通过结合预处理MRI和临床病理特征开发易于使用的模型，早期预测乳腺癌新辅助化疗（NAC）的肿瘤缩小模式。我们回顾性分析了2012年2月至2020年8月在我们医院接受NAC并进行最终手术的420名患者。手术标本的病理学发现被用作将肿瘤缩小模式分类为同心和非同心收缩的金标准。形态学和动力学MRI特征都被分析了。进行了单变量和多变量分析，选择关键的临床病理和MRI特征，用于预处理预测回归模式。使用逻辑回归和六种机器学习方法构建预测模型，并通过接收器操作特性曲线评估其性能。选择了两个临床病理变量和三个MRI特征作为独立预测因素构建预测模型。七种预测模型的表观曲线下面积（AUC）在0.669-0.740之间。逻辑回归模型的AUC为0.708（95%置信区间[CI]：0.658-0.759），决策树模型的AUC最高，达到0.740（95% CI：0.691-0.787）。对于内部验证，七个模型的乐观校正AUC在0.592-0.684之间。逻辑回归模型和每种机器学习模型的AUC之间没有显着差异。结合预处理MRI和临床病理特征的预测模型对于预测乳腺癌的肿瘤回归模式很有用，可帮助选择能够从NAC获益以减少乳腺手术并修改治疗策略的患者。版权所有©2023年大学放射学协会。由Elsevier Inc.出版。保留所有权利。

To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer.We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve.Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model.Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.