research paper on leukemia

Advances in Leukemia Research

Human cells with acute myelocytic leukemia.

NCI-funded researchers are working to advance our understanding of how to treat leukemia. With progress in both targeted therapies and immunotherapies, leukemia treatment has the potential to become more effective and less toxic.

This page highlights some of the latest research in leukemia, including clinical advances that may soon translate into improved care, NCI-supported programs that are fueling progress, and research findings from recent studies.

Leukemia Treatment for Adults

The mainstays of leukemia treatment for adults have been chemotherapy , radiation therapy , and stem cell transplantation . Over the last two decades, targeted therapies have also become part of the standard of care for some types of leukemia. These treatments target proteins that control how cancer cells grow, divide, and spread. Different types of leukemia require different combinations of therapies.  For a complete list of all currently approved drugs, see Drugs Approved for Leukemia.

Although much progress has been made against some types of leukemia, others still have relatively poor rates of survival. And, as the population ages, there is a greater need for treatment regimens that are less toxic .

Acute Lymphoblastic Leukemia (ALL) Treatment

Adult acute lymphoblastic leukemia (ALL) is a type of cancer in which the bone marrow makes too many lymphocytes (a type of white blood cell). It usually gets worse quickly and needs rapid treatment. Some recent research includes:

Combining less-toxic therapies

The intensive chemotherapy treatments used for ALL have serious side effects that many older patients cannot tolerate. Targeted therapies may have fewer side effects than chemotherapy. Clinical trials, including one at NCI , are now testing whether combinations of these types of therapies can be used instead of chemotherapy for older patients with a form of ALL called B-cell ALL.

Immunotherapy

Immunotherapies are treatments that help the body’s immune system fight cancer more effectively. Immunotherapy strategies being used or tested in ALL include:

CAR T-cell therapy

CAR T-cell therapy is a type of treatment in which a patient’s own immune cells are genetically modified to treat their cancer.

• Currently, one type of CAR T cell therapy is  approved for the treatment of some children and young adults with ALL. They are now being explored for use in older adults with B-cell ALL. 
• A second CAR T-cell therapy has been approved for adults with a type of ALL called B-cell precursor ALL that has not responded to treatment or has returned after previous treatment.

CAR T cell therapies are now being explored for other uses in ALL. For example, scientists hope that it will be possible to use CAR T-cell therapy to delay—or even replace—stem-cell transplantation in older, frailer patients.

Bispecific T-cell engagers

Another immunotherapy being tested in ALL is bispecific T-cell engagers (BiTEs). These drugs attach to immune cells and cancer cells, enabling the immune cells to easily find and destroy the cancer cell by bringing them closer together.

Once such BiTE, called blinatumomab (Blincyto) , was recently shown to improve survival for people with ALL who are in remission after chemotherapy, even when there is no trace of their disease.

Improving treatment for adolescents and young adults (AYAs)

An intensive treatment regimen developed for children with ALL has been found to also improve outcomes for newly diagnosed AYA patients . The pediatric regimen more than doubled the median length of time people lived without their cancer returning compared with an adult treatment regimen. Further studies are now testing the addition of targeted therapies to the combination .

Acute Myeloid Leukemia (AML) Treatment

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. It can cause a buildup of abnormal red blood cells, white blood cells, or platelets.

AML tends to be aggressive and is harder to treat than ALL. However, AML cells sometimes have gene changes that cause the tumors to grow but can be targeted with new drugs. Researchers are starting to look at whether genomic sequencing of tumor cells can help doctors choose the best treatment (such as chemotherapy, targeted therapy, stem-cell transplant, or a combination of therapies) for each patient. Scientists are also testing other ways to treat AML.

New Treatment Option for Some People with AML

Combining ivosidenib with chemo is effective for AML with an IDH1 gene mutation.

Targeted therapies

Targeted therapies recently approved to treat AML with certain gene changes include  Enasidenib (Idhifa) ,  Olutasidenib (Rezlidhia) ,  Ivosidenib (Tibsovo) ,  Venetoclax (Venclexta) ,  Gemtuzumab ozogamicin (Mylotarg) ,  Midostaurin (Rydapt) ,  Gilteritinib (Xospata) ,  Glasdegib (Daurismo) , and  Quizartinib (Vanflyta) . 

Other ways to treat AML

• Testing newer targeted therapies.  Researchers continue to develop new drugs to shut down proteins that some leukemias need to grow. For example, new drugs called menin inhibitors stop cancer-promoting genes from being expressed. 
• Studying ways to target AML cells indirectly. These include testing ways to make cancer cells more vulnerable to new and existing treatments.
• Targeting AML and related conditions. A type of less-aggressive cancer called myelodysplastic syndrome (MDS) can eventually progress to AML. Researchers are testing HDAC inhibitors and other drugs that alter how genes are switched on and off in both MDS and AML.
• Reducing side effects. Some older adults cannot tolerate the intensive treatments most commonly used for AML. Studies have recently found that several drug combinations can help older people with AML live longer while avoiding many serious side effects. New treatments to relieve symptoms of MDS have also been developed.
• Immunotherapy. CAR T-cells and BiTEs are being tested in people with AML.

Chronic Myelogenous Leukemia (CML) Treatment

Chronic myelogenous leukemia (CML) is a type of cancer in which the bone marrow makes too many granulocytes (a type of white blood cell). These granulocytes are abnormal and can build up in the blood and bone marrow so there is less room for healthy white blood cells, red blood cells, and platelets. CML usually gets worse slowly over time.

Blocking an abnormal protein

Most people with CML have a specific chromosome alteration called the Philadelphia chromosome , which produces an abnormal protein that drives the growth of leukemia cells. Targeted therapies that block this abnormal protein— imatinib (Gleevec) , nilotinib (Tasigna) , dasatinib (Sprycel) , and ponatinib (Iclusig) —have radically changed the outlook for people with CML, who now have close to a normal life expectancy.

Testing new combination therapies

Some people with CML continue to have detectable cancer cells in their body even after long-term treatment with drugs that target the protein produced by the Philadelphia chromosome. NCI-sponsored trials are testing whether the addition of immunotherapy or other targeted therapies to these drugs can reduce the number of CML cells in such patients.

Looking at whether patients can stop taking therapy

Researchers have found that some drugs that target the protein produced by the Philadelphia chromosome can be safely stopped in some CML patients rather than taken for life. These patients must undergo regular testing to ensure the disease has not come back.

Chronic Lymphocytic Leukemia (CLL) Treatment

Like ALL, chronic lymphocytic leukemia (CLL) is a type of cancer in which the bone marrow makes too many lymphocytes (a type of white blood cell). But unlike ALL, CLL is slow growing and worsens over time.

Targeted therapy

Ibrutinib (Imbruvica) . The targeted therapy ibrutinib (Imbruvica) was the first non-chemotherapy drug approved to treat CLL. It shuts down a signaling pathway called the B-cell receptor signaling pathway, which is commonly overactive in CLL cells. Depending on people’s age , ibrutinib may be given in combination with another targeted drug, rituximab (Rituxan) .

Clinical trials have shown that ibrutinib benefits both younger and older patients with CLL.

Venetoclax (Venclexta) and obinutuzumab (Gazyva) . In 2019, the Food and Drug Administration (FDA) approved the second chemotherapy-free initial treatment regimen for CLL , containing the targeted therapies venetoclax (Venclexta) and obinutuzumab (Gazyva) .

Other combinations of these drugs plus ibrutinib are now being used or tested for CLL, including •    ibrutinib and venetoclax in people with newly diagnosed CLL •    ibrutinib, obinutuzumab, and venetoclax in older adults with newly diagnosed CLL •    ibrutinib and obinutuzumab with or without venetoclax in younger adults with newly diagnosed CLL

An ongoing trial at NCI is also testing whether giving the combination of venetoclax and obinutuzumab to some people with CLL before symptoms develop can help them live longer overall.

Zanubrutinib (Brukinsa) . In early 2023, the FDA approved a drug that works in a similar manner to ibrutinib, called zanubrutinib (Brukinsa) , for people with CLL. A large study showed that zanubrutinib alone has fewer side effects and is more effective than ibrutinib for people whose leukemia has returned after initial treatment. More research is now needed to understand how to best combine zanubrutinib with other newer therapies, such as venetoclax.

CAR T-cell therapy is also being tested in adults with CLL. Researchers would like to know if using this type of immunotherapy early in the course of treatment would be more effective than waiting until the cancer recurs.

Hairy Cell Leukemia (HCL) Treatment

Hairy cell leukemia (HCL) is a type of cancer in which the bone marrow makes too many lymphocytes (a type of white blood cell). The disease is called hairy cell leukemia because the abnormal lymphocytes look "hairy" when viewed under a microscope. This rare type of leukemia gets worse slowly, or sometimes does not get worse at all.

Combinations of drugs

Researchers are studying combinations of drugs to treat HCL. For example, in a recent small study, a combination of two targeted therapies— vemurafenib (Zelboraf) and rituximab (Rituxan) — led to long-lasting remissions for most participants with HCL that had come back after previous treatments. More drug combinations are currently being tested in clinical trials.

