Literature DB >> 28936704

Extraction of the subpleural lung region from computed tomography images to detect interstitial lung disease.

Tae Iwasawa1, Yuma Iwao2, Tamiko Takemura3, Koji Okudela4, Toshiyuki Gotoh5, Tomohisa Baba6, Takashi Ogura6, Mari S Oba7.   

Abstract

PURPOSE: To quantify lesions in the subpleural lung region (SubPL) on computed tomography (CT) images and to evaluate whether they are useful for detecting interstitial lung disease (ILD).
MATERIALS AND METHODS: The subjects were 40 patients with idiopathic pulmonary fibrosis (IPF) diagnosed by multidisciplinary methods and 35 age-matched patients without ILDs. The lungs and SubPL were extracted from CT images using a Gaussian histogram normalized correlation system and evaluated for the mean CT attenuation value (CTmean) and the percentage of high attenuation area (%HAA), exceeding -700 Hounsfield units. The H pattern was defined as a honeycomb appearance and/or fibrosis with traction bronchiectasis, and the H-pattern volume ratios for the whole lung and the 2-mm-wide SubPL were measured. The utility of the SubPL for detecting ILD was evaluated by receiver operating characteristic (ROC) analysis.
RESULTS: The areas under the ROC curves (AUCs) of CTmean and %HAA for the SubPL were greater than those for the whole lung. The AUCs for the whole lung and the SubPL were 0.990 and 0.994, respectively, for H-pattern volume; 0.875 and 0.994, respectively, for CTmean; and 0.965 and 0.991, respectively, for %HAA.
CONCLUSION: The SubPL extraction method may be helpful for distinguishing patients with ILD from those without ILD.

Entities:  

Keywords:  Computed tomography; Computer-aided design; Idiopathic pulmonary fibrosis; Interstitial; Lung disease; Lungs

Mesh:

Year:  2017        PMID: 28936704     DOI: 10.1007/s11604-017-0683-2

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  17 in total

1.  Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment.

Authors:  Alan C Best; Anne M Lynch; Carmen M Bozic; David Miller; Gary K Grunwald; David A Lynch
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2.  Collaborative radiologic and histopathologic assessment of fibrotic lung disease.

Authors:  Jeffrey R Galvin; Aletta Ann Frazier; Teri J Franks
Journal:  Radiology       Date:  2010-06       Impact factor: 11.105

3.  Quantitative CT evaluation in patients with combined pulmonary fibrosis and emphysema: correlation with pulmonary function.

Authors:  Shin Matsuoka; Tsuneo Yamashiro; Shoichiro Matsushita; Akiyuki Kotoku; Atsuko Fujikawa; Kunihiro Yagihashi; Yasuo Nakajima
Journal:  Acad Radiol       Date:  2015-02-27       Impact factor: 3.173

4.  Standardizing CT lung density measure across scanner manufacturers.

Authors:  Huaiyu Heather Chen-Mayer; Matthew K Fuld; Bernice Hoppel; Philip F Judy; Jered P Sieren; Junfeng Guo; David A Lynch; Antonio Possolo; Sean B Fain
Journal:  Med Phys       Date:  2017-02-21       Impact factor: 4.071

5.  The importance of subpleural fibrosis in the prognosis of patients with idiopathic interstitial pneumonias.

Authors:  Tae Iwasawa; Tamiko Takemura; Koji Okudera; Toshiyuki Gotoh; Yuma Iwao; Hideya Kitamura; Tomohisa Baba; Takashi Ogura; Mari S Oba
Journal:  Eur J Radiol       Date:  2017-02-22       Impact factor: 3.528

6.  CT analysis of the effect of pirfenidone in patients with idiopathic pulmonary fibrosis.

Authors:  Tae Iwasawa; Takashi Ogura; Fumikazu Sakai; Tetsu Kanauchi; Takanobu Komagata; Tomohisa Baba; Toshiyuki Gotoh; Satoshi Morita; Takuya Yazawa; Tomio Inoue
Journal:  Eur J Radiol       Date:  2012-03-31       Impact factor: 3.528

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

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
Journal:  Eur Radiol       Date:  2012-08-24       Impact factor: 5.315

8.  Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis.

Authors:  Fabien Maldonado; Teng Moua; Srinivasan Rajagopalan; Ronald A Karwoski; Sushravya Raghunath; Paul A Decker; Thomas E Hartman; Brian J Bartholmai; Richard A Robb; Jay H Ryu
Journal:  Eur Respir J       Date:  2013-04-05       Impact factor: 16.671

9.  Multicenter study of quantitative computed tomography analysis using a computer-aided three-dimensional system in patients with idiopathic pulmonary fibrosis.

Authors:  Tae Iwasawa; Tetsu Kanauchi; Toshiko Hoshi; Takashi Ogura; Tomohisa Baba; Toshiyuki Gotoh; Mari S Oba
Journal:  Jpn J Radiol       Date:  2015-11-06       Impact factor: 2.374

10.  Genome-wide association study of subclinical interstitial lung disease in MESA.

Authors:  Ani Manichaikul; Xin-Qun Wang; Li Sun; Josée Dupuis; Alain C Borczuk; Jennifer N Nguyen; Ganesh Raghu; Eric A Hoffman; Suna Onengut-Gumuscu; Emily A Farber; Joel D Kaufman; Dan Rabinowitz; Karen D Hinckley Stukovsky; Steven M Kawut; Gary M Hunninghake; George R Washko; George T O'Connor; Stephen S Rich; R Graham Barr; David J Lederer
Journal:  Respir Res       Date:  2017-05-18
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  2 in total

1.  Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales.

Authors:  Yupeng Xu; Yi Zhang; Ke Bi; Zhiyu Ning; Lisha Xu; Mengjun Shen; Guoying Deng; Yin Wang
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

Review 2.  Smoking-related lung abnormalities on computed tomography images: comparison with pathological findings.

Authors:  Tae Iwasawa; Tamiko Takemura; Takashi Ogura
Journal:  Jpn J Radiol       Date:  2017-12-15       Impact factor: 2.374

  2 in total

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