机构：[1]Department of Ultrasound, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China[2]Department of Ultrasound, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China[3]Department of Rehabilitation Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China[4]Department of Ultrasound, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China[5]School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
Background: The American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) used with ultrasonography cannot guide the individual management of solid breast tumors, but preoperative core biopsy categories (CBCs) can. We aimed to use machine learning to analyze clinical and ultrasonic features for predicting CBCs and to aid in the development of a new ultrasound (US) imaging reporting system for solid tumors of the breast. Methods: This retrospective study included women with solid breast tumors who underwent US-guided core needle biopsy from March 1, 2019, to December 31, 2019. All patients were randomly assigned to a training or validation cohort (7:3 ratio). CBC was predicted using 5 machine learning models: random forest (RF), support vector machine (SVM), k-nearest-neighbor (KNN), multilayer perceptron (MLP), and ridge regression (RR). In the validation cohort, the area under the curve (AUC) and accuracy were ascertained for every algorithm. Based on AUC values, the optimal algorithm was determined, and the features' importance was depicted.Results: A total of 1,082 female patients were included (age range, 12-96 years; mean age +/- standard deviation, 42.22 +/- 13.37 years). The proportion of the 4 CBCs was 4% (44/1,185) for the B1 group, 60% (714/1,185) for the B2 group, 5% (57/1,185) for the B3 group, and 31% (370/1,185) for the B5 group. In the validation cohort, AUCs of the optimal algorithm constructed RF were 0.78, 0.88, 0.64, and 0.92 for B1, B2, B3, and B5, respectively, with an accuracy of 0.82. Conclusions: Machine learning could strongly predict CBC, particularly in B2 and B5 categories of solid breast tumors, with RF being the optimal machine learning model.
基金：
Guangdong Medical Science and Technology Research Fund [A2022225, B2022089, C2018001]
第一作者机构：[1]Department of Ultrasound, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China[2]Department of Ultrasound, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
共同第一作者：
通讯作者：
通讯机构：[1]Department of Ultrasound, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China[2]Department of Ultrasound, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China[*1]Department of Ultrasound, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, No. 16 Jichang Road, Guangzhou 510405, China[*2]Department of Ultrasound, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou 510080, China.
推荐引用方式(GB/T 7714)：
Liang Ting,Shen Junhui,Wang Jiexin,et al.Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning[J].QUANTITATIVE IMAGING IN MEDICINE AND SURGERY.2023,13(4):2634-+.doi:10.21037/qims-22-877.
APA：
Liang, Ting,Shen, Junhui,Wang, Jiexin,Liao, Weilin,Zhang, Zhi...&Liu, Kebing.(2023).Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,13,(4)
MLA：
Liang, Ting,et al."Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning".QUANTITATIVE IMAGING IN MEDICINE AND SURGERY 13..4(2023):2634-+
