30P AI-based response monitoring in gastroenteropancreatic neuroendocrine tumors

Neuroendocrine tumors (NETs) display relatively slow growth and heterogeneous treatment responses. Recent advances in systemic and locoregional therapies have shown efficacy in delaying progression. Imaging, while crucial for follow-up, faces challenges with conventional evaluation criteria (RECIST), criticized for variability in target lesion selection and measurements. AI-enabled automated segmentations may provide a time-efficient, accurate, and reproducible method for treatment response monitoring. Two AI segmentation models were trained to delineate liver metastases in NET patients, following an iterative training-radiologist correction scheme: a portal venous phase (PVP) model (trained on 65 scans) and an arterial phase (AP) model (trained on 36 scans). AP model was trained in three different settings. The training dataset was from a previously published study. External validation involved testing the final models on an independent dataset with follow-up data from three exemplary patients with long follow-ups. The evaluation utilized the Dice Correlation Coefficient Score (DSC) quantifying the overlap between predicted and ground truth, coefficient of determination (r2). PVP model DSCs were 66%, 77%, and 93% for each patient, respectively. Correlation between AI volume and radiologist volume was high per lesion (r2=0.93), and total tumor burden (r2=0.79). The three settings used for the AP models showed similar results in terms of median DSC (87%, 88%, 91%). Both PVP and AP models exhibited variability in DSC due to lesion density and enhancement differences and heterogeneity, with PVP excelling in identifying the hypodense lesions and AP performing better in hyperdense lesions. Differences were observed between RECIST and AI-volume trends in a preliminary comparison. This pilot study shows promising preliminary results for the creation of response criteria based on AI-quantified volumetry in NET tumors, with the algorithm being able to reproduce the results of experienced radiologists. Future outlook includes extension of the study cohort and formal comparison with RECIST 1.1.