Treatment Response Assessment According to Updated PROMISE Criteria in Patients with Metastatic Prostate Cancer Using an Automated Imaging Platform for Identification, Measurement, and Temporal Tracking of Disease
We used an artificial intelligence (AI) tool to analyze scans for prostate cancer before and after treatment to see if we could track how cancer spots respond to treatment. We found that the AI approach was successful in tracking individual tumor changes, showing which tumors disappeared, and identifying new tumors in response to prostate cancer treatment.
Authors
- Sonny Tsai
- Jesus E. Juarez (ORCID: https://orcid.org/0000-0002-9604-992X)
- Nathanael Kane
- Johan Brynolfsson (ORCID: https://orcid.org/0000-0002-2125-1349)
- Matthew B. Rettig (ORCID: https://orcid.org/0000-0002-7394-3056)
- Ida Sonni (ORCID: https://orcid.org/0000-0002-8802-7704)
- Gholam R. Berenji
- Nicholas G. Nickols (ORCID: https://orcid.org/0000-0002-3131-9212)
- Hannicka Sahlstedt
- Sai Duriseti (ORCID: https://orcid.org/0000-0002-8073-5132)
- Cecil M. Benitez (ORCID: https://orcid.org/0009-0005-2895-7386)
Institutions
- University of California, Los Angeles (US)
- VA Greater Los Angeles Healthcare System (US)
Publication Details
- Journal
- European Urology Oncology
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1016/j.euo.2024.10.011
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.23
Funders
- Lantheus Medical Imaging