为了研究磁共振成像（MRI）放射组学特征在预测局部晚期食管鳞状细胞癌（ESCC）术前新辅助化疗（NACT）患者的病理学反应方面的作用，我们纳入了2015年3月至2019年10月期间接受NACT的患者。每位患者在NACT前一周内和NACT结束后2-3周内进行食管MRI扫描，然后进行手术前的检查。从T2-TSE-BLADE中提取的放射组学特征被随机分为训练集和验证集，比例为7:3。根据病灶进展性肿瘤消退分级（TRG），患者被分为两组：良性应答者组（GR，TRG 0+1）和差性应答者组（非GR，TRG 2+3）。我们构建了NACT前后模型（Pre/Post-model）和Delta-NACT模型（Delta-model）。利用Kruskal-Wallis方法选择特征，利用逻辑回归方法开发最终模型。共纳入108例ESCC患者，从107个放射组学特征中分别选择了3/2/4个特征来构建Pre/Post/Delta模型。所选择的放射组学特征在GR组和非GR组之间具有统计学差异。在训练集中，Delta模型的最高曲线下面积（AUC）为0.851，在验证集中为0.831。在三个模型中，Pre模型在训练集和验证集中的表现最差（AUC为0.466和0.596），而Post模型在训练集和验证集中比Pre模型表现更好（AUC为0.753和0.781）。基于MRI的放射组学模型可以预测ESCC患者在NACT后的病理学反应，其中Delta模型表现出最佳的预测效果。MRI放射组学特征可以作为评估食管癌患者新辅助化疗疗效的有用工具，特别适用于筛选可能从新辅助化疗中获益的应答者患者。基于T2WI-TSE-BLADE的MRI放射组学特征可以潜在地预测ESCC患者对NACT的病理学反应。Delta模型表现出了最佳的病理学反应预测能力，其次是Post模型，其具有更好的预测能力，而Pre模型在预测TRG方面表现较差。© 2023. 版权所有，由欧洲放射学学会（European Society of Radiology）独家许可使用。

To investigate the MRI radiomics signatures in predicting pathologic response among patients with locally advanced esophageal squamous cell carcinoma (ESCC), who received neoadjuvant chemotherapy (NACT).Patients who underwent NACT from March 2015 to October 2019 were prospectively included. Each patient underwent esophageal MR scanning within one week before NACT and within 2-3 weeks after completion of NACT, prior to surgery. Radiomics features extracted from T2-TSE-BLADE were randomly split into the training and validation sets at a ratio of 7:3. According to the progressive tumor regression grade (TRG), patients were stratified into two groups: good responders (GR, TRG 0 + 1) and poor responders (non-GR, TRG 2 + 3). We constructed the Pre/Post-NACT model (Pre/Post-model) and the Delta-NACT model (Delta-model). Kruskal-Wallis was used to select features, logistic regression was used to develop the final model.A total of 108 ESCC patients were included, and 3/2/4 out of 107 radiomics features were selected for constructing the Pre/Post/Delta-model, respectively. The selected radiomics features were statistically different between GR and non-GR groups. The highest area under the curve (AUC) was for the Delta-model, which reached 0.851 in the training set and 0.831 in the validation set. Among the three models, Pre-model showed the poorest performance in the training and validation sets (AUC, 0.466 and 0.596), and the Post-model showed better performance than the Pre-model in the training and validation sets (AUC, 0.753 and 0.781).MRI-based radiomics models can predict the pathological response after NACT in ESCC patients, with the Delta-model exhibiting optimal predictive efficacy.MRI radiomics features could be used as a useful tool for predicting the efficacy of neoadjuvant chemotherapy in esophageal carcinoma patients, especially in selecting responders among those patients who may be candidates to benefit from neoadjuvant chemotherapy.• The MRI radiomics features based on T2WI-TSE-BLADE could potentially predict the pathologic response to NACT among ESCC patients. • The Delta-model exhibited the best predictive ability for pathologic response, followed by the Post-model, which similarly had better predictive ability, while the Pre-model performed less well in predicting TRG.© 2023. The Author(s), under exclusive licence to European Society of Radiology.