An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer
Our DBSI-based artificial intelligence model accurately predicted csPCa on biopsy and can be combined with PI-RADS to potentially reduce unnecessary prostate biopsies.
Authors
- Joel Vetter (ORCID: https://orcid.org/0000-0002-5914-3845)
- Joseph E. Ippolito (ORCID: https://orcid.org/0000-0003-1913-5573)
- Aleksandra Klim
- Sheng‐Kwei Song (ORCID: https://orcid.org/0000-0003-1050-7449)
- Jason Frankel (ORCID: https://orcid.org/0000-0003-3938-8981)
- R. Cody Weimholt
- Zachary L. Smith (ORCID: https://orcid.org/0000-0002-5945-3288)
- Arnold Bullock
- Gerald L. Andriole (ORCID: https://orcid.org/0000-0003-4033-2352)
- Kainen L. Utt
- Eric H. Kim (ORCID: https://orcid.org/0000-0002-0284-8342)
- Karla Bergeron
Institutions
- University of Nevada, Reno (US)
- Washington University in St. Louis (US)
- Kaiser Permanente Walnut Creek Medical Center (US)
- Mallinckrodt (United States) (US)
Publication Details
- Journal
- The Journal of Urology
- Published
- 2025-02-11
- DOI
- https://doi.org/10.1097/ju.0000000000004456
- Citations
- 4
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.07