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Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study

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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
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关键词: Ultrasound shear-wave elastography (SWE) breast cancer cancer screening Breast Imaging Reporting and Data System (BI-RADS)

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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.

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出版当年[2021]版:
大类 | 3 区 医学
小类 | 3 区 核医学
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 核医学
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出版当年[2020]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2020版] 出版当年五年平均 出版前一年[2019版] 出版后一年[2021版]

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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
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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
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