探讨基于机器学习的放射组学对于预测局部晚期宫颈癌 (LACC) 患者接受同步放化疗 (CCRT) 的无病生存 (DFS) 和总生存 (OS) 的价值。 在这项多中心研究中，700 名患有对接受 CCRT 并持续随访的 IB2-IVA 宫颈癌进行回顾性分析。收集 T2WI 序列中原发病灶及其周围 5 mm 区域的三维放射组学特征。使用六种机器学习方法构建最佳放射组学模型，以准确预测 LACC 患者 CCRT 后的 DFS 和 OS。最终，TCGA和GEO数据库被用来探索放射组学预测宫颈癌进展和生存的机制。本研究遵循 CLEAR 进行报告，并使用 RQS 和 METRICS 评估其质量。在 DFS 预测中，RSF 模型结合肿瘤和瘤周放射组学表现出最佳的预测效果，预测 1 年、3 年和训练集、验证集和测试集的 5 年 DFS 分别为 0.986、0.989、0.990 和 0.884、0.838、0.823 和 0.829、0.809、0.841。在 OS 预测中，GBM 模型表现最好，AUC 分别为 0.999、0.995、0.978、0.981、0.975、0.837、0.904、0.860、0.905。 TCGA和GEO中的差异基因提示放射组学模型的预测可能与KDELR2和HK2有关。基于机器学习的放射组学模型有助于预测LACC患者CCRT后的DFS和OS，并且肿瘤和癌旁信息的结合具有更高的预测性疗效，可为宫颈癌患者的治疗决策提供可靠的依据。版权所有 © 2024 Elsevier Ltd. 保留所有权利。

To investigate the value of machine learning-based radiomics for predicting disease-free survival (DFS) and overall survival (OS) undergoing concurrent chemoradiotherapy (CCRT) for patients with locally advanced cervical cancer (LACC).In this multicentre study, 700 patients with IB2-IVA cervical cancer who underwent CCRT with ongoing follow-up were retrospectively analyzed. Three-dimensional radiomics features of primary lesions and its surrounding 5 mm region in T2WI sequences were collected. Six machine learning methods were used to construct the optimal radiomics model for accurate prediction of DFS and OS after CCRT in LACC patients. Eventually, TCGA and GEO databases were used to explore the mechanisms of radiomics in predicting the progression and survival of cervical cancer. This study adhered CLEAR for reporting and its quality was assessed using RQS and METRICS.In the prediction of DFS, the RSF model combined tumor and peritumor radiomics demonstrated the best predictive efficacy, with the AUC for predicting 1-year, 3-year, and 5-year DFS in the training, validation, and test sets of 0.986, 0.989, 0.990, and 0.884, 0.838, 0.823, and 0.829, 0.809, 0.841, respectively. In the prediction of OS, the GBM model best performer, with AUC of 0.999, 0.995, 0.978, and 0.981, 0.975, 0.837, and 0.904, 0.860, 0.905. Differential genes in TCGA and GEO suggest that the prediction of radiomics model may be associated with KDELR2 and HK2.Machine learning-based radiomics models help to predict DFS and OS after CCRT in LACC patients, and the combination of tumor and peritumor information has higher predictive efficacy, which can provide a reliable basis for therapeutic decision-making in cervical cancer patients.Copyright © 2024 Elsevier Ltd. All rights reserved.