{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T16:00:14Z","timestamp":1787932814228,"version":"build-2784847793"},"reference-count":98,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T00:00:00Z","timestamp":1737763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning regimen, and prediction of post-transplant outcomes. Machine learning approaches, including decision trees, random forests, and neural networks, have demonstrated potential in improving donor compatibility algorithms, mortality and relapse prediction, and GVHD risk stratification. Integrating \u201comics\u201d data with ML models has enabled the identification of novel biomarkers and the development of highly accurate predictive tools, supporting personalized treatment strategies. Despite promising advancements, challenges persist, including data standardization, algorithm interpretability, and ethical considerations regarding patient privacy. While ML holds promise for revolutionizing HSCT management, addressing these barriers through multicenter collaborations and regulatory frameworks remains essential for broader clinical adoption. In addition, the potential of ML can cope with some challenges such as data harmonization, patients\u2019 data protection, and availability of adequate infrastructure. Future research should prioritize larger datasets, multimodal data integration, and robust validation methods to fully realize ML\u2019s transformative potential in HSCT.<\/jats:p>","DOI":"10.3390\/cancers17030395","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T07:46:29Z","timestamp":1737963989000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1661-2065","authenticated-orcid":false,"given":"Luca","family":"Garuffo","sequence":"first","affiliation":[{"name":"Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy"},{"name":"CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Leoni","sequence":"additional","affiliation":[{"name":"Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy"},{"name":"CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4716-9925","authenticated-orcid":false,"given":"Roberto","family":"Gatta","sequence":"additional","affiliation":[{"name":"Department of Clinical and Experimental Sciences, University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3494-2624","authenticated-orcid":false,"given":"Simona","family":"Bernardi","sequence":"additional","affiliation":[{"name":"Unit of Blood Disease and Stem Cell Transplantation, Department of Clinical and Experimental Sciences, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy"},{"name":"CREA (Centro di Ricerca Emato-Oncologica AIL), ASST Spedali Civili of Brescia, 25123 Brescia, Italy"},{"name":"National Center for Gene Therapy and Drugs Based on RNA Technology\u2014CN3, 35122 Padua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1038\/s41409-022-01702-w","article-title":"Hematopoietic stem cell transplantation and cellular therapies for autoimmune diseases: Overview and future considerations from the Autoimmune Diseases Working Party (ADWP) of the European Society for Blood and Marrow Transplantation (EBMT)","volume":"57","author":"Alexander","year":"2022","journal-title":"Bone Marrow Transpl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Polverelli, N., Farina, M., D\u2019Adda, M., Damiani, E., Grazioli, L., Leoni, A., Malagola, M., Bernardi, S., and Russo, D. (2022). How We Manage Myelofibrosis Candidates for Allogeneic Stem Cell Transplantation. Cells, 11.","DOI":"10.3390\/cells11030553"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108589","DOI":"10.1016\/j.clim.2020.108589","article-title":"Successful hematopoietic stem cell transplantation for complete CTLA-4 haploinsufficiency due to a de novo monoallelic 2q33.2-2q33.3 deletion","volume":"220","author":"Lougaris","year":"2020","journal-title":"Clin. Immunol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0076-6879(06)19007-2","article-title":"Hematopoietic stem cells","volume":"419","author":"Hawley","year":"2006","journal-title":"Methods Enzym."},{"key":"ref_5","first-page":"108","article-title":"Pattern of bone marrow regeneration following chemotherapy for acute myeloid leukemia","volume":"18","author":"Islam","year":"1987","journal-title":"J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1056\/NEJMra052638","article-title":"Hematopoietic stem-cell transplantation","volume":"354","author":"Copelan","year":"2006","journal-title":"N. Engl. J. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.bbmt.2005.09.004","article-title":"National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: I. Diagnosis and staging working group report","volume":"11","author":"Filipovich","year":"2005","journal-title":"Biol. Blood Marrow Transpl."},{"key":"ref_8","unstructured":"Justiz Vaillant, A.A., and Modi, P.M.O. (2024, December 12). Graft-Versus-Host Disease, Available online: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK538235\/."