MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study
The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.
Authors
- Rong Na (ORCID: https://orcid.org/0000-0001-7470-5108)
- Xiaohao Ruan (ORCID: https://orcid.org/0000-0001-8373-5237)
- Chun‐Ting Lai (ORCID: https://orcid.org/0000-0002-1963-0673)
- Chi Yao (ORCID: https://orcid.org/0000-0002-0734-6560)
- Hoi-Lung Wong
- Jiacheng Liu (ORCID: https://orcid.org/0000-0003-0378-2311)
- Danfeng Xu (ORCID: https://orcid.org/0000-0002-4175-1009)
- Da Huang (ORCID: https://orcid.org/0000-0001-9021-7438)
- Chiu‐Fung Tsang
- T.Y. Ng
- Tsun-Tsun Chun
- Sze-Ho Ho
Institutions
- Chinese University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Queen Mary Hospital (CN)
- Ruijin Hospital (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Cancers
- Published
- 2024-08-23
- DOI
- https://doi.org/10.3390/cancers16172944
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.83
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality