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TRUVACE RECORD VERSION
record: TRV-2026-1222
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-30T06:56:28.109640Z
status: published
lens: trace
sector: health
headline: Evaluation of the detection accuracy of a fully automated AI-based lesion segmentation tool in whole-body FDG PET/CT for lymphoma
dek: 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…
gain_title: 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.
problem_title: Automated tool detected fewer lesions than manual expert delineation (711 vs 848) with sensitivity of 82.9%, indicating missed lesions in lymphoma PET/CT.
trace_subject: lesion detection accuracy and quantitative concordance of fully automated AI segmentation versus manual delineation in whole-body FDG PET/CT for lymphoma patients
gain_reading: 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.
gain_evidence: The AI model achieved an excellent DSC (89.2%), sensitivity (82.9) and PPV (96.5) which indicated balanced detection performance. | The fully automated AI-based segmentation tool demonstrated good-to-excellent lesion detection accuracy and high quantitative concordance with manual segmentation in whole-body FDG PET/CT for lymphoma patients. | Strong correlations were observed for all quantitative parameters (r > 0.95, p 0.94; ICC > 0.94; 95% CI: 0.93-0.99)
problem_reading: Automated tool detected fewer lesions than manual expert delineation (711 vs 848) with sensitivity of 82.9%, indicating missed lesions in lymphoma PET/CT.
problem_evidence: A total of 711 AI and 848 manually segmented lesions were analysed.
quick_read: By September 2026, researchers retrospectively evaluated a fully automated AI-based lesion segmentation tool using a hybrid 2D-3D nnUNet with ResNet18 backbone on whole-body FDG PET/CT scans from 72 lymphoma patients. Compared with manual delineations by experienced physicians, the model segmented 711 lesions versus 848 manual lesions and achieved DSC 89.2%, sensitivity 82.9, PPV 96.5, with strong correlations for PET metrics.

High concordance suggests potential to reduce manual segmentation burden and standardize quantitative PET metrics in lymphoma care, but the lower lesion count and 82.9% sensitivity indicate missed lesions. As a single-scanner retrospective study, generalizability, region-specific performance, and impact on clinical decision-making remain uncertain and require further refinement.
limitation: 
tag: Dual reading
key_points: Retrospective analysis of 72 lymphoma patients (mean age 39.2 ± 18.6 years; 33 females, 39 males; 43.1% Hodgkin lymphoma, 56.9% non-Hodgkin lymphoma) scanned on uMI550 digital PET/CT. | AI used hybrid 2D-3D deep learning model based on nnUNet architecture with ResNet18 backbone and post-processing to reduce false positives. | 711 AI-segmented lesions versus 848 manually segmented lesions compared using F1-score, sensitivity, PPV, Pearson correlation, concordance correlation, ICC, and Bland-Altman analysis. | DSC showed moderate negative correlation with liver SNR (r = -0.40)
rundown: The study analyzed whole-body FDG PET/CT scans from 72 lymphoma patients acquired on a uMI550 digital PET/CT system, with 43.1% Hodgkin and 56.9% non-Hodgkin lymphoma. Manual delineations by experienced physicians served as reference for 848 lesions versus 711 AI lesions.

Performance metrics included DSC 89.2%, sensitivity 82.9 and PPV 96.5, with strong quantitative agreement (r > 0.95, ICC > 0.94, 95% CI: 0.93-0.99) and minimal bias on Bland-Altman. Authors noted moderate negative correlation between DSC and liver SNR and called for region-specific training.
sources:
- peer_reviewed | Zeitschrift für Medizinische Physik | https://doi.org/10.1016/j.zemedi.2026.09.002 | 2026-09-27
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