ArticleCancers2025
A Multicenter Machine Learning-Based Predictive Model of Acute Toxicity in Prostate Cancer Patients Undergoing Salvage Radiotherapy (ICAROS Study).
Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Diagnostic Gap in Rural Maternal Health: Initial Validation of a Parsimonious Clinical Model for Hypertensive Disorders of Pregnancy in a Honduran Hospital.Diagnostics (Basel, Switzerland) · 2026Article
- Voxel-Based Dose-Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective.Journal of clinical medicine · 2025Article
- The Surgical Imprint: How Operative Trauma May Shape Radiation Tolerance After Prostatectomy.Cancers · 2025Article
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16 authors.
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Abstract
backgroundThis study aimed to develop a predictive model for acute gastrointestinal (GI) and genitourinary (GU) toxicity in prostate cancer patients treated with salvage radiotherapy (SRT) post-prostatectomy, using machine learning techniques to identify key prognostic factors.
methodsA multicenter retrospective study analyzed 454 patients treated with SRT from three Italian radiotherapy centers. Acute toxicity was assessed using Radiation Therapy Oncology Group criteria. Predictors of grade ≥ 2 toxicity were identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Classification and Regression Tree (CART) modeling. The analyzed variables included surgical technique, clinical target volume (CTV) to planning target volume (PTV) margins, extent of lymphadenectomy, radiotherapy technique, and androgen-deprivation therapy (ADT).
resultsNo patients experienced grade ≥ 4 toxicity, and grade 3 toxicity was below 1% for both GI and GU events. The primary determinant of acute toxicity was the surgical technique. Open prostatectomy was associated with significantly higher grade ≥ 2 GI (41.8%) and GU (35.9%) toxicity compared to laparoscopic/robotic approaches (18.9% and 12.2%, respectively). A CTV-to-PTV margin ≥ 10 mm further increased toxicity, particularly when combined with extensive lymphadenectomy. SRT technique and ADT were additional predictors in some subgroups.
conclusionsSRT demonstrated excellent tolerability. Surgical technique, CTV-to-PTV margin, and treatment parameters were key predictors of toxicity. These findings emphasize the need for personalized treatment strategies integrating surgical and radiotherapy factors to minimize toxicity and optimize outcomes in prostate cancer patients.
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