A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model
To our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.
Authors
- Xinyu Cao (ORCID: https://orcid.org/0009-0007-2359-6379)
- Haiming Huang (ORCID: https://orcid.org/0000-0002-2825-4614)
- Bing Zheng
- Min Xu (ORCID: https://orcid.org/0000-0001-9455-9494)
- Zhongxin Zhang (ORCID: https://orcid.org/0000-0001-7320-0669)
- Xiang Liu (ORCID: https://orcid.org/0000-0003-2682-3075)
Institutions
- Nantong University (CN)
- Affiliated Hospital of Nantong University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-08
- DOI
- https://doi.org/10.3389/fonc.2025.1538854
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Jiangsu Commission of Health
- Nantong University
- Nantong Municipal Commission of Health and Family Planning
- Bijzonder Onderzoeksfonds UGent