Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Cancer Imaging. 2024 Jul 30;24(1):98. doi: 10.1186/s40644-024-00746-z.
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.
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）的反应和预后存在差异。本研究旨在对 MRI 上的 TNBC 异质性进行特征描述，并建立预测病理完全缓解（pCR）和预后的放射基因组模型。
本回顾性研究纳入在复旦大学附属肿瘤医院接受新辅助化疗的 TNBC 患者作为放射组学开发队列（n=315）；其中，纳入具有遗传数据的患者作为放射基因组学开发队列（n=98）。两个队列的研究人群以 7:3 的比例随机分为训练集和验证集。外部验证队列（n=77）纳入来自 DUKE 和 I-SPY 1 数据库的患者。通过肿瘤内亚区和肿瘤周围区域的特征来描述空间异质性。通过肿瘤体的动力学特征来描述血流动力学异质性。通过逻辑回归选择特征后，建立了三个放射组学模型。模型 1 包括亚区和肿瘤周围特征，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，建立了两个融合模型（PRM：病理-放射组学模型；GPRM：基因组-病理-放射组学模型）。使用 AUC 和决策曲线分析评估模型性能。通过 Kaplan-Meier 曲线和多变量 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）在预测 pCR 方面表现更好，在训练集、内部验证集和外部验证集中的 AUC 分别为 0.87、0.79 和 0.78。GPRM 在训练集（AUC=0.97，P=0.015）和验证集（AUC=0.93，P=0.019）中预测 pCR 的表现最好。模型 3、PRM 和 GPRM 可以根据无病生存情况对患者进行分层，预测非 pCR 与预后不良相关（P=0.034、0.001 和 0.019）。
DCE-MRI 特征描述的多尺度异质性可有效预测 TNBC 患者的 pCR 和预后。放射基因组模型可作为一种有价值的生物标志物，提高预测性能。
