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

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach

International Journal of Radiation Oncology*Biology*Physics(2020)10.1016/j.ijrobp.2020.10.005Source: DataRank Database

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach is a research paper published in International Journal of Radiation Oncology*Biology*Physics (2020). On theSindex it has a DataRank of 0. It has been cited 79 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access79 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 (5 grants)Impact (FWCI 7.56)OA: hybridTopics (49)IDs (PubMed, PMC, OpenAlex)SDGs (2)

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 (11)

Beth M. Beadle ORCID,Adam S. Garden ORCID,Heath D. Skinner ORCID,Jinzhong Yang ORCID,Dong Joo Rhee ORCID

Live enrichment

PubMed:33068690 PMC:PMC9472456 OpenAlex:https://openalex.org/W3092818728

NCI NIH HHS

Grant: L30 CA242657

NCI NIH HHS

Grant: UH3 CA202665

NCI NIH HHS

Grant: UH2 CA202665

National Institutes of Health

Grant: 5UH3CA202665-05

The Radiation Planning Assistant (RPA) for Radiation Therapy Planning in Low- and Middle-Income Countries

National Institutes of Health

FWCI

7.56

Citation Percentile

1.0%

Citation Trend

2020

2021

2022

2023

2024

2025

2026

hybridLicense: cc-by-nc-nd

journalhttp://www.redjournal.org/article/S0360301620343923/pdf

publisherhttp://www.redjournal.org/article/S0360301620343923/pdf

publisherhttps://api.elsevier.com/content/article/PII:S0360301620343923?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0360301620343923?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.ijrobp.2020.10.005

Fields of Study

Head and Neck Cancer StudiesAdvanced Radiotherapy TechniquesRadiomics and Machine Learning in Medical Imaging03 medical and health sciences0302 clinical medicineDeep LearningHead and Neck NeoplasmsHumansLymph NodesPharynxRadiotherapy Planning, Computer-AssistedReproducibility of ResultsRetrospective StudiesTomography, X-Ray Computed

MeSH Terms

Deep LearningHead and Neck NeoplasmsHumansLymph NodesPharynxRadiotherapy Planning, Computer-AssistedRetrospective StudiesTomography, X-Ray ComputedReproducibility of Results

Keywords

WorkflowHead and neck cancerLymph nodeRadiation therapyHead and neckMedical physicsMedicineDeep learningRadiation treatment planningArtificial intelligenceNode (physics)CancerComputer scienceRadiologyInternal medicineSurgeryEngineeringHead and Neck NeoplasmsRadiotherapy Planning, Computer-AssistedHumansPharynxReproducibility of ResultsLymph NodesTomography, X-Ray ComputedArticleRetrospective Studies

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Good health and well-being