Artificial intelligence to predict outcome after [177Lu]Lu-PSMA for metastatic castration-resistant prostate cancer: Preliminary results from a multicentric prospective study.

e17077 Background: [ 177 Lu]Lu-PSMA-617 was approved for metastatic castration-resistant prostate cancer, but only 46% of patients achieved a PSA response in the phase 3 VISION trial. Identifying patients unlikely to benefit from PSMA-targeted therapeutic radiopharmaceuticals is an urgent unmet need. Methods: Patients with mCRPC who progressed on taxane-based chemotherapy and ARSI, received baseline [ 68 Ga]Ga-PSMA-11 PET/CT, were VISION-eligible, and underwent [ 177 Lu]Lu-PSMA-617 or [ 177 Lu]Lu-PSMA-I&T were prospectively enrolled at 2 academic institutions. We report here the interim results of the University of Grenoble-Alpes cohort only (IRB: CEMEN 202406). Primary outcome was the prognostic value of an artificial intelligence-based technology (SelectPSMA, NucsAI) for PSA50 (≥50% decline from baseline), PSA progression-free survival (PSA-PFS; time to PSA progression by PCWG3), and overall survival (OS) after [ 177 Lu]Lu-PSMA radiopharmaceuticals. The Fisher's exact test and Kaplan-Meier analysis with log-rank test were used to test associations between SelectPSMA output and outcome data. Results: Of 72 mCRPC patients screened, 60 (83%) were enrolled between August 2023 and September 2024 and received [ 177 Lu]Lu-PSMA. 57/60 (95%) patients were treated previously with taxanes, while all 60 patients had received ARSIs. The median follow-up in survivors was 9.5 (IQR 7.1-15.1) mo and 47/60 (78%) patients achieved PSA progression at last follow-up. 38/60 (63%) patients achieved PSA50, the median (95%CI) PSA-PFS was 5.1 (3.3-7.1). 9/60 (15%) patients were classified by SelectPSMA as non-responders (PSMA-NR) and 51/60 (85%) as likely responders (PSMA-R). PSMA-NR was associated with significantly lower likelihood to achieve PSA50 (22% vs 71%; p=0.009) and shorter PSA-PFS (median (95%CI): 1.2 (0.7-NR) vs 6.6 (4.6-9.5) mo; p<0.001) compared to PSMA-R. At the time of this analysis, OS data was immature with low number of events in PSMA-NR group (n=4). Conclusions: Preliminary results show that SelectPSMA, an AI-based technology, identified patients with lower likelihood of PSA response and shorter progression-free survival after [ 177 Lu]Lu-PSMA radiopharmaceuticals. Results of multi-centric analysis including mature OS data will be presented at the conference. Clinical trial information: CEMEN 202406 .

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Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e17077
Primary Topic
Prostate Cancer Treatment and Research
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article
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article

Artificial intelligence to predict outcome after [177Lu]Lu-PSMA for metastatic castration-resistant prostate cancer: Preliminary results from a multicentric prospective study.

Lena M. Unterrainer, Julien Leenhardt, Andrei Gafita, Lilja B. Sólnes et al.
1 links
Journal of Clinical Oncology
Prostate Cancer Treatment and Research
article

Artificial intelligence to predict outcome after [177Lu]Lu-PSMA for metastatic castration-resistant prostate cancer: Preliminary results from a multicentric prospective study.

Lena M. Unterrainer, Julien Leenhardt, Andrei Gafita, Lilja B. Sólnes, Désirèe Deandreis, Mathieu Laramas, Guido Rovera, Emmanuelle Jacquet, Loïc Djaileb, Nicolas De Leiris, Channing Judith Paller, Anne‐Kristina Mercier
article en

Abstract

e17077 Background: [ 177 Lu]Lu-PSMA-617 was approved for metastatic castration-resistant prostate cancer, but only 46% of patients achieved a PSA response in the phase 3 VISION trial. Identifying patients unlikely to benefit from PSMA-targeted therapeutic radiopharmaceuticals is an urgent unmet need. Methods: Patients with mCRPC who progressed on taxane-based chemotherapy and ARSI, received baseline [ 68 Ga]Ga-PSMA-11 PET/CT, were VISION-eligible, and underwent [ 177 Lu]Lu-PSMA-617 or [ 177 Lu]Lu-PSMA-I&T were prospectively enrolled at 2 academic institutions. We report here the interim results of the University of Grenoble-Alpes cohort only (IRB: CEMEN 202406). Primary outcome was the prognostic value of an artificial intelligence-based technology (SelectPSMA, NucsAI) for PSA50 (≥50% decline from baseline), PSA progression-free survival (PSA-PFS; time to PSA progression by PCWG3), and overall survival (OS) after [ 177 Lu]Lu-PSMA radiopharmaceuticals. The Fisher's exact test and Kaplan-Meier analysis with log-rank test were used to test associations between SelectPSMA output and outcome data. Results: Of 72 mCRPC patients screened, 60 (83%) were enrolled between August 2023 and September 2024 and received [ 177 Lu]Lu-PSMA. 57/60 (95%) patients were treated previously with taxanes, while all 60 patients had received ARSIs. The median follow-up in survivors was 9.5 (IQR 7.1-15.1) mo and 47/60 (78%) patients achieved PSA progression at last follow-up. 38/60 (63%) patients achieved PSA50, the median (95%CI) PSA-PFS was 5.1 (3.3-7.1). 9/60 (15%) patients were classified by SelectPSMA as non-responders (PSMA-NR) and 51/60 (85%) as likely responders (PSMA-R). PSMA-NR was associated with significantly lower likelihood to achieve PSA50 (22% vs 71%; p=0.009) and shorter PSA-PFS (median (95%CI): 1.2 (0.7-NR) vs 6.6 (4.6-9.5) mo; p<0.001) compared to PSMA-R. At the time of this analysis, OS data was immature with low number of events in PSMA-NR group (n=4). Conclusions: Preliminary results show that SelectPSMA, an AI-based technology, identified patients with lower likelihood of PSA response and shorter progression-free survival after [ 177 Lu]Lu-PSMA radiopharmaceuticals. Results of multi-centric analysis including mature OS data will be presented at the conference. Clinical trial information: CEMEN 202406 .

Journal of Clinical OncologyVol. 43(16_suppl)
Johns Hopkins University (US), Centre Hospitalier Universitaire de Grenoble (FR), Institut Gustave Roussy (FR), LMU Klinikum (DE), Sidney Kimmel Comprehensive Cancer Center (US), Ludwig-Maximilians-Universität München (DE), Université Grenoble Alpes (FR)
Openalex Percentile: Top 80%
Prostate Cancer Treatment and Research
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