J Radiat Res. 2022 Jan 20;63(1):98-106. doi: 10.1093/jrr/rrab104.
We retrospectively assessed whether magnetic resonance imaging (MRI) radiomics combined with clinical parameters can improve the predictability of out-of-field recurrence (OFR) of cervical cancer after chemoradiotherapy. The data set was collected from 204 patients with stage IIB (FIGO: International Federation of Gynecology and Obstetrics 2008) cervical cancer who underwent chemoradiotherapy at 14 Japanese institutes. Of these, 180 patients were finally included for analysis. OFR-free survival was calculated using the Kaplan-Meier method, and the statistical significance of clinicopathological parameters for the OFR-free survival was evaluated using the log-rank test and Cox proportional-hazards model. Prediction of OFR from the analysis of diffusion-weighted images (DWI) and T2-weighted images of pretreatment MRI was done using the least absolute shrinkage and selection operator (LASSO) model for engineering image feature extraction. The accuracy of prediction was evaluated by 5-fold cross-validation of the receiver operating characteristic (ROC) analysis. Para-aortic lymph node metastasis (p = 0.003) was a significant prognostic factor in univariate and multivariate analyses. ROC analysis showed an area under the curve (AUC) of 0.709 in predicting OFR using the pretreatment status of para-aortic lymph node metastasis, 0.667 using the LASSO model for DWIs and 0.602 using T2 weighted images. The AUC improved to 0.734 upon combining the pretreatment status of para-aortic lymph node metastasis with that from the LASSO model for DWIs. Combining MRI radiomics with clinical parameters improved the accuracy of predicting OFR after chemoradiotherapy for locally advanced cervical cancer.
我们回顾性评估了磁共振成像（MRI）放射组学结合临床参数是否可以提高宫颈癌放化疗后野外复发（OFR）的预测能力。该数据集来自在 14 家日本机构接受放化疗的 204 名 IIB 期（FIGO：国际妇产科联合会 2008 年）宫颈癌患者。其中，最终有 180 名患者被纳入分析。采用 Kaplan-Meier 法计算 OFR 无复发生存率，采用对数秩检验和 Cox 比例风险模型评估临床病理参数对 OFR 无复发生存率的统计学意义。采用最小绝对值收缩和选择算子（LASSO）模型对弥散加权成像（DWI）和预处理 MRI 的 T2 加权图像进行分析，对 OFR 进行预测。通过Receiver Operating Characteristic（ROC）分析的 5 倍交叉验证评估预测的准确性。腹主动脉旁淋巴结转移（p=0.003）是单因素和多因素分析中的显著预后因素。ROC 分析显示，使用腹主动脉旁淋巴结转移的预处理状态预测 OFR 的曲线下面积（AUC）为 0.709，使用 DWI 的 LASSO 模型为 0.667，使用 T2 加权图像为 0.602。当将腹主动脉旁淋巴结转移的预处理状态与 DWI 的 LASSO 模型结合使用时，AUC 提高至 0.734。将 MRI 放射组学与临床参数相结合，提高了局部晚期宫颈癌放化疗后 OFR 的预测准确性。
