The impact of next-generation sequencing for diagnosis and disease understanding of myeloid malignancies

References

• Duncavage EJ, Bagg A, Hasserjian RP, et al. Genomic profiling for clinical decision making in myeloid neoplasms and acute leukemia. Blood. 2022;140(21):2228–2247. doi: 10.1182/blood.2022015853
   PubMed Web of Science ®Google Scholar
• Cho YU. The role of next-generation sequencing in hematologic malignancies. Blood Res. 2024;59(1):11. doi:10.1007/s44313-024-00010-0
   PubMed Web of Science ®Google Scholar
• Duployez N, Preudhomme C. Monitoring molecular changes in the management of myelodysplastic syndromes. Br J Haematol. 2024. doi: 10.1111/bjh.19614
   PubMed Web of Science ®Google Scholar
• Scott S, Travis D, Whitby L, et al. Measurement of BCR-ABL1 by RT-qPCR in chronic myeloid leukaemia: findings from an international EQA programme. Br J Haematol. 2017;177(3):414–422. doi:10.1111/bjh.14557
   PubMed Web of Science ®Google Scholar
• Hourigan CS. Achieving MRD negativity in AML: how important is this and how do we get there? Hematology Am Soc Hematol Educ Program. 2022;2022(1):9–14. doi: 10.1182/hematology.2022000323
   PubMedGoogle Scholar
• Stahl M, Derkach A, Farnoud N, et al. Molecular predictors of immunophenotypic measurable residual disease clearance in acute myeloid leukemia. Am J Hematol. 2023;98(1):79–89. doi: 10.1002/ajh.26757
   PubMed Web of Science ®Google Scholar
• Uy GL, Duncavage EJ, Chang GS, et al. Dynamic changes in the clonal structure of MDS and AML in response to epigenetic therapy. Leukemia. 2017;31(4):872–881. doi: 10.1038/leu.2016.282
   PubMed Web of Science ®Google Scholar
• Akkari YMN, Baughn LB, Dubuc AM, et al. Guiding the global evolution of cytogenetic testing for hematologic malignancies. Blood. 2022;139(15):2273–2284. doi: 10.1182/blood.2021014309
   PubMed Web of Science ®Google Scholar
• Herlin MK, Yones SA, Kjeldsen E, et al. What is abnormal in normal karyotype acute myeloid leukemia in children? Analysis of the mutational landscape and prognosis of the TARGET-AML cohort. Genes (Basel). 2021;12(6):792. doi: 10.3390/genes12060792
   PubMed Web of Science ®Google Scholar
• Nimer SD. Is it important to decipher the heterogeneity of normal karyotype AML? Best Pract Res Clin Haematol. 2008;21(1):43–52. doi:10.1016/j.beha.2007.11.010
   PubMed Web of Science ®Google Scholar
• Grimwade D, Hills RK, Moorman AV, et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the united kingdom medical research council trials. Blood. 2010;116(3):354–365. doi: 10.1182/blood-2009-11-254441
   PubMed Web of Science ®Google Scholar
• Godley L, Sukhanova M, Raca G, et al. Cytogenetics and genetic abnormalities. In: Kaushansky K, Prchal J, and Press O, et al., editors. Williams Hematology. New York, NY: McGraw Hill Education; 2016. p. 173–190.
