Evidence map›Paper›PMID 41251734›Full record

ArticleAbdominal radiology (New York)2026

Analysis of deep learning-based segmentation of lymph nodes on full-dose and reduced-dose body CT.

Lindsey H Bloom, Tejas Sudharshan Mathai, Bohan Liu, Brandon Khoury, Naiya Patel, Olivia Wei, Praveen T S Balamuralikrishna, Benjamin Hou, Justin Solomon, Darko Pucar and 3 more

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

13 authors.

Lindsey H BloomRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.
Tejas Sudharshan MathaiRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA. tejas.s.mathai@gmail.com.
Bohan LiuWalter Reed National Military Medical Center, Bethesda, USA.
Brandon KhouryWalter Reed National Military Medical Center, Bethesda, USA.
Naiya PatelGeorge Washington University Hospital, Washington, DC, USA.
Olivia WeiGeorge Washington University Hospital, Washington, DC, USA.
Praveen T S BalamuralikrishnaRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.
Benjamin HouDivision of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, USA.
Justin SolomonCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke Medical Center, Durham, USA.
Darko PucarRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.
Ehsan SameiCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke Medical Center, Durham, USA.
Elizabeth C JonesRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.
Ronald M SummersRadiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe performance of fully automated deep learning-based models for the detection and segmentation of lymph nodes (LNs) on full- and simulated reduced-dose CT was validated.

methodsA total of 15,341 LNs were annotated in 151 patient CTs (age 52 ± 14 years, 87 males) from the public TCIA NIH CT Lymph Nodes dataset. Two 3D nnU-Net models were trained on 90 CT scans: (1) only full dose CTs (NoAugmentation), and (2) both full- and reduced-dose CTs (Augmentation). Dose reduction from 75% to 5% of the full-dose was simulated using a noise-addition tool. Performance was validated on the remaining 61 CTs and an external TCIA Mediastinal LNQ dataset (120 CTs, 64 females).

resultsOn 61 full-dose CTs, the Augmentation model detected all LNs with 67.3% precision and 84.6% sensitivity. For all LNs and large nodes (short axis diameter ≥ 8 mm), Dice Similarity Coefficient (DSC) was 0.83 ± 0.07 and 0.80 ± 0.14, while Hausdorff Distance (HD) error was 1.47 ± 0.91 mm and 3.2 ± 2.28 mm, respectively. Performance decreased with dose reduction (p < 0.01), reaching 73.8% detection sensitivity and 0.75 DSC at 5% dose. Statistically significant differences between Augmentation vs. NoAugmentation models were seen for all nodes (p < 0.001) and small nodes (p < 0.05) at 10% and 5% doses. On the external LNQ dataset, the Augmentation model attained a DSC of 0.76 ± 0.12 and HD of 4.7 ± 3.23 (p < 0.01) for all LNs.

conclusionDegraded image quality impacted nodal delineation on reduced-dose CT. Performance improved when a model trained on both full- and reduced-dose CTs was used.

Indexed as

Deep LearningLymphatic MetastasisLymph NodesRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultFemaleHumansMaleMiddle AgedRadiation DosageSensitivity and SpecificityCTDeep learningLymph nodesReduced doseSegmentation

Identifiers

PMID41251734
PMCPMC13109288

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