Quantitative assessment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-03

AUTHORS

Ra Gyoung Yoon, Joon Beom Seo, Namkug Kim, Hyun Joo Lee, Sang Min Lee, Young Kyung Lee, Jae Woo Song, Jin Woo Song, Dong Soon Kim

ABSTRACT

OBJECTIVES: To evaluate the usefulness of a texture-based automated quantification system (AQS) for evaluating the extent and interval change of regional disease patterns on initial and follow-up high-resolution computed tomographies (HRCTs) of fibrotic interstitial pneumonia (FIP). METHODS: Eighty-nine patients with clinically and/or biopsy confirmed usual interstitial pneumonia (UIP) (n = 71) and non-specific interstitial pneumonia (NSIP) (n = 18) were included. An AQS to quantify five disease patterns (ground-glass opacity [GGO], reticular opacity [RO], honeycombing [HC], emphysema [EMPH], consolidation [CONS]) and normal lung was developed. The extent and interval changes of each disease pattern, FS (fibrosis score), TA (total abnormal lung fraction) of entire lung on initial and 1-year follow-up HRCTs were quantified. The agreement between the results of AQS and two readers was assessed. Results of AQS were correlated with forced vital capacity (FVC) and carbon monoxide diffusing capacity (DLco). RESULTS: The Intraclass correlation coefficient (ICC) study revealed acceptable agreement between visual assessment and AQS (r = 0.78, 0.66 for HC; 0.76, 0.61 for FS; 0.64, 0.68 for TA, initial and follow-up HRCTs, respectively). Linear regression analysis revealed the extent of HC, TA on initial CT, interval changes of FS contributed negatively to DLco, and interval changes of FS, TA contributed negatively to FVC. CONCLUSIONS: Our AQS is comparable with visual assessment for evaluating the disease extent and the interval changes of FIP on HRCT. More... »

PAGES

692-701

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-012-2634-8

DOI

http://dx.doi.org/10.1007/s00330-012-2634-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052304477

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22918563


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00330-012-2634-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00330-012-2634-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-012-2634-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-012-2634-8'


 

This table displays all metadata directly associated to this object as RDF triples.

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