Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, People's Republic of China.
Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan Road #89, Nanshan District, Shenzhen, 518000, People's Republic of China.
Radiat Oncol. 2023 Oct 31;18(1):179. doi: 10.1186/s13014-023-02368-4.
To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI).
In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.
The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87).
Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.
本研究旨在利用新辅助治疗（NAT）前后多参数磁共振成像（mpMRI），开发并验证预测局部晚期直肠癌（LARC）患者肿瘤对 NAT 反应的影像组学模型。
本多中心研究共纳入来自两个独立中心的 563 例患者。中心 1 的 453 例患者分为训练集和测试集，中心 2 的其余 110 例患者作为外部验证集。采集患者治疗前和治疗后的 mpMRI 用于特征提取。使用机器学习从训练队列构建影像组学模型。在测试队列和独立的外部验证队列中验证模型的准确性。使用曲线下面积（AUC）、敏感性、特异性、阳性预测值和阴性预测值评估模型性能。
使用治疗前 mpMRI 构建的模型在训练队列（AUC=0.84）、测试队列（AUC=0.81）和外部验证队列（AUC=0.79）中对预测 NAT 无反应具有良好的准确性。使用治疗前和治疗后 mpMRI 构建的模型在训练队列（AUC=0.86）、测试队列（AUC=0.87）和外部验证队列（AUC=0.87）中对预测病理完全缓解具有强大的诊断价值。
使用多期和多参数 MRI 构建的模型能够以较高的准确性和稳健性预测肿瘤对 NAT 的反应，这可能有助于 LARC 的个体化管理。
