Ultrasound in medicine & biology · 2025
Prediction of HER2 Expression in Urothelial Carcinoma of the Bladder: Are Ultrasound-based Radiomic Features Significant?
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Zhuang Jiajing, Chen Yuefan, Zheng Wenting, Lian Guangtian, Zhu Yifan, Zhang Huiping, Fan Xiaoqing, Fu Fen, Ye Qin
Аннотация
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.
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.