{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:22:09Z","timestamp":1777893729198,"version":"3.51.4"},"reference-count":15,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This paper investigates the application of\n                    <jats:italic>Survival Analysis<\/jats:italic>\n                    (SA) techniques to forecast outcomes after\n                    <jats:italic>autologous Hematopoietic Stem Cell Transplantation<\/jats:italic>\n                    (aHSCT) for\n                    <jats:italic>Multiple Myeloma<\/jats:italic>\n                    (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the\n                    <jats:italic>Concordance Index<\/jats:italic>\n                    (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that while all models performed well based on the C-index, a detailed examination revealed variations in how each model processed data. Specifically, the Coxnet and Random Survival Forest models exhibited a more thorough use of clinical variables, whereas the gradient boosting models appeared to rely on a narrower range of features, potentially limiting their ability to differentiate between patients with comparable profiles. Risk predictions categorized patients into low, moderate, and high-risk levels. For lower-risk patients, the procedure showed positive outcomes, while higher-risk individuals were predicted to have limited survival benefits, recommending alternative treatments. Lastly, we propose future research to expand these models into time-to-event estimations, offering additional support for decision-making by predicting patient life expectancy post-transplant, considering their pre-transplant clinical attributes.\n                  <\/jats:p>","DOI":"10.1515\/jib-2024-0053","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T03:19:49Z","timestamp":1748834389000},"source":"Crossref","is-referenced-by-count":2,"title":["Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma"],"prefix":"10.1515","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9009-7324","authenticated-orcid":false,"given":"Jose Mar\u00eda","family":"Belmonte","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Faculty of Computer Science , 16751 University of Murcia , 30100 , Murcia , Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1471-8828","authenticated-orcid":false,"given":"Miguel","family":"Blanquer","sequence":"additional","affiliation":[{"name":"Hematology Department , Hospital Virgen de la Arrixaca and IMIB , 30120 , Murcia , Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7265-3508","authenticated-orcid":false,"given":"Gregorio","family":"Bernab\u00e9","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Computer Science , 16751 University of Murcia , 30100 , Murcia , Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5844-4163","authenticated-orcid":false,"given":"Fernando","family":"Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Faculty of Computer Science , University of Murcia , 30100 , Murcia , Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6388-2835","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Computer Science , 16751 University of Murcia , 30100 , Murcia , Spain"}]}],"member":"374","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"2025102907580409485_j_jib-2024-0053_ref_001","doi-asserted-by":"crossref","unstructured":"Cline, MJ, Gale, RP, Stiehm, ER, Opelz, G, Young, LS, Feig, SA, et al.. Bone marrow transplantation in man. Ann Intern Med 1975;83:691\u2013708. https:\/\/doi.org\/10.7326\/0003-4819-83-5-691.","DOI":"10.7326\/0003-4819-83-5-691"},{"key":"2025102907580409485_j_jib-2024-0053_ref_002","doi-asserted-by":"crossref","unstructured":"Gahrton, G, Tura, S, Flesch, M, Gratwohl, A, Gravett, P, Lucarelli, G, et al.. Bone marrow transplantation in multiple myeloma: report from the European cooperative group for bone marrow transplantation. Blood 1987;69:1262\u20134. https:\/\/doi.org\/10.1182\/blood.v69.4.1262.1262.","DOI":"10.1182\/blood.V69.4.1262.1262"},{"key":"2025102907580409485_j_jib-2024-0053_ref_003","doi-asserted-by":"crossref","unstructured":"Cogliano-Shutta, NA, Broda, EJ, Gress, JS. Bone marrow transplantation. An overview and comparison of autologous, syngeneic, and allogeneic treatment modalities. 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In: Proceedings of the 18th international conference on Practical Applications of Computational Biology & Bioinformatics (PACBB). Lecture Notes in Networks and Systems. Springer-Verlag, Salamanca, Spain; 2024. To be published.","DOI":"10.1007\/978-3-031-87873-2_11"},{"key":"2025102907580409485_j_jib-2024-0053_ref_007","doi-asserted-by":"crossref","unstructured":"Muhsen, IN, ElHassan, T, Hashmi, SK. Artificial intelligence approaches in hematopoietic cell transplantation: a review of the current status and future directions. Turk J Haematol 2018;35:152\u20137. https:\/\/doi.org\/10.4274\/tjh.2018.0123.","DOI":"10.4274\/tjh.2018.0123"},{"key":"2025102907580409485_j_jib-2024-0053_ref_008","doi-asserted-by":"crossref","unstructured":"Muhsen, IN, Jagasia, M, Toor, AA, Hashmi, SK. Registries and artificial intelligence: investing in the future of hematopoietic cell transplantation. 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