{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:12:26Z","timestamp":1783440746271,"version":"3.54.6"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm\u2019s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 \u00b1 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model\u2019s predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.<\/jats:p>","DOI":"10.3390\/cancers16111975","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T05:35:20Z","timestamp":1716442520000},"page":"1975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology"],"prefix":"10.3390","volume":"16","author":[{"given":"Dania","family":"Daye","sequence":"first","affiliation":[{"name":"Massachusetts General Hospital, Boston, MA 02114, USA"},{"name":"Harvard Medical School, Boston, MA 02115, USA"},{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Regina","family":"Parker","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satvik","family":"Tripathi","sequence":"additional","affiliation":[{"name":"Massachusetts General Hospital, Boston, MA 02114, USA"},{"name":"Harvard Medical School, Boston, MA 02115, USA"},{"name":"Athinoula A. 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