An External, Independent Validation of anO-(2-[18F]Fluoroethyl)-l-Tyrosine PET Automatic Segmentation Network on a Single-Center, Prospective Dataset of Patients with Glioblastoma
Year: 2025
Type: article
Abstract: The goal of this study was to conduct an external, independent validation of an O-(2-[18F]fluoroethyl)-l-tyrosine ([18F]FET) PET automatic segmentation network on a cohort of patients with glioblastoma. Methods: Twenty-four patients with glioblastoma were included in this study who underwent a total of 52 [18F]FET PET scans (preradiotherapy, n = 23; preradiotherapy retest, n = 9; follow-up, n = 20). Biologic tumor volume (BTV) delineation was performed by an expert nuclear medicine physician and an automatic segmentation network. Physician and automated quantitative metrics (BTV, mean tumor-to-background ratio [TBRmean], lesion SUVmean, and background SUVmean) were assessed with Pearson correlation and Bland–Altman analysis (bias, limits of agreement [LoA]). Automated and physician segmentation overlap was assessed with spatial and distance-based metrics. Results: BTV and TBRmean Pearson correlation was excellent for all time points (range, 0.92–0.98). In 2 patients with frontal lobe lesions, the network segmented the transverse sinus. Bland–Altman analysis showed network underestimation of physician-derived BTVs (absolute bias, 2.7 cm3, LoA, −13.1–18.5 cm3; relative bias, 27.9%, LoA, −95.3%–151.2%) and deviations for TBRmean were small (absolute bias, 0.03, LoA, −0.25–0.30; relative bias, 0.83%, LoA −14.27%–15.93%). Median Dice similarity coefficient, surface Dice similarity coefficient, Hausdorff distance, 95th percentile Hausdorff distance, and mean absolute surface distance were 0.83, 0.95, 10.94 mm, 3.62 mm, and 0.88 mm, respectively. Conclusion: Automated quantitative analysis was highly correlated with physician assessment; however, volume underestimation and erroneous segmentations may impact radiotherapy treatment planning and response assessment. Further training on a representative local dataset would likely be required for multicenter implementation.
Source: Journal of Nuclear Medicine
Institutions: The University of Western Australia, Royal Brisbane and Women's Hospital, Sir Charles Gairdner Hospital, UNSW Sydney, The University of Melbourne, La Trobe University, Austin Health, Olivia Newton-John Cancer Research Institute, Forschungszentrum Jülich, RWTH Aachen University, University of Cologne, University Hospital Cologne, Universitätsklinikum Aachen
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409131171