Leukemia Treatment for Children

For the two most common types of leukemia, AML and ALL, standard leukemia treatments for children have been chemotherapy, radiation therapy, and stem-cell transplant. Despite great improvements in survival for children with many types of leukemia, some treatments don't always work. Also, some children later experience a relapse of their disease. Others live with the side effects of chemotherapy and radiation therapy for the rest of their lives, highlighting the need for less toxic treatments.

Now researchers are focusing on targeted drugs and immunotherapies for the treatment of leukemia in children. Newer chemotherapy drugs are also being tested.

Targeted Therapies

Targeted therapies that have been approved or are being studied for children with leukemia include:

• imatinib (Gleevec) and dasatinib (Sprycel), which are  approved for the treatment of children with CML  as well as those with a specific type of ALL. The approvals are for children whose cancer cells have the Philadelphia chromosome. 
• sorafenib (Nexavar) , which has been studied in combination with standard chemotherapy for children with AML whose leukemia has changes in a gene called FLT3. The addition of sorafenib to standard treatment was safe, and its addition may improve survival time free from leukemia. Other ongoing clinical trials are testing drugs that target FLT3 more specifically than sorafenib (such as gilteritinib).
• larotrectinib (Vitrakvi) , which is being tested in children with leukemia that has a specific change in a gene called NTRK . 

More possible targets for the treatment of childhood cancers are discovered every year, and many new drugs that could potentially be used to treat cancers that have these targets are being tested through the Pediatric Preclinical In Vivo Testing Consortium (PIVOT) .

CAR T-cell therapy has recently generated great excitement for the treatment of children with relapsed ALL. One CAR T-cell therapy, tisagenlecleucel (Kymriah) , was approved in 2017 for some children with relapsed ALL.

Researchers continue to address remaining challenges about the use of CAR T-cell therapy in children with leukemia:

• Sometimes, leukemia can become resistant to tisagenlecleucel. Researchers in NCI’s Pediatric Oncology Branch have developed CAR T cells that target leukemia cells in a different way. An  ongoing clinical trial is testing whether the combination of these two types of CAR T cells can provide longer-lasting remissions.
• CAR T cells are currently only approved for use in leukemia that has relapsed or proved resistant to standard treatment. A clinical trial from COG is now testing tisagenlecleucel as part of first-line therapy in children with ALL at high risk of relapse.
• More research is needed to understand which children who receive CAR T cells are at high risk of developing resistance to treatment. Researchers also plan to test whether strategies such as combining CAR T-cell therapy with other immunotherapies may help prevent resistance from developing. 
• Other research, both in NCI’s Pediatric Oncology Branch and at other institutions, is focused on creating CAR T-cell therapies that work for children with other types of childhood leukemia, such as AML. Several clinical trials of these treatments, including one led by NCI researchers , are now under way.

Two other drugs that use the body’s immune system to fight cancer have shown promise for children with leukemia:

• In clinical trials, the drug was shown to be more effective than chemotherapy in treating ALL that has relapsed in children and young adults.
• An NCI-sponsored trial is now testing the drug as part of treatment for newly diagnosed ALL in children, adolescents, and young adults .
• A drug called inotuzumab ozogamicin (Besponsa)  is being tested in children with relapsed B-cell ALL. This drug consists of an antibody that can bind to cancer cells linked to a drug that can kill those cells. An NCI-sponsored trial is also testing the drug as part of treatment for newly diagnosed ALL in children and adolescents at higher risk of relapse.

Chemotherapy

In addition to targeted therapies and immunotherapies, researchers are also working to develop new chemotherapy drugs for leukemia and find better ways to use existing drugs. In 2018, a large clinical trial showed that adding the drug nelarabine (Arranon) to standard chemotherapy improves survival for children and young adults newly diagnosed with T-cell ALL.

Other drugs are being tested that may make standard chemotherapy drugs more effective. These drugs include venetoclax , which has been approved for older adults with some types of leukemia and is now being tested in children .

Survivorship

Children’s developing brains and bodies can be particularly sensitive to the harmful effects of cancer treatment. Because many children treated for cancer go on to live long lives, they may be dealing with these late effects for decades to come.

The NCI-funded Childhood Cancer Survivor Study , ongoing since 1994, tracks the long-term harmful effects of treatments for childhood cancer and studies ways to minimize these effects. NCI also funds research into addressing ways to help cancer survivors cope with and manage health issues stemming from cancer treatment, as well into altering existing treatment regimens to make them less toxic in the long term.

For example, one study found that, in children with ALL, radiation therapy to prevent the cancer from returning in the brain is likely unnecessary . The study found that radiation can even be omitted for children at the highest risk of the cancer coming back, reducing the risk of future problems with thinking and memory, hormone dysfunction, and other side effects of radiation to the brain.

Preventing and Treating Graft Versus Host Disease

Many people with leukemia—both adults and children—have a stem-cell transplant as part of their treatment. If the new stem cells come from a donor, the immune cells they produce may be able to attack any cancer cells that remain in the body.

But sometimes, immune cells produced by donor stem cells attack healthy tissues of the body instead. This condition, called graft versus host disease ( GVHD ), can affect nearly every organ and can cause many painful and debilitating symptoms. 

In recent years, several drugs have been approved by the FDA for the treatment of GVHD, including:

•    ibrutinib, which is also used as a treatment for some types of leukemia •     ruxolitinib (Jakafi) •     belumosudil (Rezurock)

Researchers are also testing ways to prevent GVHD from developing in the first place. For example, a recent study found that removing certain immune cells from donated stem cells before they are transplanted may reduce the risk of chronic GVHD without any apparent increase in the likelihood of relapse.

NCI-Supported Research Programs

Many NCI-funded researchers working at the NIH campus and across the United States and the world are seeking ways to address leukemia more effectively. Some research is basic, exploring questions as diverse as the biological underpinnings of cancer. And some is more clinical, seeking to translate this basic information into improving patient outcomes. The programs listed below are a small sampling of NCI’s research efforts in leukemia.

NCI’s Leukemia Specialized Programs of Research Excellence (SPORE) promotes collaborative, interdisciplinary research. SPORE grants involve both basic and clinical/applied scientists working together. They support the efficient movement of basic scientific findings into clinical settings, as well as studies to determine the biological basis for observations made in individuals with cancer or in populations at risk for cancer.

The Pediatric Immunotherapy Discovery and Development Network (PI-DDN) is working to discover and characterize new targets for immunotherapies, design experimental models to test the effectiveness of pediatric immunotherapies, develop new immunotherapy treatments, and improve the understanding of tumor immunity in pediatric cancer patients. The PI-DDN was established as part of the Cancer Moonshot initiative.

The Fusion Oncoproteins in Childhood Cancers (FusOnC2) Consortium is also part of the Cancer Moonshot initiative. The consortium of collaborating research teams will work to advance the understanding of how five important fusion oncoproteins help drive pediatric cancers, including leukemia, and apply this knowledge towards developing drugs that target these proteins.

NCI has also formed partnerships with the pharmaceutical industry, academic institutions, and individual investigators for the early clinical evaluation of innovative cancer therapies. The Experimental Therapeutics Clinical Trials Network (ETCTN) was created to evaluate these therapies using a coordinated, collaborative approach to early-phase clinical trials.

The Pediatric Early-Phase Clinical Trials Network was established to help identify and develop effective new drugs for children and adolescents with cancer. The network’s focus is on phase I and early phase II trials, as well as pilot studies of novel drugs and treatment regimens to determine their tolerability.

NCI’s Pediatric Preclinical In Vivo Testing Consortium (PIVOT) develops mouse models to allow early, rapid testing of new drugs for pediatric cancers, including leukemia. The models are all derived from tissue samples taken from patients’ tumors. The consortium partners both with commercial drug companies and with drug development efforts at universities and cancer centers.

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program uses a comprehensive approach to determine the genetic changes that drive childhood cancers. The goal of the program is to use data to guide the development of effective, less toxic therapies. TARGET is organized into disease-specific teams, including those for ALL and AML.

Researchers in NCI’s Division of Cancer Epidemiology and Genetics (DCEG)  investigate novel, molecular biomarkers for leukemia, as well as clarify relationships of established risk factors. Studies include those looking at environmental and workplace exposure, families with multiple leukemia cases, and inherited bone marrow failure syndromes to name a few.

Clinical Trials

NCI funds and oversees both early- and late-phase clinical trials to develop new treatments and improve patient care. Search NCI-Supported Clinical Trials to find leukemia-related trials now accepting patients. 

Leukemia Research Results

The following are some of our latest news articles on leukemia research:

• Quizartinib Approval Adds New Treatment Option for AML, Including in Older Patients
• Blinatumomab Increases Survival for Infants with an Aggressive Type of ALL
• Revumenib Shows Promise in Treating Advanced Acute Myeloid Leukemia
• Help Desk for Oncologists Treating People with a Rare Leukemia Pays Big Dividends
• Zanubrutinib’s Approval Improves Targeted Treatment for CLL
• Trial Suggests Expanded Role for Blinatumomab in Treating ALL

View the full list of Leukemia Research Results and Study Updates .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Leukaemia articles from across Nature Portfolio

Leukaemia is a type of blood cancer, which starts in blood-forming tissue, such as the bone marrow, and causes large numbers of immature blood cells to be produced and enter the bloodstream. Leukaemia is subdivided into different subtypes according to cellular maturity (acute or chronic) and cell type (lymphocytic or myeloid).