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1182\/blood.V120.21.4159.4159","article-title":"Clinical Outcomes of Haploidentical Donor Compared with Unrelated and HLA-Matched Related Donor Hematopoietic Stem Cell Transplantation for Hematologic Malignancies","volume":"120","author":"Luo","year":"2012","journal-title":"Blood"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s41409-019-0684-0","article-title":"Autologous haematopoietic stem cell transplantation and other cellular therapy in multiple sclerosis and immune-mediated neurological diseases: Updated guidelines and recommendations from the EBMT Autoimmune Diseases Working Party (ADWP) and the Joint Acc","volume":"55","author":"Sharrack","year":"2020","journal-title":"Bone Marrow Transpl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1182\/blood-2014-02-514778","article-title":"Conditioning regimens for hematopoietic cell transplantation: One size does not fit all","volume":"124","author":"Gyurkocza","year":"2014","journal-title":"Blood"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1200\/JCO.2014.59.1339","article-title":"Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Stud","volume":"33","author":"Shouval","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bernardi, S., Vallati, M., and Gatta, R. (2024). Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?. Cancers, 16.","DOI":"10.3390\/cancers16050848"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-030-16391-4_11","article-title":"Artificial Intelligence and Personalized Medicine","volume":"178","author":"Schork","year":"2019","journal-title":"Cancer Treat. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gupta, V., Braun, T.M., Chowdhury, M., Tewari, M., and Choi, S.W. (2020). A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT). Sensors, 20.","DOI":"10.3390\/s20216100"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s41467-017-02465-5","article-title":"A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia","volume":"9","author":"Lee","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Press, A. (2014). Introduction to Probability and Statistics for Engineers and Scientists, Elsevier.","DOI":"10.1016\/B978-0-12-394811-3.50001-0"},{"key":"ref_18","unstructured":"Russel, S., and Norvig, P. (2016). Artificial Intelligence: A Modern Approach, Pearson and M. Education Limited."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0304-3800(98)00149-5","article-title":"An artificial neural network approach to spatial habitat modelling with interspecific interaction","volume":"116","year":"1999","journal-title":"Ecol. Modell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s00428-022-03430-4","article-title":"The International Consensus Classification of acute myeloid leukemia","volume":"482","author":"Weinberg","year":"2023","journal-title":"Virchows Arch."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1038\/s41374-019-0325-7","article-title":"Machine-based detection and classification for bone marrow aspirate differential counts: Initial development focusing on nonneoplastic cells","volume":"100","author":"Chandradevan","year":"2020","journal-title":"Lab. Investig."},{"key":"ref_22","first-page":"1350","article-title":"A non-classical translocation involving 17q12 (retinoic acid receptor alpha) in acute promyelocytic leukemia (APML) with atypical features","volume":"8","author":"Corey","year":"1994","journal-title":"Leukemia"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1182\/blood-2010-01-264432","article-title":"BCOR as a novel fusion partner of retinoic acid receptor alpha in a t(X;17)(p11;q12) variant of acute promyelocytic leukemia","volume":"116","author":"Yamamoto","year":"2010","journal-title":"Blood"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/0165-4608(89)90133-7","article-title":"A new variant translocation 11;17 in a patient with acute promyelocytic leukemia together with t(7;12)","volume":"43","author":"Najfeld","year":"1989","journal-title":"Cancer Genet. Cytogenet."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1007\/s12288-017-0844-6","article-title":"Myeloperoxidase Deficient Acute Promyelocytic Leukemia: Report of Two Cases","volume":"34","author":"Rastogi","year":"2018","journal-title":"Indian J. Hematol. Blood Transfus."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.3324\/haematol.2022.280406","article-title":"Diagnosis of acute promyelocytic leukemia based on routine biological parameters using machine learning","volume":"107","author":"Cheli","year":"2022","journal-title":"Haematologica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1093\/ajcp\/aqab086","article-title":"A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia with Retrospective Electronic Health Records Data","volume":"156","author":"Hauser","year":"2021","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.1182\/bloodadvances.2020002230","article-title":"Artificial intelligence-based morphological fingerprinting of megakaryocytes: A new tool for assessing disease in MPN patients","volume":"4","author":"Sirinukunwattana","year":"2020","journal-title":"Blood Adv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kimura, K., Ai, T., Horiuchi, Y., Matsuzaki, A., Nishibe, K., Marutani, S., Saito, K., Kaniyu, K., Takehara, I., and Uchihashi, K. (2021). Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-82826-9"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1002\/jcp.1040910303","article-title":"Conditions controlling the proliferation of haemopoietic stem cells in vitro","volume":"91","author":"Dexter","year":"1977","journal-title":"J. Cell Physiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1182\/blood.V46.1.65.65","article-title":"The relative spatial distributions of CFUs and CFUc in the normal mouse femur","volume":"46","author":"Lord","year":"1975","journal-title":"Blood"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1126\/science.75570","article-title":"Endosteal marrow: A rich source of hematopoietic stem cells","volume":"199","author":"Gong","year":"1978","journal-title":"Science"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1172\/JCI110734","article-title":"Hemopoietic colony-forming cells in umbilical cord blood with extensive capability to generate mono- and multipotential hemopoietic progenitors","volume":"70","author":"Nakahata","year":"1982","journal-title":"J. Clin. Investig."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.htct.2023.03.021","article-title":"Impact of waitlist time on post-HSCT survival: A cohort study at a hospital in southern Brazil","volume":"46","author":"Silva","year":"2024","journal-title":"Hematol. Transfus. Cell Ther."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1038\/s41409-023-01943-3","article-title":"Hematopoietic cell transplantation and cellular therapies in Europe 2021. The second year of the SARS-CoV-2 pandemic. A Report from the EBMT Activity Survey","volume":"58","author":"Passweg","year":"2023","journal-title":"Bone Marrow Transpl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.3324\/haematol.2024.285002","article-title":"Continuous and differential improvement in worldwide access to hematopoietic cell transplantation: Activity has doubled in a decade with a notable increase in unrelated and non-identical related donors","volume":"109","author":"Atsuta","year":"2024","journal-title":"Haematologica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1182\/blood.V75.3.555.555","article-title":"Graft-versus-leukemia reactions after bone marrow transplantation","volume":"75","author":"Horowitz","year":"1990","journal-title":"Blood"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1038\/s41409-021-01287-w","article-title":"Changes in Stem Cell Transplant activity and procedures during SARS-CoV2 pandemic in Italy: An Italian Bone Marrow Transplant Group (GITMO) nationwide analysis (TransCOVID-19 Survey)","volume":"56","author":"Russo","year":"2021","journal-title":"Bone Marrow Transpl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Malagola, M., Polverelli, N., Gandolfi, L., Zollner, T., Bernardi, S., Zanaglio, C., Re, F., Morello, E., Turra, A., and Isidori, A. (2020). Severe Acute Respiratory Syndrome Coronavirus-2 Pandemia: Facts and Perspectives in a Bone Marrow Transplant Unit. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.01626"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/leu.2012.210","article-title":"Allogeneic stem cell transplantation for older advanced MDS patients: Improved survival with young unrelated donor in comparison with HLA-identical siblings","volume":"27","author":"Zabelina","year":"2013","journal-title":"Leukemia"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1001\/jamaoncol.2021.6846","article-title":"Relapse and Disease-Free Survival in Patients with Myelodysplastic Syndrome Undergoing Allogeneic Hematopoietic Cell Transplantation Using Older Matched Sibling Donors vs Younger Matched Unrelated Donors","volume":"8","author":"Kim","year":"2022","journal-title":"JAMA Oncol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"843.e1","DOI":"10.1016\/j.jtct.2022.08.022","article-title":"Limited Contribution of Donor Characteristics to One-Year Survival After Hematopoietic Stem Cell Transplantation","volume":"28","author":"Israeli","year":"2022","journal-title":"Transpl. Cell Ther."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1093\/humupd\/dmr005","article-title":"Secondary recurrent miscarriage and H-Y immunity","volume":"17","author":"Nielsen","year":"2011","journal-title":"Hum. Reprod. Updat."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1093\/cid\/ciu364","article-title":"of the E.G. for B.; et al. Donor cytomegalovirus status influences the outcome of allogeneic stem cell transplant: A study by the European group for blood and marrow transplantation","volume":"59","author":"Ljungman","year":"2014","journal-title":"Clin. Infect. Dis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1016\/j.bbmt.2016.05.020","article-title":"Impact of Donor and Recipient Cytomegalovirus Serostatus on Outcomes of Antithymocyte Globulin-Conditioned Hematopoietic Cell Transplantation","volume":"22","author":"Kalra","year":"2016","journal-title":"Biol. Blood Marrow Transpl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Farina, M., Bernardi, S., Gandolfi, L., Zanaglio, C., Morello, E., Turra, A., Zollner, T., Gramegna, D., Rambaldi, B., and Cattina, F. (2020). Case Report: Late Onset of Myelodysplastic Syndrome From Donor Progenitor Cells After Allogeneic Stem Cell Transplantation. Which Lessons Can We Draw From the Reported Case?. