A machine learning-based model that integrates imaging uptake features, radiomics, and biomarkers accurately predicts how much radiation is absorbed by patients undergoing prostate-specific membrane antigen (PSMA) radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC).
“One of the biggest challenges in radioligand therapy is that patients can receive very different radiation doses despite being prescribed the same treatment activity,” said Amit Nautiyal, PhD, scientist and National Institute for Health and Care Research fellow at University Hospital Southampton and the University of Southampton in the U.K.
“Our findings suggest that information already available before treatment, such as 18F-PSMA PET/CT imaging and routine clinical biomarkers, may help predict how radiation will be distributed within tumors and healthy organs.”
Nautiyal told Inside Precision Medicine that, in the future, the model “could support more personalized treatment planning, helping to maximize radiation delivery to tumors while minimizing unnecessary radiation exposure to healthy tissues. Ultimately, the goal is to improve treatment effectiveness while reducing the risk of side effects.”
At present, the only way to determine how much radiation has been absorbed by the tumor and surrounding organs such as the kidneys and salivary glands is to use post-treatment imaging and dosimetry calculations, which can be time-consuming and resource intensive.
“Our approach aims to use information already available before treatment, such as positron emission tomography/computed tomography (PET/CT) scans and routine clinical data, to estimate likely absorbed doses before therapy begins,” said Nautiyal.
He and his team integrated 18F-PSMA PET/CT uptake data (total lesion uptake, tumor-to-organ ratios), radiomics features (Gray-Level Co-Occurrence Matrix), and biomarker information (estimated glomerular filtration rate) into a machine learning-based hierarchical mixed-effects model to provide pretherapy predictions of absorbed dose in tumors and organs at risk during ¹⁷⁷Lu-PSMA RLT.
The model, which was presented at the Society of Nuclear Medicine and Molecular Imaging 2026 Annual Meeting, incorporated data from nine patients with mCRPC referred for ¹⁷⁷Lu-PSMA RLT, contributing 57 tumors, 36 salivary glands, and 18 kidneys for analysis.
At the end of cycle 1, ¹⁷⁷Lu-PSMA dosimetry showed that the mean absorbed dose was 11.0 Gy for tumors, 1.8 Gy for salivary glands, and 3.9 Gy for kidneys.
For tumors, the models achieved a mean absolute error (MAE) of 3.2 Gy for the prediction of absorbed dose, meaning that, on average, the predicted tumor dose differed from the measured dose by approximately 3.2 Gy.
By comparison, the MAE was 0.3 Gy for salivary glands and 0.1 Gy for kidneys.
“Given the biological complexity of metastatic prostate cancer and the relatively small study cohort, we consider this an encouraging result,” said Nautiyal, “Tumor dose prediction is inherently challenging because different tumor lesions can behave quite differently, even within the same patient. By contrast, organs such as the kidneys and salivary glands generally exhibit more consistent uptake patterns, which likely contributed to the higher predictive accuracy observed.”
The Bayesian R² values, which indicate how much of the variation in absorbed dose can be explained by the model, were 0.73 for tumors, 0.93 for salivary glands, and 0.99 for kidneys.
The researchers also calculated the 95% Highest Density Interval (HDI) for the model, which indicates whether the uncertainty estimates produced by the model are realistic. The HDIs were 0.89, 1, and 1, for tumors, salivary glands and kidneys, respectively, meaning that, for tumors, about 89% of observed absorbed doses fell within the range predicted by the model.
“This suggests that the model is not only making reasonable predictions but is also providing realistic estimates of how confident it is in those predictions, said Nautiyal. “This is particularly important in healthcare, where understanding uncertainty is often as important as the prediction itself.”
The researchers say that, taken together, the findings support the robustness of the model. They also carried out a leave-one-patient-out analysis, which showed that performance remained stable even when individual patients were excluded from model development and then used for testing.
“This suggests that the model is learning broader patterns rather than simply memorizing the training data,” noted Nautiyal.
Although the results are promising, the researchers acknowledge that this was an early proof-of-concept study and further work is needed before the model can be used routinely in clinical practice.
They now plan to evaluate the model in larger patient populations from multiple centers in the U.K., perform independent external validation, and investigate how predicted absorbed doses correlate with clinical outcomes.
Nautiyal concluded: “If future studies continue to show promising results, predictive tools of this type could eventually support treatment planning and patient stratification in molecular radiotherapy. The aim is to help clinicians make more informed treatment decisions before therapy begins and move towards more personalized radioligand therapy.”
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