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Positron Emission Tomography

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Enhancing PRRT Outcome Prediction in Neuroendocrine Tumors: Aggregated Multi-Lesion PET Radiomics Incorporating

Maziar Sabouri^1,2, Ghasem Hajianfar³, Omid Gharibi^1,2

¹Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

|December 11, 2025

Summary

Predicting treatment response in Neuroendocrine Tumors (NETs) using Peptide Receptor Radionuclide Therapy (PRRT) is improved by selecting and aggregating radiomic features from multiple lesions. This approach enhances prediction accuracy for disease progression and Time to Progression (TTP).

Keywords:

Neuroendocrine Tumors PET PRRT feature aggregation radiomics

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Area of Science:

Radiomics
Medical Imaging
Oncology

Background:

Peptide Receptor Radionuclide Therapy (PRRT) with [177Lu]Lu-DOTA-TATE is a key treatment for advanced Neuroendocrine Tumors (NETs).
Predicting individual patient response to PRRT is challenging due to significant inter-lesion heterogeneity.
Standardized methods for using multi-lesion radiomics to predict progression and Time to Progression (TTP) are lacking.

Purpose of the Study:

To evaluate the efficacy of aggregating radiomic features from multiple PET-identified lesions for predicting disease progression and TTP in PRRT-treated NET patients.
To investigate optimal lesion selection and feature aggregation strategies for improving predictive model performance.

Main Methods:

Eighty-one NETs patients with multiple lesions underwent pre-treatment PET/CT imaging.
Lesions were segmented and ranked using various metrics (SUVmin, SUVmean, SUVmax, volume).
Radiomic features were extracted and aggregated using stacked and statistical methods, with eight classification and five survival models employed under nested cross-validation.

Main Results:

Sorting lesions by SUVmin (descending) improved progression prediction, while aggregating features from the top five lesions by SUVmean enhanced TTP prediction.
The best progression prediction model was Logistic Regression with Recursive Feature Elimination (recall: 0.75, specificity: 0.77).
The highest TTP prediction concordance index (0.68) was achieved with a Random Survival Forest using statistically aggregated features from the top five SUVmean-ranked lesions. Gray Level Size Zone Matrix features were consistently predictive.

Conclusions:

Informed lesion selection and tailored aggregation strategies significantly enhance the predictive performance of radiomics models for progression and TTP in PRRT-treated NET patients.
These approaches can improve model accuracy and better capture tumor heterogeneity.
The findings support more personalized and practical implementation of PRRT.