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.564521"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7051","DOI":"10.4081\/hr.2017.7051","article-title":"Single step multiple genotyping by MALDI-TOF mass spectrometry, for evaluation of minor histocompatibility antigens in patients submitted to allogeneic stem cell transplantation from HLA-matched related and unrelated donor","volume":"9","author":"Cattina","year":"2017","journal-title":"Hematol. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1038\/bmt.2016.162","article-title":"Unrelated donor search prognostic score to support early HLA consultation and clinical decisions","volume":"51","author":"Wadsworth","year":"2016","journal-title":"Bone Marrow Transpl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1200\/CCI.20.00185","article-title":"Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning","volume":"5","author":"Logan","year":"2021","journal-title":"JCO Clin. Cancer Inf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1016\/j.bbmt.2018.01.038","article-title":"Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection","volume":"24","author":"Buturovic","year":"2018","journal-title":"Biol. Blood Marrow Transpl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/s41409-022-01871-8","article-title":"Identifying the optimal conditioning intensity for stem cell transplantation in patients with myelodysplastic syndrome: A machine learning analysis","volume":"58","author":"Shimomura","year":"2023","journal-title":"Bone Marrow Transpl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3664","DOI":"10.1182\/blood-2014-01-552984","article-title":"Validation and refinement of the Disease Risk Index for allogeneic stem cell transplantation","volume":"123","author":"Armand","year":"2014","journal-title":"Blood"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1002\/ajh.24677","article-title":"Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort","volume":"92","author":"Shouval","year":"2017","journal-title":"Am. J. Hematol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5058","DOI":"10.1002\/cam4.2401","article-title":"Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach","volume":"8","author":"Fuse","year":"2019","journal-title":"Cancer Med."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shouval, R., Labopin, M., Unger, R., Giebel, S., Ciceri, F., Schmid, C., Esteve, J., Baron, F., Gorin, N.C., and Savani, B. (2016). Prediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150637"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/bmt.2016.142","article-title":"Identification of high-risk amino-acid substitutions in hematopoietic cell transplantation: A challenging task","volume":"51","author":"Marino","year":"2016","journal-title":"Bone Marrow Transpl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1038\/s41375-018-0229-3","article-title":"Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation","volume":"33","author":"Ritari","year":"2019","journal-title":"Leukemia"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1200\/JCO.20.02810","article-title":"Personalized Prediction Model to Risk Stratify Patients with Myelodysplastic Syndromes","volume":"39","author":"Nazha","year":"2021","journal-title":"J. Clin. Oncol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e961","DOI":"10.1097\/HS9.0000000000000961","article-title":"Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes","volume":"7","author":"Mora","year":"2023","journal-title":"Hemasphere"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Goswami, C., Poonia, S., Kumar, L., and Sengupta, D. (2019). Staging System to Predict the Risk of Relapse in Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation. Front. Oncol., 9.","DOI":"10.3389\/fonc.2019.00633"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3324\/haematol.2018.193441","article-title":"Machine learning reveals chronic graft-versus-host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies","volume":"104","author":"Gandelman","year":"2019","journal-title":"Haematologica"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e2100156","DOI":"10.1200\/CCI.21.00156","article-title":"Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients with Acute Myeloid Leukemia Posthematopoietic Cell Transplantation","volume":"6","author":"Arabyarmohammadi","year":"2022","journal-title":"JCO Clin. Cancer Inf."},{"key":"ref_63","first-page":"2455","article-title":"A BLSTM with Attention Network for Predicting Acute Myeloid Leukemia Patient\u2019s Prognosis using Comprehensive Clinical Parameters","volume":"2019","author":"Lu","year":"2019","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6732","DOI":"10.1182\/bloodadvances.2023010404","article-title":"Prognostic model using 18F-FDG PET radiomics predicts progression-free survival in relapsed\/refractory Hodgkin lymphoma","volume":"7","author":"Driessen","year":"2023","journal-title":"Blood Adv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.1182\/bloodadvances.2021005800","article-title":"Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning","volume":"6","author":"Iwasaki","year":"2022","journal-title":"Blood Adv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1182\/blood.2021013054","article-title":"Signatures of GVHD and relapse after posttransplant cyclophosphamide revealed by immune profiling and machine learning","volume":"139","author":"McCurdy","year":"2022","journal-title":"Blood"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"345","DOI":"10.31661\/jbpe.v0i0.2012-1244","article-title":"An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation","volume":"11","author":"Salehnasab","year":"2021","journal-title":"J. Biomed. Phys. