Xu Hao, Yang Ao, Kang Min, Lai Hua, Zhou Xinzhu, Chen Zhe, Lin Libo, Zhou Peng, Deng Heping
Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Department of Radiology, Sichuan Provincial Maternity and Child Health Care Hospital, Chengdu, China.
Sci Rep. 2025 Apr 27;15(1):14720. doi: 10.1038/s41598-025-98155-0.
Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.
区分乳腺癌(BC)的管腔亚型仍然具有挑战性。因此,本研究的目的是探讨利用动态对比增强磁共振成像(DCE-MRI)获得的瘤内和瘤周特征建立的放射组学特征,在术前鉴别BC患者管腔型与非管腔型的可行性。回顾性纳入了来自三家医院的305例病理确诊的BC患者。然后使用LASSO方法选择特征,并计算每位患者的放射组学评分(radscore)。基于radscore,分别建立了瘤内、瘤周以及瘤内和瘤周联合的放射组学特征。通过受试者工作特征(ROC)曲线和决策曲线分析验证了放射组学特征的性能。对于预测分子亚型,瘤内放射组学特征在训练集、内部验证集和外部验证集中的AUC分别为0.817、0.838和0.883。瘤周放射组学特征在训练集、内部验证集和外部验证集中的AUC分别为0.863、0.895和0.889。瘤内和瘤周联合放射组学特征在训练集、内部验证集和外部验证集中的AUC分别为0.956、0.945和0.896。瘤内和瘤周联合放射组学特征相对于瘤内放射组学特征的额外贡献值具有统计学意义[在内部验证集中,净重新分类改善(NRI)为0.300(95%CI:0.117-0.482),P = 0.001;在外部验证集中,NRI为0.224(95%CI:0.038-0.410),P = 0.018]。这些结果表明,结合瘤内和瘤周特征的放射组学特征在预测乳腺癌管腔型方面表现良好。