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

Cecil M Benitez; Hannicka Sahlstedt; Ida Sonni; Johan Brynolfsson; Gholam Reza Berenji; Jesus Eduardo Juarez; Nathanael Kane; Sonny Tsai; Matthew Rettig; Nicholas George Nickols; Sai Duriseti

doi:10.1016/j.euo.2024.10.011

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

Eur Urol Oncol. 2024 Nov 8:S2588-9311(24)00240-2. doi: 10.1016/j.euo.2024.10.011. Online ahead of print.

Authors

Affiliations

¹ Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA.
² Lantheus, Lund, Sweden.
³ VA Greater Los Angeles Healthcare System, Department of Nuclear Medicine, Los Angeles, CA, USA; Department of Radiological Sciences, University of California-Los Angeles, Los Angeles, CA, USA.
⁴ VA Greater Los Angeles Healthcare System, Department of Nuclear Medicine, Los Angeles, CA, USA.
⁵ Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
⁶ Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
⁷ Department of Hematology-Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Department of Urology, University of California-Los Angeles, Los Angeles, CA, USA.
⁸ Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Urology, University of California-Los Angeles, Los Angeles, CA, USA.
⁹ Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA. Electronic address: sduriseti@mednet.ucla.edu.

PMID: 39521638
DOI: 10.1016/j.euo.2024.10.011

Abstract

Background and objective: Prostate-specific membrane antigen (PSMA) molecular imaging is widely used for disease assessment in prostate cancer (PC). Artificial intelligence (AI) platforms such as automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) identify and quantify locoregional and distant disease, thereby expediting lesion identification and standardizing reporting. Our aim was to evaluate the ability of the updated aPROMISE platform to assess treatment responses based on integration of the RECIP (Response Evaluation Criteria in PSMA positron emission tomography-computed tomography [PET/CT]) 1.0 classification.

Methods: The study included 33 patients with castration-sensitive PC (CSPC) and 34 with castration-resistant PC (CRPC) who underwent PSMA-targeted molecular imaging before and ≥2 mo after completion of treatment. Tracer-avid lesions were identified using aPROMISE for pretreatment and post-treatment PET/CT scans. Detected lesions were manually approved by an experienced nuclear medicine physician, and total tumor volume (TTV) was calculated. Response was assessed according to RECIP 1.0 as CR (complete response), PR (partial response), PD (progressive disease), or SD (stable disease). KEY FINDINGS AND LIMITATIONS: aPROMISE identified 1576 lesions on baseline scans and 1631 lesions on follow-up imaging, 618 (35%) of which were new. Of the 67 patients, aPROMISE classified four as CR, 16 as PR, 34 as SD, and 13 as PD; five cases were misclassified. The agreement between aPROMISE and clinician validation was 89.6% (κ = 0.79).

Conclusions and clinical implications: aPROMISE may serve as a novel assessment tool for treatment response that integrates PSMA PET/CT results and RECIP imaging criteria. The precision and accuracy of this automated process should be validated in prospective clinical studies.

Patient summary: 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.

Keywords: Artificial intelligence; Automated; Positron emission tomography; Prostate-specific membrane antigen.

Published by Elsevier B.V.