乳腺癌患者对新辅助化疗（NAC）表现出不同的反应模式。然而，乳腺癌患者对 NAC 的不同肿瘤反应模式是否可以预测生存结果尚不确定。我们的目的是开发和验证表明肿瘤缩小和治疗反应的放射组学特征，以改善生存分析。这项回顾性多队列研究包括三个数据集。开发数据集由来自 255 名患者的术前和早期 NAC DCE-MRI 数据组成，用于创建基于成像特征的多任务模型，用于预测肿瘤缩小模式和病理完全缓解 (pCR)。患者被分类为 pCR、具有同心收缩 (CS) 的非 pCR 或具有非 CS 的非 pCR，预测性能通过曲线下面积 (AUC) 来衡量。预后验证数据集 (n = 174) 用于使用多变量 Cox 模型评估影像特征对总生存 (OS) 和无复发生存 (RFS) 的预后价值。分析基因表达数据（基因组验证数据集，n = 112）以确定反应模式的生物学基础。多任务学习模型利用 17 个放射组学特征，预测肿瘤缩小的 AUC 为 0.886，预测 pCR 的 AUC 为 0.760。实现 pCR 的患者具有最佳的生存结果，而具有 CS 模式的非 pCR 患者比非 CS 患者具有更好的生存率，OS 和 RFS 存在显着差异（分别为 p = 0.00012 和 p = 0.00063）。基因表达分析强调了 IL-17 和雌激素信号通路在反应变异中的参与。放射组学特征可有效预测乳腺癌患者的 NAC 反应模式，并与特定的生存结果相关。非 pCR 患者的 CS 模式表明生存率更高。© 2024。作者。

Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis.This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns.The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability.Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.© 2024. The Author(s).