Journal of clinical ultrasound : JCU · 2025
Analysis of Ultrasound Features for Breast Cancers With Different Risk Categories and Evaluation of a New Predicting Method.
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Hu Wenjie, Liu Lu, Zhao Ting, Lin Jie, Wang Yong, Li Fan, Ding Hong
Аннотация
OBJECTIVES: To explore pathology and ultrasound features of breast cancers with different risk categories. To establish and validate a nomogram primarily based on grayscale ultrasound features for non-invasive preoperative prediction of high-risk breast cancers and for rapid individual risk assessment and clinical decision making.
METHODS: A total of 685 breast malignant lesions were enrolled in this study. All lesions were classified according to the St. Gallen risk categories criteria. The pathology and ultrasound features were compared among different risk groups. A multifactorial Logistic model and a nomogram primarily based on grayscale ultrasound were established. Then prediction ability was evaluated.
RESULTS: In training cohort, the ultrasound features with significant differences were selected again through Lasso regression. Then, age, maximum diameter in ultrasound, posterior echo attenuation, spiculate margin and suspicious axillary lymph nodes were selected to establish the prediction model and nomogram. The areas under the curve in training cohort and internal test cohort were 0.833 and 0.827. Diagnostic sensitivity, specificity, accuracy, positive likelihood ratio and negative likelihood ratio were 75.6%, 76.6%, 76.4%, 41.4% and 93.5%, respectively.
CONCLUSIONS: Breast cancers with different risk categories exhibit distinct pathology and ultrasound features. The prediction model and nomogram have good and stable diagnostic efficiency.
METHODS: A total of 685 breast malignant lesions were enrolled in this study. All lesions were classified according to the St. Gallen risk categories criteria. The pathology and ultrasound features were compared among different risk groups. A multifactorial Logistic model and a nomogram primarily based on grayscale ultrasound were established. Then prediction ability was evaluated.
RESULTS: In training cohort, the ultrasound features with significant differences were selected again through Lasso regression. Then, age, maximum diameter in ultrasound, posterior echo attenuation, spiculate margin and suspicious axillary lymph nodes were selected to establish the prediction model and nomogram. The areas under the curve in training cohort and internal test cohort were 0.833 and 0.827. Diagnostic sensitivity, specificity, accuracy, positive likelihood ratio and negative likelihood ratio were 75.6%, 76.6%, 76.4%, 41.4% and 93.5%, respectively.
CONCLUSIONS: Breast cancers with different risk categories exhibit distinct pathology and ultrasound features. The prediction model and nomogram have good and stable diagnostic efficiency.