A Pre-Treatment CT-Based Radiomic Model Using SULF1 Polymorphisms to Predict Platinum Resistance in Advanced High-Grade Ovarian Severe Cancer Patients

Abstract

Ovarian cancer is a significant global health concern, with an approximate annual incidence of 300,000 new cases. The predominant form of ovarian cancer is high-grade serous ovarian cancer, which constitutes most cases. The standard treatment for this kind of cancer often involves the administration of platinum-based chemotherapy, a therapeutic strategy that initially yields a response rate ranging from 70% to 80%. The issue of platinum resistance, which is associated with an unfavorable prognosis, continues to be a serious worry. Therefore, accurately predicting resistance is of utmost importance in a therapeutic setting. This work acknowledges the significant importance of SULF1 polymorphisms as prospective biomarkers in anticipating platinum resistance. One of the primary obstacles in this domain has been the absence of accurate prediction models. As a response, this research presents the Pre-Treatment CT-based Radiomic Model (PT-CT-RM), an innovative methodology that integrates genetic and radiomic information to forecast platinum resistance. The findings obtained via several iterations indicate an average HRD Score of 29.83%, a Mutation Frequency of 14.42 mutations per megabase, an SNV Count of 267.85, and a TMB of 20.89 mutations per megabase. The results indicate a gradual rise in genomic instability and mutation density, which is suggestive of the underlying processes that contribute to platinum resistance. The comprehensive strategy PT-CT-RM uses shows potential in advancing patient-specific therapy options, providing a renewed sense of optimism in enhancing outcomes for individuals diagnosed with high-grade serous ovarian cancer.