Novel somatic mutations in blood driving age-related clonal hematopoiesis

An analysis of somatic mutations in blood exomes from 200,618 individuals revealed previously unrecognized genes driving clonal hematopoiesis. Mutations in these genes showed age-dependent clonal expansion and their presence correlated with heightened risks of infection, death and hematological malignancy.

Related Subjects

• Acute lymphocytic leukaemia
• Acute myeloid leukaemia
• Chronic lymphocytic leukaemia
• Chronic myeloid leukaemia
• Hairy cell leukaemia

Latest Research and Reviews

Protein arginine methyltransferase 2 controls inflammatory signaling in acute myeloid leukemia

PRMT2 is a key regulator of inflammation in acute myeloid leukemia. PRMT2-deficient cells exhibit a pro-inflammatory profile associated with STAT3 activation.

• Camille Sauter
• Thomas Morin
• Laurent Delva

Construction of a digital twin of chronic graft vs. host disease patients with standard of care

• Yi-Bin Chen
• Jonathan Peachey

Philadelphia-chromosome positive acute lymphoblastic leukemia: ten frequently asked questions

• Iman Abou Dalle
• Nour Moukalled
• Ali Bazarbachi

Siglec-6 as a therapeutic target for cell migration and adhesion in chronic lymphocytic leukemia

Siglec-6 is often overexpressed in chronic lymphocytic leukaemia (CLL), but its role is unclear. Here, the author report that Siglec-6 regulates the migration and adhesion of CLL B cells via interaction with sialyl Tn on bone marrow stromal cells driving invasion which could be therapeutically targeted using a Siglec-6/CD3-bispecfiic antibody.

• Jessica Nunes
• Rakeb Tafesse
• Natarajan Muthusamy

Pre-emptive detection and evolution of relapse in acute myeloid leukemia by flow cytometric measurable residual disease surveillance

• Nicholas McCarthy
• Sylvie Freeman

Combination of menin and kinase inhibitors as an effective treatment for leukemia with NUP98 translocations

• Hongzhi Miao
• Jolanta Grembecka

News and Comment

Haploidentical-cord blood stem cell transplantation versus haploidentical stem cell transplantation for non-CR acute leukemia patients: a multicenter study

Clonal evolution and blastic plasmacytoid dendritic cell neoplasm: malignancies of divergent hematopoietic lineages emerging from a common founding clone

• Svenja Denker
• Axel Künstner
• Niklas Gebauer

Hairy cell leukemia variant and WHO classification correspondence Re: 5 th edition WHO classification haematolymphoid tumors: lymphoid neoplasms

• Michael Grever
• Leslie Andritsos
• Pier Luigi Zinzani

Ponatinib superior to imatinib in Ph + ALL

• Peter Sidaway

Lower relapse incidence with HAPLO versus MSD or MUD HCTs for AML patients with KMT2A rearrangement: a study from the Global Committee and the ALWP of the EBMT

• Myriam Labopin
• Norbert Claude Gorin

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques

• Review article
• Published: 13 September 2022
• Volume 30 , pages 251–270, ( 2023 )

Cite this article

• Rohini Raina 1 ,
• Naveen Kumar Gondhi 1 ,
• Chaahat 2 ,
• Dilbag Singh 3 ,
• Manjit Kaur 3 &
• Heung-No Lee 3  

2019 Accesses

18 Citations

Explore all metrics

Acute leukemia is a cancer that starts in the bone marrow and is characterized by an abnormal growth of white blood cells. It is a disease that affects people all over the world. Hematologist study blood smears from patients to appropriately diagnose this anomaly. The methods used for diagnosis can be influenced by factors including the hematologist's experience and level of weariness, resulting in nonstandard results and even inaccuracies. The automatic detection of acute leukemia will produce robust results with precise accuracy. This systematic review gives a thorough investigation of the deep learning method for the classification and detection of acute leukemia. The systematic review adopted the PRISMA principle. Four online open source databases were utilized to find comparable articles, and a query featuring relevant keywords was created for the search purpose. Relevant publications were chosen from the search results based on inclusion and exclusion criteria to find answers to the four evolving research questions. The findings of the various studies were examined using the research questions that had been created.F1score and accuracy have been used as a performance matrix for the comparison purpose of CNMC and ALL IDB and self-acquired datasets. Consequently, various challenges faced by the authors have been highlighted. This systematic review article consists of a summary of the various automated detection and classification of acute leukemia in terms of four research questions. Different steps before classification like preprocessing, augmentation, segmentation, and feature extraction with various challenges faced by the author's different datasets and various challenges have been discussed in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

A Concise Review of Acute Myeloid Leukemia Recognition Using Machine Learning Techniques

Leukemia Detection Using Machine and Deep Learning Through Microscopic Images—A Review

A review on computer aided detection and classification of leukemia

Abhishek A, Jha RK, Sinha R, Jha K (2022) Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques. Biomed Signal Process Control 72:103341

Article   Google Scholar  

Eckardt JN, Bornhäuser M, Wendt K, Middeke JM (2020) Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects. Blood Adv 4(23):6077–6085

Rajpurohit S, Patil S, Choudhary N, Gavasane S, Kosamkar P (2018) Identification of acute lymphoblastic leukemia in microscopic blood image using image processing and machine learning algorithms. In: 2018 International conference on advances in computing, communications and informatics (ICACCI). pp. 2359–2363

Zolfaghari M, Sajedi H (2022) A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells. Multimedia Tools and Applications, pp 1–31

Jewani K, Boddu K, Gumani P, Solapure K (2018) Detection of diseases via blood analysis using Image processing Techniques. In: 2018 International conference on smart city and emerging technology (ICSCET). IEEE.

Wang DL (2022) BDB sciences. https://www.bdbiosciences.com/en-nl/learn/clinical/blood-cancers/leukemia .

Ghaderzadeh M, Asadi F, Hosseini A, Bashash D, Abolghasemi H, Roshanpour A (2021) Machine learning in detection and classification of leukemia using smear blood images: a systematic review. Sci Program 1–14

Rosales-Pérez A (2022) A review on machine learning techniques for acute leukemia classification. In: Biosignal processing and classification using computational learning and intelligence. Elsevier, pp 429–446

Khan M, Naeem Z, Sajid Z (2020) Insights into acute myeloid leukemia: critical analysis on its wide aspects, vol 3, pp 1–9.

Kulhalli R, Savadikar C, Garware B (2019) Toward automated classification of b-acute lymphoblastic leukemia. In ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, pp 63–72

Rehman A, Abbas N, Saba T, Rahman SI, Mehmood Z, Kolivand H (2018) Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech 81(11):1310–1317

Abunadi I, Senan EM (2022) Multi-method diagnosis of blood microscopic sample for early detection of acute lymphoblastic leukemia based on deep learning and hybrid techniques. Sensors 22(4):1629

Abhishek A, Santhanam N, Jha RK, Sinha R, Jha K (2022) Multi Class Classification of Acute Leukemia using Transfer Learning. In 2022 International Conference for Advancement in Technology (ICONAT). IEEE

Chin Neoh S, Srisukkham W, Zhang L, Todryk S, Greystoke B, Peng Lim C, Alamgir Hossain M, Aslam N (2015) An intelligent decision support system for leukaemia diagnosis using microscopic blood images. Sci Rep 5(1):1–14

Patil AP (2022) A concise review of acute myeloid leukemia recognition using machine learning techniques. Cyber Intell Inform Retriev, pp 417–424

Dharani T, Hariprasath S (2018) Diagnosis of leukemia and its types using digital image processing techniques. In: 2018 3rd International conference on communication and electronics systems (ICCES). pp 275–279

Su J, Han J, Song J (2021) A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders. Comput Med Imaging Graph 90:101912

Saleem S, Amin J, Sharif M, Anjum MA, Iqbal M, Wang SH (2021) A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00473-z

Vogado Rodrigo LHS, Veras Flavio MS, Araujo Romuere HD, Silva Kelson RV, Aires RT (2018) Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Eng Appl Artif Intell 72:415–422

Angelakis A, Soulioti I (2021) Diagnosis of acute myeloid leukaemia using machine learning . arXiv:2108.07396

Sivalingam NP, Chinnasamy S, SuruliMuniyandi T (2022) An effective chronic lymphocytic leukemia detection method using hybrid optimization aware random multimodal deep learning. Concurrency and Computation: Practice and Experience, p. e7012

Sashank GVS, Jain C, Venkateswaran N (2021) Detection of acute lymphoblastic leukemia by utilizing deep learning methods . Machine vision and augmented intelligence—theory and applications: select proceedings of MAI 2021, 796, 453.

