Noninvasive Assessment of Ki-67 Expression in Breast Cancer Using Ultrasound Radiomics: A Multi-institutional Study.

Purpose: This study aimed to develop and rigorously evaluate machine learning models using ultrasound (US) breast cancer (BC) images to predict Ki-67 expression. Additionally, the study sought to identify independent factors influencing Ki-67 expression, with further test conducted through external datasets. Materials and Methods: This study analyzed US images of BC from 347 patients (training set: n=230; external test set: n=117) from Shenzhen People's Hospital and Guangxi Academy of Medical Sciences. Radiomic features were extracted using manual region-of-interest delineation and the Pyradiomics package. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) and decision tree analysis, resulting in 16 features. Machine learning models-logistic regression (LR), support vector machine (SVM), and multilayer perceptron (MLP)-were developed, and their performance was assessed using the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and decision curve analysis. Statistical analysis included univariate and multivariate logistic regression. Results: Three machine learning models (LR, SVM, and MLP) were developed to predict Ki-67 expression from US images. The LR model demonstrated the best diagnostic performance, with an area under the curve of 0.800 in external test set. Key predictors of Ki-67 expression included ill-defined maximum mass diameter and human epidermal growth factor receptor 2 expression, along with other significant clinical variables. Conclusion: This dual-center study demonstrates the potential of radiomics models based on US BC images to predict Ki-67 expression accurately. As a non-invasive diagnostic tool, this approach offers valuable support for clinical decision-making and personalized treatment planning in BC patients.