Abstract
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.
Highlights
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC)
Studies based on computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT from single slice tumor segmentations[15,16,17,18,19,20,21] and whole-volume tumor segmentations[14,22,23,24,25] have identified radiomic signatures associated with high-risk features and poor prognosis[15,16,17,18,20,21]
Fifty-three radiomic features were extracted from manually segmented primary tumors depicted at MRI in 138 EC patients
Summary
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). 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-numberhigh/p53-altered). The aim of this study was to develop a novel radiogenomics approach using noninvasive, preoperative whole-volume tumor MRI for expedited radiomic based individual risk assessment and develop a corresponding prognostic gene expression signature in EC patients. We aimed to assess whether ML-based automated whole-volume tumor segmentations yield similar radiomic profiles that may be linked to the same gene expression signature
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