   Google Scholar
• Schanz J, Tüchler H, Solé F, et al. New comprehensive cytogenetic scoring system for primary myelodysplastic syndromes (MDS) and oligoblastic acute myeloid leukemia after MDS derived from an international database merge. J Clin Oncol. 2012;30(8):820–829. doi: 10.1200/JCO.2011.35.6394
   PubMed Web of Science ®Google Scholar
• Cumbo C, Tota G, Anelli L, et al. TP53 in Myelodysplastic syndromes: recent biological and clinical findings. Int J Mol Sci. 2020;21(10):3432. doi: 10.3390/ijms21103432
   PubMed Web of Science ®Google Scholar
• Arber DA, Orazi A, Hasserjian RP, et al. International consensus classification of myeloid neoplasms and acute leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200–1228. doi: 10.1182/blood.2022015850
   PubMed Web of Science ®Google Scholar
• Khoury JD, Solary E, Abla O, et al. The 5th edition of the world health organization classification of haematolymphoid tumours: myeloid and Histiocytic/Dendritic neoplasms. Leukemia. 2022;36(7):1703–1719. doi: 10.1038/s41375-022-01613-1
   PubMed Web of Science ®Google Scholar
• Sahoo SS, Kozyra EJ, Wlodarski MW. Germline predisposition in myeloid neoplasms: unique genetic and clinical features of GATA2 deficiency and SAMD9/SAMD9L syndromes. Best Pract Res Clin Haematol. 2020;33(3):101197. doi: 10.1016/j.beha.2020.101197
   PubMed Web of Science ®Google Scholar
• Sahoo SS, Pastor VB, Goodings C, et al. Clinical evolution, genetic landscape and trajectories of clonal hematopoiesis in SAMD9/SAMD9L syndromes. Nat Med. 2021;27(10):1806–1817. doi: 10.1038/s41591-021-01511-6
   PubMed Web of Science ®Google Scholar
• Yang H, Garcia-Manero G, Sasaki K, et al. High-resolution structural variant profiling of myelodysplastic syndromes by optical genome mapping uncovers cryptic aberrations of prognostic and therapeutic significance. Leukemia. 2022;36(9):2306–2316. doi: 10.1038/s41375-022-01652-8
   PubMed Web of Science ®Google Scholar
• Levy B, Baughn LB, Akkari Y, et al. Optical genome mapping in acute myeloid leukemia: a multicenter evaluation. Blood Adv. 2023;7(7):1297–1307. doi: 10.1182/bloodadvances.2022007583
   PubMed Web of Science ®Google Scholar
• Kivioja J, Malani D, Kumar A, et al. FLT3-ITD allelic ratio and HLF expression predict FLT3 inhibitor efficacy in adult AML. Sci Rep. 2021;11(1):23565. doi: 10.1038/s41598-021-03010-7
   PubMed Web of Science ®Google Scholar
• Ayala R, Carreño-Tarragona G, Barragán E, et al. Impact of FLT3–ITD mutation status and its ratio in a cohort of 2901 patients undergoing upfront intensive chemotherapy: a PETHEMA registry study. Cancers (Basel). 2022;14(23):5799. doi: 10.3390/cancers14235799
   PubMed Web of Science ®Google Scholar
• Stone RM, Mandrekar SJ, Sanford BL, et al. Midostaurin plus chemotherapy for acute myeloid leukemia with a FLT3 mutation. N Engl J Med. 2017;377(5):454–464. doi: 10.1056/NEJMoa1614359
   PubMed Web of Science ®Google Scholar
• Zhao JC, Agarwal S, Ahmad H, et al. A review of FLT3 inhibitors in acute myeloid leukemia. Blood Rev. 2022;52:100905. doi:10.1016/j.blre.2021.100905
   PubMed Web of Science ®Google Scholar
• Salmoiraghi S, Cavagna R, Zanghì P, et al. High throughput molecular characterization of normal karyotype acute myeloid leukemia in the context of the prospective trial 02/06 of the northern italy leukemia group (NILG). Cancers (Basel). 2020;12(8):2242. doi: 10.3390/cancers12082242
   PubMed Web of Science ®Google Scholar
• Liquori A, Lesende I, Palomo L, et al. A single-run next-generation sequencing (NGS) assay for the simultaneous detection of both gene mutations and large chromosomal abnormalities in patients with Myelodysplastic syndromes (MDS) and related myeloid neoplasms. Cancers (Basel). 2021;13(8):1947. doi: 10.3390/cancers13081947
