ArticleScientific data2022
A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
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.
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.
Who cites it
83 citing papers in PubMed.
- An [18F]-FDG PET/CT Image Dataset for Artificial Intelligence-Based Classification of Lung Cancer.Journal of biomedical physics & engineering · 2026Article
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning-Based Lung Cancer Segmentation and Volumetric Analysis Using CT Imaging: A Comprehensive Survey.Annals of biomedical engineering · 2026Review
- A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions.Scientific data · 2026Article
- A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma.European journal of nuclear medicine and molecular imaging · 2026Article
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Imaging foundation model for universal enhancement of non-ideal measurement CT.Nature communications · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Article
- A longitudinal whole-body CT dataset with manually annotated tumor lesions.Scientific data · 2026Article
- Multi-modal segment anything model (mmSAM) for tumor segmentation in multi-tracer oncologic PET/CT.EJNMMI physics · 2026Article
- Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based Models and Diffusion Approaches.Bioengineering (Basel, Switzerland) · 2026Review
- Adaptive patch sampling and location-aware reasoning for whole body PET-CT multi-organ segmentation.Scientific reports · 2026Article
- CT-to-PET Synthesis in the Head-Neck and Thoracic Region via Conditional 3D Latent Diffusion Modeling.Bioengineering (Basel, Switzerland) · 2026Article
- Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.Communications medicine · 2026Article
- Sharing a whole-/total-body [Scientific data · 2026Article
- UNSUPERVISED ADAPTATION FROM FDG TO PSMA PET/CT FOR 3D LESION DETECTION UNDER LABEL SHIFT.ArXiv · 2026Article
- PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports.Scientific data · 2026Article
- AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe a publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) and 513 studies without PET-positive malignant lesions (negative controls)) acquired between 2014 and 2018 were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner. The imaging protocol consisted of a whole-body FDG-PET acquisition and a corresponding diagnostic CT scan. All FDG-avid lesions identified as malignant based on the clinical PET/CT report were manually segmented on PET images in a slice-per-slice (3D) manner. We provide the anonymized original DICOM files of all studies as well as the corresponding DICOM segmentation masks. In addition, we provide scripts for image processing and conversion to different file formats (NIfTI, mha, hdf5). Primary diagnosis, age and sex are provided as non-imaging information. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT data and provide the trained deep learning model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.