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Objective <p>To develop an interpretable artificial intelligence (AI)-based machine learning model integrating <sup>18</sup>F-fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) radiomics, metabolic parameters, and clinical biomarkers for predicting residual disease after primary debulking surgery of ovarian cancer.</p> Methods <p>This multicenter retrospective study included 203 epithelial ovarian cancer patients (2017–2024), including 153 in the training cohort and 50 in the validation cohort. Radiomic features and metabolic parameters were extracted from preoperative PET/CT, and clinical variables were from medical records. After Synthetic Minority Over-sampling Technique (SMOTE) balancing and minimum Redundancy Maximum Relevance (mRMR)-based feature selection, seven models were trained using nested 5 × 10 cross-validation. Performance was assessed by Area Under the Curve (AUC), Brier score, and decision curve analysis (DCA), with comparison to the conventional criteria such as the Suidan criteria or Fagotti criteria. Survival outcomes were analyzed using Kaplan-Meier and Cox regression.</p> Results <p>The SVM model (constructed using the support vector machine method) achieved superior discrimination (AUC 0.896 [95% CI 0.842–0.937] for training; 0.872 [0.792–0.932] for validation), significantly outperforming the Suidan criteria (AUC 0.696, <i>P</i> &lt; 0.001; DeLong’s test). Key predictors included tumor spatial heterogeneity (Elongation, Least Axis Length) and metabolic activity (SUVmax), alongside CA-125 and albumin. The model demonstrated excellent calibration (Brier score 0.119) and clinical net benefit across thresholds (4–85%). Moreover, high-risk patients had increased mortality risk (<i>P</i> &lt; 0.001).</p> Conclusion <p>The PET/CT-based model improves preoperative prediction of residual disease in ovarian cancer, potentially reducing nontherapeutic surgeries. Automated lesion segmentation and multicenter validation are needed for clinical translation.</p> Trial registration <p> NCT06533709 (ClinicalTrials.gov, Registration Date: 2024-08-01).</p>

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The optimal SVM model from multi-model comparison outperforms conventional criteria in preoperative prediction of ovarian cancer residual disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Dongdong Ye,
  • Shaodan Lin,
  • Zhuyan Shao,
  • Yunyun Liu,
  • Bin Wu,
  • Chuying Huo,
  • Dongdong Xu,
  • Suen Wai Pang,
  • Chunxian Huang,
  • Jingyan Li,
  • Jiachen Liu,
  • Lijuan Wang,
  • Tao Zhu,
  • Huaiwu Lu

摘要

Objective

To develop an interpretable artificial intelligence (AI)-based machine learning model integrating 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics, metabolic parameters, and clinical biomarkers for predicting residual disease after primary debulking surgery of ovarian cancer.

Methods

This multicenter retrospective study included 203 epithelial ovarian cancer patients (2017–2024), including 153 in the training cohort and 50 in the validation cohort. Radiomic features and metabolic parameters were extracted from preoperative PET/CT, and clinical variables were from medical records. After Synthetic Minority Over-sampling Technique (SMOTE) balancing and minimum Redundancy Maximum Relevance (mRMR)-based feature selection, seven models were trained using nested 5 × 10 cross-validation. Performance was assessed by Area Under the Curve (AUC), Brier score, and decision curve analysis (DCA), with comparison to the conventional criteria such as the Suidan criteria or Fagotti criteria. Survival outcomes were analyzed using Kaplan-Meier and Cox regression.

Results

The SVM model (constructed using the support vector machine method) achieved superior discrimination (AUC 0.896 [95% CI 0.842–0.937] for training; 0.872 [0.792–0.932] for validation), significantly outperforming the Suidan criteria (AUC 0.696, P < 0.001; DeLong’s test). Key predictors included tumor spatial heterogeneity (Elongation, Least Axis Length) and metabolic activity (SUVmax), alongside CA-125 and albumin. The model demonstrated excellent calibration (Brier score 0.119) and clinical net benefit across thresholds (4–85%). Moreover, high-risk patients had increased mortality risk (P < 0.001).

Conclusion

The PET/CT-based model improves preoperative prediction of residual disease in ovarian cancer, potentially reducing nontherapeutic surgeries. Automated lesion segmentation and multicenter validation are needed for clinical translation.

Trial registration

NCT06533709 (ClinicalTrials.gov, Registration Date: 2024-08-01).