Machine Learning-Based Dose Prediction in [177Lu]Lu-PSMA-617 Therapy by Integrating Biomarkers and Radiomic Features from [68Ga]Ga-PSMA-11 Positron Emission Tomography/Computed Tomography

Yazdani, Elmira; Sadeghi, Mahdi; Karamzade-Ziarati, Najme; Jabari, Parmida; Amini, Payam; Vosoughi, Habibeh; Akbari, Malihe Shahbazi; Asadi, Mahboobeh; Kheradpisheh, Saeed Reza; Geramifar, Parham

Abstract

The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for metastatic castration-resistant prostate cancer (mCRPC) patients undergoing [ Lu]Lu-PSMA-617 (Lu-PSMA) radioligand therapy (RLT). By leveraging radiomic features (RFs) from [ Ga]Ga-PSMA-11 (Ga-PSMA) PET/CT scans and clinical biomarkers (CBs), the approach has the potential to improve patient selection and tailor dosimetry-guided therapy. Twenty patients with mCRPC underwent Ga-PSMA PET/CT scans prior to the administration of an initial 6.8±0.4 GBq dose of the first Lu-PSMA RLT cycle. Post-therapy dosimetry involved sequential scintigraphy imaging at approximately 4, 48, and 72 h, along with a SPECT/CT image at around 48 h, to calculate time-integrated activity (TIA) coefficients. Monte Carlo (MC) simulations, leveraging the Geant4 application for tomographic emission (GATE) toolkit, were employed to derive ADs. The ML models were trained using pretherapy RFs from Ga-PSMA PET/CT and CBs as input, while the ADs in kidneys and lesions (n=130), determined using MC simulations from scintigraphy and SPECT imaging, served as the ground truth. Model performance was assessed through leave-one-out cross-validation (LOOCV), with evaluation metrics including R² and root mean squared error (RMSE). The mean delivered ADs were 0.88 ± 0.34 Gy/GBq for kidneys and 2.36 ± 2.10 Gy/GBq for lesions. Combining CBs with the best RFs produced optimal results: the extra trees regressor (ETR) was the best ML model for predicting kidney ADs, achieving an RMSE of 0.11 Gy/GBq and an R² of 0.87. For lesion ADs, the gradient boosting regressor (GBR) performed best, with an RMSE of 1.04 Gy/GBq and an R² of 0.77. Integrating pretherapy Ga-PSMA PET/CT RFs with CBs shows potential in predicting ADs in RLT. To personalize treatment planning and enhance patient stratification, it is crucial to validate these preliminary findings with a larger sample size and an independent cohort. [Abstract copyright: Copyright © 2025. Published by Elsevier Inc.]

Citation

Yazdani, E., Sadeghi, M., Karamzade-Ziarati, N., Jabari, P., Amini, P., Vosoughi, H., Akbari, M. S., Asadi, M., Kheradpisheh, S. R., & Geramifar, P. (2025). Machine Learning-Based Dose Prediction in [177Lu]Lu-PSMA-617 Therapy by Integrating Biomarkers and Radiomic Features from [68Ga]Ga-PSMA-11 Positron Emission Tomography/Computed Tomography. International Journal of Radiation Oncology - Biology - Physics, https://doi.org/10.1016/j.ijrobp.2025.05.014

• Journal Article Type: Article
• Acceptance Date: May 10, 2025
• Online Publication Date: May 18, 2025
• Publication Date: May 18, 2025
• Deposit Date: Jun 23, 2025
• Publicly Available Date: May 19, 2026
• Journal: International journal of radiation oncology, biology, physics
• Print ISSN: 0360-3016
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.ijrobp.2025.05.014
• Keywords: Radiomics, Prostate cance, Dosimetry, [(177)Lu]Lu-PSMA-617 Radioligand Therapy, Theranostics, [(68)Ga]Ga-PSMA-11 PET/CT, Machine learning
• Public URL: https://keele-repository.worktribe.com/output/1276846
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0360301625004808?via%3Dihub