Application of multimodal ultrasound in risk assessment of breast masses: construction and validation of the BI-RADS prediction model.
- 1Department of Radiology, Jinjiang Municipal Hospital, Jinjiang, China.
- 2Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- 3Department of Radiation Oncology, Jinjiang Municipal Hospital, Jinjiang, China.
Abstract
Despite the fact that the diagnosis and treatment levels of breast cancer in first-tier cities have reached international standards and artificial intelligence (AI)-assisted diagnosis has been introduced in some regions, economically underdeveloped areas and primary hospitals still face significant bottlenecks in diagnosis and treatment. Standardized risk stratification tools that are tailored to the Chinese population are not yet in common use. Ultrasound, the primary equipment for community breast cancer screening in China, was adopted to develop a simplified quantitative Chinese ultrasound BI-RADS (Breast Imaging-Reporting and Data System) risk stratification model based on the epidemiological features of Chinese breast diseases for precise screening. Data of 1,716 patients who underwent ultrasound examination at Ruijin Hospital from August 2019 to September 2020 were collected. The most suspicious lesion identified in each patient was selected for analysis. Taking the ultrasound features of breast masses as independent variables and the pathological benign/malignant status of breast masses as dependent variables, multivariate logistic regression analysis was performed. The regression coefficients were converted to integers as the assigned scores. The total score of each mass was obtained by summing the scores of all indicators, and the malignant rate of masses corresponding to each total score was calculated. The construction of the clinical prediction model for BI-RADS classification involved the implementation of stratification, which was based on the malignant rate of each total score. The following indicators were finally included in the multimodal ultrasound BI-RADS model: ill-defined margin (1 point), spiculation (2 points), microcalcification (2 points), irregular shape (1 point), abundant blood flow (1 point), posterior acoustic shadowing (1 point), and hard strain elastography or maximum shear wave elastography (SWE) ≥87.66 kPa (1 point). The total score ranged from 0 to 8 points, with higher scores, indicating higher malignant rate of the mass. The stratification of multimodal ultrasound BI-RADS was conducted according to the malignant probability. The new model demonstrated high levels of sensitivity and specificity, and was able to effectively differentiate between benign and malignant breast tumors. The area under the receiver operating characteristic curve (AUC) was 0.881. The multimodal ultrasound BI-RADS model provides a novel quantitative method for the risk assessment of breast tumors and has significant clinical application value.
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BibTeX
@article{xie2026application,
title = {{Application of multimodal ultrasound in risk assessment of breast masses: construction and validation of the BI-RADS prediction model.}},
author = {Yamie Xie and Guiping Zhang and Huan Wang and Xiaoxiao Zhang and Weiwei Zhan},
journal = {Quantitative imaging in medicine and surgery},
year = {2026},
month = sep,
doi = {10.21037/qims-2025-1-2831},
url = {https://europepmc.org/article/MED/42997939},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Application of multimodal ultrasound in risk assessment of breast masses: construction and validation of the BI-RADS prediction model.
AU - Yamie Xie
AU - Guiping Zhang
AU - Huan Wang
AU - Xiaoxiao Zhang
AU - Weiwei Zhan
PY - 2026
DA - 2026/09/29
JO - Quantitative imaging in medicine and surgery
DO - 10.21037/qims-2025-1-2831
UR - https://europepmc.org/article/MED/42997939
AB - Despite the fact that the diagnosis and treatment levels of breast cancer in first-tier cities have reached international standards and artificial intelligence (AI)-assisted diagnosis has been introduced in some regions, economically underdeveloped areas and primary hospitals still face significant bottlenecks in diagnosis and treatment. Standardized risk stratification tools that are tailored to the Chinese population are not yet in common use. Ultrasound, the primary equipment for community breast cancer screening in China, was adopted to develop a simplified quantitative Chinese ultrasound BI-RADS (Breast Imaging-Reporting and Data System) risk stratification model based on the epidemiological features of Chinese breast diseases for precise screening. Data of 1,716 patients who underwent ultrasound examination at Ruijin Hospital from August 2019 to September 2020 were collected. The most suspicious lesion identified in each patient was selected for analysis. Taking the ultrasound features of breast masses as independent variables and the pathological benign/malignant status of breast masses as dependent variables, multivariate logistic regression analysis was performed. The regression coefficients were converted to integers as the assigned scores. The total score of each mass was obtained by summing the scores of all indicators, and the malignant rate of masses corresponding to each total score was calculated. The construction of the clinical prediction model for BI-RADS classification involved the implementation of stratification, which was based on the malignant rate of each total score. The following indicators were finally included in the multimodal ultrasound BI-RADS model: ill-defined margin (1 point), spiculation (2 points), microcalcification (2 points), irregular shape (1 point), abundant blood flow (1 point), posterior acoustic shadowing (1 point), and hard strain elastography or maximum shear wave elastography (SWE) ≥87.66 kPa (1 point). The total score ranged from 0 to 8 points, with higher scores, indicating higher malignant rate of the mass. The stratification of multimodal ultrasound BI-RADS was conducted according to the malignant probability. The new model demonstrated high levels of sensitivity and specificity, and was able to effectively differentiate between benign and malignant breast tumors. The area under the receiver operating characteristic curve (AUC) was 0.881. The multimodal ultrasound BI-RADS model provides a novel quantitative method for the risk assessment of breast tumors and has significant clinical application value.
SN - 2223-4292
SN - 2223-4306
ER -