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Artificial intelligence (AI) may decrease 18F-FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.
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Introduction: Peripheral correlates of age-associated cognitive decline are important tools in the screening for potentially abnormal courses of cognitive aging. Since salivary gland function is controlled by the autonomic and central nervous system, associations between cognitive changes and salivary gland hypofunction were tested in two groups of middle-aged men in late midlife, who differed substantially with respect to their midlife performance in verbal intelligence when compared with their performance in young adulthood. Materials and Methods: Participants (n = 193) were recruited from the Danish Metropolit Cohort of men born in 1953. Based on their individual change in performance in two previously administered intelligence tests, they were allocated to one group of positive and one group of negative outliers in midlife cognition scores, indicating no decline versus decline in test performance. All participants underwent a clinical oral examination including assessments of their dental, periodontal, and mucosal conditions. Whole and parotid saliva flow rates were measured, and the number of systemic diseases and medication intake as well as daytime and nocturnal xerostomia were registered. Results: Participants with decline in cognitive test performance in midlife had significantly lower unstimulated whole saliva flow rates, higher prevalence of hyposalivation and daytime xerostomia and a higher caries experience than participants with no decline in midlife performance. Daytime and nocturnal xerostomia were associated with daily intake of medication and alcohol. Discussion: Overall, hyposalivation, xerostomia and poor dental status distinguished a group of men displaying relative decline in cognitive performance from a group of men without evidence of cognitive decline. Thus, hyposalivation and poor dental health status may represent potential correlates of age-related cognitive decline in late midlife, provided that other causes can be excluded.