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Background <p>The tumor-stroma ratio (TSR) has emerged as a promising prognostic biomarker in epithelial ovarian cancer (EOC); however, its preoperative assessment remains challenging.</p> Objective <p>To develop a non-invasive CT-based radiomics machine learning model for preoperative TSR prediction and to evaluate its association with platinum resistance and survival outcomes in EOC.</p> Methods <p>This retrospective study included 172 patients with pathologically confirmed EOC. TSR was histologically classified as stroma-rich (≥ 50%) or stroma-poor (&lt; 50%). A total of 718 radiological features—including 4 conventional imaging features and 714 quantitative radiomic descriptors—were extracted from contrast-enhanced CT images, along with 26 clinical variables. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression, and a linear support vector machine (SVM) classifier was constructed. The dataset was randomly divided into a training cohort (70%) and validation cohort (30%). Model performance was evaluated using five-fold cross-validation in the training cohort and tested on the independent validation cohort. Associations between the predicted TSR and clinical outcomes were analyzed using multivariable logistic and Cox regression models to assess the clinical value of the model.</p> Results <p>Stroma-rich tumors were significantly associated with advanced FIGO stage, poorer differentiation, ascites, lymph node metastasis, worse completeness of cytoreduction, and platinum resistance. The SVM model achieved a mean cross-validated AUC of 0.83 ± 0.08 in the training cohort and an AUC of 0.83 in the independent validation cohort. Although histological TSR demonstrated superior statistical potency in survival discrimination, the predicted TSR remained an independent predictor of platinum resistance, progression-free survival (PFS) and overall survival (OS) in the preoperative setting.</p> Conclusions <p>The proposed CT-based radiomics model enables reliable, non-invasive estimation of TSR and provides a biologically interpretable imaging biomarker for risk stratification in EOC. Radiomics-predicted TSR may help identify patients at increased risk of platinum resistance and poor prognosis, supporting individualized treatment planning.</p>

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Tumor stroma ratio-based radiomics model for predicting platinum resistance and prognosis in epithelial ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Wang Xiang,
  • Hui Guo,
  • Yan Sun,
  • Xia Jin,
  • Hucheng Li,
  • Shuang Xia

摘要

Background

The tumor-stroma ratio (TSR) has emerged as a promising prognostic biomarker in epithelial ovarian cancer (EOC); however, its preoperative assessment remains challenging.

Objective

To develop a non-invasive CT-based radiomics machine learning model for preoperative TSR prediction and to evaluate its association with platinum resistance and survival outcomes in EOC.

Methods

This retrospective study included 172 patients with pathologically confirmed EOC. TSR was histologically classified as stroma-rich (≥ 50%) or stroma-poor (< 50%). A total of 718 radiological features—including 4 conventional imaging features and 714 quantitative radiomic descriptors—were extracted from contrast-enhanced CT images, along with 26 clinical variables. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression, and a linear support vector machine (SVM) classifier was constructed. The dataset was randomly divided into a training cohort (70%) and validation cohort (30%). Model performance was evaluated using five-fold cross-validation in the training cohort and tested on the independent validation cohort. Associations between the predicted TSR and clinical outcomes were analyzed using multivariable logistic and Cox regression models to assess the clinical value of the model.

Results

Stroma-rich tumors were significantly associated with advanced FIGO stage, poorer differentiation, ascites, lymph node metastasis, worse completeness of cytoreduction, and platinum resistance. The SVM model achieved a mean cross-validated AUC of 0.83 ± 0.08 in the training cohort and an AUC of 0.83 in the independent validation cohort. Although histological TSR demonstrated superior statistical potency in survival discrimination, the predicted TSR remained an independent predictor of platinum resistance, progression-free survival (PFS) and overall survival (OS) in the preoperative setting.

Conclusions

The proposed CT-based radiomics model enables reliable, non-invasive estimation of TSR and provides a biologically interpretable imaging biomarker for risk stratification in EOC. Radiomics-predicted TSR may help identify patients at increased risk of platinum resistance and poor prognosis, supporting individualized treatment planning.