Qu Jinrong, Ma Ling, Lu Yanan, Wang Zhaoqi, Guo Jia, Zhang Hongkai, Yan Xu, Liu Hui, Kamel Ihab R, Qin Jianjun, Li Hailiang
Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China.
Advanced Application Team, GE Healthcare, Shanghai, 201203, China.
Discov Oncol. 2022 Jan 8;13(1):3. doi: 10.1007/s12672-022-00464-7.
To assess volumetric DCE-MRI radiomics nomogram in predicting response to neoadjuvant chemotherapy (nCT) in EC patients.
This retrospective analysis of a prospective study enrolled EC patients with stage cT1N + M0 or cT2-4aN0-3M0 who received DCE-MRI within 7 days before chemotherapy, followed by surgery. Response assessment was graded from 1 to 5 according to the tumor regression grade (TRG). Patients were stratified into responders (TRG1 + 2) and non-responders (TRG3 + 4 + 5). 72 radiomics features and vascular permeability parameters were extracted from DCE-MRI. The discriminating performance was assessed with ROC. Decision curve analysis (DCA) was used for comparing three different models.
This cohort included 82 patients, and 72 tumor radiomics features and vascular permeability parameters acquired from DCE-MRI. mRMR and LASSO were performed to choose the optimized subset of radiomics features, and 3 features were selected to create the radiomics signature that were significantly associated with response (P < 0.001). AUC of combining radiomics signature and DCE-MRI performance in the training (n = 41) and validation (n = 41) cohort was 0.84 (95% CI 0.57-1) and 0.86 (95% CI 0.74-0.97), respectively. This combined model showed the best discrimination between responders and non-responders, and showed the highest positive and positive predictive value in both training set and test set.
The radiomics features are useful for nCT response prediction in EC patients.
评估容积动态对比增强磁共振成像（DCE-MRI）的影像组学列线图在预测子宫内膜癌（EC）患者新辅助化疗（nCT）反应中的作用。
本研究对一项前瞻性研究进行回顾性分析，纳入cT1N+M0期或cT2-4aN0-3M0期的EC患者，这些患者在化疗前7天内接受了DCE-MRI检查，随后接受手术。根据肿瘤退缩分级（TRG）将反应评估分为1至5级。患者被分为反应者（TRG1+2）和无反应者（TRG3+4+5）。从DCE-MRI中提取72个影像组学特征和血管通透性参数。用ROC评估鉴别性能。决策曲线分析（DCA）用于比较三种不同模型。
该队列包括82例患者，从DCE-MRI中获取了72个肿瘤影像组学特征和血管通透性参数。采用最小冗余最大相关（mRMR）和套索（LASSO）算法选择影像组学特征的优化子集，选择了3个特征创建与反应显著相关的影像组学特征标签（P<0.001）。在训练队列（n=41）和验证队列（n=41）中，影像组学特征标签与DCE-MRI性能相结合的曲线下面积（AUC）分别为0.84（95%CI 0.57-1）和0.86（95%CI 0.74-0.97）。该联合模型在反应者和无反应者之间显示出最佳的鉴别能力，在训练集和测试集中均显示出最高的阳性似然比和阳性预测值。
影像组学特征有助于预测EC患者的nCT反应。