   PubMed Web of Science ®Google Scholar
• Mareschal S, Palau A, Lindberg J, et al. Challenging conventional karyotyping by next-generation karyotyping in 281 intensively treated patients with AML. Blood Adv. 2021;5(4):1003–1016. doi: 10.1182/bloodadvances.2020002517
   PubMed Web of Science ®Google Scholar
• Bidet A, Quessada J, Cuccuini W, et al. Cytogenetics in the management of acute myeloid leukemia and histiocytic/dendritic cell neoplasms: guidelines from the groupe francophone de cytogénétique Hématologique (GFCH). Curr Res Transl Med. 2023;71(4):103421. doi: 10.1016/j.retram.2023.103421
   PubMed Web of Science ®Google Scholar
• Arber DA, Campo E, Jaffe ES. Advances in the classification of myeloid and lymphoid neoplasms. Virchows Arch. 2023;482(1):1–9. doi:10.1007/s00428-022-03487-1
   PubMed Web of Science ®Google Scholar
• Weinberg OK, Porwit A, Orazi A, et al. The international consensus classification of acute myeloid leukemia. Virchows Arch. 2023;482(1):27–37. doi: 10.1007/s00428-022-03430-4
   PubMed Web of Science ®Google Scholar
• Döhner H, Wei AH, Appelbaum FR, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345–1377. doi: 10.1182/blood.2022016867
   PubMed Web of Science ®Google Scholar
• Cheng WY, Li JF, Zhu YM, et al. Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia. Proc Natl Acad Sci USA. 2022;119(49):e2211429119. doi: 10.1073/pnas.2211429119
   PubMed Web of Science ®Google Scholar
• Samorodnitsky E, Jewell BM, Hagopian R, et al. Evaluation of hybridization capture versus amplicon-based methods for whole-exome sequencing. Hum Mutat. 2015;36(9):903–914. doi: 10.1002/humu.22825
   PubMed Web of Science ®Google Scholar
• Hung SS, Meissner B, Chavez EA, et al. Assessment of capture and amplicon-based approaches for the development of a targeted next-generation sequencing pipeline to personalize lymphoma management. J Mol Diagn. 2018;20(2):203–214. doi: 10.1016/j.jmoldx.2017.11.010
   PubMed Web of Science ®Google Scholar
• Casbon JA, Osborne RJ, Brenner S, et al. A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids Res. 2011;39(12):e81. doi:10.1093/nar/gkr217
   PubMed Web of Science ®Google Scholar
• Hiatt JB, Pritchard CC, Salipante SJ, et al. Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation. Genome Res. 2013;23(5):843–854. doi: 10.1101/gr.147686.112
   PubMed Web of Science ®Google Scholar
• Ye K, Schulz MH, Long Q, et al. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics. 2009;25(21):2865–2871. doi:10.1093/bioinformatics/btp394
   PubMed Web of Science ®Google Scholar
• Chen X, Schulz-Trieglaff O, Shaw R, et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016;32(8):1220–1222. doi: 10.1093/bioinformatics/btv710
   PubMed Web of Science ®Google Scholar
• Eisfeldt J, Vezzi F, Olason P, et al. TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data. F1000Res. 2017;6:664. doi:10.12688/f1000research.11168.1
   PubMedGoogle Scholar
• Cameron DL, Schröder J, Penington JS, et al. GRIDSS: sensitive and specific genomic rearrangement detection using positional de bruijn graph assembly. Genome Res. 2017;27(12):2050–2060. doi: 10.1101/gr.222109.117
   PubMed Web of Science ®Google Scholar
• Cucchi DGJ, Denys B, Kaspers GJL, et al. RNA-based FLT3-ITD allelic ratio is associated with outcome and ex vivo response to FLT3 inhibitors in pediatric AML. Blood. 2018;131(22):2485–2489. doi: 10.1182/blood-2017-12-819508
   PubMed Web of Science ®Google Scholar
• Pollyea DA, Altman JK, Assi R, et al. Acute myeloid leukemia, version 3.2023, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2023;21(5):503–513. doi: 10.6004/jnccn.2023.0025
