Interstitial Lung Disease, Quantitative CT Analysis and Artificial Intelligence Applications, Radiomics
PDF
Cite
Share
Request
Invited Review
P: 162-176
April 2024

Interstitial Lung Disease, Quantitative CT Analysis and Artificial Intelligence Applications, Radiomics

Trd Sem 2024;12(1):162-176
1. Dokuz Eylül Üniversitesi Tıp Fakültesi, Radyoloji Anabilim Dalı, İzmir, Türkiye
No information available.
No information available
Received Date: 28.08.2023
Accepted Date: 18.03.2024
Publish Date: 02.05.2024
PDF
Cite
Share
Request

ABSTRACT

In the last decade, advances in artificial intelligence technology, especially deep learning, have created new opportunities in medical image analysis. The number of studies in this field is increasing day by day and the performance of artificial intelligence is being improved. The aim of the studies is to develop new imaging biomarkers and to create reliable image analysis tools. It has been shown that early and accurate diagnosis of interstitial lung diseases, determination of severity and prediction of prognosis can be possible by the analysis of high-resolution chest computed tomography images with machine learning method, which is a subset of artificial intelligence. Despite all these promising developments, there are still some challenges to be overcome. One of the most important is the need for large and high-quality datasets to develop high-performance models. For this reason, there is a need for the creation of national data pools and international cooperation. Optimal collection, storage, sharing and management of the obtained digital imaging data should be ensured. In addition, measures should be taken to prevent personal data privacy violations.

Keywords:
Deep learning, interstitial lung disease, machine learning, radiomics, artificial intelligence

