{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:20:22Z","timestamp":1788877222406,"version":"build-2803163510"},"reference-count":39,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"8","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec>\n            <jats:title>PURPOSE<\/jats:title>\n            <jats:p>Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-relevant toxicity during the course of treatment.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS<\/jats:title>\n            <jats:p>Clinical records were retrieved from a single-center, consecutive cohort of patients who underwent neoadjuvant treatment for early breast cancer. We developed and validated machine learning algorithms to predict grade 3 or 4 toxicity (anemia, neutropenia, deviation of liver enzymes, nephrotoxicity, thrombopenia, electrolyte disturbance, or neuropathy). We used 10-fold cross-validation to develop two algorithms (logistic regression with elastic net penalty [GLM] and support vector machines [SVMs]). Algorithm predictions were compared with documented toxicity events and diagnostic performance was evaluated via area under the curve (AUROC).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS<\/jats:title>\n            <jats:p>\n              A total of 590 patients were identified, 432 in the development set and 158 in the validation set. The median age was 51 years, and 55.8% (329 of 590) experienced grade 3 or 4 toxicity. The performance improved significantly when adding referenced treatment information (referenced regimen, referenced summation dose intensity product) in addition to patient and tumor variables: GLM AUROC 0.59 versus 0.75,\n              <jats:italic>P<\/jats:italic>\n              = .02; SVM AUROC 0.64 versus 0.75,\n              <jats:italic>P<\/jats:italic>\n              = .01.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSION<\/jats:title>\n            <jats:p>The individual risk of treatment-relevant toxicity can be predicted using machine learning algorithms. We demonstrate a promising way to improve efficacy and facilitate proactive toxicity management of systemic cancer treatment.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1200\/cci.24.00010","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T21:00:15Z","timestamp":1734987615000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":7,"title":["Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment"],"prefix":"10.1200","author":[{"given":"Lie","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas M.","family":"Deutsch","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Heidelberg University Hospital, 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Heidelberg University Hospital, Heidelberg, Germany"},{"name":"National Center for Tumor Diseases, Heidelberg University Hospital and German Cancer Research Center, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2605-0060","authenticated-orcid":true,"given":"Michael","family":"Golatta","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany"},{"name":"Breast Centre Heidelberg, Klinik St Elisabeth, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joerg","family":"Heil","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany"},{"name":"Breast Centre Heidelberg, Klinik St Elisabeth, Heidelberg, 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