Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study
机构:[1]Guangdong Second Hosp Tradit Chinese Med, Dept Med Technol, Guangdong Key Lab Tradit Chinese Med Res & Dev, Guangzhou 510095, Peoples R China[2]Jinan Univ, Affiliated Hosp 1, Med Imaging Ctr, Guangzhou, Peoples R China[3]Anhui Med Univ, Affiliated Hosp 1, Dept Obstet & Gynecol, Hefei, Peoples R China[4]Sun Yat Sen Univ, Affiliated Hosp 1, Breast Dis Ctr, Guangzhou 510080, Peoples R China中山大学附属第一医院
Background: Ultrasound is commonly used in breast cancer screening but lacks quantification ability and diagnostic power due to its low specificity, which can lead to overdiagnosis and unnecessary biopsies. This study evaluated the diagnostic efficacy and clinical utility of adding shear-wave elastography (SWE) to the screening of the Breast Imaging Reporting and Data System (BI-RADS) category 4 breast cancer. Methods: A machine learning-based diagnostic model was constructed using data retrospectively collected from 3 independent cohorts with features selected using lasso regression and support vector machine-recursive feature elimination algorithms. Propensity score matching (PSM) was used to preclude confounding baseline characteristics between malignant and benign lesions. A decision curve analysis (DCA) was used to evaluate the clinical benefit of the diagnostic model in identifying high-risk tumor patients for intervention while simultaneously avoiding overtreatment of low-risk patients with integrative evaluation using a net benefit value and treatment reduction rate. Results: In our training center, a total of 122 patients were enrolled, and 577 breast tumors were collected. The comparison between malignant and benign lesions revealed significant differences in patient age, tumor size, resistance index (RI), and elasticity values. The maximum elasticity value (Emax) was identified as an independent diagnostic feature and was included in the diagnostic model. The combination of Emax with BI-RADS category 4 demonstrated a significantly better diagnostic efficacy than the BI-RADS category alone [BI-RADS+Emax: AUC: 0.908, 95% confidence interval (CI), 0.842-0.974; BI-RADS: AUC =0.862, 95% CI, 0.784-0.94; P=0.024] and significantly increased the clinical benefit for patients and policy makers by effectively reducing overdiagnosis and biopsy rates. In the BI-RADS category 4A subgroup, adding Emax to breast cancer screening benefited patients and showed a greater absolute benefit than did the BI-RADS category alone when used for patients with a higher probability of cancer (>0.403), demonstrating a 50% overtreatment reduction. Conclusions: Adding Emax to BI-RADS category 4 breast cancer screening using SWE significantly reduced overdiagnosis and biopsy rates compared with the BI-RADS category alone, especially for BI-RADS 4A patients.
基金:
Medical Scientific Research Foundation of Guangdong Province [B2020184]
第一作者机构:[1]Guangdong Second Hosp Tradit Chinese Med, Dept Med Technol, Guangdong Key Lab Tradit Chinese Med Res & Dev, Guangzhou 510095, Peoples R China
共同第一作者:
通讯作者:
通讯机构:[1]Guangdong Second Hosp Tradit Chinese Med, Dept Med Technol, Guangdong Key Lab Tradit Chinese Med Res & Dev, Guangzhou 510095, Peoples R China[4]Sun Yat Sen Univ, Affiliated Hosp 1, Breast Dis Ctr, Guangzhou 510080, Peoples R China[*1]Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China[*2]Department of Medical Technology, Guangdong Key Laboratory of Traditional Chinese Medicine Research and Development, Guangdong Second Hospital of Traditional Chinese Medicine, Guangzhou 510095, China
推荐引用方式(GB/T 7714):
Tang Yi,Liang Minjie,Tao Li,et al.Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study[J].QUANTITATIVE IMAGING IN MEDICINE AND SURGERY.2022,12(2):1223-1234.doi:10.21037/qims-21-341.
APA:
Tang, Yi,Liang, Minjie,Tao, Li,Deng, Minjun&Li, Tianfu.(2022).Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study.QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,12,(2)
MLA:
Tang, Yi,et al."Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study".QUANTITATIVE IMAGING IN MEDICINE AND SURGERY 12..2(2022):1223-1234