Triple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and develop a radiogenomic model for predicting both pathological complete response (pCR) and prognosis. In this retrospective study, TNBC patients who underwent neoadjuvant chemotherapy at Fudan University Shanghai Cancer Center were enrolled as the radiomic development cohort (n = 315); among these patients, those whose genetic data were available were enrolled as the radiogenomic development cohort (n = 98). The study population of the two cohorts was randomly divided into a training set and a validation set at a ratio of 7:3. The external validation cohort (n = 77) included patients from the DUKE and I-SPY 1 databases. Spatial heterogeneity was characterized using features from the intratumoral subregions and peritumoral region. Hemodynamic heterogeneity was characterized by kinetic features from the tumor body. Three radiomics models were developed by logistic regression after selecting features. Model 1 included subregional and peritumoral features, Model 2 included kinetic features, and Model 3 integrated the features of Model 1 and Model 2. Two fusion models were developed by further integrating pathological and genomic features (PRM: pathology-radiomics model; GPRM: genomics-pathology-radiomics model). Model performance was assessed with the AUC and decision curve analysis. Prognostic implications were assessed with Kaplan‒Meier curves and multivariate Cox regression. Among the radiomic models, the multiregional model representing multiscale heterogeneity (Model 3) exhibited better pCR prediction, with AUCs of 0.87, 0.79, and 0.78 in the training, internal validation, and external validation sets, respectively. The GPRM showed the best performance for predicting pCR in the training (AUC = 0.97, P = 0.015) and validation sets (AUC = 0.93, P = 0.019). Model 3, PRM and GPRM could stratify patients by disease-free survival, and a predicted nonpCR was associated with poor prognosis (P = 0.034, 0.001 and 0.019, respectively). Multiscale heterogeneity characterized by DCE-MRI could effectively predict the pCR and prognosis of TNBC patients. The radiogenomic model could serve as a valuable biomarker to improve the prediction performance.

中文翻译：

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基于定量异质性的术前放射基因组模型，用于预测接受新辅助化疗的三阴性乳腺癌患者的结局


三阴性乳腺癌（TNBC）具有高度异质性，导致患者对新辅助化疗（NAC）的反应和预后不同。本研究试图描述 MRI 上 TNBC 的异质性，并开发用于预测病理完全缓解 (pCR) 和预后的放射基因组模型。在这项回顾性研究中，在复旦大学附属肿瘤医院接受新辅助化疗的 TNBC 患者被纳入放射组学发展队列（n = 315）；在这些患者中，那些可获得遗传数据的患者被纳入放射基因组开发队列（n = 98）。两个队列的研究人群按7:3的比例随机分为训练集和验证集。外部验证队列 (n = 77) 包括来自 DUKE 和 I-SPY 1 数据库的患者。使用肿瘤内亚区域和肿瘤周围区域的特征来表征空间异质性。血流动力学异质性以肿瘤体的动力学特征为特征。选择特征后通过逻辑回归开发了三种放射组学模型。模型1包括亚区域和瘤周特征，模型2包括动力学特征，模型3整合了模型1和模型2的特征。通过进一步整合病理和基因组特征，开发了两种融合模型（PRM：病理学-放射组学模型；GPRM：基因组学） -病理学-放射组学模型）。通过 AUC 和决策曲线分析评估模型性能。通过 Kaplan-Meier 曲线和多元 Cox 回归评估预后意义。在放射组学模型中，代表多尺度异质性的多区域模型（模型3）表现出更好的pCR预测，AUC分别为0.87、0.79和0。训练、内部验证和外部验证集中分别有 78 个。 GPRM 在训练集（AUC = 0.97，P = 0.015）和验证集（AUC = 0.93，P = 0.019）中显示出预测 pCR 的最佳性能。模型 3、PRM 和 GPRM 可以根据无病生存率对患者进行分层，预测的 nonpCR 与不良预后相关（P 分别 = 0.034、0.001 和 0.019）。以DCE-MRI为特征的多尺度异质性可以有效预测TNBC患者的pCR和预后。放射基因组模型可以作为有价值的生物标志物来提高预测性能。