Zhang Ling, Cai Peiqiang, Hou Jingyu, Luo Ma, Li Yonggang, Jiang Xinhua
Department of Radiology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, People's Republic of China.
Department of Liver Surgery, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, People's Republic of China.
Cancer Manag Res. 2021 Mar 25;13:2785-2796. doi: 10.2147/CMAR.S300627. eCollection 2021.
A practical prognostic prediction model is absent for hepatocellular carcinoma (HCC) patients after curative ablation. We aimed to develop a radiomics model based on gadoxetic acid disodium-enhanced magnetic resonance (MR) images to predict HCC recurrence after curative ablation.
We retrospectively enrolled 132 patients with HCC who underwent curative ablation. Patients were randomly divided into the training (n = 92) and validation (n = 40) cohorts. Radiomic features were extracted from gadoxetic acid disodium-enhanced MR images of the liver before curative ablation, and various baseline clinical characteristics were collected. Cox regression and random survival forests were used to construct models that incorporated radiomic features and/or clinical characteristics. The predictive performance of the different models was compared using the concordance index (C-index) and decision curves analysis (DCA). A cutoff derived from the combined model was used for risk categorization, and recurrence-free survival (RFS) was compared between groups using the Kaplan-Meier survival curve analysis.
Twenty radiomic features and four clinical characteristics were identified and used for model construction. The radiomics model constructed by tumoral and peritumoral radiomic features had better predictive performance (C-index 0.698, 95% confidence interval [CI] 0.640-0.755) compared with the clinical model (C-index 0.614, 95% CI 0.499-0.695), while the combined model had the best predictive performance (C-index 0.706, 95% CI 0.638-0.763). A better net benefit was observed with the combined model compared with the other two models according to the DCA. Distinct RFS distributions were observed when patients were categorized based on the cutoff derived from the combined model (Log rank test, p = 0.007).
The radiomics model which combined radiomic features extracted from gadoxetic acid disodium-enhanced MR images with clinical characteristics could predict HCC recurrence after curative ablation.
目前缺乏用于预测肝细胞癌（HCC）患者根治性消融术后预后的实用预测模型。我们旨在基于钆塞酸二钠增强磁共振（MR）图像开发一种放射组学模型，以预测HCC根治性消融术后的复发情况。
我们回顾性纳入了132例行根治性消融的HCC患者。将患者随机分为训练组（n = 92）和验证组（n = 40）。从根治性消融术前肝脏的钆塞酸二钠增强MR图像中提取放射组学特征，并收集各种基线临床特征。采用Cox回归和随机生存森林构建纳入放射组学特征和/或临床特征的模型。使用一致性指数（C指数）和决策曲线分析（DCA）比较不同模型的预测性能。从联合模型得出的临界值用于风险分类，并使用Kaplan-Meier生存曲线分析比较组间无复发生存期（RFS）。
识别出20个放射组学特征和4个临床特征并用于模型构建。与临床模型（C指数0.614，95%置信区间[CI] 0.499 - 0.695）相比，由肿瘤及瘤周放射组学特征构建的放射组学模型具有更好的预测性能（C指数0.698，95% CI 0.640 - 至0.755），而联合模型具有最佳预测性能（C指数0.706，95% CI 0.638 - 0.763）。根据DCA，联合模型与其他两个模型相比观察到更好的净效益。当根据联合模型得出的临界值对患者进行分类时，观察到不同的RFS分布（对数秩检验，p = 0.007）。
将钆塞酸二钠增强MR图像中提取的放射组学特征与临床特征相结合的放射组学模型可预测HCC根治性消融术后的复发情况。