Sunil S, Sonu P (2022) An effective approach for detecting acute lymphoblastic leukemia using deep convolutional neural networks. In: International conference on computational techniques and applications. pp 23–24

Jha KK, Dutta HS (2019) Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images. Comput Methods Programs Biomed 179:104987

Kalaiselvi TC, Kumar DS, Subhashri KS, Siddharth SM (2020) Classification of leukemia using convolution neural network. Eur J Mol Clin Med 7(4):1286–1293

Google Scholar  

Sajana T et al. (2020) Classification of leukemia patients with different clinical presentation of blood cells. Materials Today

Mora JJM, Martın M, Systematic review of the state of the art regarding the identification of cancer cells of the leukemia type with digital image processing. Res Comput Sci 149(4):69–79

Khalil AJ, Abu-Naser SS (2022) Diagnosis of blood cells using deep learning. Int J Acad Eng Res (IJAER) 6(2):11–13

Sahlol AT, Kollmannsberger P, Ewees AA (2020) Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci Rep 10(1):2536

Abas SM, Abdulazeez AM (2021) Detection and classification of leukocytes in leukemia using YOLOv2 with CNN. Asian J Res Comput Sci, 64–75

Sidhom J-W, Siddarthan IJ, Lai BS, Luo A, Hambley BC, Bynum J, Duffield AS, Streiff MB, Moliterno AR, Imus P, Gocke CB (2021) Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features. NPJ Precision Oncol 5(1):1–8

Loey M, Naman M, Zayed H (2020) Deep transfer learning in diagnosing leukemia in blood cells. Computers 9(2):29

Rawat J, Singh A, Bhadauria HS, Kumar I (2014) Comparative analysis of segmentation algorithms for leukocyte extraction in the acute Lymphoblastic Leukemia images. In: 2014 International conference on parallel, distributed and grid computing. IEEE

Kassani SH, Kassani PH, Wesolowski MJ, Schneider KA, Deters R (2019) A hybrid deep learning architecture for leukemic B-lymphoblast classification. In: 2019 International conference on information and communication technology convergence (ICTC). IEEE

Ouyang N, Wang W, Ma L, Wang Y, Chen Q, Yang S, Xie J, Su S, Cheng Y, Cheng Q, Zheng L (2021) Diagnosing acute promyelocytic leukemia by using convolutional neural network. Clin Chim Acta 512:1–6

Mondal C, Hasan M, Jawad M, Dutta A, Islam M, Awal M, Ahmad M (2021) Acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks. arXiv:2105.03995

Shaheen M, Khan R, Biswal RR, Ullah M, Khan A, Uddin MI, Zareei M, Waheed A (2021) Acute myeloid leukemia (AML) detection using alexnet model. Complexity 2021:1–8

Sneha D, Alagu S, Chrompet C, A novel segmentation approach for acute lymphocytic leukemia detection using deep learning.

Acharya V, Ravi V, Pham TD, Chakraborty C (2021) Peripheral blood smear analysis using automated computer-aided diagnosis system to identify acute myeloid leukemia. IEEE Transactions on Engineering and Management 1–14

Mirmohammadi P, Ameri M, Shalbaf A (2021) Recognition of acute lymphoblastic leukemia sand lymphocytes cell subtypes in microscopic images using random forest classifier. Phys Eng Sci Med 44(2):433–441

Khandekar R, Shastry P, Jaishankar S, Faust O, Sampathila N (2021) Automated blast cell detection for acute lymphoblastic leukemia diagnosis. Biomed Signal Process Control 68:102690

Kumar I, Bhatt C, Vimal V, Qamar S (2021) Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear. J Intell Fuzzy Syst, 1–14

Aftab MO, Awan MJ, Khalid S, Javed R, Shabir H (2021) Executing spark BigDL for leukemia detection from microscopic images using transfer learning, pp 216–220

GGenovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F (2021) Acute lymphoblastic leukemia detection based on adaptive unsharpening and deep learning. pp 1205–1209

Jiang Z, Dong Z, Wang L, Jiang W (2021) Method for diagnosis of acute lymphoblastic leukemia based on ViT-CNN ensemble model. Comput Intell Neurosci 2021:7529893

Chowdhury AB, Roberson J, Hukkoo A, Bodapati S (2020) Automated complete blood cell count and malaria pathogen detection using convolution neural network. IEEE Robot Automat Lett 5(2):1047–1054

Harun NH, Bakar JA, Abd Wahab Z, Osman MK, Harun H (2020) Color image enhancement of acute leukemia cells in blood microscopic image for leukemia detection sample. In: 2020 IEEE 10th symposium on computer applications & industrial electronics (ISCAIE) pp 24–29

Liu Y, Long F (2019) Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning. In ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, pp 113–121

Das PK, Meher S (2021) An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia. Expert Syst Appl 183:115311

Loey M, Naman MR, Zayed HH (2020) A survey on blood image diseases detection using deep learning. Int J Serv Sci Manag Eng Technol 11(3):18–32

Kumar D, Jain N, Khurana A, Mittal S, Satapathy SC (2020) Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access 8:142521–142531

Bibi N, Sikandar M, Ud Din I, Almogren A, Ali S (2020) IoMT-based automated detection and classification of leukemia using deep learning. J Healthc Eng 2020:6648574

Melfi Rose S, Merlin Sheeba J, Vijay Prabaharan R, Bhuvaneshwari M, Subha Hency Jose P (2021) Prognostication of acute lymphocytic leukemia (ALL) using capsule network algorithm. J Phys Conf Ser 1937(1):012021

Moosavi Tayebi R, et al (2021) Histogram of cell types: deep learning for automated bone marrow cytology. p 2107.02293.

Renuka TV, Surekha B (2021) Acute-lymphoblastic leukemia detection through deep transfer learning approach of neural network. In: Proceeding of first doctoral symposium on natural computing research. Springer pp 163–170

Rawat J, Singh A, Bhadauria HS, Virmani J, Devgun JS (2017) Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimed Tools Appl 76(18):19057–19085

Andrade AR, Vogado LH, de MS Veras R, Silva RR, Araujo FH, Medeiros FN (2019) Recent computational methods for white blood cell nuclei segmentation: a comparative study. Comput Methods Programs Biomed 173:1–14

Karami K, Akbari M, Moradi MT, Soleymani B, Fallahi H (2021) Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques. PLoS ONE 16(7):e0254976

Putzu L, Caocci G, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62(3):179–191

Negm AS, Hassan OA, Kandil AH (2018) A decision support system for Acute Leukaemia classification based on digital microscopic images. Alex Eng J 57(4):2319–2332

Kumar P, Udwadia SM (2017) Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image. In: 2017 International conference on advances in computing, communications and informatics (ICACCI). IEEE

Moshavash Z, Danyali H, Helfroush MS (2018) An automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. J Digit Imaging 31(5):702–717

Jothi G, Inbarani HH, Azar AT, Devi KR (2019) Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl 31(9):5175–5194

Pansombut T, Wikaisuksakul S, Khongkraphan K, Phon-On A (2019) Convolutional neural networks for recognition of lymphoblast cell images. Comput Intell Neurosci

Viswanathan P (2015) Fuzzy C means detection of leukemia based on morphological contour segmentation. Proc Comput Sci 58:84–90

Patel N, Mishra A (2015) Automated leukaemia detection using microscopic images. Proc Comput Sci 58:635–642

Schouten JP, Matek C, Jacobs LF, Buck MC, Bošnački D, Marr C (2021) Tens of images can suffice to train neural networks for malignant leukocyte detection. Sci Rep 11(1):1–8

Raina R, Gondhi DNK (2018) Machine learning techniques in IoT. Int J Sci Res Comput Sci Eng Inf Technol 1:117–121

Elhassan TAM, Rahim MS, Swee TT, Hashim SZ, Aljurf M (2022) Feature extraction of white blood cells using CMYK-moment localization and deep learning in acute myeloid leukemia blood smear microscopic images. IEEE Access 10:16577–16591

Jayavikash KP, Gopinath P, Nandheeswaran U, Sivabalan R, Bharathi TD (2021) Detection of Leukemia Using Machine Learning Algorithms. J Phys Conf Ser 1916:012220

Sneha D, Alagu S Chronological Sine Cosine Algorithm Based Deep CNN for Acute Lymphocytic Leukemia Detection.

Agustin RI, Arif A, Sukorini U (2021) Classification of immature white blood cells in acute lymphoblastic leukemia L1 using neural networks particle swarm optimization. Neural Comput Appl 33(17):10869–10880

Arivuselvam B, Sudha S (2022) Leukemia classification using the deep learning method of CNN. J X-Ray Sci Technol (Preprint), pp 1–19

Das PK, Nayak B, Meher S (2022) A lightweight deep learning system for automatic detection of blood cancer. Measurement 191:110762

Rezayi S et al (2021) Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods. Comput Intell Neurosci. https://doi.org/10.1155/2021/5478157

Surya Sashank GV, Jain C, Venkateswaran N (2021) Detection of acute lymphoblastic leukemia by utilizing deep learning methods. In: Bajpai MK, Singh KK, Giakos G (eds) Machine vision and augmented intelligence—theory and applications. Springer, New York, pp 453–467

Chapter   Google Scholar  

Bukhari M, Yasmin S, Sammad S, El-Latif A, Ahmed A (2022) A deep learning framework for leukemia cancer detection in microscopic blood samples using squeeze and excitation learning. Math Problems Eng

Umamaheswari D, Geetha S (2018) Segmentation and classification of acute lymphoblastic leukemia cells tooled with digital image processing and ML techniques. In: 2018 Second international conference on intelligent computing and control systems (ICICCS). IEEE.

