Predicting Response to Somatostatin Analog Therapy in GEP-NET Liver Metastases: A Machine Learning Approach with ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/CT.

BACKGROUND

Predicting response to somatostatin analogs (SSAs) in gastroenteropancreatic neuroendocrine tumors (GEP-NETs) with liver metastases (LMs) remains challenging. This study aimed to develop and validate an interpretable machine learning (ML) model based on dual-tracer (68Ga-DOTATATE and 18F-FDG) PET/CT parameters to predict octreotide response and identify key imaging predictors using SHAP analysis.

METHODS

32 GEP-NET patients with LMs who received first-line octreotide therapy were retrospectively enrolled. Quantitative PET/CT metrics, dual-tracer ratios, and lesion size were extracted from 202 hepatic lesions. Lesions were classified as disease-control (SD/PR) or disease-progression (PD) using size-change thresholds derived from RECIST 1.1. Patients were divided at the patient level into training and validation cohorts. LASSO regression was used for feature selection, followed by the construction of multiple ML models. Leave-one-patient-out cross-validation (LOPOCV) was performed to further assess model robustness, and SHAP analysis was applied for model interpretability.

RESULTS

⁶⁸Ga-DOTATATE TLR, rTLR, and primary size differed significantly between disease-control and disease-progression groups. LASSO identified six key predictive features. The XGBoost model showed the best performance in the patient-level validation cohort, with an AUC of 0.825 and an accuracy of 0.742. In LOPOCV, XGBoost achieved an AUC of 0.856 and an accuracy of 0.787. SHAP analysis revealed that ⁶⁸Ga-DOTATATE TLR, primary size, and ⁶⁸Ga-DOTATATE SUVmean were the three most important predictors.

CONCLUSION

An interpretable ML model integrating dual-tracer PET/CT parameters can effectively predict octreotide response in GEP-NETs with LMs. This approach may provide a noninvasive tool for treatment-response stratification and individualized management.