放射肿瘤学-AI MITO 小组针对妇科癌症放疗中AI预测模型临床应用所面临的挑战进行的系统综述。
完整摘要 · 中文译文
引言：放射肿瘤学-AI MITO工作组开展了一项系统综述，旨在梳理接受放疗（RT）的妇科恶性肿瘤患者中基于人工智能预测模型的现状。目的是评估现有证据、方法学质量以及已有模型的临床适用性。 方法：按照PRISMA指南，从PubMed、EMBASE和Scopus检索相关研究。方法学质量采用预测建模研究系统评价的关键评估与数据提取（CHARMS）清单进行评估，偏倚风险和适用性采用预测模型偏倚风险评估工具（PROBAST）进行评估。共识别出1402条记录，去除重复后筛选了1025项独立研究。 结果：31项研究（1.1%）符合纳入标准。其中，27项（87.0%）针对宫颈癌，2项（6.5%）针对子宫肿瘤，2项（6.5%）针对混合妇科队列。预测模型涉及临床结局（35.5%）、治疗相关毒性（29.0%）或治疗反应（29.0%）。研究纳入了3-114个预测因子，使用了临床、剂量学和影像组学特征，且大多来自回顾性数据集。LASSO是最常用的变量筛选方法（25.8%）。所有研究均报告了内部验证，而仅有两项（6.5%）进行了外部验证。根据CHARMS，13项研究（41.9%）显示高偏倚风险；性能报告异质性较大，通常受限于样本量小和预测因子筛选欠佳。 结论：基于人工智能的预测建模在妇科放疗中仍受限于方法学异质性、有限的外部验证以及缺乏可临床部署的工具。推进临床转化需要严格的模型开发、透明的报告、稳健的验证以及由专家共识支持的可解释工具。
完整摘要 · 英文原文
INTRODUCTION: The Radiation Oncology-AI MITO group conducted a systematic review to map the current landscape of AI-based predictive modelling in patients with gynaecological malignancies treated with radiotherapy (RT). The aim was to evaluate available evidence, methodological quality, and the clinical applicability of existing models. METHODS: Relevant studies were retrieved from PubMed, EMBASE, and Scopus following PRISMA guidelines. Methodological quality was assessed using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist, while risk of bias and applicability were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 1402 records were identified, and 1025 unique studies were screened after duplicate removal. RESULTS: Thirty-one studies (1.1%) met eligibility criteria. Of these, 27 (87.0%) focused on cervical cancer, 2 (6.5%) on uterine tumours, and 2 (6.5%) on mixed gynaecological cohorts. Predictive models addressed clinical outcomes (35.5%), treatment-related toxicities (29.0%), or treatment response (29.0%). Studies incorporated 3-114 predictors, using clinical, dosimetric, and radiomic features, mostly from retrospective datasets. LASSO was the most frequent variable-selection method (25.8%). All studies reported internal validation, whereas only two (6.5%) performed external validation. According to CHARMS, 13 studies (41.9%) showed high risk of bias; performance reporting was heterogeneous, commonly limited by small sample sizes and suboptimal predictor selection. CONCLUSIONS: AI-based predictive modelling in gynaecological RT remains constrained by methodological variability, limited external validation, and a lack of clinically deployable tools. Advancing clinical translation requires rigorous model development, transparent reporting, robust validation, and interpretable tools supported by expert consensus.