Abir WH, Uddin M, Khanam FR, Tazin T, Khan MM, Masud M, Aljahdali S (2022) Explainable AI in diagnosing and anticipating leukemia using transfer learning method. Comput Intell Neurosci

Sakthiraj FSK (2021) Autonomous leukemia detection scheme based on hybrid convolutional neural network model using learning algorithm. Wireless Pers Commun

Reena MR, Ameer PM (2020) Localization and recognition of leukocytes in peripheral blood: a deep learning approach. Comput Biol Med 126:104034

Sipes R, Li D (2018) Using convolutional neural networks for automated fine grained image classification of acute lymphoblastic leukemia, pp 157–161

Boldú L, Merino A, Acevedo A, Molina A, Rodellar J (2021) A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. Comput Methods Programs Biomed 202:105999

Genovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F (2021) Histopathological transfer learning for acute lymphoblastic leukemia detection, pp 1–6

Zhou M, Wu K, Yu L, Xu M, Yang J, Shen Q, Liu B, Shi L, Wu S, Dong B, Wang H (2021) Development and evaluation of a leukemia diagnosis system using deep learning in real clinical scenarios. Front Pediatr 9:693676

Eckardt J-N, Schmittmann T, Riechert S, Kramer M, Sulaiman AS, Sockel K, Kroschinsky F, Schetelig J (2022) Deep learning identifies acute promyelocytic leukemia in bone marrow smears. BMC Cancer 22(1):1–11

Fabio Scotti RDL, Piuri V (2010) https://homes.di.unimi.it/scotti/all/ .

Anubha Gupta RG (2019) https://competitions.codalab.org/competitions/20395 .

Acharya V, Ravi V, Pham TD, Chakraborty C (2021) Peripheral blood smear analysis using automated computer-aided diagnosis system to identify acute myeloid leukemia. IEEE Trans Eng Manag

Marzahl C, Aubreville M, Voigt J, Maier A (2019) Classification of leukemic b-lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, pp 13–22

Ding Y, Yang Y, Cui Y (2019) Deep learning for classifying of white blood cancer. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 33–41

Shi T, Wu L, Zhong C, Wang R, Zheng W (2019) Ensemble convolutional neural networks for cell classification in microscopic images. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 43–51

Verma E, Singh V (2019) ISBI challenge 2019: convolution neural networks for b-all cell classification. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 131–139

Khan MA, Choo J (2019) Classification of cancer microscopic images via convolutional neural networks. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 141–147

Honnalgere A, Nayak G (2019) Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 1–12

Prellberg J, Kramer O (2019) Acute lymphoblastic leukemia classification from microscopic images using convolutional neural networks. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 53–61

Pan Y, Liu M, Xia Y, Shen D (2019) Neighborhood-correction algorithm for classification of normal and malignant cells. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, Singapore, 73–82

Kumar Y, Gupta S, Singla R, Hu YC (2021) A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. Arch Comput Methods Eng 221:1–28

Shaheen M, Khan R, Biswal RR, Ullah M, Khan A, Uddin MI, Zareei M, Waheed A (2021) Acute myeloid leukemia (AML) detection using AlexNet model. Complexity. https://doi.org/10.1155/2021/6658192

Hahn TV, Mechefske CK (2021) Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder. Int J Hydromech 4(1):69–98

Xu Y, Li Y, Li C (2021) Electric window regulator based on intelligent control. J Artif Intell Technol 1:198–206

Singh D, Kumar V, Kaur M, Jabarulla MY, Lee HN (2021) Screening of COVID-19 suspected subjects using multi-crossover genetic algorithm based dense convolutional neural network. IEEE Access 9:142566–142580

Jie D, Zheng G, Zhang Y, Ding X, Wang L (2021) Spectral kurtosis based on evolutionary digital filter in the application of rolling element bearing fault diagnosis. Int J Hydromech 4(1):27

Singh PK (2022) Data with non-Euclidean geometry and its characterization. J Artif Intell Technol 2:3–8

Kaur M, Singh D, Kumar V, Gupta BB, Abd-El-Latif AA (2021) Secure and energy efficient-based E-health care framework for green internet of things. IEEE Trans Green Commun Netw 5(3):1223–1231

Balakrishna A, Mishra PK (2021) Modelling and analysis of static and modal responses of leaf spring used in automobiles. Int J Hydromech 4(4):350–367

Mondal SC, Marquez PLC, Tokhi MO (2021) Analysis of mechanical adhesion climbing robot design for wind tower inspection. J Artif Intell Technol 1(4):219–227

Kaushik H, Singh D, Kaur M, Alshazly H, Zaguia A, Hamam H (2021) Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models. IEEE Access 9:108276–108292

Download references

Acknowledgements

This work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean government (MSIP) (NRF‑2021R1A2B5B03002118) and This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the ITRC (Information Technology Research Center) support program(IITP‑2021‑0‑01835) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation).

Author information

Authors and affiliations.

Shri Mata Vaishno Devi University, Katra, India

Rohini Raina & Naveen Kumar Gondhi

CGC College of Engineering, Landran, Mohali, India

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea

Dilbag Singh, Manjit Kaur & Heung-No Lee

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Heung-No Lee .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Raina, R., Gondhi, N.K., Chaahat et al. A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques. Arch Computat Methods Eng 30 , 251–270 (2023). https://doi.org/10.1007/s11831-022-09796-7

Download citation

Received : 06 April 2022

Accepted : 13 July 2022

Published : 13 September 2022

Issue Date : January 2023

DOI : https://doi.org/10.1007/s11831-022-09796-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Acute myeloid leukemia: Treatment and research outlook for 2021 and the MD Anderson approach

Affiliation.

• 1 Department of Leukemia, MD Anderson Cancer Center, Houston, Texas.
• PMID: 33734442
• DOI: 10.1002/cncr.33477

The unraveling of the pathophysiology of acute myeloid leukemia (AML) has resulted in rapid translation of the information into clinical practice. After more than 40 years of slow progress in AML research, the US Food and Drug Administration has approved nine agents for different AML treatment indications since 2017. In this review, we detail the progress that has been made in the research and treatment of AML, citing key publications related to AML research and therapy in the English literature since 2000. The notable subsets of AML include acute promyelocytic leukemia (APL), core-binding factor AML (CBF-AML), AML in younger patients fit for intensive chemotherapy, and AML in older/unfit patients (usually at the age cutoff of 60-70 years). We also consider within each subset whether the AML is primary or secondary (therapy-related, evolving from untreated or treated myelodysplastic syndrome or myeloproliferative neoplasm). In APL, therapy with all-trans retinoic acid and arsenic trioxide results in estimated 10-year survival rates of ≥80%. Treatment of CBF-AML with fludarabine, high-dose cytarabine, and gemtuzumab ozogamicin (GO) results in estimated 10-year survival rates of ≥75%. In younger/fit patients, the "3+7" regimen (3 days of daunorubicin + 7 days of cytarabine) produces less favorable results (estimated 5-year survival rates of 35%; worse in real-world experience); regimens that incorporate high-dose cytarabine, adenosine nucleoside analogs, and GO are producing better results. Adding venetoclax, FLT3, and IDH inhibitors into these regimens has resulted in encouraging preliminary data. In older/unfit patients, low-intensity therapy with hypomethylating agents (HMAs) and venetoclax is now the new standard of care. Better low-intensity regimens incorporating cladribine, low-dose cytarabine, and other targeted therapies (FLT3 and IDH inhibitors) are emerging. Maintenance therapy now has a definite role in the treatment of AML, and oral HMAs with potential treatment benefits are also available. In conclusion, AML therapy is evolving rapidly and treatment results are improving in all AML subsets as novel agents and strategies are incorporated into traditional AML chemotherapy. LAY SUMMARY: Ongoing research in acute myeloid leukemia (AML) is progressing rapidly. Since 2017, the US Food and Drug Administration has approved 10 drugs for different AML indications. This review updates the research and treatment pathways for AML.

Keywords: acute myelogenous leukemia; new drugs; progress; research; therapy.

© 2021 American Cancer Society.

