Evidence map›Paper›PMID 40127518›Full record

ArticleComputers in biology and medicine2025

Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.

Fahmida Haque, Alex Chen, Nathan Lay, Jorge Carrasquillo, Esther Mena, Liza Lindenberg, Julia E Segal, Philip C Eclarinal, Sara Talvacchio, Alberta Derkyi and 6 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Fahmida HaqueArtificial Intelligence Resource, National Cancer Institute, National Institute of Health, Bethesda, MD, 20814, USA.
Alex ChenArtificial Intelligence Resource, National Cancer Institute, National Institute of Health, Bethesda, MD, 20814, USA.
Nathan LayArtificial Intelligence Resource, National Cancer Institute, National Institute of Health, Bethesda, MD, 20814, USA.
Jorge CarrasquilloMolecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Esther MenaMolecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Liza LindenbergMolecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Julia E SegalSection on Medical Neuroendocrinology National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, 20892, MD, USA.
Philip C EclarinalMolecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Sara TalvacchioSection on Medical Neuroendocrinology National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, 20892, MD, USA.
Alberta DerkyiSection on Medical Neuroendocrinology National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, 20892, MD, USA.
Peter L ChoykeMolecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Karel PacakSection on Medical Neuroendocrinology National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, 20892, MD, USA; AKESO, Prague 5, Czech Republic.
Rosandra N KaplanPediatric Oncology Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Frank I LinMolecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Baris TurkbeyArtificial Intelligence Resource, National Cancer Institute, National Institute of Health, Bethesda, MD, 20814, USA; Molecular Imaging Branch, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Stephanie A HarmonArtificial Intelligence Resource, National Cancer Institute, National Institute of Health, Bethesda, MD, 20814, USA.

Funding

LAB RESEARCH TRAINING IN PEDIATRIC ONCOLOGY-HEMATOLOGYT32CA060441 · NCI · JOHNS HOPKINS UNIVERSITY · PI ALAN D FRIEDMAN · 1993 to 2026
$14.9M
Intramural NIH HHS Z99 CA999999NCI NIH HHS T32 CA060441
6 · The paper itself

Abstract

backgroundThis study develops a deep learning-based automated lesion segmentation model for whole-body 3D

methodA publicly available lesion-annotated dataset of 1014 whole-body FDG-PET/CT images was used to train, validate, and test (70:10:20) eight configurations with 3D U-Net as the backbone architecture. The best-performing model on the test set was further evaluated on 3 different unseen cohorts consisting of osteosarcoma or neuroblastoma (OS cohort) (n = 13), pediatric solid tumors (ST cohort) (n = 14), and adult Pheochromocytoma/Paraganglioma (PHEO cohort) (n = 40). Both lesion-level and patient-level statistical analyses were conducted to validate the performance of the model on different cohorts.

resultsThe best performing 3D full resolution nnUNet model achieved a lesion-level sensitivity and DISC of 71.70 % and 0.40 for the test set, 97.83 % and 0.73 for ST, 40.15 % and 0.36 for OS, and 78.37 % and 0.50 for the PHEO cohort. For the test set and PHEO cohort, the model has missed small volume and lower uptake lesions (p < 0.01), whereas no statistically significant differences (p > 0.05) were found in the false positive (FP) and false negative lesions volume and uptake for the OS and ST cohort. The predicted total lesion glycolysis is slightly higher than the ground truth because of FP calls, which experts can easily check and reject.

conclusionThe developed deep learning-based automated lesion segmentation AI model which utilizes 3D_FullRes configuration of the nnUNet framework showed promising and reliable performance for the whole-body FDG-PET/CT images.

Indexed as

Deep LearningFluorodeoxyglucose F18Image Processing, Computer-AssistedNeoplasmsPositron Emission Tomography Computed TomographyWhole Body ImagingAdolescentAdultChildCohort StudiesFemaleHumansMaleMiddle AgedFluorodeoxyglucose F18Artificial intelligenceDeep learningFDG PET/CTOncology

Identifiers

PMID40127518
PMCPMC12107610

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.