Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

Abstract

Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials.

Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer

“…Nevertheless, these public databases allow for the analysis of associations between radiophenotypes and genomic data, and this approach has been applied to OC. 11,16,18 and selection, modeling for the identification of biomarkers associated with clinical outcomes, 71,72 model validation, 73,74 and analyses of correlations between image-based phenotypes and tumoral molecular features. 31,32 Furthermore, deep learning algorithms with self-learning ability enable the automatic extraction and analysis of significant imaging features from large-scale radiological images, thereby saving time and manpower.…”

“…Although it is the largest publicly available medical imaging database, it only includes CT and MRI data for 143 cases of ovarian tumors. Nevertheless, these public databases allow for the analysis of associations between radiophenotypes and genomic data, and this approach has been applied to OC 11,16,18 …”

Radiomics Analysis in Ovarian Cancer: A Narrative Review

“…The iRCG model had the best platinum resistance classification accuracy (AUROC 0.78, 95% CI 0.77 to 0.80]. CluDiss was associated with PFS (HR 1.03, 95% CI 1.01 to 1.05) (85).…”

Precision radiogenomics: fusion biopsies to target tumour habitats in vivo

“…5 For example, in high-grade serous ovarian cancer (HGSOC), our previous work shows that patients with higher degrees of heterogeneity between habitats on CT scans have significantly worse prognosis. 6 Habitat-based heterogeneity in HGSOC lesions has been shown to capture underlying differences in phylogenetic evolution 7 and T-cell infiltration 8 in several pioneering studies, but the difficulty of the experiments has significantly limited the number of patients included. Crucially, these studies have so far focused on tissue obtained from surgical tissue samples, which are easier to obtain as the resected specimen is directly accessible.…”