Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Predicting the hypoxic volume of head and neck tumors from fluorodeoxyglucose positron emission tomography images using artificial intelligence

Physics and Imaging in Radiation Oncology(2025)10.1016/j.phro.2025.100769Source: DataRank Database

Predicting the hypoxic volume of head and neck tumors from fluorodeoxyglucose positron emission tomography images using artificial intelligence is a research paper published in Physics and Imaging in Radiation Oncology (2025). On theSindex it has a DataRank of 0.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access0 citations

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (7 grants)Impact (FWCI 0.00)OA: goldTopics (27)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (8)

Milan Grkovski ORCID,Heiko Schöder ORCID,Aditya Apte ORCID,John L. Humm ORCID,Nancy Y. Lee ORCID

Live enrichment

PubMed:40584457 PMC:PMC12206312 OpenAlex:https://openalex.org/W4409534661

NIH NCI

Grant: P30 CA008748

Breast Cancer Research Foundation

Grant: U54CA274291

Breast Cancer Research Foundation

Grant: MATH-23-001

NCI NIH HHS

Grant: R01 CA258821

National Institutes of Health

Grant: 1U54CA274291-01

Dynamics of Immune Response in Irradiated Rectal Cancer

National Institutes of Health

Grant: 5R01CA258821-02

Safer lung cancer radiotherapy delivery using novel artificial intelligence methods

National Institutes of Health

Grant: 2P30CA008748-43

MOUSE GENETICS

FWCI

0.00

Citation Percentile

0.1%

goldLicense: cc-by-nc-nd

journalhttps://www.phiro.science/article/S2405-6316(25)00074-0/pdf

publisherhttps://www.phiro.science/article/S2405-6316(25)00074-0/pdf

publisherhttps://api.elsevier.com/content/article/PII:S2405631625000740?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2405631625000740?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.phro.2025.100769

Fields of Study

Medical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical ImagingCancer, Hypoxia, and Metabolism03 medical and health sciences0302 clinical medicine

Keywords

Positron emission tomographyFluorodeoxyglucoseHead and neckNuclear medicineHead and neck cancerVolume (thermodynamics)Positron emissionMedicineRadiologyRadiation therapyPhysicsSurgeryHypoxiaFdgFmisoDeep LearningGenerative Adversarial NetworkMedical physics. Medical radiology. Nuclear medicineR895-920Neoplasms. Tumors. Oncology. Including cancer and carcinogensOriginal Research ArticleRC254-282