An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models.
This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound images and machine learning models.
Ultrasound images of 437 patients with surgically and pathologically confirmed BC were retrospectively analyzed, including 144 HER2-positive and 293 HER2-negative cases, which were used as a training and validation dataset. Key features highly correlated with HER2-positive status were identified and selected using the least absolute shrinkage and selection operator (LASSO), t-test, and principal component analysis (PCA). After the selection of relevant features, the dataset was randomly split into five equal parts for five-fold cross-validation to identify the optimal machine learning method and hyperparameters. A predictive model was then developed based on ultrasound imaging and radiomics features. After feature selection and model development, an additional cohort of 88 patients from other hospitals was utilized as an external validation dataset. The model's internal validation performance was assessed through receiver operating characteristic (ROC) curve analysis, and metrics including area under the curve (AUC), sensitivity, and specificity were calculated. The generalizability of the model was further evaluated using the external validation.
Five radiomics features were found to correlate with HER2-positive status in BC and used for model construction. Among the machine learning models generated, the best predictive model achieved area under the ROC curve values of 0.893 (95% confidence interval [CI], 0.860-0.920) in the training and validation dataset and 0.854 (95% CI, 0.775-0.927) in the external validation dataset.
Machine learning models based on ultrasound radiomics features have potential clinical value for predicting HER2-positive status in BC.
Ultrasound images of 437 patients with surgically and pathologically confirmed BC were retrospectively analyzed, including 144 HER2-positive and 293 HER2-negative cases, which were used as a training and validation dataset. Key features highly correlated with HER2-positive status were identified and selected using the least absolute shrinkage and selection operator (LASSO), t-test, and principal component analysis (PCA). After the selection of relevant features, the dataset was randomly split into five equal parts for five-fold cross-validation to identify the optimal machine learning method and hyperparameters. A predictive model was then developed based on ultrasound imaging and radiomics features. After feature selection and model development, an additional cohort of 88 patients from other hospitals was utilized as an external validation dataset. The model's internal validation performance was assessed through receiver operating characteristic (ROC) curve analysis, and metrics including area under the curve (AUC), sensitivity, and specificity were calculated. The generalizability of the model was further evaluated using the external validation.
Five radiomics features were found to correlate with HER2-positive status in BC and used for model construction. Among the machine learning models generated, the best predictive model achieved area under the ROC curve values of 0.893 (95% confidence interval [CI], 0.860-0.920) in the training and validation dataset and 0.854 (95% CI, 0.775-0.927) in the external validation dataset.
Machine learning models based on ultrasound radiomics features have potential clinical value for predicting HER2-positive status in BC.
Authors
Zhang Zhang, Yuan Yuan, Hu Hu, Chen Chen, Xiao Xiao, Huang Huang, Yu Yu, Lu Lu, Shen Shen, Fu Fu
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