A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI) Model to Predict Castration-Resistant Prostate Cancer Progression
The Dense-MFAI model proposed in this study has the ability to more accurately predict whether a PCa patient could progress to CRPC. This model can assist urologists in developing the most appropriate treatment plan and prognostic measures.
Authors
- Shouliang Qi (ORCID: https://orcid.org/0000-0003-0977-1939)
- Aritrick Chatterjee (ORCID: https://orcid.org/0000-0003-4022-7106)
- Haoming Zhuang (ORCID: https://orcid.org/0000-0001-7835-0578)
- Xiaobing Fan (ORCID: https://orcid.org/0000-0001-9004-029X)
- Wei Qian (ORCID: https://orcid.org/0000-0002-9563-721X)
- Zhe Zhang (ORCID: https://orcid.org/0000-0001-8220-8443)
- Ying Ma (ORCID: https://orcid.org/0000-0002-3105-375X)
- Dianning He (ORCID: https://orcid.org/0000-0002-0867-6238)
- Bixuan Xia
- Jing Liu (ORCID: https://orcid.org/0000-0002-3255-3019)
Institutions
- University of Chicago (US)
- First Hospital of China Medical University (CN)
- China Medical University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Cancers
- Published
- 2025-05-03
- DOI
- https://doi.org/10.3390/cancers17091556
- Citations
- 4
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 6.07
Funders
- National Natural Science Foundation of China
- China Medical University
- Fundamental Research Funds for the Central Universities