Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

作者：Wenlong Ming, Yanhui Zhu, Yunfei Bai, Wanjun Gu, Fu‐Yu Li, Zixi Hu, Tiansong Xia, Zuolei Dai, Xiafei Yu, Huamei Li, Yu Gu, Shaoxun Yuan, Rongxin Zhang, Haitao Li, Wenyong Zhu, Jianing Ding, Xiao Sun, Yun Liu, Hongde Liu, Xiaoan Liu · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.943326 · 被引用次数：22 · 研究领域：Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis、Breast Cancer Treatment Studies

Background To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery ( n = 174) and validation cohorts ( n = 72). Six external datasets ( n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features ( p -value < 0.001). With eight imaging-associated genes ( CHEK1 , TTK , CDC45 , BUB1B , PLK1 , E2F1 , CDC20 , and CDC25A ), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis ( p -values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning cl...