Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach
The combination of multimodal imaging data with peritumoral features moderately improved the accuracy of prostate cancer classification. This model provides a non-invasive and effective diagnostic tool for clinical use and supports future personalized treatment decisions.
Authors
- Hongcheng Shi (ORCID: https://orcid.org/0000-0003-4813-4262)
- Chengchao Shou (ORCID: https://orcid.org/0000-0002-2206-0843)
- Huadi Zhou
- Yue Zhang (ORCID: https://orcid.org/0000-0001-7856-7451)
- Xiao Liu (ORCID: https://orcid.org/0009-0004-7336-0194)
- Mei Xie (ORCID: https://orcid.org/0000-0001-5605-8867)
- Yue Hu (ORCID: https://orcid.org/0000-0002-9240-6483)
- Meng Tang (ORCID: https://orcid.org/0009-0005-5671-252X)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Zhejiang Hospital (CN)
- Guang’anmen Hospital (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1371/journal.pone.0323752
- Citations
- 2
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.04