Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China.
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
J Transl Med. 2024 Jul 8;22(1):637. doi: 10.1186/s12967-024-05487-y.
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.
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能预测生存结局尚不确定。我们旨在开发和验证放射组学特征，以指示肿瘤缩小和治疗反应，从而改善生存分析。
本回顾性多队列研究包括三个数据集。开发数据集由 255 例患者术前和早期 NAC DCE-MRI 数据组成，用于创建基于成像特征的多任务模型，以预测肿瘤退缩模式和病理完全缓解（pCR）。患者分为 pCR、非 pCR 伴同心性退缩（CS）和非 pCR 伴非 CS，通过曲线下面积（AUC）测量预测性能。预后验证数据集（n=174）用于使用多变量 Cox 模型评估成像特征对总生存（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。
使用 17 个放射组学特征的多任务学习模型，预测肿瘤退缩的 AUC 为 0.886，预测 pCR 的 AUC 为 0.760。达到 pCR 的患者具有最佳的生存结局，而 CS 模式的非 pCR 患者的生存结局优于非 CS 患者，OS 和 RFS 差异有统计学意义（p=0.00012 和 p=0.00063）。基因表达分析突出了 IL-17 和雌激素信号通路在反应变异性中的作用。
放射组学特征可有效预测乳腺癌患者 NAC 的反应模式，并与特定的生存结局相关。非 pCR 患者中的 CS 模式表明生存更好。