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chandra, G., Wang, J., Siirtola, P., and R\u00f6ning, J. (2024). Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia. BMC Med. Res. Methodol., 24.","DOI":"10.1186\/s12874-024-02237-y"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3626","DOI":"10.1182\/bloodadvances.2019000934","article-title":"Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation","volume":"3","author":"Arai","year":"2019","journal-title":"Blood Adv."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s43856-023-00299-5","article-title":"A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation","volume":"3","author":"Jo","year":"2023","journal-title":"Commun. Med."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Rowley, S.D., Gunning, T.S., Pelliccia, M., Della Pia, A., Lee, A., Behrmann, J., Bangolo, A., Jandir, P., Zhang, H., and Kaur, S. (2024). Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation. Cancers, 16.","DOI":"10.3390\/cancers16071357"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e2317188","DOI":"10.1001\/jamanetworkopen.2023.17188","article-title":"Analysis of Antibiotic Exposure and Development of Acute Graft-vs-Host Disease Following Allogeneic Hematopoietic Cell Transplantation","volume":"6","author":"Rashidi","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3612","DOI":"10.1182\/bloodadvances.2022007715","article-title":"A diagnostic classifier for pediatric chronic graft-versus-host disease: Results of the ABLE\/PBMTC 1202 study","volume":"7","author":"Cuvelier","year":"2023","journal-title":"Blood Adv."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1038\/s41409-020-0848-y","article-title":"Machine learning applied to gene expression analysis of T-lymphocytes in patients with cGVHD","volume":"55","author":"Lumbreras","year":"2020","journal-title":"Bone Marrow Transpl."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Partanen, J., Hyv\u00e4rinen, K., Bickeb\u00f6ller, H., Bogunia-Kubik, K., Crossland, R.E., Ivanova, M., Perutelli, F., and Dressel, R. (2020). Review of Genetic Variation as a Predictive Biomarker for Chronic Graft-Versus-Host-Disease After Allogeneic Stem Cell Transplantation. Front. Immunol., 11.","DOI":"10.3389\/fimmu.2020.575492"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liu, N., Liu, Y., Logan, B., Xu, Z., Tang, J., and Wang, Y. (2019). Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network. Sci. Rep., 9.","DOI":"10.1038\/s41598-018-37142-0"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s00277-024-05755-3","article-title":"Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT","volume":"103","author":"Shao","year":"2024","journal-title":"Ann. Hematol."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Musia\u0142, K., Stojanowski, J., Mi\u015bkiewicz-Bujna, J., Ka\u0142wak, K., and Ussowicz, M. (2023). KIM-1, IL-18, and NGAL, in the Machine Learning Prediction of Kidney Injury among Children Undergoing Hematopoietic Stem Cell Transplantation-A Pilot Study. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms242115791"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Musia\u0142, K., Stojanowski, J., Augustynowicz, M., Mi\u015bkiewicz-Migo\u0144, I., Ka\u0142wak, K., and Ussowicz, M. (2024). Assessment of Risk Factors for Acute Kidney Injury with Machine Learning Tools in Children Undergoing Hematopoietic Stem Cell Transplantation. J. Clin. Med., 13.","DOI":"10.3390\/jcm13082266"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1038\/s41409-022-01583-z","article-title":"Prediction and recommendation by machine learning through repetitive internal validation for hepatic veno-occlusive disease\/sinusoidal obstruction syndrome and early death after allogeneic hematopoietic cell transplantation","volume":"57","author":"Lee","year":"2022","journal-title":"Bone Marrow Transpl."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"57.e1","DOI":"10.1016\/j.jtct.2022.10.007","article-title":"Machine learning algorithm as a prognostic tool for venous thromboembolism in allogeneic transplant patients","volume":"29","author":"Deng","year":"2023","journal-title":"Transpl. Cell Ther."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e2034","DOI":"10.1002\/onco.13969","article-title":"Validation of a Post-Transplant Lymphoproliferative Disorder Risk Prediction Score and Derivation of a New Prediction Score Using a National Bone Marrow Transplant Registry Database","volume":"26","author":"Lee","year":"2021","journal-title":"Oncologist"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e2017036","DOI":"10.4084\/mjhid.2017.036","article-title":"Bacterial Blood Stream Infections Negatively Impact on Outcome of Patients Treated with Allogeneic Stem Cell Transplantation: 6 Years Single-Centre Experience","volume":"9","author":"Malagola","year":"2017","journal-title":"Mediterr. J. Hematol. Infect. Dis."