{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:04:27Z","timestamp":1784217867149,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate estimation of prognosis and life expectancy is essential in patients with advanced cancer, as it guides clinical decision\u2010making and helps avoid unnecessary interventions while facilitating timely integration of palliative and supportive care. Palliative radiotherapy plays a key role within multidisciplinary management, offering effective and well\u2010tolerated symptom relief for complications such as pain, bleeding, and obstruction, with treatment strategies closely tailored to expected survival. Although recent advances in machine learning have improved prognostic accuracy by modeling complex variable interactions, their application in palliative care settings remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To aid clinical decision\u2010making, we developed a decision tree multi\u2010classifier to predict the mortality at 3, 24, and 52\u00a0weeks following palliative radiotherapy for bone metastases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 573 adults diagnosed with metastatic cancer were analyzed. The primary endpoint was the overall survival (OS) defined as the number of months from treatment to death event. Four clinically relevant classes were defined: Class 0 (OS: \u2264 3\u00a0weeks), Class 1 (OS: 3\u201324\u00a0weeks), Class 2 (OS: 24\u201352\u00a0weeks) and Class 3 (OS \u2265 52\u00a0weeks). Candidate covariate predictors consisted of 65 clinical, dosimetric and laboratory variables. Two supervised decision tree machine\u2010learning models were trained and validated using the Python package. A SHapley Additive exPlanations (SHAP) explanaibility analysis was performed to infer the global and local feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The SHAP analysis selected three laboratory variables, the interleukin8, haemoglobin and lymphocytes count as the first three ranked variables representing the major impact on OS in each of the four classes and accounting for more than 80% of contribution. In all classes, higher chance of OS was associated with low values of interleukin8 (IL8) and higher values of haemoglobin (HEM) and lymphocytes count (LYMPH). Pre\u2010treatment values of IL8\u00a0&gt;\u00a036.7 relocated more than 50% of patients with survival\u00a0&lt;\u00a03\u00a0weeks and only 1.5% of patient with survival\u00a0&gt;\u00a052\u00a0weeks. On the other hand, pre\u2010treatment values of IL8\u00a0&lt;\u00a019 relocated about 92% of patients with survival\u00a0&gt;\u00a052\u00a0weeks. Patients are then additionally separated based on the lymphocytes count (LYMPH). LYMPH values higher than 7.5 will drive the probability of survival\u00a0&gt;\u00a052\u00a0weeks still over 90% while it drops down to 2.1% for LYMPH\u00a0&lt;\u00a07.5.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An explainable machine learning approach based on decision trees is able to predict the survival at different timing after radiotherapy in patients with advanced cancer. This approach provides an intelligible explanation of individualized risk prediction, helping clinicians to identify the best strategy for patient stratification and treatment selection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70606","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T18:32:05Z","timestamp":1783535525000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning decision tree models for multiclass classification of prognosis in patient undergoing palliative radiotherapy for bone metastases"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6711-350X","authenticated-orcid":false,"given":"Savino","family":"Cilla","sequence":"first","affiliation":[{"name":"Medical Physics Unit Responsible Research Hospital  Campobasso 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