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Peer-Reviewed Publication
PLoS One2026;21(6):e0349825.January 1, 2026Journal Article

Deep learning models built from PSMA PET of the primary tumor can predict synchronous and metachronous prostate cancer metastases.

Jesus E Juarez Casillas1, Maryam Nezafat2, Cecil M Benitez1, Kamil Rzechowski2, Nathanael Kane1,3, Karl Sjöstrand2, Aseem Anand2, Ida Sonni3,4, Gholam R Berenji3, Sai Duriseti1,3, Matthew B Rettig5,6,7, Nicholas G Nickols1,3,7
1Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.
2Exini Diagnostics (subsidiary of Lantheus Holdings, Inc), Bedford, Massachusetts, United States of America.
3Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States of America.
4Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.
5Department of Medicine, VA Greater Los Angeles Healthcare System, Los Angeles, California, United States of America.
6Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.
7Department of Urology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.
PubMed PMC DOI PDF

Abstract

OBJECTIVE: The objective was to develop prognostic models that included convolutional neural networks (CNN) derived from 18F-DCFPyL (PSMA) PET imaging of the primary tumor uptake patterns to prognose early metastatic progression after curative intent treatment for localized prostate cancer. METHODS: Due to the lack of sufficient cases with adequate follow-up and metastatic events to derive this m…

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