Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches

Zorzi, Giulia; Berta, Luca; Rizzetto, Francesco; Mattia, Cristina De; Felisi, M.; Carrazza, Stefano; Molteni, Silvia Nerini; Vismara, Chiara; Scaglione, Francesco; Vanzulli, Angelo; Torresin, A.; Colombo, P.E.

doi:10.1186/s41747-022-00317-6

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Radiomics analysis of computed chest tomographic images appears able to differentiate COVID-19 from other viral pneumonias. 84 Whether the heterogeneity found in COVID-19 that kills previously healthy young adults as well as the elderly with comorbid illness is unique remains unknown.…”

Section: Discussionmentioning

confidence: 99%

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

Murthy,

Gordon,

Lowry

et al. 2024

BMJ Open

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ObjectiveIn order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as the SARS-CoV-2 pandemic progressed, document their changing response to the virus and its variants over time, and identify factors most importantly associated with mortality after hospital admission.DesignObservational study using a prospective hospital systemwide COVID-19 database.Setting15-hospital US health system.Participants26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022.Main outcome measures60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission.ResultsHospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant.ConclusionsHospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients’ demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.

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Section: Discussionmentioning

confidence: 99%

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

Murthy,

Gordon,

Lowry

et al. 2024

BMJ Open

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“…Radiomics data were extracted with Pyradiomics, which is an open-source software implemented in Python 3.6 able to extract radiomics features from two-or three-dimensional medical images [22]. This platform has been widely used by several researchers to evaluate the predictive value of radiomics in several diseases including SARS-CoV-2 pneumonia [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection

Ciarmiello,

Tutino,

Giovannini

et al. 2023

JCM

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Aim: To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance. Results: Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9–37), while the low-risk group did not reach the median survival of 50% (p < 0.001). Conclusions: A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.

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A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia

Jiang,

Chen,

Zhang

et al. 2024

BMC Med Imaging

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Background Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model for the early detection of SCAP to enable timely intervention and improve patient outcomes. Methods A retrospective study was conducted on 115 CAP and SCAP patients at Southern Medical University Shunde Hospital from January to December 2021. Using the Pyradiomics package, 107 radiomic features were extracted from CT scans, refined via intra-class and inter-class correlation coefficients, and narrowed down using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The predictive performance of the radiomics-based model was assessed through receiver operating characteristic (ROC) analysis, employing machine learning classifiers such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), trained and validated on datasets split 7:3, with a training set ( n = 80) and a validation set ( n = 35). Results The radiomics model exhibited robust predictive performance, with the RF classifier achieving superior precision and accuracy compared to LR, SVM, and KNN classifiers. Specifically, the RF classifier demonstrated a precision of 0.977 (training set) and 0.833 (validation set), as well as an accuracy of 0.925 (training set) and 0.857 (validation set), suggesting its superior performance in both metrics. Decision Curve Analysis (DCA) was utilized to evaluate the clinical efficacy of the RF classifier, demonstrating a favorable net benefit within the threshold ranges of 0.1 to 0.8 for the training set and 0.2 to 0.7 for the validation set. Conclusions The radiomics model developed in this study shows promise for early SCAP detection and can improve clinical decision-making.

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Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches

Cited by 5 publications

References 42 publications

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection

A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia

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