Background: There remains a critical need for prognostic biomarkers of treatment response in epithelial ovarian cancer (EOC). The KELIM score, derived from the rate of CA-125 elimination during the first 100 days of treatment, is a clinically available biomarker of treatment response to platinum-based chemotherapy, its utility is limited by the need for post-treatment data. Tumor-stroma proportion (TSP) has emerged as a prognostic biomarker across several malignancies. Studies from our group have shown that high TSP (≥50% stroma content assessed by pathologist evaluation, TSPmanual) is associated with platinum resistance and poor survival in EOC at diagnosis and before treatment.
Methods: We compared the prognostic value of TSP and KELIM by analyzing manual pathologist (TSPmanual) and artificial intelligence-derived assessments (TSPauto) on digitized images from a cohort of EOC specimens.
Results: In this cohort, we showed the prognostic significance of TSPmanual, confirming prior findings. Furthermore, TSPauto and TSPmanual assessments were highly concordant (94% agreement, Cohen's Kappa 0.89, p<0.001), providing a highly reproducible, automated approach. Unlike KELIM, which was only associated with platinum resistance, high TSPauto was significantly associated with poor survival (HR 1.99, p = 0.02).
Conclusion: These findings support AI-derived TSP as a pre-treatment prognostic biomarker for EOC that complements KELIM.
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