Ensemble Machine Learning Predicts Platinum Resistance in Ovarian Cancer Using Laboratory Data

Platinum resistance remains a critical bottleneck in ovarian cancer management, yet reliable pre-treatment predictive tools are lacking. Existing markers like the platinum-free interval are retrospective, while genomic profiling is often cost-prohibitive. This study aimed to develop an accessible, machine learning-based dynamic weighted fusion (DWF) model using routine laboratory data to provide bidirectional risk stratification, particularly to reliably rule out platinum resistance before treatment initiation.

In this retrospective study (2019-2023), seventy baseline clinical features were collected to differentiate platinum-resistant from platinum-sensitive ovarian cancer patients. We developed a DWF framework that dynamically integrates the top-performing classifiers from a library of 168 algorithms (combining 14 feature selection and 12 machine learning methods). Class imbalance was addressed via oversampling, and model efficacy was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity.

The DWF model achieved a robust AUC of 0.760 (95% CI: 0.683-0.837), outperforming all individual base classifiers. Subgroup analysis demonstrated highly consistent overall discrimination across initial treatment strategies (AUC of 0.755 for primary debulking surgery and 0.761 for neoadjuvant chemotherapy). Feature interpretation highlighted that resistance is driven by synergistic dysregulation of systemic inflammation and hypercoagulability, rather than single biomarkers.

The proposed DWF model effectively leverages low-cost, standardized clinical data to serve as a robust bidirectional stratification tool. Its exceptional ability to rule out resistance provides clinicians with the evidence-based confidence to proceed with standard therapies, while its high-risk alerts identify candidates for early therapeutic adjustments and enhanced surveillance in ovarian cancer care.