Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
Commun Biol. 2021 Dec 6;4(1):1363. doi: 10.1038/s42003-021-02894-5.
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.
预测对于子宫内膜癌 (EC) 的准确诊断和个体化治疗至关重要。我们采用影像基因组学将术前磁共振成像 (MRI，n=487 例患者) 与组织学、转录组学和分子生物标志物 (n=550 例患者) 相结合，旨在通过包括 866 例 EC 患者的研究中识别侵袭性肿瘤特征。对手动（放射科医生）分割的肿瘤进行全容积肿瘤放射组学分析 (n=138 例患者) 产生的聚类可识别出具有高危组织学特征和不良生存的患者。基于完全自动化的机器学习 (ML) 肿瘤分割算法的放射组学分析 (n=336 例患者) 重现了相同的放射组学预后组。从这些放射组学风险组中定义了一个 11 基因高风险签名，并在同源验证队列 (n=554 例患者) 中重现了其预后作用，并与癌症基因组图谱 (TCGA) 分子分类（高拷贝数/ p53 改变）一致，提示不良预后。我们得出结论，基于 MRI 的综合影像基因组学分析可提供精细的肿瘤特征描述，有助于预测预后并指导 EC 的未来治疗策略。
