Nilai Hounsfield Unit (HU) CT-Scan pada Lesi Paru-Paru Pasien Suspek COVID-19
Mahfud Edy Widiatmoko(1*), Shelsa Ramadhanti(2)
(1) Jurusan Teknik Radiodiagnostik dan Radioterapi, Poltekkes Kemenkes Jakarta II
(2) Program Studi Teknologi Radiologi Pencitraan, Poltekkes Kemenkes Jakarta II
(*) Corresponding Author
Abstract
Latar Belakang: National Health Commission of China menyatakan bahwa Computed Tomography (CT) memiliki peranan penting dalam hal menegakkan diagnosis dan pemantauan prognosis pada pasien COVID-19 karena memiliki sensitivitas diagnostik tinggi sebesar 97,2% dan menjadi pelengkap dari pengujian RT-PCR. Gambaran CT thorax pada pasien dengan lesi paru-paru suspek COVID-19 terlihat nodul konsolidasi dan Ground Glass Opacities (GGO) di sebagian area. Sebaran nodul GGO pada COVID-19 diklasifikasikan dengan istilah CO-RADS. Karakteristik nodul lesi dapat dianalisis kepadatan jaringan dengan nilai Hounsfield Unit (HU).
Tujuan: Mengetahui nilai Houndsfield Unit (HU) CT pada lesi paru-paru pasien suspek COVID-19 berdasarkan kategori CO-RADS.
Metode: Penelitian ini menggunakan studi cross‑sectional berdasarkan data sekunder hasil rekonstruksi gambar pemeriksaan CT thorax dengan klinis suspek pneumonia COVID-19 tahun 2021 dan jumlah sampel 40 kasus.
Hasil: Hasil rata-rata nilai HU pada kategori CO-RADS 4, 5, dan 6, berturut-turut, -203,00 HU, -168,97 HU), dan -133,57 HU). Berdasarkan uji statistik, nilai p < 0,05 yang artinya bahwa rata-rata nilai HU ketiga kategori CO-RADS berbeda secara signifikan.
Kesimpulan: Ada beda tingkatan klasifikasi CO-RADS 4-6, yaitu bahwa semakin tinggi tingkatan kategori CO-RADS, semakin tinggi pula nilai HU CT pada lesi paru-paru.Keywords
Full Text:
PDFReferences
Caruso, et al (2020) ‘Chest CT Features of COVID-19 in Rome, Italy’, Radiology, 296(2), pp. E79–E85. Available at: https://doi.org/10.1148/radiol.2020201237.
Chaolin Huang et. al (2020) ‘Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China’, The Lancet, 395(20), pp. 497–506.
Chattopadhyay, S. (2022) ‘An Approach to Identify Regions of Interest in Chest X-Ray Images of COVID-19 Patients and Its Clinical Validation: An Indian Study’, Artificial Intelligence Evolution, 3(1), pp. 41–53. Available at: https://doi.org/10.37256/aie.3120221331.
Chenyang, L. et al. (2015) ‘Automatic detection of the pulmonary nodules from CT images’, IntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference, pp. 742–746. Available at: https://doi.org/10.1109/IntelliSys.2015.7361223.
Hafsa, N.E. (2021) Diagnostic tools and automated decision support systems for COVID-19, Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology. Elsevier Inc. Available at: https://doi.org/10.1016/B978-0-323-90959-4.00002-X.
Heshui, S. et al. (2020) ‘Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study’, Lancet Infect Dis, 20(4), pp. 19–21.
Jasper Fuk-Woo Chan, et. a. (2020) ‘A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster’, The Lancet, 395(10223), pp. 514–523. Available at: https://doi.org/10.1016/S0140-6736(20)30154-9.
Jung, H. (2021) ‘Basic Physical Principles and Clinical Applications of Computed Tomography’, Progress in Medical Physics, 32(1), pp. 1–17. Available at: https://doi.org/10.14316/pmp.2021.32.1.1.
Prokop, M. et al. (2020) ‘CO-RADS-A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation Original research’, Radiology, (1), pp. 1–37.
Putra, G.D. et al. (2022) ‘Analisis Nilai Ct-Number Pada Ct-Scan Thorax Dengan Kasus Covid-19’, JRI (Jurnal Radiografer Indonesia), 5(1), pp. 15–20. Available at: https://doi.org/10.55451/jri.v5i1.102.
Qi, C. et al. (2020) ‘Diagnostic technologies for COVID-19: a review’, RSC Advances, 10(58), pp. 35257–35264. Available at: https://doi.org/10.1039/d0ra06445a.
Santura, I. et al. (2021) ‘Chest computed tomography versus rt-pcr in early diagnostics of covid-19 – a systematic review with meta-analysis’, Polish Journal of Radiology, 86(1), pp. 518–531. Available at: https://doi.org/10.5114/pjr.2021.109074.
Sun, P. et al. (2020) ‘Understanding of COVID-19 based on current evidence’, Journal of Medical Virology, 92(6), pp. 548–551. Available at: https://doi.org/10.1002/jmv.25722.
Torretta, S. et al. (2021) ‘Diagnosis of SARS-CoV-2 by RT-PCR Using Different Sample Sources: Review of the Literature’, Ear, Nose and Throat Journal, 100(2_suppl), pp. 131S-138S. Available at: https://doi.org/10.1177/0145561320953231.
Vikram Rao, B. et al. (2021) ‘The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital’, Insights into Imaging, 12(1). Available at: https://doi.org/10.1186/s13244-020-00957-5.
Xingzhi Xie, MD, et. a. (2020) ‘Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing’, Radiology, 296(2), pp. E41–E45. Available at: https://doi.org/10.1148/radiol.2020200343.
Yicheng Fang, MD, et. a. (2020) ‘Sensitivity of Chest CT for COVID.19: Comparasion to RT.PCR’, Radiology, 296, pp. 15–17.
Zhao, W. et al. (2020) ‘Relation between chest CT findings and clinical conditions of coronavirus disease (covid-19) pneumonia: A multicenter study’, American Journal of Roentgenology, 214(5), pp. 1072–1077. Available at: https://doi.org/10.2214/AJR.20.22976.
DOI: https://doi.org/10.22146/jkesvo.78738
Article Metrics
Abstract views : 2973 | views : 1763Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Jurnal Kesehatan Vokasional
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Jurnal Kesehatan Vokasional with registered number ISSN 2541-0644 (print), ISSN 2599-3275 (online) published by the Departement of Health Information Management and Services, Vocational College, Universitas Gadjah Mada