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Koronavirüs hastalığı 2019 ve diğer viral pnömonilerde tespit edilen buzlu cam opasitelerinin bilgisayarlı tomografi doku analizi bulgularının karşılaştırılması

Yıl 2025, Cilt: 35 Sayı: 6, 1063 - 1071, 31.12.2025
https://doi.org/10.54005/geneltip.1452582

Öz

Amaç: Koronavirüs Hastalığı 2019’da (COVID-19) ve diğer viral pnömonilerde tespit edilen buzlu cam opasitelerini (BCO) ayırt etmede bilgisayarlı tomografi doku analizi (BTTA) bulgularının yararlılığını araştırmak.
Materyaller ve Yöntemler: COVID-19 ve COVID-19 dışı viral pnömoni için torasik bilgisayarlı tomografi (BT) incelemesi yapılan 120 hasta çalışmaya dahil edildi. Akciğer parankiminin heterojenitesi BT doku analizi özel yazılımı (Olea Medical, Fransa) ile değerlendirildi. CTTA, 8 first order intensitesi ve 3 gri seviye co-occurrence matrikse dayalı özellikleri içeriyordu.
Bulgular: Kontrast değeri COVID-19 BCO’da daha yüksekti (p = 0,006). Ortalama, medyan, skewness ve inverse difference moment (IDM) değerleri COVID-19 dışı BCO’da daha yüksekti (sırasıyla p değerleri 0, 0.001, 0.021 ve 0.006), ortalama mutlak sapma, varyans ve kontrast değerleri COVID-19 hastalarının normal akciğer parankimasında daha yüksekti (sırasıyla p değerleri 0.011, 0.023 ve 0.006). Her iki grupta hem BCO’dan hem de normal akciğer parankimasından elde edilen doku parametrelerinin ROC analizinde, doku parametrelerinin duyarlılık değerleri %58,3 ile %66,7 arasında değişirken, özgüllük değerleri %50 ile %66,7 arasında değişti.
Sonuç: BTTA parametreleri olan ortalama, medyan, skewness ve IDM değerleri COVID-19 ve COVID-19 dışı viral pnömonileri ayırt etmek için kullanılabilir.

Kaynakça

  • 1. Han R, Huang L, Jiang H, et al. Early Clinical and CT Manifestations of Coronavirus Disease 2019 (COVID-19) Pneumonia. AJR Am J Roentgenol. 2020;215:338-43.
  • 2. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507-13.
  • 3. Song F, Shi N, Shan F, et al. Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology. 2020;295:210-7.
  • 4. Rubin EJ, Baden LR, Morrissey S, Campion EW. Medical Journals and the 2019-nCoV Outbreak. N Engl J Med. 2020;382:866.
  • 5. Loeffelholz MJ, Tang YW. Laboratory diagnosis of emerging human coronavirus infections - the state of the art. Emerg Microbes Infect. 2020;9:747-56.
  • 6. Chung M, Bernheim A, Mei X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020;295:202-7.
  • 7. Shi H, Han X, Zheng C. Evolution of CT Manifestations in a Patient Recovered from 2019 Novel Coronavirus (2019-nCoV) Pneumonia in Wuhan, China. Radiology. 2020;295:20.
  • 8. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics. 2017;37:1483-503.
  • 9. Park YS, Seo JB, Kim N, et al. Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test. Invest Radiol. 2008;43:395-402.
  • 10. Digumarthy SR, Padole AM, Lo Gullo R, et al. CT texture analysis of histologically proven benign and malignant lung lesions. Medicine (Baltimore). 2018;97.
  • 11. Kloth C, Henes J, Xenitidis T, et al. Chest CT texture analysis for response assessment in systemic sclerosis. Eur J Radiol. 2018;101:50-8.
  • 12. Kloth C, Thaiss WM, Beck R, et al. Potential role of CT-textural features for differentiation between viral interstitial pneumonias, pneumocystis jirovecii pneumonia and diffuse alveolar hemorrhage in early stages of disease: a proof of principle. BMC Med Imaging. 2019;19:39.
  • 13. Corman VM, Landt O, Kaiser M, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25.
  • 14. Fang Y, Zhang H, Xie J, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020;296.
  • 15. Ai T, Yang Z, Hou H, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296.
  • 16. Zu ZY, Jiang MD, Xu PP, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology. 2020;296.
  • 17. Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. AJR Am J Roentgenol. 2020;215:87-93.
  • 18. Cheng Z, Lu Y, Cao Q, et al. Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China. AJR Am J Roentgenol. 2020;215:121-6.
  • 19. Hani C, Trieu NH, Saab I, et al. COVID-19 pneumonia: A review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 2020;101:263-8.
  • 20. Tanaka N, Matsumoto T, Kuramitsu T, et al. High resolution CT findings in community-acquired pneumonia. J Comput Assist Tomogr. 1996;20:600-8.
  • 21. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266:177-84.
  • 22. Yip C, Landau D, Kozarski R, et al. Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. Radiology. 2014;270:141-8.
  • 23. Zhang H, Graham CM, Elci O, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology. 2013;269:801-9.
  • 24. Park SO, Seo JB, Kim N, et al. Comparison of usual interstitial pneumonia and nonspecific interstitial pneumonia: quantification of disease severity and discrimination between two diseases on HRCT using a texture-based automated system. Korean J Radiol. 2011;12:297-307.
  • 25. Wei W, Hu XW, Cheng Q, Zhao YM, Ge YQ. Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics. Eur Radiol. 2020;30:6788-96.
  • 26. Yasar H, Ceylan M. A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods. Multimed Tools Appl. 2021;80:5423-47.
  • 27. Fanni SC, Volpi F, Colligiani L, et al. Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters. Diagnostics (Basel). 2024;14.
  • 28. Nagpal P, Guo J, Shin KM, et al. Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia. BJR Open. 2021;3:20200043.
  • 29. Gaudêncio AS, Vaz PG, Hilal M, et al. Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy. Biomed Signal Process Control. 2021;68:102582.

