Noninvasive identification of HER2 status by integrating multiparametric MRI-based radiomics model with the vesical imaging-reporting and data system (VI-RADS) score in bladder urothelial carcinoma

• Published in: Abdominal imaging Vol. 50; no. 7; pp. 3126 - 3136
• Main Authors: Luo, Cheng, Li, Shurong, Han, Yichao, Ling, Jian, Wu, Xuanling, Chen, Lingwu, Wang, Daohu, Chen, Junxing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2025; Springer Nature B.V
• Subjects: Aged; Aged, 80 and over; Biomarkers; Bladder; Breast cancer; Carcinoma, Transitional Cell - diagnostic imaging; Carcinoma, Transitional Cell - metabolism; Chemotherapy; Data systems; ErbB-2 protein; Female; Gastroenterology; Hepatology; Humans; Imaging; Immunohistochemistry; Machine learning; Magnetic resonance imaging; Male; Medical imaging; Medical prognosis; Medicine; Medicine & Public Health; Metastasis; Middle Aged; Multiparametric Magnetic Resonance Imaging - methods; Nomograms; Open source software; Prediction models; Radiology; Radiology Information Systems; Radiomics; Receptor, ErbB-2 - metabolism; Retrospective Studies; Statistical analysis; Urinary Bladder Neoplasms - diagnostic imaging; Urinary Bladder Neoplasms - metabolism; Urothelial carcinoma; Multimodal radiomics; HER2 status; Vesical imaging-reporting and data system; Multiparametric MRI; Bladder urothelial carcinoma
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04767-x

Purpose HER2 expression is crucial for the application of HER2-targeted antibody–drug conjugates. This study aims to construct a predictive model by integrating multiparametric magnetic resonance imaging (mpMRI) based multimodal radiomics and the Vesical Imaging-Reporting and Data System (VI-RADS) score for noninvasive identification of HER2 status in bladder urothelial carcinoma (BUC). Methods A total of 197 patients were retrospectively enrolled and randomly divided into a training cohort ( n  = 145) and a testing cohort ( n  = 52). The multimodal radiomics features were derived from mpMRI, which were also utilized for VI-RADS score evaluation. LASSO algorithm and six machine learning methods were applied for radiomics feature screening and model construction. The optimal radiomics model was selected to integrate with VI-RADS score to predict HER2 status, which was determined by immunohistochemistry. The performance of predictive model was evaluated by receiver operating characteristic curve with area under the curve (AUC). Results Among the enrolled patients, 110 (55.8%) patients were demonstrated with HER2-positive and 87 (44.2%) patients were HER2-negative. Eight features were selected to establish radiomics signature. The optimal radiomics signature achieved the AUC values of 0.841 (95% CI 0.779–0.904) in the training cohort and 0.794 (95%CI 0.650–0.938) in the testing cohort, respectively. The KNN model was selected to evaluate the significance of radiomics signature and VI-RADS score, which were integrated as a predictive nomogram. The AUC values for the nomogram in the training and testing cohorts were 0.889 (95%CI 0.840–0.938) and 0.826 (95%CI 0.702–0.950), respectively. Conclusion Our study indicated the predictive model based on the integration of mpMRI-based radiomics and VI-RADS score could accurately predict HER2 status in BUC. The model might aid clinicians in tailoring individualized therapeutic strategies.