   PubMed Web of Science ®Google Scholar
• Kim B, Kim E, Lee ST, et al. Detection of recurrent, rare, and novel gene fusions in patients with acute leukemia using next-generation sequencing approaches. Hematol Oncol. 2020;38(1):82–88. doi: 10.1002/hon.2709
   PubMed Web of Science ®Google Scholar
• Qu X, Yeung C, Coleman I, et al. Comparison of four next generation sequencing platforms for fusion detection: oncomine by ThermoFisher, AmpliSeq by illumina, FusionPlex by ArcherDX, and QIAseq by QIAGEN. Cancer Genet. 2020;243:11–18. doi:10.1016/j.cancergen.2020.02.007
   PubMed Web of Science ®Google Scholar
• Arindrarto W, Borràs DM, de Groen RAL, et al. Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing. Leukemia. 2021;35(1):47–61. doi: 10.1038/s41375-020-0762-8
   PubMed Web of Science ®Google Scholar
• Heydt C, Wölwer CB, Velazquez Camacho O, et al. Detection of gene fusions using targeted next-generation sequencing: a comparative evaluation. BMC Med Genomics. 2021;14(1):62. doi: 10.1186/s12920-021-00909-y
   PubMed Web of Science ®Google Scholar
• Cilloni D, Petiti J, Rosso V, et al. Digital PCR in myeloid malignancies: ready to replace quantitative PCR? Int J Mol Sci. 2019;20(9):2249. doi: 10.3390/ijms20092249
   PubMed Web of Science ®Google Scholar
• Coccaro N, Tota G, Anelli L, et al. Digital PCR: a reliable tool for analyzing and monitoring hematologic malignancies. Int J Mol Sci. 2020;21(9):3141. doi: 10.3390/ijms21093141
   PubMed Web of Science ®Google Scholar
• Brunetti C, Anelli L, Zagaria A, et al. Droplet digital PCR is a reliable tool for monitoring minimal residual disease in acute promyelocytic leukemia. J Mol Diagn. 2017;19(3):437–444. doi: 10.1016/j.jmoldx.2017.01.004
   PubMed Web of Science ®Google Scholar
• Chin L, Wong CYG, Gill H. Targeting and monitoring acute myeloid leukaemia with nucleophosmin-1 (NPM1) mutation. Int J Mol Sci. 2023;24(4):3161. doi: 10.3390/ijms24043161
   PubMed Web of Science ®Google Scholar
• Cabrera K, Gole J, Leatham B, et al. Analytical performance and concordance with next-generation sequencing of a rapid, multiplexed dPCR panel for the detection of DNA and RNA biomarkers in non-small-cell lung cancer. Diagnostics (Basel). 2023;13(21):3299. doi: 10.3390/diagnostics13213299
   PubMedGoogle Scholar
• Spencer DH, Tyagi M, Vallania F, et al. Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data. J Mol Diagn. 2014;16(1):75–88. doi: 10.1016/j.jmoldx.2013.09.003
   PubMed Web of Science ®Google Scholar
• Peng Q, Xu C, Kim D, et al. Targeted single primer enrichment sequencing with single end duplex-UMI. Sci Rep. 2019;9(1):4810. doi:10.1038/s41598-019-41215-z
   PubMedGoogle Scholar
• Patkar N, Kakirde C, Shaikh AF, et al. Clinical impact of panel-based error-corrected next generation sequencing versus flow cytometry to detect measurable residual disease (MRD) in acute myeloid leukemia (AML). Leukemia. 2021;35(5):1392–1404. doi: 10.1038/s41375-021-01131-6
   PubMed Web of Science ®Google Scholar
• Li Y, Solis-Ruiz J, Yang F, et al. NGS-defined measurable residual disease (MRD) after initial chemotherapy as a prognostic biomarker for acute myeloid leukemia. Blood Cancer J. 2023;13(1):59. doi: 10.1038/s41408-023-00833-7
   PubMed Web of Science ®Google Scholar
• Gurbuxani S, Hochman MJ, DeZern AE, et al. The times, they are A-Changing: the impact of next-generation sequencing on diagnosis, classification, and prognostication of myeloid malignancies with focus on myelodysplastic syndrome, AML, and germline predisposition. Am Soc Clin Oncol Educ Book. 2023;43(43):e390026. doi: 10.1200/EDBK_390026
   PubMedGoogle Scholar
