Abstract
This study aimed to investigate whether establishing a machine learning (ML) model based on contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) radiomic features could predict human epidermal growth factor receptor 2-positive breast cancer (BC). Eighty-eight patients diagnosed with invasive BC who underwent preoperative CE-CBBCT were retrospectively enrolled. Patients were randomly assigned to the training and testing cohorts at a ratio of approximately 7:3. A total of 1046 quantitative radiomics features were extracted from the CE-CBBCT images using PyRadiomics. Z-score normalization was used to standardize the radiomics features, and Pearson correlation coefficient and one-way analysis of variance were used to explore the significant features. Six ML algorithms (support vector machine, random forest [RF], logistic regression, adaboost, linear discriminant analysis, and decision tree) were used to construct optimal predictive models. Receiver operating characteristic curves were constructed and the area under the curve (AUC) was calculated. Four top-performing radiomic models were selected to develop the 6 predictive features. The AUC values for support vector machine, linear discriminant analysis, RF, logistic regression, adaboost, and decision tree were 0.741, 0.753, 1.000, 0.752, 1.000, and 1.000, respectively, in the training cohort, and 0.700, 0.671, 0.806, 0.665, 0.706, and 0.712, respectively, in the testing cohort. Notably, the RF model exhibited the highest predictive ability with an AUC of 0.806 in the testing cohort. For the RF model, the DeLong test showed statistically significant differences in the AUC between the training and testing cohorts (Z = 2.105, P = .035). The ML model based on CE-CBBCT radiomics features showed promising predictive ability for human epidermal growth factor receptor 2-positive BC, with the RF model demonstrating the best diagnostic performance.