Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Zhejiang University, Hangzhou, 310000, Zhejiang, China.
Comput Biol Med. 2024 Jul;177:108593. doi: 10.1016/j.compbiomed.2024.108593. Epub 2024 May 11.
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.
探讨基于机器学习的放射组学在预测行同期放化疗（CCRT）的局部晚期宫颈癌（LACC）患者无病生存（DFS）和总生存（OS）中的价值。
本多中心研究回顾性分析了 700 例接受 CCRT 并持续随访的 IB2-IVA 宫颈癌患者。采集 T2WI 序列原发灶及其周围 5mm 区域的三维放射组学特征。采用 6 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 DFS 和 OS。最终，使用 TCGA 和 GEO 数据库探讨放射组学预测宫颈癌进展和生存的机制。本研究遵循 CLEAR 报告规范，并使用 RQS 和 METRICS 进行质量评估。
在预测 DFS 方面，RSF 模型联合肿瘤和肿瘤周围放射组学显示出最佳预测效能，在训练集、验证集和测试集中预测 1 年、3 年和 5 年 DFS 的 AUC 分别为 0.986、0.989、0.990 和 0.884、0.838、0.823。在预测 OS 方面，GBM 模型表现最佳，AUC 分别为 0.999、0.995、0.978 和 0.981、0.975、0.837。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 相关。
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，肿瘤和肿瘤周围信息的联合具有更高的预测效能，可为宫颈癌患者的治疗决策提供可靠依据。
