PET Inter-Lesion Radiomics Aggregation for Enhanced PRRT Response Prediction in Neuroendocrine Tumors

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Peptide Receptor Radionuclide Therapy (PRRT) using [${ }^{177}$Lu]Lu-DOTA-TATE has significantly improved outcomes for patients with advanced neuroendocrine tumors (NETs), yet predicting therapeutic response remains challenging. This study investigates whether aggregating radiomic features of different lesions extracted from pre-treatment somatostatin receptor PET/CT scans can predict disease progression and time to progression (TTP) in NET patients receiving PRRT. A retrospective analysis was conducted on 81 patients, with segmented lesions sorted based on standardized uptake values ($\text{SUV}_{\text {max }}, \text{SUV}_{\text {mean }}, \text{SUV}_{\text {min }}$) and volume. Radiomic features were extracted from the top 1, 3, and 5 lesions per patient, and two aggregation strategies-stacked and statistical-were applied. Classification models were trained using eight machine learning algorithms incorporating three feature selection methods within a nested cross-validation framework. For TTP prediction, five survival models employing three feature selection methods were used within the same cross-validation scheme. Results showed that stacking features from the top three lesions sorted by$\text{SUV}_{\text {min }}$and input into a K-Nearest Neighbors model provided the highest progression prediction accuracy (AUCC$=0.75$). For TTP, the best performance was achieved by a Random Survival Forest model trained on statistically aggregated features from the top 5 lesions sorted by SUV${ }_{\text {mean }}(\mathrm{C}$-index$=0.68)$. Overall, incorporating radiomic data from multiple lesions using aggregation methods enhanced model performance in both tasks, highlighting the importance of lesion selection and feature aggregation in progression and survival prediction for PRRT-treated NET patients.

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