Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Abstract

ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility

“…Additionally, one paper (1.8%) described both differential diagnosis and prognostic prediction. We found that assessments of differential diagnosis and prognostic prediction were both commonly performed; thus, 16 articles focusing on the differential diagnosis of OC [ 24 – 26 , 28 - 32 , 34 , 35 , 39 , 40 , 42 , 44 , 45 , 47 ] and 13 that described studies on prognostic factors [ 52 , 53 , 55 – 57 , 64 , 66 – 68 , 71 – 74 ] were subjected to separate meta-analyses.

Fig.

…”

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

“…Using non-contrast CT images, Li et al [149] constructed and externally validated a radiomics model and nomogram with good-to-excellent performance (AUC of 0.83 and 0.95, respectively) for the differential diagnosis between benign and malignant ovarian tumours. A large study including 1329 patients with ovarian tumours provided an AUC of 0.91 of the machine-learning-based radiomics model for the differentiation between the benign and malignant tumours on contrast-enhanced CT [150]. Furthermore, a multicentric study involving 665 patients from four centres reported an AUC of 0.836 for differentiating high-grade and non-high-grade serous carcinoma [151].…”