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

被引:4
|
作者
Iwasawa, Tae [1 ]
Iwao, Yuma [2 ]
Takemura, Tamiko [3 ]
Okudela, Koji [4 ]
Gotoh, Toshiyuki [5 ]
Baba, Tomohisa [6 ]
Ogura, Takashi [6 ]
Oba, Mari S. [7 ]
机构
[1] Kanagawa Cardiovasc & Resp Ctr, Dept Radiol, Kanazawa Ku, 6-16-1 Tomioka Higashi, Yokohama, Kanagawa 2368651, Japan
[2] Natl Inst Quantum & Radiol Sci & Technol, Chiba, Japan
[3] Japanese Red Cross Med Ctr, Dept Pathol, Tokyo, Japan
[4] Yokohama City Univ, Grad Sch Med, Dept Pathol, Yokohama, Kanagawa, Japan
[5] Yokohama Natl Univ, Grad Sch Environm & Informat Sci, Yokohama, Kanagawa, Japan
[6] Kanagawa Cardiovasc & Resp Ctr, Dept Resp Med, Yokohama, Kanagawa, Japan
[7] Toho Univ, Dept Biostat & Epidemiol, Sch Med, Yokohama, Kanagawa, Japan
关键词
Lungs; Lung disease; Interstitial; Idiopathic pulmonary fibrosis; Computed tomography; Computer-aided design; IDIOPATHIC PULMONARY-FIBROSIS; AUTOMATED QUANTIFICATION; CT; PNEUMONIA; DIAGNOSIS; PATTERNS; SYSTEM;
D O I
10.1007/s11604-017-0683-2
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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). 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. 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. The SubPL extraction method may be helpful for distinguishing patients with ILD from those without ILD.
引用
收藏
页码:681 / 688
页数:8
相关论文
共 50 条
  • [21] The place of high-resolution computed tomography imaging in the investigation of interstitial lung disease
    Jeny, Florence
    Brillet, Pierre-Yves
    Kim, Young-Wouk
    Freynet, Olivia
    Nunes, Hilario
    Valeyre, Dominique
    EXPERT REVIEW OF RESPIRATORY MEDICINE, 2019, 13 (01) : 79 - 94
  • [22] High resolution computed tomography patterns in interstitial lung disease (ILD): prevalence and prognosis
    Almeida, Renata Fragomeni
    Watte, Guilherme
    Marchiori, Edson
    Altmayer, Stephan
    Pacini, Gabriel Sartori
    Barros, Marcelo Cardoso
    Paza Junior, Aldo
    Runin, Adalberto Sperb
    Garces Gamarra Salem, Moacyr Christopher
    Hochhegger, Bruno
    JORNAL BRASILEIRO DE PNEUMOLOGIA, 2020, 46 (05)
  • [23] Interactive High-resolution Computed Tomography Digital Atlas of Interstitial Lung Disease
    Walker, Christopher M.
    Chung, Jonathan H.
    Wall, Corey
    Pipavath, Sudhakar N.
    Chapman, Teresa
    Reddy, Gautham P.
    Stern, Eric J.
    Godwin, J. David
    Weinberger, Ed
    ACADEMIC RADIOLOGY, 2011, 18 (11) : 1453 - 1460
  • [24] Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images
    Lai, Yexin
    Liu, Xueyu
    Hou, Fan
    Han, Zhiyong
    E, Linning
    Su, Ningling
    Du, Dianrong
    Wang, Zhichong
    Zheng, Wen
    Wu, Yongfei
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2024, 32 (02) : 323 - 338
  • [25] Underreporting of Interstitial Lung Abnormalities on Lung Cancer Screening Computed Tomography
    Oldham, Justin M.
    Adegunsoye, Ayodeji
    Khera, Satinderpal
    Lafond, Elyse
    Noth, Imre
    Strek, Mary E.
    Kadoch, Michael
    Chung, Jonathan H.
    ANNALS OF THE AMERICAN THORACIC SOCIETY, 2018, 15 (06) : 764 - 766
  • [26] Clustering of lung diseases in the family of interstitial lung disease patients
    Terwiel, Michelle
    Grutters, Jan C.
    van Moorsel, Coline H. M.
    BMC PULMONARY MEDICINE, 2022, 22 (01)
  • [27] Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease
    Hwang, Hye Jeon
    Kim, Hyunjong
    Seo, Joon Beom
    Ye, Jong Chul
    Oh, Gyutaek
    Lee, Sang Min
    Jang, Ryoungwoo
    Yun, Jihye
    Kim, Namkug
    Park, Hee Jun
    Lee, Ho Yun
    Yoon, Soon Ho
    Shin, Kyung Eun
    Lee, Jae Wook
    Kwon, Woocheol
    Sun, Joo Sung
    You, Seulgi
    Chung, Myung Hee
    Gil, Bo Mi
    Lim, Jae-Kwang
    Lee, Youkyung
    Hong, Su Jin
    Choi, Yo Won
    KOREAN JOURNAL OF RADIOLOGY, 2023, 24 (08) : 807 - 820
  • [28] Quantitative evaluation of disease severity in connective tissue disease-associated interstitial lung disease by dual-energy computed tomography
    Chen, Ling
    Zhu, Min
    Lu, Haiyan
    Yang, Ting
    Li, Wanjiang
    Zhang, Yali
    Xie, Qibing
    Li, Zhenlin
    Wan, Huajing
    Luo, Fengming
    RESPIRATORY RESEARCH, 2022, 23 (01)
  • [29] Computed tomography machine learning classifier correlates with mortality in interstitial lung disease
    Moran-Mendoza, Onofre
    Singla, Abhishek
    Kalra, Angad
    Muelly, Michael
    Reicher, Joshua J.
    RESPIRATORY INVESTIGATION, 2024, 62 (04) : 670 - 676
  • [30] The Role of Computed Tomography Honeycombing in Profiling Disease Progression in Chronic Interstitial Lung Disease
    Perez, Evans R. Fernandez
    ANNALS OF THE AMERICAN THORACIC SOCIETY, 2019, 16 (05) : 546 - 548