本研究旨在探讨磁共振成像（MRI）放射组学特征在新辅助治疗期间不同时间点对直肠癌患者病理完全缓解（pCR）的预测效力。此外，我们旨在使用机器学习开发和验证一个放射组学时空模型（RSTM），以预测患者的pCR。回顾性分析了83例直肠癌患者的临床和影像数据，并根据其术后病理结果将患者分为pCR和非pCR患者。所有患者在新辅助治疗前后接受了一次MRI检查，以提取放射组学特征，包括治疗前、治疗后和Delta特征。 Delta特征是指治疗前和治疗后特征之差与治疗前特征的比率。在基于上述三种特征类型的特征维度约简后，使用机器学习方法构建了RSTM，并根据曲线下面积（AUC）评估了其性能。治疗前、治疗后和Delta特征构建的各个基本模型的AUC值分别为0.771、0.681和0.871。它们的敏感性值分别为0.727、0.864和0.909，特异性值分别为0.803、0.492和0.656。结合治疗前、治疗后和Delta特征构建的基本模型的AUC、灵敏度和特异性值分别为0.901、0.909和0.803。根据结合特征构建的K最近邻（KNN）分类器构建的RSTM的AUC、灵敏度和特异性值分别为0.944、0.871和0.983。 Delong检验表明，RSTM的性能与治疗前、治疗后和Delta模型有显着差异（P <0.05），但与三种基本模型的结合（P >0.05）没有显着差异。基于新辅助治疗前后和Delta特征的结合特征构建的KNN分类器的RSTM对新辅助治疗的pCR具有最佳预测效力。它可能成为协助个体化管理直肠癌患者的新型临床工具。©2023.作者等。

In this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space-time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.Clinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).The AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).The RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.© 2023. The Author(s).