Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images
We developed a deep learning feature-based stacking model to predict lymphovascular invasion in urothelial carcinoma of the bladder patients using CT. Max cross sections from three dimensions of the CT image are used to train the CNN model. We made comparisons across six CNN networks, including ResNet50.
Authors
- Yang Lv (ORCID: https://orcid.org/0000-0002-6490-9438)
- Qing Jiang (ORCID: https://orcid.org/0000-0003-1875-6263)
- Fajin Lv (ORCID: https://orcid.org/0000-0002-5746-168X)
- Mingzhao Xiao (ORCID: https://orcid.org/0000-0001-6989-5971)
- Huayun Liu (ORCID: https://orcid.org/0009-0006-0639-8509)
- Yingjie Xv (ORCID: https://orcid.org/0000-0001-5585-6058)
- Zongjie Wei
- Li Feng (ORCID: https://orcid.org/0000-0002-8742-0838)
- Bangxin Xiao
- Quanhao He (ORCID: https://orcid.org/0000-0002-8231-8199)
- Canjie Peng
- Qiao Xv
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-05-18
- DOI
- https://doi.org/10.1186/s13244-025-01988-6
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Chongqing Medical University