Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy

磁共振成像 医学 新辅助治疗 化疗 乳腺癌 肿瘤科 核磁共振 内科学 放射科 癌症 物理
作者
Peng Lin,Wei-Jun Wan,Tong Kang,Lian-feng Qin,Qiu-xue Meng,Xiaoxin Wu,Hongyan Qin,Yi-qun Lin,Yun He,Hong Yang
出处
期刊:Radiologia Medica [Springer Nature]
卷期号:128 (2): 171-183
标识
DOI:10.1007/s11547-023-01595-9
摘要

To identify molecular basis of four parameters obtained from dynamic contrast-enhanced magnetic resonance imaging, including functional tumor volume (FTV), longest diameter (LD), sphericity, and contralateral background parenchymal enhancement (BPE). Pretreatment-available gene expression profiling and different treatment timepoints MRI features were integrated for Spearman correlation analysis. MRI feature-related genes were submitted to hypergeometric distribution-based gene functional enrichment analysis to identify related Kyoto Encyclopedia of Genes and Genomes annotation. Gene set variation analysis was utilized to assess the infiltration of distinct immune cells, which were used to determine relationships between immune phenotypes and medical imaging phenotypes. The clinical significance of MRI and relevant molecular features were analyzed to identify their prediction performance of neoadjuvant chemotherapy (NAC) and prognostic impact. Three hundred and eighty-three patients were included for integrative analysis of MRI features and molecular information. FTV, LD, and sphericity measurements were most positively significantly correlated with proliferation-, signal transmission-, and immune-related pathways, respectively. However, BPE did not show marked correlation relationships with gene expression alteration status. FTV, LD and sphericity all showed significant positively or negatively correlated with some immune-related processes and immune cell infiltration levels. Sphericity decreased at 3 cycles after treatment initiation was also markedly negatively related to baseline sphericity measurements and immune signatures. Its decreased status could act as a predictor for prediction of response to NAC. Different MRI features capture different tumor molecular characteristics that could explain their corresponding clinical significance.
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