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18F-FP-CIT is a high-resolution imaging marker of nigrostriatal neuronal integrity, differentiating Parkinsonism with loss of dopaminergic terminals (presynaptic Parkinsonian syndrome [PS]) from Parkinsonism without nigrostriatal degeneration (non-PS). We assessed the diagnostic accuracy of 18F-FP-CIT PET in patients with clinically uncertain PS (CUPS) at the first visit. Among the 272 patients who underwent 18F-FP-CIT PET imaging at the first visit between September 2008 and July 2012, 111 had CUPS (age, 62.6 ± 10.5 y; male:female, 45:66; symptom duration, 13.1 ± 8.8 months). Uncertainty criteria included only one of the three cardinal signs of Parkinsonism, two signs without bradykinesia, or atypical signs. The baseline clinical and 18F-FP-CIT PET imaging diagnostic accuracy was compared with the accuracy of clinical diagnosis after > 2-year follow-up. Nuclear medicine physicians assessed the 18F-FP-CIT PET images visually. Focal dopamine transporter binding deficit in the posterior putamen was considered PS. Bilateral symmetric striatum without focal deficit, suggesting normal 18F-FP-CIT PET, and focal deficits elsewhere in the striatum suggesting vascular Parkinsonism were considered non-PS. Seventy-nine patients had PS, and 32 did not. Baseline clinical diagnosis included PS in 45 patients, non-PS in 24, and inconclusive in 42. Among patients in whom initial clinical diagnosis (PS or non-PS) was possible, the sensitivity, specificity, and accuracy of the baseline clinical and 18F-FP-CIT PET imaging diagnoses were 54.4, 50.0, and 53.2%, and 98.7, 100, and 99.1%, respectively. The respective positive and negative predictive values were 95.6 and 66.7%, and 100 and 97.0%. Among those with initially inconclusive diagnosis, 64.2% were eventually diagnosed with PS while 35.7% were diagnosed with non-PS. The final clinical diagnosis of these patients all matched those made by 18F-FP-CIT PET imaging, except in one patient with scan without evidence of dopaminergic deficit (SWEDD). 18F-FP-CIT PET diagnosis was more accurate than clinical diagnosis, reducing the false-negative and inconclusive clinical diagnosis rates at baseline in patients with CUPS. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop and validate post- and preoperative models for predicting recurrence after curative-intent surgery using an FDG PET-CT metabolic parameter to improve the prognosis of patients with synchronous colorectal cancer liver metastasis (SCLM). Methods: In this retrospective multicenter study, consecutive patients with resectable SCLM underwent upfront surgery between 2006 and 2015 (development cohort) and between 2006 and 2017 (validation cohort). In the development cohort, we developed and internally validated the post- and preoperative models using multivariable Cox regression with an FDG metabolic parameter (metastasis-to-primary-tumor uptake ratio [M/P ratio]) and clinicopathological variables as predictors. In the validation cohort, the models were externally validated for discrimination, calibration, and clinical usefulness. Model performance was compared with that of Fong's clinical risk score (FCRS). Results: A total of 374 patients (59.1 ± 10.5 years, 254 men) belonged in the development cohort and 151 (60.3 ± 12.0 years, 94 men) in the validation cohort. The M/P ratio and nine clinicopathological predictors were included in the models. Both postoperative and preoperative models showed significantly higher discrimination than FCRS (p <.05) in the external validation (time-dependent AUC = 0.76 [95% CI 0.68–0.84] and 0.76 [0.68–0.84] vs. 0.65 [0.57–0.74], respectively). Calibration plots and decision curve analysis demonstrated that both models were well calibrated and clinically useful. The developed models are presented as a web-based calculator (https://cpmodel.shinyapps.io/SCLM/) and nomograms. Conclusions: FDG metabolic parameter-based prognostic models are well-calibrated recurrence prediction models with good discriminative power. They can be used for accurate risk stratification in patients with SCLM. Key Points: • In this multicenter study, we developed and validated prediction models for recurrence in patients with resectable synchronous colorectal cancer liver metastasis using a metabolic parameter from FDG PET-CT. • The developed models showed good predictive performance on external validation, significantly exceeding that of a pre-existing model. • The models may be utilized for accurate patient risk stratification, thereby aiding in therapeutic decision-making. [ABSTRACT FROM AUTHOR]