{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T19:06:48Z","timestamp":1792004808484,"version":"4.4.0"},"reference-count":55,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Medicine and Pharmacy \u201cCarol Davila\u201d"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: The use of artificial intelligence (AI) in oncology has the potential to improve decision making, particularly in managing the risk associated with targeted therapies. This study aimed to develop and validate a machine learning-based clinical decision support system (CDSS) capable of predicting complications associated with Bevacizumab or its biosimilars and to translate the resulting predictive model into a clinically applicable tool. Methods: A prospective observational study was conducted on 395 records from patients treated with Bevacizumab or biosimilars for solid tumors. Pretherapeutic variables, such as demographic data, medical history, tumor characteristics and laboratory findings, were retrieved from medical records. Several machine learning models (logistic regression, Random Forest, XGBoost) were trained using 70\/30 and 80\/20 data splits. Their predictive performances were compared using accuracy, AUC-ROC, sensitivity, specificity, F1-scores and error rate. The best-performing model was used to derive a logistic-based risk score, which was further implemented as an interactive HTML form. Results: The optimized Random Forest model trained on the 80\/20 split demonstrated the best balance between accuracy (70.63%), sensitivity (66.67%), specificity (73.85%), and AUC-ROC (0.75). The derived logistic risk score showed good performance (AUC-ROC = 0.720) and calibration. It identified variables, such as age \u2265 65, anemia, elevated urea, leukocytosis, tumor differentiation, and stage, as significant predictors of complications. The final tool provides clinicians with an easy-to-use, offline form that estimates individual risk levels and stratifies patients into low-, intermediate-, or high-risk categories. Conclusions: This study offers a proof of concept for developing AI-supported predictive tools in oncology using real-world data. The resulting logistic risk score and interactive form can assist clinicians in tailoring therapeutic decisions for patients receiving targeted therapies, enhancing the personalization of care without replacing clinical judgment.<\/jats:p>","DOI":"10.3390\/diagnostics15182391","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:53:42Z","timestamp":1758300822000},"page":"2391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Using Artificial Intelligence to Develop Clinical Decision Support Systems\u2014The Evolving Road of Personalized Oncologic Therapy"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9225-8030","authenticated-orcid":false,"given":"Elena","family":"Chitoran","sequence":"first","affiliation":[{"name":"Medicine School, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"General Surgery and Surgical Oncology Department I, Bucharest Institute of Oncology \u201cProf. Dr. Alexandru Trestioreanu\u201d, 022328 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-1951","authenticated-orcid":false,"given":"Vlad","family":"Rotaru","sequence":"additional","affiliation":[{"name":"Medicine School, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"General Surgery and Surgical Oncology Department I, Bucharest Institute of Oncology \u201cProf. Dr. Alexandru Trestioreanu\u201d, 022328 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6538-3114","authenticated-orcid":false,"given":"Aisa","family":"Gelal","sequence":"additional","affiliation":[{"name":"Medicine School, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"General Surgery and Surgical Oncology Department I, Bucharest Institute of Oncology \u201cProf. Dr. Alexandru Trestioreanu\u201d, 022328 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-0651","authenticated-orcid":false,"given":"Sinziana-Octavia","family":"Ionescu","sequence":"additional","affiliation":[{"name":"Medicine School, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"General Surgery and Surgical Oncology Department I, Bucharest Institute of Oncology \u201cProf. Dr. Alexandru Trestioreanu\u201d, 022328 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4269-4269","authenticated-orcid":false,"given":"Giuseppe","family":"Gullo","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, University of Palermo, 90146 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5740-1209","authenticated-orcid":false,"given":"Daniela-Cristina","family":"Stefan","sequence":"additional","affiliation":[{"name":"Medicine School, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurentiu","family":"Simion","sequence":"additional","affiliation":[{"name":"Medicine School, \u201cCarol Davila\u201d University of Medicine and Pharmacy, 050474 Bucharest, Romania"},{"name":"General Surgery and Surgical Oncology Department I, Bucharest Institute of Oncology \u201cProf. Dr. Alexandru Trestioreanu\u201d, 022328 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104143","DOI":"10.1016\/j.critrevonc.2023.104143","article-title":"Evaluation of clinical decision support systems in oncology: An updated systematic review","volume":"192","author":"Nafees","year":"2023","journal-title":"Crit. 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