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Background <p>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<sub>manual</sub>) is associated with platinum resistance and poor survival in EOC at diagnosis and before treatment.</p> Methods <p>We compared the prognostic value of TSP and KELIM by analyzing manual pathologist (TSP<sub>manual</sub>) and artificial intelligence–derived assessments (TSP<sub>auto</sub>) on digitized images from a cohort of EOC specimens.</p> Results <p>In this cohort, we showed the prognostic significance of TSP<sub>manual</sub>, confirming prior findings. Furthermore, TSP<sub>auto</sub> and TSP<sub>manual</sub> assessments were highly concordant (94% agreement, Cohen’s Kappa 0.89, <i>p</i>&lt;0.001), providing a highly reproducible, automated approach. Unlike KELIM, which was only associated with platinum resistance, high TSP<sub>auto</sub> was significantly associated with poor survival (HR 1.99, <i>p</i> = 0.02).</p> Conclusion <p>These findings support AI-derived TSP as a pre-treatment prognostic biomarker for EOC that complements KELIM.</p>

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STAR (stroma-tumor AI risk) assessment: association of AI-derived tumor-stroma proportion with patient survival provides added prognostic value beyond KELIM in epithelial ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Arpit Aggarwal,
  • Morgann Madill,
  • Mayukhmala Jana,
  • Tilak Pathak,
  • Timothy K. Starr,
  • Boris Winterhoff,
  • Katelyn M. Tessier,
  • Britt K. Erickson,
  • Andrew C. Nelson,
  • Emil Lou,
  • Anant Madabhushi,
  • Martina Bazzaro

摘要

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.