Using deep learning to differentiate among histology renal tumor types in computed tomography scans
This study demonstrated the efficacy of using deep learning models for the classification of renal tumor subtypes from contrast-enhanced CT images. While the models showed promising accuracy, further development is necessary to improve their clinical applicability.
Authors
- Shih-Chun Cheng
- Hung-Cheng Kan (ORCID: https://orcid.org/0000-0003-1503-9971)
- See‐Tong Pang (ORCID: https://orcid.org/0000-0002-2064-0828)
- I‐Hung Shao (ORCID: https://orcid.org/0000-0002-4832-0113)
- Po‐Hung Lin (ORCID: https://orcid.org/0000-0001-8245-4302)
- Tzuo‐Yau Fan (ORCID: https://orcid.org/0000-0002-6420-055X)
- Cheng‐Keng Chuang (ORCID: https://orcid.org/0000-0001-6154-1220)
- Kai‐Jie Yu (ORCID: https://orcid.org/0000-0003-3656-8856)
- Syu‐Jyun Peng (ORCID: https://orcid.org/0000-0001-5002-6581)
- Liang‐Kang Huang (ORCID: https://orcid.org/0000-0002-7634-6740)
- Yuan‐Cheng Chu (ORCID: https://orcid.org/0000-0002-1210-0522)
- Ying‐Hsu Chang
- Chun-Te Wu
Institutions
- Chang Gung University (TW)
- Taipei Medical University Hospital (TW)
- Chang Gung Memorial Hospital (TW)
- Taipei Municipal YangMing Hospital (TW)
- Linkou Chang Gung Memorial Hospital (TW)
- Taipei Medical University (TW)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1186/s12880-025-01606-3
- Citations
- 9
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 13.67