Sanchay Jain

Introduction: Peptide Receptor Radionuclide Therapy (PRRT) with [¹⁷⁷Lu]Lu-DOTA-TATE is effective in treating advanced Neuroendocrine Tumors (NETs), yet predicting individual response in this treatment remains a challenge due to inter-lesion heterogeneity. There is a lack of standardized, effective methods for using multi-lesion radiomics to predict progression and Time to Progression (TTP) in PRRT-treated patients. This study evaluated how aggregating radiomic features from multiple PET-identified lesions can be used to predict disease progression (event [progression and death] vs. event-free) and TTP. Methods: Eighty-one NETs patients with multiple lesions underwent pre-treatment PET/CT imaging. Lesions were segmented and ranked by minimum Standard Uptake Value (SUV_min) (both descending and ascending), SUV_mean, SUV_max, and volume (descending). From each sorting, the top one, three, and five lesions were selected. For the selected lesions, radiomic features were extracted (using the Pyradiomics library) and lesion aggregation was performed using stacked vs. statistical methods. Eight classification models along with three feature selection methods were used to predict progression, and five survival models and three feature selection methods were used to predict TTP under a nested cross-validation framework. Results: The overall appraisal showed that sorting lesions based on SUV_min (descending) yields better classification performance in progression prediction. This is in addition to the fact that aggregating features extracted from all the lesions, as well as the top five lesions sorted by SUV_mean, lead to the highest overall performance in TTP prediction. The individual appraisal in progression prediction models trained on the single top lesion sorted by SUV_min (descending) showed the highest recall and specificity despite data imbalance. The best-performing model was the Logistic Regression (LR) classifier with Recursive Feature Elimination (RFE) (recall: 0.75, specificity: 0.77). In TTP prediction, the highest concordance index was obtained using a Random Survival Forest (RSF) trained on statistically aggregated features from the top five lesions ranked by SUV_mean, selected via Univariate C-Index (UCI) (C-index = 0.68). Across both tasks, features from the Gray Level Size Zone Matrix (GLSZM) family were consistently among the most predictive, highlighting the importance of spatial heterogeneity in treatment response. Conclusions: This study demonstrates that informed lesion selection and tailored aggregation strategies significantly impact the predictive performance of radiomics-based models for progression and TTP prediction in PRRT-treated NET patients. These approaches can potentially enhance model accuracy and better capture tumor heterogeneity, supporting more personalized and practical PRRT implementation.

Paraneoplastic syndromes (PNSs) are pathologic conditions produced by neoplasms not attributable to tumor invasion or metastasis. The clinical manifestations of PNSs can precede the diagnosis; these symptoms may serve as early indicators of underlying malignancy. Standard imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), have limited sensitivity in detecting small or early-stage PNS-associated tumors. FDG PET/CT identifies hypermetabolic lesions suggestive of malignancy and, therefore, facilitates early diagnosis, refined treatment planning, and potentially prolonged patient survival. This review evaluates the diagnostic accuracy, clinical utility, and emerging role of FDG PET/CT in detecting occult malignancies. Syndrome-targeted applications discussed include limbic encephalitis, cerebellar degeneration, Lambert-Eaton myasthenic syndrome, Cushing’s syndrome, hypercalcemia of malignancy, dermatomyositis, and tumor-induced osteomalacia. In addition, the limitations of FDG PET/CT, including false-positive or false-negative findings, are reviewed, while newer PET tracers, like ⁶⁸Ga-DOTATATE, are also highlighted. Ultimately, FDG PET/CT has transformed clinical decision-making, enabling more timely interventions and improved patient management in the context of PNSs. Future directions in imaging, including PET/MRI and ongoing refinements in tracer design, promise to further enhance diagnostic precision, and therapeutic outcomes are also discussed.