Artificial intelligence to codify lung CT in Covid-19 patients

Belfiore, Maria Paola; Urraro, Fabrizio; Grassi, Roberta; Giacobbe, Giuliana; Patelli, Gianluigi; Cappabianca, Salvatore; Reginelli, Alfonso

doi:10.1007/s11547-020-01195-x

Cited by 100 publications

(89 citation statements)

References 15 publications

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“…This software is designed to quantify pulmonary emphysema in patients with chronic obstructive pulmonary disease. In our case, we analyzed the CT scans of patients with suspicious COVID-19 pneumonia by pre-setting threshold values of Hounsfield Unit in order to obtain a segmentation of both lungs and a quantitative evaluation of Emphysema (− 1024/− 977; blue) [ 18 ], residual healthy lung parenchyma (− 977/− 703; yellow) [ 19 ], GGO (− 703/− 368; pink) and consolidation (− 100/5; red) [ 20 – 22 ]. Finally, volumes for both right and left lungs were calculated (Fig.…”

Section: Methodsmentioning

confidence: 99%

Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis

et al. 2020

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Objective To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual scoring system to predict prognosis in patients with COVID-19 pneumonia. Materials and methods This study included 103 (41 women and 62 men; 68.8 years of mean age—range, 29–93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission in addition to clinical and laboratory findings recording. All chest CT examinations were reviewed using a structured report. Moreover, using an artificial intelligence tool we performed an automatic segmentation on CT images based on Hounsfield unit to calculate residual healthy lung parenchyma, ground-glass opacities (GGO), consolidations and emphysema volumes for both right and left lungs. Two expert radiologists, in consensus, attributed at the CT pulmonary disease involvement a severity score using a scale of 5 levels; the score was attributed for GGO and consolidation for each lung, and then, an overall radiological severity visual score was obtained summing the single score. Univariate and multivariate regression analysis was performed. Results Symptoms and comorbidities did not show differences statistically significant in terms of patient outcome. Instead, SpO2 was significantly lower in patients hospitalized in critical conditions or died while age, HS CRP, leukocyte count, neutrophils, LDH, d-dimer, troponin, creatinine and azotemia, ALT, AST and bilirubin values were significantly higher. GGO and consolidations were the main CT patterns (a variable combination of GGO and consolidations was found in 87.8% of patients). CT COVID-19 disease was prevalently bilateral (77.6%) with peripheral distribution (74.5%) and multiple lobes localizations (52.0%). Consolidation, emphysema and residual healthy lung parenchyma volumes showed statistically significant differences in the three groups of patients based on outcome (patients discharged at home, patients hospitalized in stable conditions and patient hospitalized in critical conditions or died) while GGO volume did not affect the patient's outcome. Moreover, the overall radiological severity visual score (cutoff ≥ 8) was a predictor of patient outcome. The highest value of R-squared (R2 = 0.93) was obtained by the model that combines clinical/laboratory findings at CT volumes. The highest accuracy was obtained by clinical/laboratory and CT findings model with a sensitivity, specificity and accuracy, respectively, of 88%, 78% and 81% to predict discharged/stable patients versus critical/died patients. Conclusion In conclusion, both CT visual score and computerized software-based quantification of the consolidation, emphysema and residual healthy lung parenchyma on chest CT images were independent predictors of outcome in patients with COVID-19 pneumonia.

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Section: Methodsmentioning

confidence: 99%

Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis

et al. 2020

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“…We believe that the combination of quantitative analysis data, lung volumes, and structured report items, with the help of deep learning techniques, will enable digital patient models to be extracted; therefore, the progression of the disease and the possible response to pharmacological treatments could be studied from these predictive and prognostic models. For this reason, many research groups have launched single and multicenter studies for the application of artificial intelligence on CT data [6,33,[51][52][53][54][55].…”

Section: Discussionmentioning

confidence: 99%

Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal

et al. 2020

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Objectives: The need of a standardized reporting scheme and language, in imaging of COVID-19 pneumonia, has been welcomed by major scientific societies. The aim of the study was to build the reporting scheme of chest CT in COVID-19 pneumonia. Methods: A team of experts, of the Italian Society of Medical and Interventional Radiology (SIRM), has been recruited to compose a consensus panel. They used a modified Delphi process to build a reporting scheme and expressed a level of agreement for each section of the report. To measure the internal consistency of the panelist ratings for each section of the report, a quality analysis based on the average inter-item correlation was performed with Cronbach's alpha (Cα) correlation coefficient. Results: The overall mean score of the experts and the sum of score were 3.1 (std.dev. ± 0.11) and 122 in the second round, and improved to 3.75 (std.dev. ± 0.40) and 154 in the third round. The Cronbach's alpha (Cα) correlation coefficient was 0.741 (acceptable) in the second round and improved to 0.789 in the third round. The final report was built in the management of radiology report template (MRRT) and includes n = 4 items in the procedure information, n = 5 items in the clinical information, n = 16 in the findings, and n = 3 in the impression, with overall 28 items. Conclusions: The proposed structured report could be of help both for expert radiologists and for the less experienced who are faced with the management of these patients. The structured report is conceived as a guideline, to recommend the key items/findings of chest CT in COVID-19 pneumonia.

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“…It provides the classification of voxels based on Hounsfield Units and a color-coded display of the segmented regions. We analyzed the CT scans by pre-setting a threshold value of Hounsfield Units in order to obtain a quantification for both lungs of emphysema ( − 1024/ − 977; blue) [ 20 ], of healthy residual lung parenchyma ( − 977/ − 703; yellow) [ 21 , 22 ], of GGO ( − 703/ − 368; pink) and of consolidation (− 100/5; red) [ 21 , 23 – 25 ] (Fig. 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, the diagnosis of viral pneumonia based on chest CT may be used to recommend patient isolation. Further, chest CT has an important role in the management of patients with suspected SARSCoV-2 infection, identifying patients with severe lung involvement of viral pneumonia at CT; this could direct the clinician versus an anticipation of mild invasive ventilation that has been demonstrated effective in reducing severity of pneumonia [18][19][20][21][22][23][24][25][26][27]. The main CT characteristics reported in the recent literature and individuated in patients affected by COVID-19 pneumonia were ground-glass opacity (GGO), consolidation, reticulation/thickened interlobular septa, nodules and lesion distribution in left, right or bilateral lungs [17][18][19].…”

Section: Introductionmentioning

confidence: 99%