An innovative approach for predicting prostate cancer Gleason grading: machine learning-based fusion of multimodal ultrasound, clinical and laboratory indicators
Ultrasonography, being noninvasive, radiation-free, and cost-effective, demonstrates high clinical feasibility for implementation in routine practice. The predictive model established through multimodal ultrasound parameters effectively predicts the Gleason grade of prostate cancer.
Authors
- Mengying Tong (ORCID: https://orcid.org/0000-0001-7723-8306)
- Guangzhen Wu (ORCID: https://orcid.org/0000-0002-2300-8465)
- Xiaochen Qi
- Wen-Long Xie
- Ying Che (ORCID: https://orcid.org/0000-0002-0044-4168)
- Lin Zhong (ORCID: https://orcid.org/0000-0002-1822-6919)
- Liying Guo (ORCID: https://orcid.org/0000-0001-5959-3566)
Institutions
- Dalian Medical University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1186/s40001-025-03426-1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00