Abstract
Objectives
To compare computed tomography (CT)–based radiomics for preoperatively differentiating type I and II epithelial ovarian cancers (EOCs) using different machine learning classifiers and to construct and validate the best diagnostic model.
Methods
A total of 470 patients with EOCs were included retrospectively. Patients were divided into a training dataset (N = 329) and a test dataset (N = 141). A total of 1316 radiomics features were extracted from the portal venous phase of contrast-enhanced CT images for each patient, followed by dimension reduction of the features. The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), naïve Bayes (NB), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) classifiers were trained to obtain the radiomics signatures. The performance of each radiomics signature was evaluated and compared by the area under the receiver operating characteristic curve (AUC) and relative standard deviation (RSD). The best radiomics signature was selected and combined with clinical and radiological features to establish a combined model. The diagnostic value of the combined model was assessed.
Results
The LR-based radiomics signature performed well in the test dataset, with an AUC of 0.879 and an accuracy of 0.773. The combined model performed best in both the training and test datasets, with AUCs of 0.900 and 0.934 and accuracies of 0.848 and 0.823, respectively.
Conclusion
The combined model showed the best diagnostic performance for distinguishing between type I and II EOCs preoperatively. Therefore, it can be a useful tool for clinical individualized EOC classification.
Key Points
• Radiomics features extracted from computed tomography (CT) could be used to differentiate type I and II epithelial ovarian cancers (EOCs).
• Machine learning can improve the performance of differentiating type I and II EOCs.
• The combined model exhibited the best diagnostic capability over the other models in both the training and test datasets.
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Abbreviations
- EOCs:
-
Epithelial ovarian cancers
- GLCM:
-
Gray-level co-occurrence matrix
- GLDM:
-
Gray-level dependence matrix
- GLRLM:
-
Gray-level run-length matrix
- GLSZM:
-
Gray-level size zone matrix
- ICC:
-
Intraclass correlation coefficient
- KNN:
-
K-nearest neighbor
- LR:
-
Logistic regression
- NB:
-
Naïve Bayes
- NGTDM:
-
Neighboring gray-tone difference matrix
- OC:
-
Ovarian cancer
- RF:
-
Random forest
- RSD:
-
Relative standard deviation
- SVM:
-
Support vector machines
- VOI:
-
Volume of interest
- XGBoost:
-
eXtreme Gradient Boosting
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Funding
This work was supported by the Chinese National Key Research and Development Project (Grant No. 2021YFC2500400 and Grant No. 2021YFC2500402), the Breeding Project of National Natural Science Foundation of China, Tianjin Medical University Cancer Institute and Hospital (Grant No. 210207), and the Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A).
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The scientific guarantor of this publication is Dr. Zhaoxiang Ye.
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Li, J., Li, X., Ma, J. et al. Computed tomography–based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers. Eur Radiol 33, 5193–5204 (2023). https://doi.org/10.1007/s00330-022-09318-w
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DOI: https://doi.org/10.1007/s00330-022-09318-w