CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors

• Published in: European radiology Vol. 32; no. 10; pp. 6953 - 6964
• Main Authors: Zheng, Yunlin, Zhou, Di, Liu, Huan, Wen, Ming
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022; Springer Nature B.V
• Subjects: Benign; Computed tomography; Decision analysis; Decision making; Decision trees; Diagnostic Radiology; Feature extraction; Head and Neck; Image enhancement; Imaging; Internal Medicine; Interventional Radiology; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Oral cancer; Radiology; Radiomics; Reduction; Regression analysis; Support vector machines; Training; Tumors; Ultrasound; Computed tomography; Parotid neoplasms; Machine learning; Radiomics
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-08830-3

Objectives This study aimed to explore and validate the value of different radiomics models for differentiating benign and malignant parotid tumors preoperatively. Methods This study enrolled 388 patients with pathologically confirmed parotid tumors (training cohort: n = 272; test cohort: n = 116). Radiomics features were extracted from CT images of the non-enhanced, arterial, and venous phases. After dimensionality reduction and selection, radiomics models were constructed by logistic regression (LR), support vector machine (SVM), and random forest (RF). The best radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. A combined model was constructed by incorporating radiomics and clinical features. Model performances were assessed by ROC curve analysis, and decision curve analysis (DCA) was used to estimate the models’ clinical values. Results In total, 2874 radiomic features were extracted from CT images. Ten radiomics features were deemed valuable by dimensionality reduction and selection. Among radiomics models, the SVM model showed greater predictive efficiency and robustness, with AUCs of 0.844 in the training cohort; and 0.840 in the test cohort. Ultimate clinical features constructed a clinical model. The discriminatory capability of the combined model was the best (AUC, training cohort: 0.904; test cohort: 0.854). Combined model DCA revealed optimal clinical efficacy. Conclusions The combined model incorporating radiomics and clinical features exhibited excellent ability to distinguish benign and malignant parotid tumors, which may provide a noninvasive and efficient method for clinical decision making. Key Points The current study is the first to compare the value of different radiomics models (LR, SVM, and RF) for preoperative differentiation of benign and malignant parotid tumors. A CT-based combined model, integrating clinical-radiological and radiomics features, is conducive to distinguishing benign and malignant parotid tumors, thereby improving diagnostic performance and aiding treatment.