ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gynecological Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1571193
Multimodal Data Integration with Machine Learning for Predicting PARP Inhibitor Efficacy and Prognosis in Ovarian Cancer
Provisionally accepted
- 1Hunan Cancer Hospital, Xiangya School of Medicine, Central South University, Changsha, China
- 2Dali University, Dali, Yunnan, China
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Background: Poly(ADP)-ribose polymerase inhibitors (PARPi) have brought a significant breakthrough in the maintenance treatment of ovarian cancer. However, beyond BRCA mutation/HRD, the direct impact of other prognostic factors on PARPi response and prognosis remains inadequately characterized.We assessed PARPi prognostic factors from clinical characteristics, pathological findings, and biochemical indicators from 241 ovarian cancer patients. Cox univariate and multivariate analyses were employed to identify the factors which influencing PARPi efficacy and patients prognosis. Feature screening was conducted using correlation analysis, significance analysis, Variance Inflation Factor (VIF), and Elastic Net stability analysis. Patient-specific efficacy and prognosis prediction models were then constructed using various machine learning algorithms.Results: Total bile acids (TBAs) and CA-199 present as an independent risk factor in Cox multivariate analysis for primary and recurrent ovarian cancer patients respectively (P < 0.05). TBAs emerged as a risk factor, with each unit increase associated with a 10% rise in recurrence risk. The best-performing model has an AUC of 0.79 ± 0.09 and an AUC of 0.72 ± 0.03 for primary and recurrent ovarian cancer patients respectively. External validation(n=36) in multicenter cohorts maintained robust performance with AUC of 0.74 and an AUC of 0.70 for primary and recurrent ovarian cancer patients respectively.
Keywords: PARP inhibitors (PARPi), Prognostic factor, ovarian cancer, machine learning, Prediction model
Received: 05 Feb 2025; Accepted: 19 May 2025.
Copyright: © 2025 Xiong, Cai, Wu and Ni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xi'An Xiong, Hunan Cancer Hospital, Xiangya School of Medicine, Central South University, Changsha, China
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