文章：

基于机器学习的多中心预测模型：前列腺癌患者接受挽救性放疗急性毒性反应研究（ICAROS研究）

A Multicenter Machine Learning-Based Predictive Model of Acute Toxicity in Prostate Cancer Patients Undergoing Salvage Radiotherapy (ICAROS Study)

原文发布日期：25 June 2025

DOI: 10.3390/cancers17132142

类型: Article

开放获取: 是

英文摘要：

Background: This 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. Methods: A 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). Results: No 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. Conclusions: SRT 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.

摘要翻译： 

背景：本研究旨在利用机器学习技术，构建前列腺癌患者根治性前列腺切除术后接受挽救性放疗时发生急性胃肠道和泌尿生殖系统毒性的预测模型，以识别关键预后因素。方法：一项多中心回顾性研究分析了来自意大利三家放疗中心的454例接受挽救性放疗的患者。急性毒性采用放射治疗肿瘤学组标准进行评估。通过最小绝对收缩与选择算子回归和分类回归树模型，识别出≥2级毒性的预测因素。分析的变量包括手术技术、临床靶区至计划靶区外扩范围、淋巴结清扫范围、放疗技术以及雄激素剥夺疗法。结果：无患者发生≥4级毒性，胃肠道和泌尿生殖系统≥3级毒性发生率均低于1%。急性毒性的主要决定因素是手术技术。与腹腔镜/机器人手术相比，开放性前列腺切除术与显著更高的≥2级胃肠道毒性（41.8%）和泌尿生殖系统毒性（35.9%）相关。临床靶区至计划靶区外扩范围≥10毫米进一步增加了毒性风险，尤其是在联合广泛淋巴结清扫时。挽救性放疗技术和雄激素剥夺疗法在某些亚组中是额外的预测因素。结论：挽救性放疗耐受性良好。手术技术、临床靶区至计划靶区外扩范围和治疗参数是毒性的关键预测因素。这些发现强调需要整合手术和放疗因素制定个体化治疗策略，以最大程度减少毒性并优化前列腺癌患者的治疗结果。

原文链接：

A Multicenter Machine Learning-Based Predictive Model of Acute Toxicity in Prostate Cancer Patients Undergoing Salvage Radiotherapy (ICAROS Study)