在局部晚期直肠癌（LARC）患者中，15%-27%的病人通过新辅助化疗放射治疗(nCRT)实现病理学完全反应(pCR)，可以避免肛门切除手术。我们旨在研究使用基于MRI的前、后和差值放射学特征预测LARC患者nCRT治疗反应的有效性，并将这些放射学模型与放射科医师的视觉评估进行比较。共纳入126名在手术前接受nCRT治疗的LARC患者，并随机分为训练组(n = 84)和验证组(n = 42)。从nCRT前后的MRI T2加权图像中提取了250个放射学特征。采用Pearson相关性分析和AONVA或Relief方法来识别与pCR相关的放射学描述符。比较了5种机器学习分类器来构建放射学模型。通过多元逻辑回归分析建立了放射学范图。两名资深放射科医师独立对肿瘤退化等级进行评分，并与放射学模型进行比较。使用DeLong检验比较模型和汇总读者的曲线下面积(AUC)。最佳的nCRT前、后和差值放射学模型的AUC分别为0.717(95% CI:0.639-0.795)、0.805(95%CI:0.736-0.874)和0.724(95%CI:0.648-0.800)。基于nCRT前cN分期、nCRT前放射学评分和nCRT后放射学评分的放射学范图获得了0.852 (95%CI:0.774-0.930)的AUC，优于单个放射学模型和汇总读者 (所有p < 0.05)。放射学范图是一种有效且无创的工具，可预测LARC患者在nCRT后的pCR，优于放射科医师的诊断。版权所有© 2023年大学放射学家协会。由Elsevier Inc.出版。保留所有权利。

The 15%-27% of patients with locally advanced rectal cancer (LARC) achieved pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) and could avoid proctectomy. We aimed to investigate the effectiveness of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for LARC patients treated with nCRT and to compare these radiomic models with radiologists' visual assessment.A total of 126 patients with LARC who received nCRT before surgery were included and randomly divided into a training set (n = 84) and a validation set (n = 42). 250 radiomic features were extracted from T2-weighted images from pre- and post-nCRT MRI. Pearson correlation analysis and AONVA or Relief were used to identify radiomic descriptors associated with pCR. Five machine-learning classifiers were compared to construct radiomic models. The radiomic nomogram was built via multivariate logistic regression analysis. Two senior radiologists independently rated tumor regression grades and compared with radiomic models. Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test.The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI: 0.648-0.800), respectively. The radiomic nomogram based on pre-nCRT cN stage, pre-nCRT radscore, and post-nCRT radscore achieved an AUC of 0.852 (95%CI: 0.774-0.930), which was higher than the single radiomic models and pooled readers (all p < 0.05).The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.