Frontiers in Oncology (Jan 2025)

CT-based radiomics predicts HRD score and HRD status in patients with ovarian cancer

  • Yujiao Wu,
  • Qianhui Zhang,
  • Wenyan Jiang,
  • Yuhua Gao,
  • Bin Qu,
  • Xingling Wang

DOI
https://doi.org/10.3389/fonc.2024.1477759
Journal volume & issue
Vol. 14

Abstract

Read online

IntroductionThis study predicted HRD score and status based on intra- and peritumoral radiomics in patients with ovarian cancer (OC) for better guiding the use of PARPi in clinical.MethodsA total of 106 and 95 patients with OC were included between January 2022 and November 2023 for predicting HRD score and status, respectively. Radiomics features were extracted and quantitatively analyzed from intra- and peri-tumor regions in the CT image. Radiomics signatures (RSs) were built based on features from intra- and peri-tumor regions for predicting HRD score and status alone or in combination. Subject working characteristics (ROC) area under the curve (AUC), sensitivity (SEN), and specificity (SPE) were calculated as comparative metrics.ResultsFor predicting HRD score, 4 and 2 features were selected as the most important predictors from the intra- and peritumoral regions, respectively. For predicting HRD status, 4 features from the intratumoral region and 2 from the peritumoral region were identified as the most important predictors. Radiomics nomograms created by combining RSs and important clinical factors showed good predictive results with AUCs of 0.852 (95% confidence interval [CI]: 0.765-0.938, SEN = 0.907, SPE = 0.655) and 0.781 (95% CI: 0.621-0.941, SEN = 0.688, SPE = 0.833) in the training and validation cohort for predicting HRD score, respectively; with AUCs of 0.874 (95% CI: 0.790-0.957, SEN = 0.765, SPE = 0.867) and 0.824 (95% CI: 0.663-0.985, SEN = 0.762, SPE = 0.800) in the training and validation cohort for predicting HRD status, respectively.DiscussionCalibration curves and decision curve analysis (DCA) confirmed potential clinical usefulness of our nomograms. Our findings suggest that radiomics features derived from the CT image of OC have the potential to predict HRD score and status, and the developed nomograms can enrich the range of applicable population of PARPi, prolong progression-free survival and provide personalized treatment for OC patients.

Keywords