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

作者全名："Zheng, Yunlin; Zhou, Di; Liu, Huan; Wen, Ming"

作者地址："[Zheng, Yunlin; Zhou, Di; Wen, Ming] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China; [Liu, Huan] GE Healthcare, Shanghai 201203, Peoples R China"

通信作者："Wen, M (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing 400016, Peoples R China."

来源：EUROPEAN RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000788458100002

JCR分区：Q1

影响因子：5.9

年份：2022

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期号：　

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文献类型：Article; Early Access

关键词：Computed tomography; Parotid neoplasms; Radiomics; Machine learning

摘要："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."

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