• Gurnari C, Robin M, Godley LA, et al. Germline predisposition traits in allogeneic hematopoietic stem-cell transplantation for myelodysplastic syndromes: a survey-based study and position paper on behalf of the chronic malignancies working party of the EBMT. Lancet Haematol. 2023;10(12):e994–e1005. doi: 10.1016/S2352-3026(23)00265-X
   PubMed Web of Science ®Google Scholar
• Kanagal-Shamanna R, Schafernak KT, Calvo KR. Diagnostic work-up of hematological malignancies with underlying germline predisposition disorders (GPD). Semin Diagn Pathol. 2023;40(6):443–456. doi:10.1053/j.semdp.2023.11.004
   PubMed Web of Science ®Google Scholar
• Obiorah IE, Upadhyaya KD, Calvo KR. Germline predisposition to myeloid neoplasms: diagnostic concepts and classifications. Clin Lab Med. 2023;43(4):615–638. doi: 10.1016/j.cll.2023.06.004
   PubMed Web of Science ®Google Scholar
• Drazer MW, Kadri S, Sukhanova M, et al. Prognostic tumor sequencing panels frequently identify germ line variants associated with hereditary hematopoietic malignancies. Blood Adv. 2018;2(2):146–150. doi: 10.1182/bloodadvances.2017013037
   PubMed Web of Science ®Google Scholar
• Kraft IL, Basdag H, Koppayi A, et al. Sequential tumor molecular profiling identifies likely germline variants. Genet Med. 2024;26(3):101037. doi: 10.1016/j.gim.2023.101037
   PubMed Web of Science ®Google Scholar
• Williams LS, Williams KM, Gillis N, et al. Donor-derived malignancy and transplantation morbidity: risks of patient and donor genetics in allogeneic hematopoietic stem cell transplantation. Transpl Cell Ther. 2024;30(3):255–267. doi: 10.1016/j.jtct.2023.10.018
   PubMedGoogle Scholar
• Cai SF, Levine RL. Genetic and epigenetic determinants of AML pathogenesis. Semin Hematol. 2019;56(2):84–89. doi:10.1053/j.seminhematol.2018.08.001
   PubMed Web of Science ®Google Scholar
• DeRoin L, Cavalcante de Andrade Silva M, Petras K, et al. Feasibility and limitations of cultured skin fibroblasts for germline genetic testing in hematologic disorders. Hum Mutat. 2022;43(7):950–962. doi: 10.1002/humu.24374
   PubMed Web of Science ®Google Scholar
• Makishima H, Saiki R, Nannya Y, et al. Germ line DDX41 mutations define a unique subtype of myeloid neoplasms. Blood. 2023;141(5):534–549. doi: 10.1182/blood.2022018221
   PubMed Web of Science ®Google Scholar
• Makishima H, Bowman TV, Godley LA. DDX41-associated susceptibility to myeloid neoplasms. Blood. 2023;141(13):1544–1552. doi: 10.1182/blood.2022017715
   PubMed Web of Science ®Google Scholar
• Duncavage EJ, Schroeder MC, O’Laughlin M, et al. Genome sequencing as an alternative to cytogenetic analysis in myeloid cancers. N Engl J Med. 2021;384(10):924–935. doi: 10.1056/NEJMoa2024534
   PubMed Web of Science ®Google Scholar
• Euskirchen P, Bielle F, Labreche K, et al. Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing. Acta Neuropathol. 2017;134(5):691–703. doi: 10.1007/s00401-017-1743-5
   PubMed Web of Science ®Google Scholar
• Nattestad M, Goodwin S, Ng K, et al. Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome Res. 2018;28(8):1126–1135. doi: 10.1101/gr.231100.117
   PubMed Web of Science ®Google Scholar
• Oehler JB, Wright H, Stark Z, et al. The application of long-read sequencing in clinical settings. Hum Genomics. 2023;17(1):73. doi:10.1186/s40246-023-00522-3
   PubMed Web of Science ®Google Scholar
• Cumbo C, Minervini CF, Orsini P, et al. Nanopore targeted sequencing for rapid gene mutations detection in acute myeloid leukemia. Genes (Basel). 2019;10(12):1026. doi: 10.3390/genes10121026
   PubMed Web of Science ®Google Scholar
• Jeck WR, Lee J, Robinson H, et al. A nanopore sequencing-based assay for rapid detection of gene fusions. J Mol Diagn. 2019;21(1):58–69. doi: 10.1016/j.jmoldx.2018.08.003
