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Background <p>Characterisation of CT detected ovarian masses is challenging with overlapping imaging features, unreliable biomarker or clinical presentation. We proposed a two-staged CT-based radiomics model to identify early-stage ovarian carcinoma (ES-OC) and sub-classify different types of benign ovarian masses (BOM).</p> Methods <p>Patients with histologically confirmed BOM or ES-OC (FIGO I-II) were retrospectively recruited from 5 centres. Radiomics features were derived from CT images using PyRadiomics (v3.0.1), which intrinsically resampled volumes to isotropic 1&#xa0;mm³ voxels. To reduce feature redundancy, features with high correlation (Spearman’s ρ ≥ 0.85) were excluded. Two-staged feature selection was applied. First, elastic-net regression with repeated 5-fold stratified cross-validation (100 iterations) was performed to identify highly repeatable features, followed by Mann-Whitney U testing for statistical significance. Second, Boruta algorithm with Random Forest (RF) estimator was employed over 500 iterations to robustly select features by comparing their importance to randomized shadow features. Several machine learning (ML) classifiers were evaluated using stratified 10‑fold GridSearch cross-validation with area under the curve (AUC) as tuning metric. The optimal model from each stage with highest cross-validated AUC was then evaluated on the respective test set. The AUC, calibration plot, and decision curve analysis (DCA) were employed to assess the performance and clinical utility of models.</p> Results <p>The study enrolled 483 patients with 529 lesions (ES-OC: 192 patients, 192 lesions; BOM: 291 patients, 337 lesions). In the first-stage, logistic regression (LR) algorithm was selected with high sensitivity (0.870), moderate specificity (0.719) and high AUC (0.859) in the test set. In the second-stage, support vector machines (SVM) had high diagnostic accuracy with sensitivity 0.750, specificity 0.839 and AUC 0.918. DCA identified the highest benefit at 0.20 risk threshold probability in determining ES-OC.</p> Conclusion <p>The two-staged CT-based radiomics model incorporating LR and SVM algorithms had high diagnostic efficiency in characterising ES-OC and BOM, potentially in triaging disease and personalising care.</p>

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Two-staged CT-based radiomics model in characterising early-stage ovarian carcinoma and benign ovarian masses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月1日

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  • Jiarui Zhang,
  • Rahul Singh,
  • Esther Man Fung Wong,
  • Lujun Han,
  • Grace Ho,
  • Wan Hang Keith Chiu,
  • Isaac Ming Kiu Cho,
  • Philip Pun Ching Ip,
  • Elaine Yuen Phin Lee

摘要

Background

Characterisation of CT detected ovarian masses is challenging with overlapping imaging features, unreliable biomarker or clinical presentation. We proposed a two-staged CT-based radiomics model to identify early-stage ovarian carcinoma (ES-OC) and sub-classify different types of benign ovarian masses (BOM).

Methods

Patients with histologically confirmed BOM or ES-OC (FIGO I-II) were retrospectively recruited from 5 centres. Radiomics features were derived from CT images using PyRadiomics (v3.0.1), which intrinsically resampled volumes to isotropic 1 mm³ voxels. To reduce feature redundancy, features with high correlation (Spearman’s ρ ≥ 0.85) were excluded. Two-staged feature selection was applied. First, elastic-net regression with repeated 5-fold stratified cross-validation (100 iterations) was performed to identify highly repeatable features, followed by Mann-Whitney U testing for statistical significance. Second, Boruta algorithm with Random Forest (RF) estimator was employed over 500 iterations to robustly select features by comparing their importance to randomized shadow features. Several machine learning (ML) classifiers were evaluated using stratified 10‑fold GridSearch cross-validation with area under the curve (AUC) as tuning metric. The optimal model from each stage with highest cross-validated AUC was then evaluated on the respective test set. The AUC, calibration plot, and decision curve analysis (DCA) were employed to assess the performance and clinical utility of models.

Results

The study enrolled 483 patients with 529 lesions (ES-OC: 192 patients, 192 lesions; BOM: 291 patients, 337 lesions). In the first-stage, logistic regression (LR) algorithm was selected with high sensitivity (0.870), moderate specificity (0.719) and high AUC (0.859) in the test set. In the second-stage, support vector machines (SVM) had high diagnostic accuracy with sensitivity 0.750, specificity 0.839 and AUC 0.918. DCA identified the highest benefit at 0.20 risk threshold probability in determining ES-OC.

Conclusion

The two-staged CT-based radiomics model incorporating LR and SVM algorithms had high diagnostic efficiency in characterising ES-OC and BOM, potentially in triaging disease and personalising care.