Comparison of computed tomography texture analysis findings of ground glass opacities detected in coronavirus disease 2019 and other viral pneumonias

Yıl 2025, Cilt: 35 Sayı: 6, 1063 - 1071, 31.12.2025
https://doi.org/10.54005/geneltip.1452582

Öz

Aim: To investigate the utility of computed tomography texture analysis (CTTA) findings in differentiating ground glass opacities (GGO) detected in Coronavirus Disease 2019 (COVID-19) and other viral pneumonias.

Methods: 120 patients who underwent thoracic computed tomography (CT) examination for COVID-19 and non-COVID-19 viral pneumonia were included in the study. The heterogeneity of the lung parenchyma was evaluated with CT tissue analysis special software (Olea Medical, France). CTTA included features based on 8 first order intensity and 3 gray level co-occurrence matrix.

Results: Contrast value was higher in COVID-19 GGO (p = 0.006). Mean, median, skewness and inverse difference moment (IDM) values were higher in non-COVID-19 GGO (p values 0, 0.001, 0.021 and 0.006, respectively) mean absolute deviation, variance and contrast values were higher in normal lung parenchyma of COVID-19 patients (p values 0.011, 0.023 and 0.006, respectively). In the ROC analysis of tissue parameters obtained from both GGO and normal lung parenchyma in both groups, the sensitivity values of tissue parameters ranged from 58.3% to 66.7%, while the specificity values ranged from 50% to 66.7%.

Conclusion: Mean, median, skewness and IDM values, which are CTTA parameters, can be used to distinguish COVID-19 and non- COVID-19 viral pneumonias.

