Machine Learning Models Combining [⁶⁸Ga]Ga-PSMA-11 PET Radiomics and Clinical Features to Predict Overall Survival for Metastatic Castration-resistant Prostate Cancer Treated with [¹⁷⁷Lu]Lu-PSMA Radioligand Therapy.

Authors

Lucas Edouard, Léa Papillon, Loïc Ferrer, Yann Godbert, Marie Meyer, Vincent Prega-Renaud, Céline Subreville, Nadège Anizan, Nicolas Benziane-Ouaritini, Olivier Gallinato, Mathilde Cabart, Diego Teyssonneau, Guilhem Roubaud, Thierry Colin, Paul Schwartz

Published in

European urology oncology. Oct 15, 2026. Epub Oct 15, 2026.

Abstract

Metastatic castration-resistant prostate cancer (mCRPC) remains incurable despite recent therapeutic advances. [¹⁷⁷Lu]Lu-PSMA RLT (lutetium-177 labelled prostate-specific membrane antigen radioligand therapy) provides meaningful clinical benefit in selected patients. However, ∼30% of the patients do not respond to [¹⁷⁷Lu]Lu-PSMA RLT. Although several baseline prognostic factors are well known, reliable pretherapeutic predictive biomarkers are still lacking. We developed and compared several predictive machine learning (ML) models integrating clinical, biochemical, and imaging data, including [⁶⁸Ga]Ga-PSMA-11 PET (gallium-68 labelled prostate-specific membrane antigen ligand positron emission tomography) radiomic features, to estimate overall survival (OS) in patients with mCRPC treated with [¹⁷⁷Lu]Lu-PSMA RLT.
This retrospective monocentric study included 102 patients with mCRPC treated with [¹⁷⁷Lu]Lu-PSMA between January 2022 and March 2024. Five lesions per patient were segmented on their pretherapeutic [⁶⁸Ga]Ga-PSMA-11 PET/computed tomography (CT) scans. Radiomic features were extracted following Image Biomarker Standardization Initiative guidelines. Multimodal ML models integrating clinical, genomic, biochemical, and imaging features (including radiomics) were developed to predict OS.
Median OS of the total cohort was 11.2 mo. Intralesional homogeneity, characterized by the second-order radiomic feature normalized inverse difference moment, was associated with shorter OS (p = 0.025). The best-performing ML model achieved a 12-mo area under the curve (AUC) of 0.778 by combining 22 clinical, biochemical, genomic, and radiomic features. The model leveraging PET data alone achieved strong performance, with an AUC comparable to the clinical model (0.715 vs 0.766).
ML models based on [⁶⁸Ga]Ga-PSMA-11 PET/CT, including radiomic features, offer a noninvasive, standardized, and widely accessible approach to predict OS in patients with mCRPC treated with [¹⁷⁷Lu]Lu-PSMA RLT. This study highlights the strong predictive potential of PSMA PET imaging for patient stratification in a real-world setting.

PMID:
42881113
Bibliographic data and abstract were imported from PubMed on 16 Oct 2026.

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