Truace
Subscribe

lesion detection accuracy and quantitative concordance of fully automated AI segmentation versus manual delineation in whole-body FDG PET/CT for lymphoma patients

Source article: Evaluation 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…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1222· cite & verify
Evaluation of the detection accuracy of a fully automated AI-based lesion segmentation tool in whole-body FDG PET/CT for lymphoma
TORNAI-SpectrumOfMedicalImaging by Martin Tornai. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0
Trace impact readingPositive state
P 63The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 70The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

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.

Main points

  1. 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.
  2. AI used hybrid 2D-3D deep learning model based on nnUNet architecture with ResNet18 backbone and post-processing to reduce false positives.
  3. 711 AI-segmented lesions versus 848 manually segmented lesions compared using F1-score, sensitivity, PPV, Pearson correlation, concordance correlation, ICC, and Bland-Altman analysis.
  4. DSC showed moderate negative correlation with liver SNR (r = -0.40)

The gain

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.

The problem

Automated tool detected fewer lesions than manual expert delineation (711 vs 848) with sensitivity of 82.9%, indicating missed lesions in lymphoma PET/CT.

The 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

  1. Peer-reviewedZeitschrift für Medizinische Physik2026-09-27

The debate