   PubMed Web of Science ®Google Scholar
• Sala-Torra O, Reddy S, Hung LH, et al. Rapid detection of myeloid neoplasm fusions using single-molecule long-read sequencing. PloS Glob Public Health. 2023;3(9):e0002267. doi: 10.1371/journal.pgph.0002267
   PubMedGoogle Scholar
• Norris AL, Workman RE, Fan Y, et al. Nanopore sequencing detects structural variants in cancer. Cancer Biol Ther. 2016;17(3):246–253. doi:10.1080/15384047.2016.1139236
   PubMed Web of Science ®Google Scholar
• Tse OYO, Jiang P, Cheng SH, et al. Genome-wide detection of cytosine methylation by single molecule real-time sequencing. Proc Natl Acad Sci U S A. 2021;118(5). doi: 10.1073/pnas.2019768118
   Web of Science ®Google Scholar
• Zhang J, Xie S, Xu J, et al. Cancer biomarkers discovery of methylation modification with direct high-throughput nanopore sequencing. Front Genet. 2021;12:672804. doi:10.3389/fgene.2021.672804
   PubMed Web of Science ®Google Scholar
• Karst SM, Ziels RM, Kirkegaard RH, et al. High-accuracy long-read amplicon sequences using unique molecular identifiers with nanopore or PacBio sequencing. Nat Methods. 2021;18(2):165–169. doi: 10.1038/s41592-020-01041-y
   PubMed Web of Science ®Google Scholar
• Ediriwickrema A, Gentles AJ, Majeti R. Single-cell genomics in AML: extending the frontiers of AML research. Blood. 2023;141(4):345–355. doi:10.1182/blood.2021014670
   PubMed Web of Science ®Google Scholar
• O’Sullivan JM, Mead AJ, Psaila B. Single-cell methods in myeloproliferative neoplasms: old questions, new technologies. Blood. 2023;141(4):380–390. doi:10.1182/blood.2021014668
   PubMed Web of Science ®Google Scholar
• Ediriwickrema A, Aleshin A, Reiter JG, et al. Single-cell mutational profiling enhances the clinical evaluation of AML MRD. Blood Adv. 2020;4(5):943–952. doi: 10.1182/bloodadvances.2019001181
   PubMed Web of Science ®Google Scholar
• Rutella S, Vadakekolathu J, Mazziotta F, et al. Immune dysfunction signatures predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia. J Clin Invest. 2022;132(21). doi: 10.1172/JCI159579
   PubMed Web of Science ®Google Scholar
• Li Z, Liu X, Wang L, et al. Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia. Front Immunol. 2023;14:1281687. doi: 10.3389/fimmu.2023.1281687
   PubMed Web of Science ®Google Scholar
• Gao X. Integrated analysis of single-cell RNA-Seq and bulk RNA-Seq unravels the molecular feature of tumor-associated macrophage of acute myeloid leukemia. Genet Res (Camb). 2024;2024:5539065. doi: 10.1155/2024/5539065
   PubMed Web of Science ®Google Scholar
• Guijarro F, Garrote M, Villamor N, et al. Novel tools for diagnosis and monitoring of AML. Curr Oncol. 2023;30(6):5201–5213. doi:10.3390/curroncol30060395
   PubMed Web of Science ®Google Scholar
• Selim AG, Moore AS. Molecular minimal residual disease monitoring in acute myeloid leukemia: challenges and future directions. J Mol Diagn. 2018;20(4):389–397. doi:10.1016/j.jmoldx.2018.03.005
   PubMed Web of Science ®Google Scholar
• Shimony S, Stahl M, Stone RM. Acute myeloid leukemia: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98(3):502–526. doi:10.1002/ajh.26822
   PubMed Web of Science ®Google Scholar
• Alhajahjeh A, Nazha A. Unlocking the potential of artificial intelligence in acute myeloid leukemia and myelodysplastic syndromes. Curr Hematol Malig Rep. 2024;19(1):9–17. doi:10.1007/s11899-023-00716-5
   PubMed Web of Science ®Google Scholar
• Bernardi S, Vallati M, Gatta R. Artificial intelligence-based management of adult chronic myeloid leukemia: where are we and where are we going? Cancers (Basel). 2024;16(5):848. doi: 10.3390/cancers16050848
   PubMed Web of Science ®Google Scholar