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Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification

Academic Radiology(2022)10.1016/j.acra.2022.07.001Source: DataRank Database

Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification is a research paper published in Academic Radiology (2022). On theSindex it has a DataRank of 0. It has been cited 15 times.

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15 citations

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Enrichment:Funding (3 grants)OA: closedTopics (11)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed, PMC)Annotations (1)

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Authors (6)

Thayer Alshaabi ORCID,Nick Cheney ORCID,Raúl San Jośe Estépar ORCID,Jason H. T. Bates ORCID,C. Matthew Kinsey ORCID

Live enrichment

PubMed:35933282 PMC:PMC9895123

National Institutes of Health

Grant: T32 HL076122

National Cancer Institute

Grant: F31 CA268908

National Cancer Institute

Grant: K23 HL133476

closedLicense: http://www.elsevier.com/open-access/userlicense/1.0/

publisherhttps://api.elsevier.com/content/article/PII:S1076633222003749?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S1076633222003749?httpAccept=text/plain

MeSH Terms

LungHumansLung NeoplasmsTomography, X-Ray ComputedRetrospective StudiesMultiple Pulmonary NodulesAdenocarcinoma of Lung

Keywords

Lung cancerEmphysemaLow-dose Computed TomographyRadiomics

Additional file 1 of Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

10.6084/m9.figshare.25854622.v1figshare

Additional file 1 of Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis

10.6084/m9.figshare.25854622figshare

Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification

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Authors (6)

Funding (3 grants)

Open Access

Topics

Linked Datasets (2)

Annotations (Europe PMC)