Enterprise AI Analysis

Genomics-Based AI: Moderate Accuracy with Variability

The pooled AUC for genomics-based AI models was 0.78 (95% CI: 0.66–0.89), indicating moderate predictive accuracy. HRD prediction models, such as DeepHRD (AUC = 0.81), achieved relatively strong performance, whereas BRCA-status models like NERO et al. (AUC = 0.70) exhibited lower predictive accuracy. This suggests that single-gene approaches may not provide sufficient discriminatory power for therapy selection.

The high heterogeneity observed across genomics-based AI models reflects differences in biomarker selection, dataset sources, and model validation techniques. Future AI models could incorporate spatial transcriptomics and proteomics to improve tumor heterogeneity characterization and enhance biomarker-driven patient stratification.

Immunotherapy-Focused AI Models: Promising but Heterogeneous

The pooled AUC for immunotherapy-focused AI models was 0.77 (95% CI: 0.69–0.85), with high heterogeneity (I2 = 90.0%), reflecting differences in immune biomarker selection, dataset validation, and AI methodologies. Models leveraging fibroblast-based signatures (AUC = 0.853) and ECM remodeling (AUC = 0.810) showed higher accuracy, suggesting the importance of tumor microenvironment interactions.

Despite promising results, these models rely heavily on publicly available transcriptomic datasets and vary significantly in endpoints and validation strategies. The heterogeneity in AI performance across different datasets highlights the inherent complexity of immune biomarkers.

Addressing Challenges for Clinical Translation

Despite promising results, AI models in ovarian cancer still face significant hurdles before widespread clinical adoption. Key areas for improvement include:

Standardize Workflows: Efforts to standardize radiomics workflows through AI-driven automated feature selection and harmonization of imaging databases, such as The Cancer Imaging Archive (TCIA), will be crucial for their translation into routine clinical practice.

Multi-Center Validation: The scarcity of prospective validation is due to logistical challenges, data access limitations, and the retrospective design typical of early-stage AI studies. Moving forward, the successful integration of AI into clinical practice will require prospective validation through multi-center clinical trials to ensure generalizability.

Integrate XAI: A key barrier to clinical implementation of AI in oncology remains the lack of interpretability. Explainable AI (XAI) techniques have emerged to address this challenge, offering tools to visualize or quantify how input features contribute to model predictions. Future AI models should prioritize the integration of these tools.

Long-term Monitoring: AI models must be continuously updated using real-world patient data to adapt to evolving treatment paradigms, novel biomarker discoveries, and emerging immunotherapy combinations.

Performance Comparison by AI Model Type

Our analysis revealed that the relative performance of AI model types appears to be domain-dependent. Deep learning models demonstrated superior performance in imaging-based applications, achieving an average AUC of 0.975 in radiogenomics. In contrast, traditional machine learning methods showed more stable performance in genomics-based applications.