References

1
Chen A, Karwoski RA, Gierada DS, Bartholmai BJ, Koo CW. Quantitative CT analysis of diffuse lung disease. Radiographics 2020; 40: 28-43.
2
Walsh SLF, Wells AU, Desai SR, Poletti V, Piciucchi S, Dubini A, et al. Multicentre evaluation of multidisciplinary team meeting agreement on diagnosis in diffuse parenchymal lung disease: a case-cohort study. Lancet Respir Med 2016; 4: 557-65.
3
Dack E, Christe A, Fontanellaz M, Brigato L, Heverhagen JT, Peters AA, et al. Artificial Intelligence and interstitial lung disease: diagnosis and prognosis. Invest Radiol 2023; 58: 602-9.
4
Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, et al. Artificial intelligence for interstitial lung disease analysis on chest computed tomography: a systematic review. Acad Radiol 2022; 29 (Suppl 2): S226-35.
5
Felder FN, Walsh SLF. Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges. ERJ Open Res 2023; 9: 00145-2023.
6
Milam ME, Koo CW. The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States. Clin Radiol 2023; 78: 115-22.
7
Putman RK, Hatabu H, Araki T, Gudmundsson G, Gao W, Nishino M, et al. Association between interstitial lung abnormalities and all-cause mortality. JAMA 2016; 315: 672-81.
8
Wells AU, Walsh SLF. Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis. Curr Opin Pulm Med 2022; 28: 492-7.
9
Lederer DJ, Enright PL, Kawut SM, Hoffman EA, Hunninghake G, van Beek EJ, et al. Cigarette smoking is associated with subclinical parenchymal lung disease: the Multi-Ethnic Study of Atherosclerosis (MESA)-lung study. Am J Respir Crit Care Med 2009; 180: 407-14.
10
Ash SY, Harmouche R, Vallejo DL, Villalba JA, Ostridge K, Gunville R, et al. Densitometric and local histogram based analysis of computed tomography images in patients with idiopathic pulmonary fibrosis. Respir Res 2017; 18: 45.
11
Best AC, Lynch AM, Bozic CM, Miller D, Grunwald GK, Lynch DA. Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. Radiology 2003; 228: 407-14.
12
Jacob J, Bartholmai BJ, Rajagopalan S, van Moorsel CHM, van Es HW, van Beek FT, et al. Predicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis. Am J Respir Crit Care Med 2018; 198: 767-76.
13
Wells AU, Brown KK, Flaherty KR, Kolb M, Thannickal VJ; IPF Consensus Working Group. What’s in a name? That which we call IPF, by any other name would act the same. Eur Respir J 2018; 51: 1800692.
14
Flaherty KR, Wells AU, Cottin V, Devaraj A, Walsh SLF, Inoue Y, et al. Nintedanib in progressive fibrosing interstitial lung diseases. N Engl J Med 2019; 381: 1718-27.
15
Raghu G, Remy-Jardin M, Richeldi L, Thomson CC, Inoue Y, Johkoh T, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med 2022; 205: e18-47.
16
Noble PW, Albera C, Bradford WZ, Costabel U, Glassberg MK, Kardatzke D, et al. Pirfenidone in patients with idiopathic pulmonary fibrosis (CAPACITY): two randomised trials. Lancet 2011; 377: 1760-9.
17
Richeldi L, du Bois RM, Raghu G, Azuma A, Brown KK, Costabel U, et al. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis. N Engl J Med 2014; 370:2071-82.
18
King TE Jr, Bradford WZ, Castro-Bernardini S, Fagan EA, Glaspole I, Glassberg MK, et al. A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis. N Engl J Med 2014; 370:2083-92.
19
Jo HE, Corte TJ, Calandrieello L, Silva M, Sverzelatti N, Chapman S, et al. Deep learning­based prediction of progressive fibrotic lung disease on baseline computed tomography in the Australian IPF Registry. Am J Respir Crit Care Med 2020; 201: 5994.
20
Best AC, Meng J, Lynch AM, Bozic CM, Miller D, Grunwald GK, et al. Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology 2008; 246: 935-40.
21
Obert M, Kampschulte M, Limburg R, Barańczuk S, Krombach GA. Quantitative computed tomography applied to interstitial lung diseases. Eur J Radiol 2018; 100: 99-107.
22
Hartley PG, Galvin JR, Hunninghake GW, Merchant JA, Yagla SJ, Speakman SB, et al. High-resolution CT-derived measures of lung density are valid indexes of interstitial lung disease. J Appl Physiol (1985) 1994; 76: 271-7.
23
Podolanczuk AJ, Oelsner EC, Barr RG, Hoffman EA, Armstrong HF, Austin JH, et al. High attenuation areas on chest computed tomography in community-dwelling adults: the MESA study. Eur Respir J 2016; 48: 1442-52.
24
Hatabu H, Hunninghake GM, Richeldi L, Brown KK, Wells AU, Remy-Jardin M, et al. Interstitial lung abnormalities detected incidentally on CT: a position paper from the Fleischner Society. Lancet Respir Med 2020; 8: 726-37.
25
Sverzellati N, Zompatori M, De Luca G, Chetta A, Bnà C, Ormitti F, et al. Evaluation of quantitative CT indexes in idiopathic interstitial pneumonitis using a low-dose technique. Eur J Radiol 2005; 56: 370-5.
26
Sverzellati N, Calabrò E, Chetta A, Concari G, Larici AR, Mereu M, et al. Visual score and quantitative CT indices in pulmonary fibrosis: relationship with physiologic impairment. Radiol Med 2007; 112: 1160-72.
27
Barnes H, Humphries SM, George PM, Assayag D, Glaspole I, Mackintosh JA, et al. Machine learning in radiology: the new frontier in interstitial lung diseases. Lancet Digit Health 2023; 5: e41-50.
28
Weatherley ND, Eaden JA, Stewart NJ, Bartholmai BJ, Swift AJ, Bianchi SM, et al. Experimental and quantitative imaging techniques in interstitial lung disease. Thorax 2019; 74: 611-9.
29
Rosas IO, Yao J, Avila NA, Chow CK, Gahl WA, Gochuico BR. Automated quantification of high-resolution CT scan findings in individuals at risk for pulmonary fibrosis. Chest 2011; 140: 1590-7.
30
Salisbury ML, Lynch DA, van Beek EJ, Kazerooni EA, Guo J, Xia M, et al. Idiopathic pulmonary fibrosis: the association between the adaptive multiple features method and fibrosis outcomes. Am J Respir Crit Care Med 2017; 195: 921-9.
31
Jacob J, Bartholmai BJ, Rajagopalan S, Kokosi M, Nair A, Karwoski R, et al. Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures. Eur Respir J 2017; 49: 1601011.
32
Jacob J, Bartholmai BJ, Rajagopalan S, Egashira R, Brun AL, Kokosi M, et al. Unclassifiable-interstitial lung disease: outcome prediction using CT and functional indices. Respir Med 2017; 130: 43-51.
33
Lamers RJ, Kemerink GJ, Drent M, van Engelshoven JM. Reproducibility of spirometrically controlled CT lung densitometry in a clinical setting. Eur Respir J 1998; 11: 942-5.
34
Newell JD Jr, Tschirren J, Peterson S, Beinlich M, Sieren J. Quantitative CT of interstitial lung disease. Semin Roentgenol 2019; 54: 73-9.
35
Ertel W. Introduction to artificial intelligence. 2nd ed. Cham, Switzerland: Springer; 2017.
36
Kubat M. Introductıon To Machıne Learning. Springer 2018.
37
Uppaluri R, Hoffman EA, Sonka M, Hunninghake GW, McLennan G. Interstitial lung disease: a quantitative study using the adaptive multiple feature method. Am J Respir Crit Care Med 1999; 159: 519-25.
38
Uppaluri R, Hoffman EA, Sonka M, Hartley PG, Hunninghake GW, McLennan G. Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med 1999; 160: 648-54.
39
Uppaluri R, Mitsa T, Sonka M, Hoffman EA, McLennan G. Quantification of pulmonary emphysema from lung computed tomography images. Am J Respir Crit Care Med 1997; 156: 248-54.
40
Jacob J, Bartholmai BJ, Rajagopalan S, Kokosi M, Nair A, Karwoski R, et al. Automated quantitative computed tomography versus visual computed tomography scoring in idiopathic pulmonary fibrosis: validation against pulmonary function. J Thorac Imaging 2016; 31: 304-11.
41
Moon JW, Bae JP, Lee HY, Kim N, Chung MP, Park HY, et al. Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis. Eur Radiol 2016; 26: 1368-77.
42
Bondesson D, Schneider MJ, Gaass T, Kühn B, Bauman G, Dietrich O, et al. Nonuniform fourier-decomposition MRI for ventilation- and perfusion-weighted imaging of the lung. Magn Reson Med 2019; 82: 1312-21.
43
Wang Z, Robertson SH, Wang J, He M, Virgincar RS, Schrank GM, et al. Quantitative analysis of hyperpolarized 129 Xe gas transfer MRI. Med Phys 2017; 44: 2415-28.
44
Depeursinge A, Chin AS, Leung AN, Terrone D, Bristow M, Rosen G, et al. Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution computed tomography. Invest Radiol 2015; 50: 261-7.
45
Gulli, A, Kapoor A, Pal S. Deep learning with Tensor Flow 2 and Keras. 2nd ed. Birmingham Mumbai: Pact publishing; 2019.
46
Walsh SLF, Humphries SM, Wells AU, Brown KK. Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. Lancet Respir Med 2020; 8: 1144-53.
47
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021; 8: 53.
48
Humphries SM, Yagihashi K, Huckleberry J, Rho BH, Schroeder JD, Strand M, et al. Idiopathic pulmonary fibrosis: data-driven textural analysis of extent of fibrosis at baseline and 15-month follow-up. Radiology 2017; 285: 270-8.
49
Lancaster L, Goldin J, Trampisch M, Kim GH, Ilowite J, Homik L, et al. Effects of nintedanib on quantitative lung fibrosis score in idiopathic pulmonary fibrosis. Open Respir Med J 2020; 14: 22-31.