{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T12:30:25Z","timestamp":1786365025314,"version":"build-2736575974"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":221,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Scientifica"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Machine learning survival models can outperform conventional Cox regression in heterogeneous pediatric HSCT cohorts, but clinical adoption is limited by concerns regarding information leakage, time\u2010dependent structure, and interpretability. We evaluated explainable survival modeling aligned to real\u2010world decision points before transplantation and after early post\u2010transplant complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      In a single\u2010center cohort of 839 transplant\u2010na\u00efve children (&lt; 14\u2009years) undergoing HSCT (2010\u20132016), overall survival was modeled from infusion to death or last follow\u2010up. A baseline model used only pretransplant variables (age at HSCT, primary diagnostic group, stem cell source, and donor HLA type). To prevent leakage from postinfusion events, a day\u2009+\u2009100 landmark cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009721; 121 deaths beyond day\u2009+\u2009100) was constructed and extended with early complications known by day\u2009+\u2009100 (SOS\/VOD and acute GvHD). Cox proportional hazards, random survival forests (ranger), and randomForestSRC (rfSRC) were benchmarked using C\u2010index, integrated cumulative\/dynamic AUC (iAUC), and integrated Brier score (IBS). Cox proportional hazard assumptions were tested using Schoenfeld residuals. Model\u2010agnostic explainability (survex) included time\u2010dependent permutation importance, partial dependence survival profiles, SurvSHAP(t), ceteris paribus profiles, and additive attributions (breakdown and SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall mortality was 28.1% (236\/839). Baseline Cox violated proportional hazards (global\n                      <jats:italic>p<\/jats:italic>\n                      = 0.00181), whereas the day\u2009+\u2009100 landmark Cox did not (global\n                      <jats:italic>p<\/jats:italic>\n                      = 0.52). Baseline performance favored ML models (C\u2010index: Cox 0.673, ranger 0.677, rfSRC 0.668; iAUC: 0.698, 0.866, 0.826; IBS: 0.159, 0.126, 0.135). In the landmark setting, discrimination and accuracy improved with ML (C\u2010index: Cox 0.721, ranger 0.870, rfSRC 0.840; iAUC: 0.609, 0.668, 0.650; IBS: 0.125, 0.0969, 0.1016). Explainability analyses produced clinically coherent global and individualized risk narratives beyond day\u2009+\u2009100.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An information\u2010leakage\u2010free, landmark\u2010based XAI framework allows clinically credible survival prediction in pediatric HSCT, with random survival forests outperforming Cox regression and providing transparent cohort\u2010level and patient\u2010level explanations suitable for clinical communication and risk updating.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/sci5\/7818238","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:51:43Z","timestamp":1786362703000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark\u2010Based Machine Learning"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9581-7993","authenticated-orcid":false,"given":"Khawar","family":"Siddiqui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulrahman","family":"Al-Musa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona","family":"Al-Saleh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hawazen","family":"Al 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