基于肿瘤-肝脏比（TLR）放射组学特征和多种数据预处理方法，开发和验证18F-荧光脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F-FDG PET/CT）模型对乳腺癌新辅助化疗（NAC）疗效的预测价值。本研究回顾性纳入了多中心的193名乳腺癌患者。根据NAC的终点，我们将患者分为病理学完全缓解（pCR）组和非pCR组。所有患者在接受NAC治疗之前接受18F-FDG PET/CT影像检查，并通过手动分割和半自动绝对阈值分割对CT和PET图像感兴趣的体积（VOI）进行了分割。然后，使用pyradiomics软件包进行了VOI特征提取。基于放射组学特征来源、批次效应消除方法和离散化方法，创建了630个模型。比较和分析了数据预处理方法的差异，以确定表现最佳的模型，并通过置换检验进行进一步的测试。各种数据预处理方法对模型效果的改善贡献不同。其中，TLR放射组学特征和消除批次效应的Combat和Limma方法可以提高模型预测效果，而数据离散化则可作为进一步优化模型的潜在方法。选取了7个优秀模型，并根据每个模型在四个测试集中的AUC值和标准差选择了最优模型。最优模型预测四个测试组的AUC值介于0.7和0.77之间，置换检验的p值小于0.05。通过数据预处理消除混淆因素可以提高模型预测效果。这种开发方法的模型对于预测乳腺癌NAC的疗效是有效的。©2023年。作者（们）独家许可Springer-Verlag GmbH Germany，Springer Nature的组成部分。

To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods.One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test.A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test.It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.