无线电技术
医学
乳腺癌
乳腺摄影术
放射科
核医学
内科学
肿瘤科
癌症
接收机工作特性
作者
Shuxian Niu,Wenyan Jiang,Nannan Zhao,Tao Jiang,Yue Dong,Yahong Luo,Tao Yu,Xiran Jiang
标识
DOI:10.1007/s00432-021-03822-0
摘要
This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer. A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics. The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort. Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.
科研通智能强力驱动
Strongly Powered by AbleSci AI