Multimodal AI pipeline taxonomy across 31 high-grade serous ovarian cancer studies

Across 31 high-grade serous ovarian cancer studies, a unified multimodal pipeline has five stages: feature extraction, intra-modal aggregation, fusion, inter-modal integration, prediction head. Issues: inconsistent validation, limited reproducibility, weak explainability.

• A unified five-stage pipeline taxonomy is used to analyze 31 high-grade serous ovarian cancer studies: feature extraction, intra-modal aggregation, fusion, inter-modal integration, and prediction head.
• The approach integrates CT and MRI radiomics with histopathology and multi-omics data to model tumor biology, aiming to improve survival prediction and treatment response assessment.
• Key challenges flagged include inconsistent validation strategies, limited reproducibility, heterogeneous genomic data integration, and insufficient model explainability.

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