MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.
Authors
Affiliations (11)
Affiliations (11)
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing City, China.
- Department of Radiology, Wuxi Medical Center of Nanjing Medical University, Wuxi, China.
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Radiology, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing, 210029, China.
- Department of Urology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, 223001, China.
- Department of Urology, Suzhou Hospital Affiliated of Nanjing Medical University, Suzhou, 215200, China.
- Department of Urology, Yixing People's Hospital, Wuxi, China.
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing City, China. [email protected].
- SEUIC Technologies Co., Ltd, Nanjing, China. [email protected].
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Gulou District, Nanjing City, China. [email protected].
Abstract
Lymphovascular invasion (LVI) signifies poor prognosis in bladder cancer, yet reliable preoperative prediction remains challenging, limiting personalized treatment planning. In this multi-center retrospective study, 543 bladder cancer patients were enrolled. Of these, 473 patients from two centers were randomly split into training and internal test sets at an 8:2 ratio, while 70 patients from six additional centers constituted an independent external test set. After tumor and peritumoral segmentation, a hybrid model that integrates deep learning and radiomics features was developed. The hybrid model was compared against ten baseline models, including image-only and radiomics-only deep learning models, as well as radiomics-based machine learning approaches. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) with five-fold cross-validation. Prognostic value was evaluated by Kaplan-Meier survival analysis with the log-rank test. The hybrid model achieved AUCs of 0.77 (internal test) and 0.75 (external test), surpassing all baseline models. The model-predicted LVI status significantly stratified overall survival in the training set (p < 0.001) and the internal test set (p = 0.003), with a non-significant trend observed in the external test set (p = 0.151). The proposed non-invasive hybrid model can accurately predict LVI status preoperatively and demonstrates prognostic potential, thereby aiding risk stratification in bladder cancer.