A CT-based deep learning radiomics model for predicting HER2 expression and prognosis in non-muscle-invasive bladder cancer.
Authors
Affiliations (2)
Affiliations (2)
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Abstract
This study aimed to extract radiomics (Rad) and deep learning (DL) features from preoperative CT of patients with Non-Muscle Invasive Bladder Cancer (NMIBC) and develop models incorporating clinical characteristics to assess Human-Epidermal-Growth-Factor-Receptor-2 (HER2) expression status and prognosis in these patients. From January 2019 to December 2024, 181 patients with NMIBC were retrospectively enrolled in this study. A deep learning radio-clinical signature model (DLCS) was created by integrating DL score, Rad score, and clinicopathologic features to predict HER2 expression in NMIBC and compared with a deep learning model, a radiomic model, and a Clinical model. An additional model was built to predict Recurrence-Free Survival (RFS) in NMIBC patients. 181 patients with NMIBC were divided into a training cohort (<i>n</i> = 126) and a test cohort (<i>n</i> = 55). The DLCS model achieved the highest area under the curve (AUC) for HER2 prediction in the test cohort (AUC = 0.894 (95% CI: 0.814-0.974)). The univariate and multivariate Cox regression analyses identified both the DL score and Rad score as independent risk factors for RFS (<i>p</i> < 0.05). The DLCS model demonstrates good diagnostic performance in predicting HER2 expression, and the prognosis model can stratify the risk of tumor recurrence in patients with NMIBC.