Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions

Quantitative Imaging in Medicine and Surgery

article

Xiaosong Lan¹  Xiaoxia Wang¹  Jun Qi²  Huifang Chen¹  Xiangfei Zeng¹  Jinfang Shi¹  Daihong Liu¹  Hesong Shen¹  Jiuquan Zhang¹ 

[1] Department of Radiology , Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital;Department of Thoracic Surgery, Chongqing University Cancer Hospital, School of Medicine , Chongqing University

关键词: Dual-energy computed tomography (DECT);    breast neoplasms;    machine learning;   

DOI  :  10.21037/qims-21-39

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions. Methods: In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZeff), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λHu). Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test. Results: The univariate analysis showed that the venous phase λHu had the highest AUROC (0.88). Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88–0.99) and testing (AUROC, 0.83–0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001). Conclusions: The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.

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