aceEvaluation of the detection accuracy of a fully automated AI-based lesion segmentation tool in whole-body FDG PET/CT for lymphoma
Background This study evaluated an AI-based lesion segmentation tool using a hybrid 2D-3D deep learning model based on the nnUNet architecture with a ResNet18 backbone, incorporating post-processing to reduce false positives and segmentation-related quantitative errors. Methods Whole-body FDG PET/CT scans from 72 lymphoma patients (mean age 39.2 ± 18.6 years; 33 females, 39 males; 43.1% Hodgkin lymphoma, 56.9% non-Hodgkin lymphoma) acquired on a uMI550 digital PET/CT system were retrospectively analysed. Automat…
Fully automated AI-based lesion segmentation achieved excellent overlap and high quantitative agreement with expert manual delineation for lymphoma on whole-body FDG PET/CT.
Automated tool detected fewer lesions than manual expert delineation (711 vs 848) with sensitivity of 82.9%, indicating missed lesions in lymphoma PET/CT.
Evidence
- Peer-reviewedZeitschrift für Medizinische Physik2026-09-27
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Truvace Impact Record TRV-2026-1222, v1: “Evaluation of the detection accuracy of a fully automated AI-based lesion segmentation tool in whole-body FDG PET/CT for lymphoma.” Truvace, 2026-09-30. /record/TRV-2026-1222 (accessed at citation time). sha256 fa18ca58075495d3…
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