Kaynakça

  • 1. Han R, Huang L, Jiang H, et al. Early Clinical and CT Manifestations of Coronavirus Disease 2019 (COVID-19) Pneumonia. AJR Am J Roentgenol. 2020;215:338-43.
  • 2. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507-13.
  • 3. Song F, Shi N, Shan F, et al. Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology. 2020;295:210-7.
  • 4. Rubin EJ, Baden LR, Morrissey S, Campion EW. Medical Journals and the 2019-nCoV Outbreak. N Engl J Med. 2020;382:866.
  • 5. Loeffelholz MJ, Tang YW. Laboratory diagnosis of emerging human coronavirus infections - the state of the art. Emerg Microbes Infect. 2020;9:747-56.
  • 6. Chung M, Bernheim A, Mei X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020;295:202-7.
  • 7. Shi H, Han X, Zheng C. Evolution of CT Manifestations in a Patient Recovered from 2019 Novel Coronavirus (2019-nCoV) Pneumonia in Wuhan, China. Radiology. 2020;295:20.
  • 8. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics. 2017;37:1483-503.
  • 9. Park YS, Seo JB, Kim N, et al. Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test. Invest Radiol. 2008;43:395-402.
  • 10. Digumarthy SR, Padole AM, Lo Gullo R, et al. CT texture analysis of histologically proven benign and malignant lung lesions. Medicine (Baltimore). 2018;97.
  • 11. Kloth C, Henes J, Xenitidis T, et al. Chest CT texture analysis for response assessment in systemic sclerosis. Eur J Radiol. 2018;101:50-8.
  • 12. Kloth C, Thaiss WM, Beck R, et al. Potential role of CT-textural features for differentiation between viral interstitial pneumonias, pneumocystis jirovecii pneumonia and diffuse alveolar hemorrhage in early stages of disease: a proof of principle. BMC Med Imaging. 2019;19:39.
  • 13. Corman VM, Landt O, Kaiser M, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25.
  • 14. Fang Y, Zhang H, Xie J, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020;296.
  • 15. Ai T, Yang Z, Hou H, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296.
  • 16. Zu ZY, Jiang MD, Xu PP, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology. 2020;296.
  • 17. Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. AJR Am J Roentgenol. 2020;215:87-93.
  • 18. Cheng Z, Lu Y, Cao Q, et al. Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China. AJR Am J Roentgenol. 2020;215:121-6.
  • 19. Hani C, Trieu NH, Saab I, et al. COVID-19 pneumonia: A review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 2020;101:263-8.
  • 20. Tanaka N, Matsumoto T, Kuramitsu T, et al. High resolution CT findings in community-acquired pneumonia. J Comput Assist Tomogr. 1996;20:600-8.
  • 21. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266:177-84.
  • 22. Yip C, Landau D, Kozarski R, et al. Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. Radiology. 2014;270:141-8.
  • 23. Zhang H, Graham CM, Elci O, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology. 2013;269:801-9.
  • 24. Park SO, Seo JB, Kim N, et al. Comparison of usual interstitial pneumonia and nonspecific interstitial pneumonia: quantification of disease severity and discrimination between two diseases on HRCT using a texture-based automated system. Korean J Radiol. 2011;12:297-307.
  • 25. Wei W, Hu XW, Cheng Q, Zhao YM, Ge YQ. Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics. Eur Radiol. 2020;30:6788-96.
  • 26. Yasar H, Ceylan M. A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods. Multimed Tools Appl. 2021;80:5423-47.
  • 27. Fanni SC, Volpi F, Colligiani L, et al. Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters. Diagnostics (Basel). 2024;14.
  • 28. Nagpal P, Guo J, Shin KM, et al. Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia. BJR Open. 2021;3:20200043.
  • 29. Gaudêncio AS, Vaz PG, Hilal M, et al. Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy. Biomed Signal Process Control. 2021;68:102582.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bulaşıcı Hastalıklar, Göğüs Hastalıkları, Radyoloji ve Organ Görüntüleme
Bölüm Araştırma Makalesi
Yazarlar

Nusret Seher 0000-0003-2296-556X

Mehmet Öztürk 0000-0001-5585-1476

Mustafa Koplay 0000-0001-7513-4968

Abidin Kılınçer 0000-0001-6027-874X

Burcu Yormaz 0000-0001-6563-8337

Halil Özer 0000-0003-1141-1094

Nazlım Aktuğ Demir 0000-0002-4703-0827

Emine Uysal 0000-0001-8533-4939

Hakan Cebeci 0000-0002-2017-3166

Gönderilme Tarihi 20 Ağustos 2024
Kabul Tarihi 16 Haziran 2025
Yayımlanma Tarihi 31 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 35 Sayı: 6

Kaynak Göster

Vancouver Seher N, Öztürk M, Koplay M, Kılınçer A, Yormaz B, Özer H, vd. Comparison of computed tomography texture analysis findings of ground glass opacities detected in coronavirus disease 2019 and other viral pneumonias. Genel Tıp Derg. 2025;35(6):1063-71.