Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm
Unfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve-spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI-based cancer mapping may lead to better oncological and functional outcomes for patients.
Authors
- Joshua Shubert
- Anthony Sisk (ORCID: https://orcid.org/0000-0001-7262-3985)
- James Sayre (ORCID: https://orcid.org/0000-0001-6090-8713)
- Shannon Richardson (ORCID: https://orcid.org/0000-0002-8083-4763)
- Ely Felker (ORCID: https://orcid.org/0000-0002-9042-0628)
- Wayne Brisbane (ORCID: https://orcid.org/0000-0003-0470-5262)
- Alan Priester (ORCID: https://orcid.org/0000-0001-9188-5905)
- Sakina Mohammed Mota (ORCID: https://orcid.org/0000-0001-9590-0578)
- Leonard S. Marks (ORCID: https://orcid.org/0000-0002-7910-179X)
- Kyla P. Grunden
- Shyam Natarajan
Institutions
- University of California, Los Angeles (US)
Publication Details
- Journal
- BJUI Compass
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1002/bco2.421
- Citations
- 4
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.68
Funders
- Prostate Cancer Foundation
- National Institutes of Health
- National Cancer Institute