PubMed Disclaimer

Similar articles

• The cure of leukemia through the optimist's prism. Kantarjian HM, Jain N, Garcia-Manero G, Welch MA, Ravandi F, Wierda WG, Jabbour EJ. Kantarjian HM, et al. Cancer. 2022 Jan 15;128(2):240-259. doi: 10.1002/cncr.33933. Epub 2021 Oct 6. Cancer. 2022. PMID: 34614211 Free PMC article. Review.
• Venetoclax plus intensive chemotherapy with cladribine, idarubicin, and cytarabine in patients with newly diagnosed acute myeloid leukaemia or high-risk myelodysplastic syndrome: a cohort from a single-centre, single-arm, phase 2 trial. Kadia TM, Reville PK, Borthakur G, Yilmaz M, Kornblau S, Alvarado Y, Dinardo CD, Daver N, Jain N, Pemmaraju N, Short N, Wang SA, Tidwell RSS, Islam R, Konopleva M, Garcia-Manero G, Ravandi F, Kantarjian HM. Kadia TM, et al. Lancet Haematol. 2021 Aug;8(8):e552-e561. doi: 10.1016/S2352-3026(21)00192-7. Lancet Haematol. 2021. PMID: 34329576 Free PMC article. Clinical Trial.
• Secondary AML Emerging After Therapy with Hypomethylating Agents: Outcomes, Prognostic Factors, and Treatment Options. Richardson DR, Green SD, Foster MC, Zeidner JF. Richardson DR, et al. Curr Hematol Malig Rep. 2021 Feb;16(1):97-111. doi: 10.1007/s11899-021-00608-6. Epub 2021 Feb 20. Curr Hematol Malig Rep. 2021. PMID: 33609248 Review.
• Single-agent and combination biologics in acute myeloid leukemia. Richard-Carpentier G, DiNardo CD. Richard-Carpentier G, et al. Hematology Am Soc Hematol Educ Program. 2019 Dec 6;2019(1):548-556. doi: 10.1182/hematology.2019000059. Hematology Am Soc Hematol Educ Program. 2019. PMID: 31808888 Free PMC article. Review.
• Venetoclax-based therapies for acute myeloid leukemia. Guerra VA, DiNardo C, Konopleva M. Guerra VA, et al. Best Pract Res Clin Haematol. 2019 Jun;32(2):145-153. doi: 10.1016/j.beha.2019.05.008. Epub 2019 May 24. Best Pract Res Clin Haematol. 2019. PMID: 31203996 Free PMC article. Review.
• On the mechanism of wogonin against acute monocytic leukemia using network pharmacology and experimental validation. Wang X, Wang Y, Chen J, Wang Q, Liu Z, Yin Y, Yang T, Shen T, Sa Y. Wang X, et al. Sci Rep. 2024 May 2;14(1):10114. doi: 10.1038/s41598-024-60859-0. Sci Rep. 2024. PMID: 38698063 Free PMC article.
• Venetoclax Combination Treatment of Acute Myeloid Leukemia in Adolescents and Young Adult Patients. Chatzikalil E, Roka K, Diamantopoulos PT, Rigatou E, Avgerinou G, Kattamis A, Solomou EE. Chatzikalil E, et al. J Clin Med. 2024 Apr 1;13(7):2046. doi: 10.3390/jcm13072046. J Clin Med. 2024. PMID: 38610812 Free PMC article. Review.
• Curcumin in treatment of hematological cancers: Promises and challenges. Entezari M, Tayari A, Paskeh MDA, Kheirabad SK, Naeemi S, Taheriazam A, Dehghani H, Salimimoghadam S, Hashemi M, Mirzaei S, Samarghandian S. Entezari M, et al. J Tradit Complement Med. 2024 Jan 5;14(2):121-134. doi: 10.1016/j.jtcme.2023.10.004. eCollection 2024 Mar. J Tradit Complement Med. 2024. PMID: 38481552 Free PMC article. Review.
• Discovery of 5-trifluoromethyl-2-aminopyrimidine derivatives as potent dual inhibitors of FLT3 and CHK1. Deng M, Gao Y, Wang P, Du W, Xu G, Li J, Zhou Y, Liu T. Deng M, et al. RSC Med Chem. 2023 Dec 7;15(2):539-552. doi: 10.1039/d3md00597f. eCollection 2024 Feb 21. RSC Med Chem. 2023. PMID: 38389894
• Probing the Internalization and Efficacy of Antibody-Drug Conjugate via Site-Specific Fc-Glycan Labelling of a Homogeneous Antibody Targeting SSEA-4 Bearing Tumors. Shivatare VS, Huang HW, Tseng TH, Chuang PK, Zeng YF, Wong CH. Shivatare VS, et al. Isr J Chem. 2023 Oct;63(10-11):e202300042. doi: 10.1002/ijch.202300042. Epub 2023 Apr 22. Isr J Chem. 2023. PMID: 38348405 Free PMC article.
• Kantarjian H. Acute myeloid leukemia-major progress over four decades and glimpses into the future. Am J Hematol. 2016;91:131-145.
• Short NJ, Rytting ME, Cortes JE. Acute myeloid leukaemia. Lancet. 2018;392:593-606.
• Kadia TM, Ravandi F, Cortes J, Kantarjian H. Toward individualized therapy in acute myeloid leukemia: a contemporary review. JAMA Oncol. 2015;1:820-828.
• Dohner H, Weisdorf DJ, Bloomfield CD. Acute Myeloid Leukemia. N Engl J Med. 2015;373:1136-1152.
• Fernandez HF, Sun Z, Yao X, et al. Anthracycline dose intensification in acute myeloid leukemia. N Engl J Med. 2009;361:1249-1259.

Publication types

• Search in MeSH

Related information

• PubChem Compound (MeSH Keyword)

Grants and funding

• CA100632/MD Anderson Cancer Center Leukemia SPORE
• Charif Souki Cancer Research Grant.
• P30 CA016672/CA/NCI NIH HHS/United States

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.

Other Literature Sources

• scite Smart Citations
• Genetic Alliance
• MedlinePlus Health Information

Miscellaneous

• NCI CPTAC Assay Portal

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Warning: The NCBI web site requires JavaScript to function. more...

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Acute myeloid leukemia.

Anusha Vakiti ; Samuel B. Reynolds ; Prerna Mewawalla .

Affiliations

Last Update: April 27, 2024 .

• Continuing Education Activity

Acute myeloid leukemia (AML) is a rapidly progressing myeloid neoplasm characterized by the clonal expansion of primitive hematopoietic stem cells, known as blasts, in the bone marrow. This expansion results in ineffective erythropoiesis and megakaryopoiesis, clinically manifesting as relatively rapid bone marrow failure compared to chronic and indolent leukemias. This leads to inadequate production of red blood cells and platelets. The recent consensus guidelines established by the European LeukemiaNET (ELN) in 2022 have emphasized molecular characterization and risk stratification for individuals with AML, providing updated data on these aspects.

Treatment options vary depending on patient-specific factors, and hematopoietic stem cell transplant remains the only curative therapy. Although the administration of multiagent induction chemotherapy can achieve complete remission, allogeneic stem cell transplantation is the only established curative therapy. Despite advancements in therapeutic approaches, prognosis remains suboptimal, especially among the older populations. This activity explores the appropriate timing for considering this condition in the differential diagnosis and outlines proper evaluation methods. In addition, this activity emphasizes the crucial role of the interprofessional healthcare team in providing care for patients affected by this condition.

• Identify characteristic clinical features and laboratory findings indicative of acute myeloid leukemia during patient evaluation.
• Implement evidence-based diagnostic and therapeutic strategies for acute myeloid leukemia management in accordance with established guidelines.
• Select appropriate induction and consolidation therapies tailored to individual patient characteristics and disease risk for patients with acute myeloid leukemia.
• Collaborate with multidisciplinary healthcare teams to develop comprehensive care plans and provide support for patients throughout their acute myeloid leukemia journey.
• Introduction

Acute myeloid leukemia (AML) is a rapidly progressing myeloid neoplasm characterized by the clonal expansion of immature myeloid-derived cells, known as blasts, in the peripheral blood and bone marrow. This expansion results in ineffective erythropoiesis and megakaryopoiesis, clinically manifesting as relatively rapid bone marrow failure compared to chronic and indolent leukemias. This leads to inadequate production of red blood cells and platelets. 

Although the administration of multiagent induction chemotherapy can induce complete remission, allogeneic stem cell transplantation is the only established curative therapy. Despite advancements in therapeutic approaches, prognosis remains suboptimal, especially among the older populations. [1] [2] [3] .

The European LeukemiaNet (ELN) 2022 consensus recommendations offer a valuable framework for classifying AML based on mutational profile. [4] [5] [6]  However, before providers can truly grasp and access this framework, they need to comprehend the origins and pathways of the disease. For example, patients with high and very high-risk myelodysplastic syndrome (MDS), clinically characterized by the presence of transfusion-dependent cytopenias and peripheral blasts, are at increased risk of AML evolution and necessitate vigilant surveillance. [7]

Patients with myeloproliferative neoplasms, which include myelofibrosis, essential thrombocythemia, polycythemia vera, and chronic myeloid leukemia, may also progress or evolve into a higher-grade myeloid neoplasm such as AML. [8] Indications of such progression in these preexisting conditions vary based on the baseline clinical phenotype (eg, thrombocytosis in a patient with essential thrombocythemia), but a common presentation involves declining blood counts alongside peripheral blast elevations. Collectively, these conditions, including MDS and myeloproliferative neoplasms, as well as other disease states, such as aplastic anemia, may lead to what is termed as secondary AML. [9]

Another group of patients at risk for AML includes patients who have previously received chemotherapy for other malignancies. Patients who have been exposed to alkylating agents or radiation (eg, patients receiving breast cancer-directed cyclophosphamide) may develop MDS/AML with chromosome 5 or 7 abnormalities. Such sequelae commonly occur 5 to 7 years after exposure. [10] Other chemotherapeutic agents, particularly topoisomerase inhibitors, may also lead to AML but are associated with 11q23 rearrangements. [11] These phenomena characterize what is effectively known as therapy-related MDS/AML.

Additional environmental exposures, including radiation, tobacco smoke, and benzene, also contribute to the risk of AML. [12] Despite these known risk factors, most cases of AML still arise  de novo  without an attributable etiology.

