Prediction of HER2 Expression in Urothelial Carcinoma of the Bladder: Are Ultrasound-based Radiomic Features Significant?

OBJECTIVE

To investigate whether ultrasound-based radiomic features can be used for the prediction of human epidermal growth factor receptor 2 (HER2) expression.

METHODS

This study retrospectively analyzed the pre-operative ultrasound data of 113 patients with urothelial carcinoma of the bladder who were classified into training (n = 67) and test (n = 46) sets. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify the most discriminative radiomic features for evaluating HER2 status and seven radiomics-based machine learning models were developed. The discriminative performance of the models was evaluated using metrics including area under the receiver operator characteristic curves (AUROCs). A nomogram based on logistic regression was established to visualize the predictive model combining clinical and radiomic signatures.

RESULTS

Ultimately, seven radiomic features for HER2 status prediction were identified, six of which were derived from the wavelet images. Shapley Additive exPlanations analysis revealed that wavelet_LHH_glcm_MCC had the highest weight in predicting HER2 expression. All of the radiomics-based prediction models achieved an area under the curve of more than 0.72 in the test set. The combining nomogram exhibited areas under the curve of 0.827 (95% CI: 0.723-0.931) in the training set and 0.784 (95% CI: 0.616-0.953) in the test set, respectively.

CONCLUSION

Ultrasound-based radiomic features, especially the wavelet transform-based texture features, show potential for non-invasive HER2 status classification in urothelial carcinoma of the bladder.