Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
GE Healthcare, MR Research, Beijing, China.
Cancer Imaging. 2022 Jul 16;22(1):35. doi: 10.1186/s40644-022-00474-2.
BACKGROUND: To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). METHODS: A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell's C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. RESULTS: The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). CONCLUSIONS: The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.
背景：为了探讨磁共振成像（MRI）基于放射组学在预测接受同期放化疗（CCRT）治疗的局部晚期宫颈鳞状细胞癌（LACSC）患者生存中的价值。
方法：本研究回顾性纳入了 2014 年 1 月至 2018 年 12 月期间接受 CCRT 治疗的 185 例 LACSC 患者（训练组：n=128；测试组：n=57）。从 T2 加权成像、表观扩散系数图、动脉期和延迟期对比增强 MRI 中提取了总共 400 个放射组学特征。采用单因素 Cox 回归和最小绝对收缩和选择算子 Cox 回归来选择能够独立预测无进展生存期（PFS）和总生存期（OS）的放射组学特征和临床特征。采用 Harrell's C 指数评估预测模型的预测能力。然后生成列线图和校准曲线。使用 Kaplan-Meier 方法生成生存曲线，并使用对数秩检验进行比较。
结果：放射组学评分在预测 PFS（C 指数，训练组为 0.764，测试组为 0.762）和 OS（C 指数，训练组为 0.793，测试组为 0.750）方面的表现明显优于 2018 年 FIGO 分期系统（PFS 的 C 指数，训练组为 0.657，测试组为 0.677；OS 的 C 指数，训练组为 0.665，测试组为 0.633）和临床预测模型（PFS 的 C 指数，训练组为 0.731，测试组为 0.725；OS 的 C 指数，训练组为 0.708，测试组为 0.693）（P<0.05）。由 T 分期、淋巴结转移位置和放射组学评分构建的联合模型在预测 PFS（C 指数，训练组为 0.792，测试组为 0.809）和 OS（C 指数，训练组为 0.822，测试组为 0.785）方面的表现最佳，明显高于放射组学评分（P<0.05）。
结论：MRI 基于放射组学评分可为接受 CCRT 治疗的 LACSC 患者的 PFS 和 OS 预测提供有效信息。联合模型（包括 MRI 基于放射组学评分和临床特征）显示出最佳的预测性能。
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