• Epidemiology

The annual incidence of new cases in both men and women is approximately 4.3 per 100,000 population, totaling over 20,000 cases per year in the United States alone. [13] The median age at the time of diagnosis is about 68, with a higher prevalence observed among non-Hispanic Whites. Furthermore, males exhibit a higher incidence compared to females, with a ratio of 5:3.

• Pathophysiology

AML is characterized by the clonal proliferation of undifferentiated myeloid precursors, known as blasts, within the bone marrow compartment. Extensive research, both past and ongoing, investigates the communication pathways of these cells within the bone marrow. However, this proliferation primarily stems from the accumulation of diverse genomic and cytogenetic abnormalities. The clinical manifestations of this process result in ineffective erythropoiesis, megakaryopoiesis, and bone marrow failure. 

AML is a highly heterogeneous disease that requires individualized cytogenetic and molecular characterization. However, broadly speaking, the disease can be categorized into favorable, intermediate, or high-risk groups based on the criteria outlined in the aforementioned ELN 2022 guidelines. [6]  Genetic abnormalities that characterize favorable risk disease include chromosomal translocations t(8;21)(q22;q22.1) or inv(16)(p13.1q22). Patients who lack FLT3-ITD (internal tandem duplication) mutations without mutated NPM1 or with CEBPA (bZIP in-frame) mutations are also categorized as favorable risk.

A study even reported that NPM1 mutations were present in up to 35% of patients with AML. [14]  Intermediate-risk AML is diagnosed in the presence of any FLT3-ITD mutation or t(9;11)(p21.3;q23.3, or MLL:KMT2A rearrangement). Lastly, high-risk AML categorization can be diagnosed in the presence of several cytogenetic or molecular aberrancies, which notably include monosomy 5/del 5q or 7/deletion 7q, other monosomal or complex karyotype (≥3 unrelated abnormalities), or mutations in  ASXL1,  EZH2 , SRSF2, or  TP53 . 

Runt-related transcription factor ( RUNX1 ) is an essential component of hematopoiesis and is also known as AML1 protein or core-binding factor subunit alpha-2 ( CBFA2 ). RUNX1  is located on chromosome 21 and is frequently translocated with the ETO (Eight Two One) /RUNX1T1  gene located on chromosome 8q22, creating an AML-ETO  or t(8;21)(q22;q22) AML, which is seen in about 12% of AML cases. These mutations, commonly associated with trisomies 13 and 21, show resistance to standard induction therapy.

Mutations in isocitrate dehydrogenase ( IDH) are oncogenic and present in 15% to 20% of all AML cases and 25% to 30% of patients with cytogenetically normal AML, with a higher prevalence in older individuals. Additionally, TP53 mutations are associated with a poor prognosis and resistance to chemotherapy.

• History and Physical

Due to ineffective erythropoiesis and bone marrow failure, patients may experience various symptoms, including recurrent infections, anemia, easy bruising, excessive bleeding, headaches, and bone pain. Generalized weakness, fatigue, shortness of breath, and chest tightness may also be observed, depending on the degree of anemia. The time course associated with such symptoms is relatively rapid, often on the order of days to weeks.

Common physical examination findings in AML include pallor, bruising, and hepatosplenomegaly, while lymphadenopathy is rare. Myeloid sarcoma, a myeloid equivalent, may present as thickened, hyperpigmented, coarse skin lesions. Disseminated intravascular coagulation (DIC), characterized clinically by oral mucosal hemorrhages, purpura, extremity petechiae, and bleeding from intravenous line sites, is common in AML.

AML should be suspected in individuals presenting with rapid (within days or a few weeks) unexplained cytopenias (decreased leukocytes, hemoglobin, or platelets), circulating blast cells in peripheral blood, easy bruising or bleeding, or recurrent infections. In some cases, patients may present with renal failure due to auto-tumor lysis syndrome (auto-TLS), which, even in the absence of prior chemotherapy, is considered an oncologic emergency. [15] [16] [17] [18]  Characteristic laboratory findings indicative of auto-tumor lysis, stemming from high tumor burden and rapid cell turnover, often include elevated LDH, uric acid, potassium, and phosphorus levels.

Obtaining a peripheral blood smear is crucial when any (or all) of these features are present upon initial presentation. Characteristic features, in addition to generalized thrombocytopenia, include blasts, which are large, immature leukocytes with a high nuclear-to-cytoplasmic ratio, irregular nuclear contour, and smooth chromatin with prominent or multiple nucleoli. Blasts typically have cytoplasm that appears pale or deep blue with a variably eosinophilic hue. Additionally, the presence of schistocytes may be observed in cases of concurrent DIC.

Notably, a specific subtype of AML—acute promyelocytic leukemia (APL)—exhibits a distinctive and pathognomonic feature on peripheral blood morphology of abundant cytoplasmic Auer rods, which resemble clumps of azurophilic granules elongated like needles. Collectively, the presence of 20% or more blasts in peripheral blood, as confirmed by immunophenotyping (flow cytometry), is diagnostic of AML. Early involvement of hematologists and hematopathologists is recommended in suspected cases of AML to confirm the diagnosis.

Oncologic emergencies associated with AML include neurologic impairment, including visual deficits, and respiratory distress with parenchymal infiltrates due to leukostasis, DIC, and TLS, as previously mentioned. [19] Following the confirmation of an AML diagnosis, recommended tests should be ordered, including electrocardiography (ECG) and 2-dimensional (2D) echocardiography, to anticipate potential cardiotoxic effects (eg, from anthracycline therapies).

• Treatment / Management

Induction Therapy—General Considerations

All induction regimens discussed in forthcoming sections are potentially toxic to the bone marrow and can induce cytopenias and renal failure, particularly in the setting of either auto-tumor lysis, as discussed earlier, or TLS following chemotherapy. Electrolyte imbalances, notably hyperkalemia and hyperphosphatemia, are also common manifestations of TLS, underscoring the importance of establishing baseline cardiac structure and function through methods such as 2D echocardiography, ECG, and telemetry both before and throughout therapy. Another crucial aspect of induction therapy in AML is close hemodynamic monitoring, particularly temperature, within dedicated oncology units. This monitoring is essential as recovery from white blood cell (WBC) count can take up to 28 days, increasing the risk of neutropenic fever during this period.

Notably, before initiating induction therapy, it is crucial to involve bone marrow transplant (BMT) specialists early, particularly for patients with intermediate- or high-risk disease, according to the ELN 2022 criteria mentioned earlier. Allogeneic hematopoietic stem cell transplantation (HSCT) remains the only curative therapy for AML and should be considered for any patient with intermediate- or high-risk disease who achieves complete remission.

Induction Therapy—Regimen Selection

Induction therapy represents the standard of care for all patients with AML, and decisions regarding the selection of induction chemotherapy should not be solely based on age. In younger patients (typically aged 70 or younger), individuals who are deemed fit (ECOG performance status scale ≤2), and those with de novo AML without complex (ie, ≤3 abnormalities) or poor-risk characteristics, the preferred regimen is the "7+3" protocol. This regimen involves a continuous infusion of cytarabine (ie, Ara-C) for 7 days combined with anthracycline administration on days 1 to 3. When using daunorubicin, a dosage of 90 mg/m 2 /d has been associated with improved overall survival. [20]  

In patients with complex or poor-risk cytogenetics, secondary, or therapy-related AML, FLAG is the preferred regimen. [21]  In addition, for patients aged 18 to 75 receiving standard 7+3 induction therapy with FLT3-ITD mutations, quizartinib should also be added as per the results of QUANTUM. [22]  In older patients (typically aged 70 or older) and people who are deemed fit, the most commonly utilized regimen is a combination of a hypomethylating agent—either azacitidine or decitabine—and Bcl-2 inhibitor/BH3 mimetic therapy of venetoclax. [23]  

Adults who are deemed unfit for therapy may receive the best supportive care. If patients achieve complete remission, the hypomethylating agent + venetoclax regimen can be continued indefinitely, although the duration of treatment should be carefully evaluated through a comprehensive risk/benefit assessment involving the physician, family, and patient.

Lastly, if APL is suspected, then the treatment should be initiated with all-trans retinoic acid (ATRA), and diagnosis should be confirmed either by peripheral blood immunophenotyping, bone marrow biopsy, or fluorescence in situ hybridization (FISH) for t(15;17)/ PML::RARA . If the WBC count is more than 10 K/μL (considering high-risk by Sanz criteria), full induction therapy with ATRA + arsenic trioxide and anthracycline should not be initiated until the diagnosis is confirmed. [24]

Response Assessment

In young, fit individuals undergoing a 7+3 or FLAG-based induction regimen, a bone marrow biopsy should ideally be performed after induction therapy around the time of peripheral count recovery, particularly when the absolute neutrophil count exceeds 1000/μL and platelet count exceeds 100 K/μL with no blasts present. Complete remission can be considered if the marrow shows no morphological evidence of leukemia with less than 5% blasts by aspirate differential, consistent with peripheral blood count values. The timing of post-induction therapy bone marrow biopsy remains a topic of debate. Although some advocate for performing it upon full count recovery (which corresponds with approximately 28 days), others argue for a universal performance at day 14 to ensure that residual leukemia is not present and the marrow appears chemo-ablated (as expected). 

