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DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules

Medical Physics(2020)10.1002/mp.14445Source: DataRank Database

DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules is a dataset published in Medical Physics (2020). On theSindex it has a DataRank of 0.753, placing it in the top 26.7% of the data-sharing corpus. It has been cited 21 times, with 19 citing works in its 1-hop citation network.

Top 27%percentile

0.753DataRank

Ranks in the top 27% for downstream scientific impact

Data paper Open Access21 citations · 19 citing works in its network

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Linked data & code

doi

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 (19 grants)Impact (FWCI 0.88)OA: hybridTopics (34)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed, PMC, OpenAlex)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (1/3)

Dataset classification

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DataRank Breakdown

Base Score 62%Citation Network 38%

Base Score Contribution

0.464

From this paper's citation signal

Citation Network Contribution

0.289

From 10 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 19 citers.

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective
Seminars in Cancer Biology2023283 citations88 referencesContributes 0.064
Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets
Insights into Imaging202325 citations56 referencesContributes 0.058
Simultaneous object detection and segmentation for patient‐specific markerless lung tumor tracking in simulated radiographs with deep learning
Medical Physics202311 citations46 referencesContributes 0.054
An ISHAP-based interpretation-model-guided classification method for malignant pulmonary nodule
Knowledge-Based Systems202127 citations82 referencesContributes 0.041
Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine
Journal of Translational Medicine202498 citations123 referencesContributes 0.037

Authors (11)

Matthew C. Hancock,David Clunie ORCID,Mathias Brochhausen ORCID,Jonathan P. Bona ORCID,Justin Kirby ORCID

Live enrichment

PubMed:32772385 PMC:PMC7721965 OpenAlex:https://openalex.org/W3047773104

National Institutes of Health

Grant: 16X011

National Institutes of Health

Grant: HHSN261200800001E,

National Institutes of Health

Grant: 19X037Q

National Institutes of Health

Grant: HHSN26100071

NCI NIH HHS

Grant: U01 CA187013

NCI NIH HHS

Grant: HHSN261200800001E

NCI NIH HHS

Grant: U01 CA190234

NCI NIH HHS

Grant: U24 CA180918

NCI NIH HHS

Grant: R35 CA220523

CCR NIH HHS

Grant: HHSN261200800001C

NCI NIH HHS

Grant: U24 CA199460

NCI NIH HHS

Grant: U24 CA215109

National Institutes of Health

Grant: 5U24CA199460-05

Extensible open-source zero-footprint web viewer for oncologic imaging research

National Institutes of Health

Grant: 5U01CA190234-04

Genotype and Imaging Phenotype Biomarkers in Lung Cancer

National Institutes of Health

Grant: 5U24CA180918-02

Quantitative Image Informatics for Cancer Research (QIICR)

National Cancer Institute: U24 CA180918, U24 CA199460 and U01 CA190234

Foundation for the National Institutes of Health

National Cancer Institute: U24 CA180918, U24 CA199460 and U01 CA190234

Foundation for the National Institutes of Health

FWCI

0.88

Citation Percentile

0.8%

Citation Trend

2020

2021

2022

2023

2024

2025

2026

hybridLicense: cc-by

journalhttps://doi.org/10.1002/mp.14445

publisherhttps://doi.org/10.1002/mp.14445

publisherhttps://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14445

publisherhttps://onlinelibrary.wiley.com/doi/full-xml/10.1002/mp.14445

publisherhttps://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14445

Fields of Study

Lung Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingAI in cancer detection03 medical and health sciences0302 clinical medicineDatabases, FactualHumansLungLung NeoplasmsTomography, X-Ray Computed

MeSH Terms

HumansLungLung NeoplasmsTomography, X-Ray ComputedDatabases, Factual

Keywords

DICOMMedical imagingEncoding (memory)Computer scienceMedicineComputer graphics (images)RadiologyArtificial intelligenceLung cancerQuantitative ImagingCancer ImagingFair DataImage AnnotationsLung NeoplasmsDatabases, FactualHumansTomography, X-Ray ComputedLungMedical Physics Dataset Articles

Data From LIDC-IDRI

10.7937/k9/tcia.2015.lo9ql9sxThe Cancer Imaging Archive

Standardized representation of the TCIA LIDC-IDRI annotations using DICOM

10.7937/tcia.2018.h7umfurqThe Cancer Imaging Archive