Compact Fuzzy-Rule Decision-Level Fusion for Ovarian Cancer Survival Prediction With Controlled Modality Extension

Accurate survival risk stratification in epithelial ovarian cancer remains challenging because prognostic information is distributed across heterogeneous clinical, histopathological, radiological, and molecular scales, while modality availability is often incomplete across cohorts. We present a compact fuzzy-rule decision-level fusion framework centered on a primary clinical-histopathology survival model and extended through controlled modality-extension analyses. The primary model operates on calibrated unimodal risk scores and integrates fuzzy membership embedding, rule screening, and compact rule distillation to produce a frozen survival score for downstream use. On the clinical-histopathology complete-case subsets, the compact model achieved C-indices of 0.6771 in TCGA-OV and 0.6085 in the independent Memorial Sloan Kettering Cancer Center cohort, and yielded the strongest external discrimination among the evaluated two-modality late-fusion comparators. Paired bootstrap analysis showed significant gains over the clinical unimodal baseline and QMF, while the remaining pairwise comparisons were directionally favorable but not uniformly significant. CT radiomics, evaluated as an auxiliary modality under incomplete availability, provided only modest local refinement and did not redefine the primary model. In the matched molecular subset, transcriptomics provided substantial complementary value beyond the frozen primary score, whereas reverse incremental analysis showed that the primary model retained nonredundant prognostic information beyond the molecular score. Exploratory biological analyses linked the joint molecular extension score to attenuation of immune- and module-related programs and to enrichment of extracellular-matrix and migratory pathways in high-risk tumors. These findings support a compact, interpretable, and deployment-oriented decision-level fusion strategy for ovarian cancer survival modeling.