Neural Network-based Automated Classification of 18F-FDG PET/CT Lesions and Prognosis Prediction in Nasopharyngeal Carcinoma Without Distant Metastasis

Purpose: To evaluate the diagnostic performance of the PET Assisted Reporting System (PARS) in nasopharyngeal carcinoma (NPC) patients without distant metastasis, and to investigate the prognostic significance of the metabolic parameters. Patients and Methods: Eighty-three NPC patients who underwent pretreatment 18 F-FDG PET/CT were retrospectively collected. First, the sensitivity, specificity, and accuracy of PARS for diagnosing malignant lesions were calculated, using histopathology as the gold standard. Next, metabolic parameters of the primary tumor were derived using both PARS and manual segmentation. The differences and consistency between the 2 methods were analyzed. Finally, the prognostic value of PET metabolic parameters was evaluated. Prognostic analysis of progression-free survival (PFS) and overall survival (OS) was conducted. Results: PARS demonstrated high patient-based accuracy (97.2%), sensitivity (88.9%), and specificity (97.4%), and 96.7%, 84.0%, and 96.9% based on lesions. Manual segmentation yielded higher metabolic tumor volume (MTV) and total lesion glycolysis (TLG) than PARS. Metabolic parameters from both methods were highly correlated and consistent. ROC analysis showed metabolic parameters exhibited differences in prognostic prediction, but generally performed well in predicting 3-year PFS and OS overall. MTV and age were independent prognostic factors; Cox proportional-hazards models incorporating them showed significant predictive improvements when combined. Kaplan-Meier analysis confirmed better prognosis in the low-risk group based on combined indicators (χ² = 42.25, P < 0.001; χ² = 20.44, P < 0.001). Conclusions: Preliminary validation of PARS in NPC patients without distant metastasis shows high diagnostic sensitivity and accuracy for lesion identification and classification, and metabolic parameters correlate well with manual. MTV reflects prognosis, and its combination with age enhances prognostic prediction and risk stratification.