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      Quantitative analysis in COVID-19: report of an initial experience

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      Einstein
      Instituto Israelita de Ensino e Pesquisa Albert Einstein

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          Abstract

          A 63-year-old man came to the emergency room complaining of unverified fever and myalgia. Oropharyngeal material was collected for reverse transcription testing followed by polymerase chain reaction (RT-PCR) for coronavirus disease 2019 (COVID-19), and a chest radiography was performed (normal), and the patient was discharged to home isolation, due to his mild symptoms, until the test result. After 3 days, the patient evolved with dyspnea, a drop in oxygen saturation (95%), and measured fever (38.6°C), and a chest computed tomography was performed (Figure 1) and he was admitted to hospital. The RT-PCR test for COVID-19 was positive. Three days later, his respiratory pattern worsened, with a decrease in oxygen saturation to 90%, and he was referred to a stepdown unit and a new tomography was performed (Figure 1). An 80-row CT scanner (Aquillion Prime, Canon Medical Systems, Tochigi, Japan) was used, with the patient in supine position, during maximum inspiration, and without injection of contrast medium. The following parameters were used: reconstructions with slice-thickness of 1 mm, a tube voltage of 80 kVp to 120 kVp, and adjustable current, varying between 10 mA and 440 mA. The images of these two exams were then processed, using the 3DSlicer software to segment the normal parenchyma, ground-glass opacities and consolidation areas in both lungs in the two exams performed for the patient (Figure 1). From this, a quantitative analysis was conducted, showing in the first study a total lung volume of 4,289.62cm3, with the right lung measuring 2,214.91cm3 and the left lung measuring 2,074.71cm3. The preserved parenchyma area measured 2,110.48cm3 (95.29%) in the right lung, and 2,056.79cm3 (99.14%) in the left lung. The right lung presented with 81.01cm3 (3.66%) of ground-glass opacities and 23.42cm3 (1.06%) of consolidations; the left lung presented with 14.98cm3 (0.72%) of ground-glass opacities, and 2.95cm3 (0.14%) of consolidations. In total, the patient had 2.85% of parenchyma affected by ground-glass opacities or consolidations in the first study. In the second study, the total lung volume calculated was 3,569.85cm3, with 1,814.95cm3 in the right lung and 1,754.90cm3 in the left lung. The preserved parenchyma area measured 915.17cm3 (50.42%) in the right lung, and 1,301.17cm3 (74.15%) in the left lung. The ground-glass opacity areas totaled 857.49cm3 (47.25%) in the right lung, and 447.63cm3 (25.51%) in the left lung, and the consolidation areas totaled 42.29cm3 (2.33%) in the right lung, and 6.09cm3 (0.35%) in the left lung. In three days of progression, the patient showed an increase of 1,259.93% in ground-glass opacity volume, with 958.47% in the right lung, and 2,888.06% in the left lung, and an 83.44% increase in the consolidation volume, with 80.57% in the right lung, and 106.66% in the left lung. These numbers resulted in a 46.81% reduction in the preserved parenchyma volume, with a reduction of 56.64% in the right lung, and 36.74% in the left lung. DISCUSSION Several cases of pneumonia of unknown origin that occurred in Wuhan, China, in late 2019, led to the discovery of a new type of coronavirus (2019-nCoV), called novel coronavirus-infected pneumonia (COVID-19).(1-4) The virus quickly spread and started to affect individuals outside the initial contagion area, in other countries and, finally, on all continents, and was declared a pandemic by the World Health Organization (WHO).(1-4) The 3DSlicer software is a free tool available online for download, whose use in quantitative imaging is well-established, having even been used in the evaluation of pulmonary nodules in chest imaging.(5-7) Much has been studied since the beginning of the pandemic about the role of imaging tests in the prognosis and progressive control of COVID-19 patients, but a forceful answer has yet to be found. Our service, for instance, has been using the assessment of the tomographic progression of the disease as an auxiliary criterion in the clinical decision of hospitalization. The present case demonstrates the use of the 3DSlicer tool for the quantification of pulmonary tomographic changes, applied in the clinical monitoring of the patient, enabling an objective estimation of the involvement percentage and the progression rate of the disease. We believe that this tool can be an important resource for borderline cases or those that raise doubts about the significance of the progression. In addition, its association with artificial intelligence strategies can optimize the quantification process, rendering it possible the use of this quantification in a greater number of cases. Figure 1 Chest computed tomography and superimposed 3DSlicer software quantification images. The upper series (A) show the findings when the patient returned to the emergency room, and the lower series (B) show the findings at the time of his clinical worsening. Axial sections of the chest tomography showing multifocal pulmonary ground-glass opacities predominantly peripheral and basal, more extensive in the last study, and quantitative images generated by the 3DSlicer software superimposed over the tomographic images. The areas marked in yellow show ground-glass opacities, those marked in green are areas of normal parenchyma, and those marked in orange are areas of consolidation. The extensive progression of the findings illustrates the numerical data provided

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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            Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

            Purpose Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. Methods CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. Results The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10−16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. Conclusion Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point.
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              Author and article information

              Journal
              Einstein (Sao Paulo)
              eins
              Einstein
              Instituto Israelita de Ensino e Pesquisa Albert Einstein
              1679-4508
              2317-6385
              02 October 2020
              2020
              : 18
              : eAI5842
              Affiliations
              [1 ] orgnameHospital Israelita Albert Einstein São Paulo SP Brazil originalHospital Israelita Albert Einstein, São Paulo, SP, Brazil.
              [1 ] Brasil original Hospital Israelita Albert Einstein, São Paulo, SP, Brasil.
              Author notes
              Corresponding author: Eduardo Kaiser Ururahy Nunes Fonseca. Avenida Albert Einstein, 627/701 – Morumbi. Zip code: 05652-900 – São Paulo, SP, Brazil. Phone: (55 11) 2151-1233. E-mail: eduardo.ururahy@ 123456einstein.br
              Autor correspondente: Eduardo Kaiser Ururahy Nunes Fonseca. Avenida Albert Einstein, 627/701 – Morumbi. CEP: 05652-900 – São Paulo, SP, Brasil. Tel.: (11) 2151-1233. E-mail: eduardo.ururahy@einstein.br
              Author information
              https://orcid.org/0000-0003-2448-5826
              https://orcid.org/0000-0002-0233-0041
              https://orcid.org/0000-0002-1941-7899
              https://orcid.org/0000-0002-4193-7647
              Article
              00705
              10.31744/einstein_journal/2020AI5842
              7531899
              6af5c10d-fb3e-4f99-a6fd-a64bcf10eaf0

              This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

              History
              : 10 May 2020
              : 27 July 2020
              Page count
              Figures: 2, Tables: 0, Equations: 0, References: 7
              Categories
              Learning by Images

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