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1002\/ajh.26671","article-title":"Time-dependent prediction of mortality and cytomegalovirus reactivation after allogeneic hematopoietic cell transplantation using machine learning","volume":"97","author":"Eisenberg","year":"2022","journal-title":"Am. J. Hematol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1097\/BS9.0000000000000143","article-title":"Machine learning algorithm as a prognostic tool for Epstein-Barr virus reactivation after haploidentical hematopoietic stem cell transplantation","volume":"5","author":"Fan","year":"2023","journal-title":"Blood Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e214514","DOI":"10.1001\/jamanetworkopen.2021.4514","article-title":"Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant","volume":"4","author":"Lind","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s13073-016-0301-4","article-title":"Pretreatment gut microbiome predicts chemotherapy-related bloodstream infection","volume":"8","author":"Montassier","year":"2016","journal-title":"Genome Med."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.radonc.2013.07.007","article-title":"\u201cRapid Learning health care in oncology\u201d\u2014An approach towards decision support systems enabling customised radiotherapy\u2019","volume":"109","author":"Lambin","year":"2013","journal-title":"Radiother. Oncol."},{"key":"ref_89","unstructured":"Holmes, J.H., Bellazzi, R., Sacchi, L., and Peek, N. (2015). Distributed Learning to Protect Privacy in Multi-Centric Clinical Studies BT\u2014Artificial Intelligence in Medicine, Springer International Publishing."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"435","DOI":"10.2217\/crc.14.34","article-title":"VATE: VAlidation of high TEchnology based on large database analysis by learning machine","volume":"3","author":"Meldolesi","year":"2014","journal-title":"Color. Cancer"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Gottardelli, B., Gatta, R., Nucciarelli, L., Tudor, A.M., Tavazzi, E., Vallati, M., Orini, S., Di Giorgi, N., and Damiani, A. (2024). GEN-RWD Sandbox: Bridging the gap between hospital data privacy and external research insights with distributed analytics. BMC Med. Inf. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02549-5"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e5815","DOI":"10.1002\/pds.5815","article-title":"OpenSAFELY: A platform for analysing electronic health records designed for reproducible research","volume":"33","author":"Nab","year":"2024","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_93","first-page":"870","article-title":"VANTAGE6: An open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange","volume":"2020","author":"Martin","year":"2020","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.bbmt.2016.11.022","article-title":"Impact of Conditioning Regimen on Outcomes for Children with Acute Myeloid Leukemia Undergoing Transplantation in First Complete Remission. An Analysis on Behalf of the Pediatric Disease Working Party of the European Group for Blood and Marrow Transplanta","volume":"23","author":"Lucchini","year":"2017","journal-title":"Biol. Blood Marrow Transpl."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"e28","DOI":"10.1016\/S2352-3026(19)30157-7","article-title":"Treosulfan or busulfan plus fludarabine as conditioning treatment before allogeneic haemopoietic stem cell transplantation for older patients with acute myeloid leukaemia or myelodysplastic syndrome (MC-FludT.14\/L): A randomised, non-inferiority, phase 3","volume":"7","author":"Beelen","year":"2020","journal-title":"Lancet Haematol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"101954","DOI":"10.1016\/j.trim.2023.101954","article-title":"Nutritional intervention with TGF-beta enriched food for special medical purposes (TGF-FSMP) is associated with a reduction of malnutrition, acute GVHD, pneumonia and may improve overall survival in patients undergoing allogeneic hematopoietic stem transp","volume":"81","author":"Morello","year":"2023","journal-title":"Transpl. Immunol."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Morello, E., Guarinoni, M.G., Arena, F., Andreoli, M., Bernardi, S., Malagola, M., Turra, A., Polverelli, N., and Russo, D. (2020). A Systematic Review of the Literature and Perspectives on the Role of Biomarkers in the Management of Malnutrition After Allogeneic Hematopoietic Stem Cell Transplantation. Front. Immunol., 11.","DOI":"10.3389\/fimmu.2020.535890"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5045\/br.2018.53.1.3","article-title":"The role of allogeneic hematopoietic stem cell transplantation in the four P medicine era","volume":"53","author":"Morello","year":"2018","journal-title":"Blood Res."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/3\/395\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:56Z","timestamp":1759919756000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/3\/395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,25]]},"references-count":98,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["cancers17030395"],"URL":"https:\/\/doi.org\/10.3390\/cancers17030395","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,25]]}}}