Abstract 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, TSP manual ) 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 (TSP manual ) and artificial intelligence–derived assessments (TSP auto ) on digitized images from a cohort of EOC specimens. Results In this cohort, we showed the prognostic significance of TSP manual , confirming prior findings. Furthermore, TSP auto and TSP manual 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 TSP auto 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.
Aggarwal et al. (Fri,) studied this question.
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