In older patients undergoing induction therapy with hypomethylating agent + venetoclax, the median response time ranges from 1.2 to 1.4 months. [25] Accordingly, the initial bone marrow biopsy following induction is typically conducted after at least 2 complete cycles of therapy, each lasting 28 days. 

Consolidation Therapy

Despite achieving a complete response with optimal induction therapy, minimal residual disease often persists, necessitating consolidation therapy to mitigate the risk of relapse by eliminating residual disease. In patients who have received 7+3 induction therapy, consolidation therapy is initiated with high-dose cytarabine, also known as HiDAC. Those who received FLAG during induction should undergo additional cycles of the same regimen during consolidation. Additionally, all patients with intermediate- or high-risk disease, regardless of the regimen, who are suitable candidates, should be offered allogeneic HSCT for complete remission, overseen by experienced BMT physicians at a high-volume center. [25] [26] [27] [28]

Relapsed/Post-HSCT Acute Myeloid Leukemia

Several agents are available for patients experiencing relapsed AML with specific mutations identified through molecular sequencing techniques. Fms-like tyrosine kinase 3 (FLT3) inhibitors, such as gilteritinib, may be recommended and have demonstrated higher complete remission rates than salvage chemotherapy in this patient population. [29]  Patients with IDH1 mutations, either in the relapsed setting or among older populations, unfit individuals unsuitable for induction therapy, should be offered ivosidenib or olutasidenib. [30] [31] Similarly, individuals with IDH2 mutations under the same designation may be offered enasidenib. [32]  Sorfaneib is approved as maintenance therapy following allogeneic HSCT for patients with FLT3-ITD mutations. [33]

Transfusional Support

All blood products must undergo irradiation to prevent transfusion-related graft versus host disease.

• Differential Diagnosis

Other diseases with presentations similar to AML include Acute lymphoblastic leukemia, anemia, aplastic anemia, B-cell lymphoma, bone marrow failure, chronic myelogenous leukemia, lymphoblastic lymphoma, MDS, myelophthisic anemia, and primary myelofibrosis.

In the past, the French-American-British (FAB) system classified AML into 8 subtypes—FAB M0 to M7—as mentioned below.

• M0: Undifferentiated AML
• M1: AML with minimal maturation
• M2: AML with maturation
• M4: Acute myelomonocytic leukemia
• M5: Acute monocytic leukemia
• M6: Acute erythroid leukemia
• M7: Acute megakaryocytic leukemia

In 2016, the World Health Organization (WHO) revised the classification of AML, categorizing it into the following groups:

• AML with recurrent genetic abnormalities
• AML with myelodysplasia-related changes
• Therapy-related myeloid neoplasms
• AML, not otherwise specified (NOS)
• Myeloid sarcoma
• Myeloid proliferations related to Down syndrome.

AML with recurrent genetic abnormalities includes the following: 

• AML with t(8;21)(q22;q22.1); RUNX1-RUNX1T1
• AML with inv(16)(p13.1q22) or t(16;16)(p13.1;q22); CBFB-MYH11
• APL with t(15;17)(q22;q12); PML-RARA
• AML with t(9;11)(p21.3;q23.3); MLLT3-KMT2A
• AML with t(6;9)(p23;q24); DEK-NUP214
• AML with inv(3)(q21.3q26.2) or t(3;3)(q21.3;q26.2); GATA2, MECOM
• AML (megakaryoblastic) with t(1;22)(p13.3;q13.3); RBM15-MKL1
• AML with mutated NPM1

 AML NOS includes the following:

• AML with minimal differentiation
• AML without maturation
• AML with maturation
• Acute myelomonocytic leukemia
• Acute monoblastic/monocytic leukemia
• Pure erythroid leukemia
• Acute megakaryoblastic leukemia
• Acute basophilic leukemia
• Acute panmyelosis with myelofibrosis

Based on its etiology, AML can be categorized into 3 main types—de novo AML, which arises spontaneously; secondary AML (s-AML), which evolves from prior myeloproliferative disorders or MDS; and therapy-related AML, resulting from exposure to chemotherapeutic agents, radiation therapy, or toxins.

The latest 2022 guidelines established by the ELN are now considered the standard for leukemia classification. Perhaps a notable alteration in these guidelines is the modification to the blast threshold needed to define disease, particularly in the presence of recurrent genetic abnormalities, set at 10% or more. Additionally, new types of AML with recurrent genetic abnormalities have been included as follows:

• AML with in-frame bZIP-mutated CEBPA
• AML with t(9;22)(q34.1;q11.2)/ BCR::ABL1

Prognosis in AML depends on an individual patient's cytogenetic and molecular characterization. A favorable-risk AML, for instance, can be diagnosed in the presence of translocation of specific chromosomal material, including t(8;21), t(15;17), and inversion of chromosome 16, or t(16;16). Higher-risk cytogenetic aberrancies or mutations, such as t(6;9)(p23.3;q34.1) or mutations in  ASXL1 and  U2AF1 , generate a higher risk and indicate a less favorable prognosis. Adverse outcomes have been noted with older age, WBC count (>100,000 at diagnosis), secondary or therapy-related AML, and the presence of leukemic cells in the central nervous system.

Recent techniques, including PCR and flow cytometry, can detect the presence of minimal residual disease in patients with complete remission. Persistently elevated levels of RUNX1 - RUNX1T1 , despite induction therapy, in patients with t(8;21) AML are associated with an increased incidence of relapse.

• Enhancing Healthcare Team Outcomes

AML, although rare among adults, can rapidly lead to fatalities if not promptly diagnosed and managed expeditiously by an interprofessional healthcare team comprising oncologists, hematologists, hematopathologists, clinical pharmacists, and nurses experienced in chemotherapy administration.

Pharmacists should provide comprehensive education to the patient regarding the chemotherapeutic regimen, covering both its benefits and potential adverse effects. Meanwhile, oncology nurses are critical in treatment administration and vigilantly monitoring for any potential complications. Additionally, nurses are crucial in educating patients and their families, particularly regarding infection prevention measures such as handwashing, fruit and vegetable rinsing, avoiding crowded places, and promptly seeking medical attention if fever develops in the outpatient setting.

Interventional radiologists are crucial in placing long-term venous catheters and conducting necessary imaging studies. Primary care physicians are responsible for educating patients on infection control measures and immunizations and managing other medical conditions. Dieticians provide valuable support in nutritional management, while social workers ensure that a patient receives comprehensive support to successfully undergo treatment.

After discharge from the inpatient setting, patients are advised to convene a weekly multidisciplinary conference to assess the initial management of AML and any ongoing care, such as consolidative chemotherapy or allogeneic stem cell transplantation, if deemed appropriate. An interprofessional approach to evaluation and management is paramount for achieving optimal patient outcomes. [34] [35]

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Anusha Vakiti declares no relevant financial relationships with ineligible companies.

Disclosure: Samuel Reynolds declares no relevant financial relationships with ineligible companies.

Disclosure: Prerna Mewawalla declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Vakiti A, Reynolds SB, Mewawalla P. Acute Myeloid Leukemia. [Updated 2024 Apr 27]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

In this Page

Bulk download.

• Bulk download StatPearls data from FTP

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Similar articles in PubMed

• Acute Myeloid Leukemia (Nursing). [StatPearls. 2024] Acute Myeloid Leukemia (Nursing). Vakiti A, Reynolds SB, Mewawalla P, Wood SK. StatPearls. 2024 Jan
• A Rare Case of Acute Myeloid Leukemia Initially Presenting With Fever of Unknown Origin and Rapidly Progressing Pericardial Effusion. [Cureus. 2024] A Rare Case of Acute Myeloid Leukemia Initially Presenting With Fever of Unknown Origin and Rapidly Progressing Pericardial Effusion. Iheagwara C, Ajayi F, Otaluka ON, Cantu Lopez C, Slim J, Shaaban HS, Muhanna A, Tewoldemedhin B, Szabela M, Boghossian J. Cureus. 2024 Jan; 16(1):e51505. Epub 2024 Jan 2.
• Physician Education: Myelodysplastic Syndrome. [Oncologist. 1996] Physician Education: Myelodysplastic Syndrome. Yoshida Y. Oncologist. 1996; 1(4):284-287.
• Review An extensive pharmacokinetic, metabolic and toxicological review of elderly patients under intensive chemotherapy for acute myeloid leukemia. [Expert Opin Drug Metab Toxicol...] Review An extensive pharmacokinetic, metabolic and toxicological review of elderly patients under intensive chemotherapy for acute myeloid leukemia. Kaur I, Constance JE, Kosak KM, Spigarelli MG, Sherwin CM. Expert Opin Drug Metab Toxicol. 2015 Jan; 11(1):53-65. Epub 2014 Dec 9.
• Review Exploiting epigenetically mediated changes: Acute myeloid leukemia, leukemia stem cells and the bone marrow microenvironment. [Adv Cancer Res. 2019] Review Exploiting epigenetically mediated changes: Acute myeloid leukemia, leukemia stem cells and the bone marrow microenvironment. Kogan AA, Lapidus RG, Baer MR, Rassool FV. Adv Cancer Res. 2019; 141:213-253. Epub 2019 Jan 21.

Recent Activity

• Acute Myeloid Leukemia - StatPearls Acute Myeloid Leukemia - StatPearls

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers