Возможности денситометрии в оценке диффузных изменений паренхимы легких (обзор литературы)

Автор: Сучилова М.М., Блохин И.А., Коденко М.Р., Решетников Р.В., Николаев А.Е., Омелянская О.В., Владзимирский А.В.

Журнал: Сибирский журнал клинической и экспериментальной медицины @cardiotomsk

Статья в выпуске: 3 т.38, 2023 года.

Бесплатный доступ

Данные, полученные при проведении компьютерной томографии (КТ) органов грудной клетки, можно проанализировать не только визуально, но и численно. Количественная оценка позволяет более точно и объективно оценить степень тяжести заболевания. Наиболее изученным способом количественной оценки данных КТ является денситометрия - автоматический анализ плотностных показателей легких, выраженных в единицах Хаунсфилда. Данный обзор посвящен типам заболеваний, для которых возможна формализация диагностической задачи и применение денситометрии, а также ограничениям метода и способам их преодоления.

Денситометрия, компьютерная томография, низкодозная компьютерная томография

Короткий адрес: https://sciup.org/149143644

IDR: 149143644 | DOI: 10.29001/2073-8552-2023-39-3-23-31

Список литературы Возможности денситометрии в оценке диффузных изменений паренхимы легких (обзор литературы)

Mascalchi M., Diciotti S., Sverzellati N., Camiciottoli G., Ciccotosto C., Falaschi F. et al. Low agreement of visual rating for detailed quantification of pulmonary emphysema in whole-lung CT. Acta Radiol. 2012;53(1):53–60. DOI: 10.1258/ar.2011.110419.
Ng C.S., Desai S.R., Rubens M.B., Padley S.P., Wells A.U., Hansell D.M. Visual quantitation and observer variation of signs of small airways disease at inspiratory and expiratory CT. J. Thorac. Imaging. 1999;14(4):279–285. DOI: 10.1097/00005382-199910000-00008.
Siemienowicz M.L., Kruger S.J., Goh N.S., Dobson J.E., Spelman T.D., Fabiny R.P. Agreement and mortality prediction in high-resolution CT of diffuse fibrotic lung disease. J. Med. Imaging Radiat. Oncol. 2015;59(5):555–563. DOI: 10.1111/1754-9485.12314.
Walsh S.L.F., Hansell D.M. High-resolution CT of interstitial lung disease: A continuous evolution. Semin. Respir. Crit. Care Med. 2014;35(1):129–144. DOI: 10.1055/s-0033-1363458.
Asil K., Kalaycıoğlu B., Mahmutyazıcıoğlu K. Individual factors affecting computed tomography densitometry measurements. The International Annals of Medicine. 2018;2(12). DOI: 10.24087/IAM.2018.2.12.680.
Ringheim H., Thudium R.F., Jensen J.S., Rezahosseini O., Nielsen S.D. Prevalence of emphysema in people living with human immunodeficiency virus in the current combined antiretroviral therapy era: A systematic review. Front. Med. (Lausanne). 2022;9:897773. DOI: 10.3389/fmed.2022.897773.
Romei C., Castellana R., Conti B., Bemi P., Taliani A., Pistelli F. et al. Quantitative texture-based analysis of pulmonary parenchymal features on chest CT: comparison with densitometric indices and short-term effect of changes in smoking habit. Eur. Respir. J. 2022;60(4):2102618. DOI: 10.1183/13993003.02618-2021.
Lagrange J.L., Brassard N., Costa A., Aubanel D., Héry M., Bruneton J.N. et al. CT measurement of lung density: the role of patient position and value for total body irradiation. Int. J. Radiat. Oncol. Biol. Phys. 1987;13(6):941–944. DOI: 10.1016/0360-3016(87)90111-8.
Lynch D.A. Progress in imaging COPD, 2004–2014. Chronic Obstr. Pulm. Dis. 2014;1(1):73–82. DOI: 10.15326/jcopdf.1.1.2014.0125.
Yanase J., Triantaphyllou E. A systematic survey of computer-aided diagnosis in medicine: Past and present developments. Expert Systems with Applications. 2019;138:112821. DOI: 10.1016/j.eswa.2019.112821.
Bankman I. (ed.) Handbook of medical image processing and analysis. 2-nd ed. San Diego, United States: Elsevier Science Publishing Co Inc.; 2008:1000.
Loeh B., Brylski L.T., von der Beck D., Seeger W., Krauss E., Bonniaud P. et al. Lung CT densitometry in idiopathic pulmonary fibrosis for the prediction of natural course, severity, and mortality. Chest. 2019;155(5):972–981. DOI: 10.1016/j.chest.2019.01.019.
Hoffman E.A., Ahmed F.S., Baumhauer H., Budoff M., Carr J.J., Kronmal R. et al. Variation in the percent of emphysema-like lung in a healthy, nonsmoking multiethnic sample. The MESA lung study. Ann. Am. Thorac. Soc. 2014;11(6):898–907. DOI: 10.1513/AnnalsATS.201310-364OC.
Walsdorff M., Van Muylem A., Gevenois P.A. Effect of total lung capacity and gender on CT densitometry indexes. BJR. 2016;89(1058):20150631. DOI: 10.1259/bjr.20150631.
Avila N.A., Kelly J.A., Dwyer A.J., Johnson D.L., Jones E.C., Moss J. Lymphangioleiomyomatosis: Correlation of qualitative and quantitative thin-section CT with pulmonary function tests and assessment of dependence on pleurodesis. Radiology. 2002;223(1):189–197. DOI: 10.1148/radiol.2231010315.
Crossley D., Renton M., Khan M., Low E.V., Turner A.M. CT densitometry in emphysema: a systematic review of its clinical utility. Int. J. Chron. Obstruct. Pulmon. Dis. 2018;13:547–563. DOI: 10.2147/COPD.S143066.
Jou S.S., Yagihashi K., Zach J.A., Lynch D., Suh Y.J. Relationship between current smoking, visual CT findings and emphysema index in cigarette smokers. Clinical Imaging. 2019;53:195–199. DOI: 10.1016/j. clinimag.2018.10.024.
Эмфизема легких. Большая российская энциклопедия – электронная версия. Accessed February 8, 2023. [Pulmonary emphysema. The Big Russian Encyclopedia – electronic version. Accessed February 8, 2023]. URL: https://old.bigenc.ru/medicine/text/4935239 (13.04.2023).
Viegi G., Pistelli F., Sherrill D.L., Maio S., Baldacci S., Carrozzi L. Definition, epidemiology and natural history of COPD. Eur. Resp. J. 2007;30(5):993–1013. DOI: 10.1183/09031936.00082507.
Carr L.L., Jacobson S., Lynch D.A., Foreman M.G., Flenaugh E.L., Hersh C.P. et al. Features of COPD as predictors of lung cancer. Chest. 2018;153(6):1326–1335. DOI: 10.1016/j.chest.2018.01.049.
Николаев А.Е., Блохин И.А., Лбова О.А., Дадакина И.С., Гомболевский В.А., Морозов С.П. Три клинически значимые находки при скрининге рака легких. Туберкулез и болезни легких. 2019;97(10):37–44. DOI: 10.21292/2075-1230-2019-97-10-37-44. [Nikolaev A.E., Blokhin I.A., Lbova O.A., Dadakina I.S., Gombolevskiy V.A., Morozov S.P. Three clinically relevant findings in lung cancer screening. Tuberculosis and Lung Diseases. 2019;97(10):37–44. (In Russ.)]. DOI: 10.21292/2075-1230-2019-97-10-37-44.
Yasuura Y., Terada Y., Mizuno K., Kayata H., Hayato K., Kojima H. et al. Quantitative severity of emphysema is related to the prognostic outcome of early-stage lung cancer. Eur. J. Cardiothorac. Surg. 2022;62(5):ezac499. DOI: 10.1093/ejcts/ezac499.
Ezponda A., Casanova C., Divo M., Marín-Oto M., Cabrera C., Marín J.M. et al. Chest CT-assessed comorbidities and all-cause mortality risk in COPD patients in the BODE cohort. Respirology. 2022;27(4):286–293. DOI: 10.1111/resp.14223.
Bakker J.T., Klooster K., Vliegenthart R., Slebos D.J. Measuring pulmonary function in COPD using quantitative chest computed tomography analysis. Eur. Respir. Rev. 2021;30(161):210031. DOI: 10.1183/16000617.0031-2021.
Cavigli E., Camiciottoli G., Diciotti S., Orlandi I., Spinelli C., Meoni E. et al. Whole-lung densitometry versus visual assessment of emphysema. Eur. Radiol. 2009;19(7):1686–1692. DOI: 10.1007/s00330-009-1320-y.
Chen H., Zeng Q.S., Zhang M., Chen R.C., Xia T.T., Wang W. et al. Quantitative low-dose computed tomography of the lung parenchyma and airways for the differentiation between chronic obstructive pulmonary disease and asthma patients. RES. 2017;94(4):366–374. DOI: 10.1159/000478531.
Loh L.C., Ong C.K., Koo H.J., Lee S.M., Lee J.S., Oh Y.M. et al. A novel CT-emphysema index/FEV1 approach of phenotyping COPD to predict mortality. Int. J. Chron. Obstruct Pulmon. Dis. 2018;13:2543–2550. DOI: 10.2147/COPD.S165898.
QIBA Profile: Computed Tomography: Lung Densitometry; Alliance QIB. Radiological Society of North America; 2021. URL: https://qibawiki.rsna.org/images/a/a8/QIBA_CT_Lung_Density_Profile_090420-clean.pdf (13.04.2023).
Nguyen-Kim T.D.L., Maurer B., Suliman Y.A., Morsbach F., Distler O., Frauenfelder T. The impact of slice-reduced computed tomography on histogram-based densitometry assessment of lung fi brosis in patients with systemic sclerosis. J. Thorac. Dis. 2018;10(4):2142–2152. DOI: 10.21037/jtd.2018.04.39.
Alevizos M.K., Danoff S.K., Pappas D.A., Lederer D.J., Johnson C., Hoff man E.A. et al. Assessing predictors of rheumatoid arthritis-associated interstitial lung disease using quantitative lung densitometry. Rheumatology (Oxford). 2022;61(7):2792–2804. DOI: 10.1093/rheumatology/keab828.
Tao Q., Zhu T., Ge X., Gong S., Guo J. The application value of spiral CT lung densitometry software in the diagnosis of radiation-induced lung injury. Contrast Media & Molecular Imaging. 2021;2021:e9305508. DOI: 10.1155/2021/9305508.
Carvalho A.R.S., Guimarães A.R., Sztajnbok F.R., Rodrigues R.S., Silva B.R.A., Lopes A.J. et al. Automatic quantifi cation of interstitial lung disease from chest computed tomography in systemic sclerosis. Front. Med. (Lausanne). 2020;7:577739. DOI: 10.3389/fmed.2020.577739.
Abuladze L.R., Blokhin I.A., Gonchar A.P., Suchilova M.M., Vladzymyrskyy A.V., Gombolevskiy V.A. et al. CT imaging of HIV-associated pulmonary disorders in COVID-19 pandemic. Clinical Imaging. 2023;95:97–106. DOI: 10.1016/j.clinimag.2023.01.006.
Richeldi L., Collard H.R., Jones M.G. Idiopathic pulmonary fi brosis. Lancet. 2017;389(10082):1941–1952. DOI: 10.1016/S0140-6736(17)30866-8.
Easthausen I., Podolanczuk A., Hoff man E., Kawut S., Oelsner E., Kim J.S. et al. Reference values for high attenuation areas on chest CT in a healthy, never-smoker, multi-ethnic sample: The MESA study. Respirology. 2020;25(8):855–862. DOI: 10.1111/resp.13783.
Richeldi L., Collard H.R., Jones M.G. Idiopathic pulmonary fi brosis. Lancet. 2017;389(10082):1941–1952. DOI: 10.1016/S0140-6736(17)30866-8.
Kim G.H.J., Weigt S.S., Belperio J.A., Brown M.S., Shi Y., Lai J.H. et al. Prediction of idiopathic pulmonary fi brosis progression using early quantitative changes on CT imaging for a short term of clinical 18–24-month follow-ups. Eur. Radiol. 2020;30(2):726–734. DOI: 10.1007/s00330-019-06402-6.
Best A.C., Meng J., Lynch A.M., Bozic C.M., Miller D., Grunwald G.K. et al. Idiopathic pulmonary fi brosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology. 2008;246(3):935–940. DOI: 10.1148/radiol.2463062200.
Humphries S.M., Mackintosh J.A., Jo H.E., Walsh S.L.F., Silva M., Calandriello L. et al. Quantitative computed tomography predicts outcomes in idiopathic pulmonary fi brosis. Respirology. 2022;27(12):1045–1053. DOI: 10.1111/resp.14333.
Jacob J., Bartholmai B.J., Rajagopalan S., Kokosi M., Nair A., Karwoski R. et al. Mortality prediction in idiopathic pulmonary fi brosis: evaluation of computer-based CT analysis with conventional severity measures. Eur. Respir. J. 2017;49(1):1601011. DOI: 10.1183/13993003.01011-2016.
De Giacomi F., Raghunath S., Karwoski R., Bartholmai B.J., Moua T. Short-term automated quantifi cation of radiologic changes in the characterization of idiopathic pulmonary fi brosis versus nonspecifi c interstitial pneumonia and prediction of long-term survival. J. Thorac. Imaging. 2018;33(2):124–131. DOI: 10.1097/RTI.0000000000000317.
Чучалин А.Г., Авдеев С.Н., Айсанов З.Р., Белевский А.С., Демура С.А., Илькович М.М. и др. Диагностика и лечение идиопатического легочного фиброза. Федеральные клинические рекомендации. Пульмонология. 2016;26(4):399–419. [Chuchalin A.G., Avdeev S.N., Aisanov Z.R., Belevskiy A.S., Demura S.A., Il’kovich M.M. et al. Diagnosis and Treatment of Idiopathic Pulmonary Fibrosis. Federal Guidelines. Pulmonologiya. 2016;26(4):399–419. (In Russ.)]. DOI: 10.18093/0869-0189-2016-26-4-399-419.
Ando K., Sekiya M., Tobino K., Takahashi K. Relationship between quantitative CT metrics and pulmonary function in combined pulmonary fi brosis and emphysema. Lung. 2013;191(6):585–591. DOI: 10.1007/s00408-013-9513-1.
Wisselink H.J., Pelgrim G.J., Rook M., van den Berge M., Slump K., Nagaraj Y. et al. Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study. BJR. 2020;93(1105):20181019. DOI: 10.1259/bjr.20181019.
Choromańska A., Macura K.J. Role of computed tomography in quantitative assessment of emphysema. Pol. J. Radiol. 2012;77(1):28–36. DOI: 10.12659/pjr.882578.
Гаврилов П.В., Грива Н.А., Торкатюк Е.А. Оценка воспроизводи- мости программного анализа объема эмфиземы: сравнительный анализ результатов при оценке различными программными продуктами. Лучевая диагностика и терапия. 2021;11(4):37–43. DOI: 10.22328/2079-5343-2020-11-4-37-43. [Gavrilov P.V., Griva N.A., Torkatyuk E.A. Evaluation of the interchangeability of volumetric lung emphysema quantifi cation: comparative analysis of the evaluation results using diff erent software products. Diagnostic radiology and radiotherapy. 2021;11(4):37–43. (In Russ.)]. DOI: 10.22328/2079-5343-2020-11-4-37-43.
Гомболевский В.А., Чернина В.Ю., Блохин И.А., Николаев А.Е., Барчук А.А., Морозов С.П. Основные достижения низкодозной компьютерной томографии в скрининге рака легкого. Туберкулез и болезни легких. 2021;99(1):61–70. [Gombolevskiy V.A., Chernina V.Yu., Blokhin I.A., Nikolaev A.E., Barchuk A.A., Morozov S.P. Main achievements of low-dose computed tomography in lung cancer screening. Tuberculosis and Lung Diseases. 2021;99(1):61–70. (In Russ.)]. DOI: 10.21292/2075-1230-2021-99-1-61-70.
Gierada D.S., Bierhals A.J., Choong C.K., Bartel S.T., Ritter J.H., Das N.A. et al. Eff ects of CT section thickness and reconstruction kernel on emphysema quantifi cation relationship to the magnitude of the CT emphysema index. Acad. Radiol. 2010;17(2):146–156. DOI: 10.1016/j.acra.2009.08.007.
Cao X., Jin C., Tan T., Guo Y. Optimal threshold in low-dose CT quantifi - cation of emphysema. Eur. J. Radiol. 2020;129:109094. DOI: 10.1016/j.ejrad.2020.109094.
Jin H., Heo C., Kim J.H. Deep learning-enabled accurate normalization of reconstruction kernel eff ects on emphysema quantifi cation in lowdose CT. Phys. Med Biol. 2019;64(13):135010. DOI: 10.1088/1361-6560/ab28a1.
Kim H., Goo J.M., Ohno Y., Kauczor H.U., Hoff man E.A., Gee J.C. et al. Eff ect of reconstruction parameters on the quantitative analysis of chest computed tomography. J. Thorac. Imaging. 2019;34(2):92–102. DOI: 10.1097/RTI.0000000000000389.
Nagaraj Y., Wisselink H.J., Rook M., Cai J., Nagaraj S.B., Sidorenkov G. et al. AI-driven model for automatic emphysema detection in low-dose computed tomography using disease-specifi c augmentation. J. Digit. Imaging. 2022;35(3):538–550. DOI: 10.1007/s10278-022-00599-7.
Bak S.H., Kim J.H., Jin H., Kwon S.O., Kim B., Cha Y.K. et al. Emphysema quantifi cation using low-dose computed tomography with deep learning-based kernel conversion comparison. Eur. Radiol. 2020;30(12):6779–6787. DOI: 10.1007/s00330-020-07020-3.
Эмфизема легких: Клинические рекомендации. Российское Респираторное общество; 2021. [Lung emphysema: Clinical guidelines. Russian Respiratory Society; 2021]. URL: https://spulmo.ru/upload/kr/Emfi zema_2021.pdf (13.04.2023).
Rea G., De Martino M., Capaccio A., Dolce P., Valente T., Castaldo S. et al. Comparative analysis of density histograms and visual scores in incremental and volumetric high-resolution computed tomography of the chest in idiopathic pulmonary fi brosis patients. Radiol. med. 2021;126(4):599–607. DOI: 10.1007/s11547-020-01307-7.
Sukhija A., Mahajan M., Joshi P.C., Dsouza J., Seth N.D.N., Patil K.H. Radiographic fi ndings in COVID-19: Comparison between AI and radiologist. Indian J. Radiol. Imaging. 2021;31(Suppl 1):S87–S93. DOI: 10.4103/ijri.IJRI_777_20.
Soyer P., Fishman E.K., Rowe S.P., Patlas M.N., Chassagnon G. Does artifi cial intelligence surpass the radiologist? Diagnostic and Interventional Imaging. 2022;103(10):445–447. DOI: 10.1016/j.diii.2022.08.001.
Colombi D., Bodini F.C., Petrini M., Maffi G., Morelli N., Milanese G. et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 Pneumonia. Radiology. 2020;296(2):E86–E96. DOI: 10.1148/radiol.2020201433.
Блохин И.А., Соловьев А.В., Владзимирский A.B., Коденко М.Р., Шумская Ю.Ф., Гончар А.П. и др. Автоматический анализ поражения легких при COVID-19: сравнение стандартной и низкодозной компьютерной томографии. Сибирский журнал клинической и экспериментальной медицины. 2022;37(4):114–123. [Blokhin I.A., Solovev A.V., Vladzymyrskyy A.V., Kodenko M.R., Shumskaya Yu.F., Gonchar A.P. et al. Automated analysis of lung lesions in COVID-19: comparison of standard and low-dose CT. The Siberian Journal of Clinical and Experimental Medicine. 2022;37(4):114–123. (In Russ.)]. DOI: 10.29001/2073-8552-2022-37-4-114-123.
Шатенок М.П., Рыжов С.А., Лантух З.А., Дружинина Ю.В., Толкачев К.В. Возможности программного обеспечения для мониторинга дозовой нагрузки пациентов в лучевой диагностике. Digital Diagnostics. 2022;3(3):212−230. [Shatenok M.P., Ryzhov S.A., Lantukh Z.A., Druzhinina Yu.V., Tolkachev K.V. Patient dose monitoring software in radiology. Digital Diagnostics. 2022;3(3):212−230. (In Russ.)]. DOI: 10.17816/DD106083.
Kodenko M.R., Vasilev Y.A., Vladzymyrskyy A.V., Omelyanskaya O.V., Leonov D.V., Blokhin I.A. et al. Diagnostic accuracy of ai for opportunistic screening of abdominal aortic aneurysm in CT: A systematic review and narrative synthesis. Diagnostics. 2022;12(12):3197. DOI: 10.3390/diagnostics12123197.
Sorantin E., Grasser M.G., Hemmelmayr A., Tschauner S., Hrzic F., Weiss V. et al. The augmented radiologist: artificial intelligence in the practice of radiology. Pediatr. Radiol. 2022;52(11):2074–2086. DOI: 10.1007/s00247-021-05177-7.
Gangeh M.J., Sørensen L., Shaker S.B., Kamel M.S., de Bruijne M., Loog M. A texton-based approach for the classification of lung parenchyma in CT images. Med. Image Comput. Comput. Assist. Interv. 2010;13(Pt. 3):595–602. DOI: 10.1007/978-3-642-15711-0_74.
Soffer S., Ben-Cohen A., Shimon O., Amitai M.M., Greenspan H., Klang E. Convolutional neural networks for radiologic images: A radiologist’s guide. Radiology. 2019;290(3):590–606. DOI: 10.1148/radiol.2018180547.
Soffer S., Morgenthau A.S., Shimon O., Barash Y., Konen E., Glicksberg B.S. et al. Artificial intelligence for interstitial lung disease analysis on chest computed tomography: A systematic review. Academic Radiology. 2022;29:S226–S235. DOI: 10.1016/j.acra.2021.05.014.
Aggarwal R., Sounderajah V., Martin G., Aggarwal R., Sounderajah V., Martin G. et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit. Med. 2021;4(1):1–23. DOI: 10.1038/s41746-021-00438-z.

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