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the results and improve specificity of the power of detection. The prediction model would be a more reliable tool when compared with other existing prediction models.

Early detection of breast cancer can result in a better prognosis, lower mortality, and lower recurrence rate.[@R58] Nowadays, mammography is a routine method to detect breast cancer, but since mammography cannot detect microcalcifications, it is relatively insensitive to detect breast cancer at early stage.[@R60]

To the best of our knowledge, this is the first study that comprehensively assessed the diagnostic performance of QUS parameters to detect breast cancer using the largest study population with the longest follow-up. The main strength of this study lies in the detailed data collection from a single site, and the large sample size, which allowed us to establish an external validation group and perform stepwise logistic regression analysis. However, there are still some limitations that should be considered when interpreting the data. First, USp had lower diagnostic accuracy, which may due to the different locations of the breast lesion and US frequency, as US is a transducer-based method while QUS is a non-transducer-based method, and also due to the intersubject variability. Second, sample sizes of different QUS parameters at different points of follow-up were small, which may have made the model unstable. In addition, the model was prone to overfitting. Third, our study only included Chinese women, and whether the model is transferable to other ethnic groups is unknown. Fourth, both patient history and results from histopathology were based on the most recently performed biopsies; however, prior biopsies were not available to evaluate lesion burden, which may have affected diagnosis. Lastly, this study only focused on breast lesions with size 鈮5鈥塩m, and the conclusions cannot be applied to the general population or for larger breast lesions. Therefore, large-scale studies with more appropriate sample sizes and other ethnic groups are required to further validate the model.

Conclusion {#s5}

In conclusion, the present study shows that a QUS-based prediction model (USp and 螖USp) has good performance for diagnosing breast cancer at stage I. If the

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