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Background <p>Preoperative discrimination between endometriosis-associated ovarian cancer (EAOC) and benign ovarian endometrioma (OE) remains challenging because imaging findings overlap and CA125 is frequently elevated by benign pelvic inflammation. We hypothesized that EAOC is associated with a systemic immune-coagulation signature that could improve risk stratification beyond tumor markers alone.</p> Methods <p>We retrospectively analyzed treatment-naive patients who underwent primary surgery for ovarian masses between January 2015 and February 2025. EAOC (<i>n</i> = 49) and OE (<i>n</i> = 146) were confirmed histopathologically according to the Sampson–Scott criteria. Preoperative laboratory indices obtained within 2 weeks before surgery included fibrinogen, albumin, absolute monocyte count (AMC), CA125, and HE4; the fibrinogen-to-albumin ratio (FAR) was calculated. A multivariable logistic regression model integrating age, menopausal status, AMC, FAR, CA125, and HE4 was developed as the CO-MHF nomogram. Supplementary data-driven analyses included least absolute shrinkage and selection operator (LASSO) logistic regression, Akaike information criterion (AIC)-based stepwise selection, and add-one/drop-one marginal contribution analysis. Discrimination, calibration, and clinical utility were assessed by area under the receiver operating characteristic curve (AUC), bootstrap-derived confidence interval (CI), the Hosmer-Lemeshow test, and decision curve analysis (DCA).</p> Results <p>FAR and AMC were significantly higher in EAOC than in OE (median FAR 0.10 versus 0.07; median AMC 0.39 versus 0.31 × 10<sup>9</sup>/L). The CO-MHF model showed favorable internal discrimination (AUC 0.932, 95% CI 0.887–0.968), outperforming HE4 (0.835), FAR (0.786), CA125 (0.769), and AMC (0.657). At the Youden-derived threshold (0.468), sensitivity was 75.5% and specificity 95.9%, with a positive predictive value (PPV) of 86.0% and a negative predictive value (NPV) of 92.1%. Lower thresholds increased sensitivity to 85.7%–93.9% at the cost of reduced specificity. Supplementary data-driven analyses supported the predictor set: AIC-based stepwise selection identified the same variables, and LASSO retained all CO-MHF predictors. Calibration was acceptable (Hosmer–Lemeshow <i>P</i> = 0.225), and DCA showed greater net benefit across a range of threshold probabilities.</p> Conclusions <p>A mechanism-informed nomogram combining FAR and AMC with established tumor markers may improve preoperative risk stratification for EAOC. This host-response framework merits further evaluation in other malignancy-versus-inflammation diagnostic settings, although prospective multicenter validation and clinical threshold optimization remain necessary.</p>

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Systemic immune and coagulation amplifiers resolve diagnostic ambiguity in endometriosis-associated ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Li Luo,
  • Yanwei Tu,
  • Zirui Zhu,
  • Mingzhu Ye

摘要

Background

Preoperative discrimination between endometriosis-associated ovarian cancer (EAOC) and benign ovarian endometrioma (OE) remains challenging because imaging findings overlap and CA125 is frequently elevated by benign pelvic inflammation. We hypothesized that EAOC is associated with a systemic immune-coagulation signature that could improve risk stratification beyond tumor markers alone.

Methods

We retrospectively analyzed treatment-naive patients who underwent primary surgery for ovarian masses between January 2015 and February 2025. EAOC (n = 49) and OE (n = 146) were confirmed histopathologically according to the Sampson–Scott criteria. Preoperative laboratory indices obtained within 2 weeks before surgery included fibrinogen, albumin, absolute monocyte count (AMC), CA125, and HE4; the fibrinogen-to-albumin ratio (FAR) was calculated. A multivariable logistic regression model integrating age, menopausal status, AMC, FAR, CA125, and HE4 was developed as the CO-MHF nomogram. Supplementary data-driven analyses included least absolute shrinkage and selection operator (LASSO) logistic regression, Akaike information criterion (AIC)-based stepwise selection, and add-one/drop-one marginal contribution analysis. Discrimination, calibration, and clinical utility were assessed by area under the receiver operating characteristic curve (AUC), bootstrap-derived confidence interval (CI), the Hosmer-Lemeshow test, and decision curve analysis (DCA).

Results

FAR and AMC were significantly higher in EAOC than in OE (median FAR 0.10 versus 0.07; median AMC 0.39 versus 0.31 × 109/L). The CO-MHF model showed favorable internal discrimination (AUC 0.932, 95% CI 0.887–0.968), outperforming HE4 (0.835), FAR (0.786), CA125 (0.769), and AMC (0.657). At the Youden-derived threshold (0.468), sensitivity was 75.5% and specificity 95.9%, with a positive predictive value (PPV) of 86.0% and a negative predictive value (NPV) of 92.1%. Lower thresholds increased sensitivity to 85.7%–93.9% at the cost of reduced specificity. Supplementary data-driven analyses supported the predictor set: AIC-based stepwise selection identified the same variables, and LASSO retained all CO-MHF predictors. Calibration was acceptable (Hosmer–Lemeshow P = 0.225), and DCA showed greater net benefit across a range of threshold probabilities.

Conclusions

A mechanism-informed nomogram combining FAR and AMC with established tumor markers may improve preoperative risk stratification for EAOC. This host-response framework merits further evaluation in other malignancy-versus-inflammation diagnostic settings, although prospective multicenter validation and clinical threshold optimization remain necessary.