Los Angeles -- A new machine-learning approach for prostate-specific
membrane antigen (PSMA) treatment of metastatic castration-resistant prostate
cancer (mCRPC) could estimate radiation dose to tumors and healthy organs
before therapy begins. Using data already available from pre-therapy PET/CT
scans, this novel prediction tool could help personalize treatment plans,
improve patient selection, and reduce toxicity risk. This research was
presented at the Society of Nuclear Medicine and Molecular Imaging 2026 Annual
Meeting.
Dosimetry is critical for optimizing ⁷⁷Lu-PSMA radiopharmaceutical
therapy in mCRPC. Currently, post-therapy imaging is typically used to
calculate dosimetry; however, it is time-consuming and resource-intensive.
Pre-therapy PET/CT offers an opportunity to assess potential treatment
effectiveness and risk before therapy.
"18F-PSMA PET/CT is already routinely performed and widely
available in prostate cancer patients, but its potential to predict treatment
radiation dose has not previously been explored," said Amit Nautiyal, PhD,
scientist and National Institute for Health and Care Research (NIHR) fellow at
University Hospital Southampton and the University of Southampton in the United
Kingdom. "Our study sought to determine
if information already available from these scans could guide treatment
planning before therapy begins and support more personalized care."
In this proof-of-concept study, nine patients with mCRPC
referred for ⁷⁷Lu-PSMA radiopharmaceutical therapy were included,
contributing 57 tumors, 36 salivary glands, and 18 kidneys for analysis. Researchers
developed a machine learning mixed effects model to predict absorbed doses in
tumors and organs. Predictors included uptake-based PET metrics, radiomic
features, and clinical biomarkers. Predictive estimates were compared with dosimetry
calculated after one cycle of ⁷⁷Lu-PSMA therapy to assess
accuracy.
The pre-therapy 18F-PSMA PET/CT-based machine learning model
showed a promising ability to predict tumor and organ
absorbed dose. By combining uptake features, radiomics, and clinical biomarkers
while accounting for patient-level variability, the model shows potential for
using pre-therapy information to predict post-therapy dosimetry.
"If validated in larger studies, this approach may improve
patient selection and support better decision-making during pre-treatment
assessment, helping to optimize ⁷⁷Lu-PSMA therapy for individual
patients. More broadly, it highlights how imaging can move beyond diagnosis to
actively guiding personalized treatment," said Nautiyal.
This proof-of-concept research is part of a planned five-year program aimed at
collecting more data and developing a robust, validated model. This work was
supported by the NIHR in the, United Kingdom. Future work will focus on larger,
multi-center cohorts to refine pre-therapy absorbed dose predictions and to
perform independent validation to support patient stratification for
personalized ⁷⁷Lu-PSMA radiopharmaceutical therapy in clinical
practice.