精准地识别新辅助化疗（NAC）对pCR的产生对于决定适当的手术策略和指导乳腺癌切除范围至关重要。然而，目前没有非侵入性工具可以准确预测pCR。我们的研究旨在使用纵向多参数MRI开发集成学习模型，以预测乳腺癌中的pCR。从2015年7月至2021年12月，我们收集了每位患者的NAC前后的多参数MRI序列。我们提取了14,676个放射学信息学和4096个深度学习特征，并计算了附加的delta值特征。在原始队列（n=409）中，使用组间相关系数测试、U检验、Boruta和最小绝对收缩和选择算子回归选择每种乳腺癌亚型的最显著特征，并开发了5种机器学习分类器来准确预测每个亚型的pCR。集成学习策略用于集成单模型。模型的诊断表现在三个外部队列（n=343、170和340）中进行了评估。该研究共招募了四个中心的1262名乳腺癌患者，HR+/HER2-、HER2+和TNBC亚型的pCR率分别为10.6％（52/491）、54.3％（323/595）和37.5％（66/176）。最终，分别在HR+/HER2-、HER2+和TNBC亚型中选择了20、15和13个特征来构建机器学习模型。多层感知机（MLP）在所有亚型中产生了最佳的诊断表现。对于三种亚型，集成前、后和delta模型的堆叠模型在原始队列中的AUC分别为0.959、0.974和0.958，在外部验证队列中的AUC分别为0.882-0.908、0.896-0.929和0.837-0.901。堆叠模型在外部验证队列中的准确性为85.0％-88.9％，灵敏度为80.0％-86.3％，特异度为87.4％-91.5％。我们的研究建立了一种预测乳腺癌对NAC的反应的新工具，并取得了优异的表现。这些模型可以帮助确定乳腺癌的NAC后手术策略。本研究得到了国家自然科学基金（82171898，82103093）、高水平医院建设登峰项目（DFJHBF202109）、广东省基础与应用基础研究基金（2020A1515010346、2022A1515012277）、广州市科技计划项目（202002030236）、北京医学奖基金会（YXJL-2020-0941-0758）和北京市科技创新医药发展基金会（KC2022-ZZ-0091-5）的资助。资金来源未参与研究设计、数据收集、分析和解释、报告撰写或提交文章的决定。 ©2023作者。

Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer.From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively).A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts.Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer.This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.© 2023 The Author(s).