Quantitative CT Extent of Lung Damage in COVID-19 Pneumonia Is an Independent Risk Factor for Inpatient Mortality in a Population of Cancer Patients: A Prospective Study

Ramtohul, Toulsie; Cabel, Luc; Paolettí, Xavier; Chiche, Laurent; Moreau, Pauline; Noret, Aurélien; Vuagnat, Perrine; Chérel, P.; Tardivon, A.; Cottu, Paul; Bidard, François-Clément; Servois, Vincent

doi:10.3389/fonc.2020.01560

Cited by 13 publications

(17 citation statements)

References 30 publications

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“…Previous studies have reported that age is a risk factor for severe COVID-19. ( Cheung et al, 2005 ; Ramtohul et al, 2020 ; Nawar et al, 2020 ) Fei Zhou et al analyzed 191 patients with severe COVID-19 requiring hospitalization. In this cohort, patients had a median age of 56.0 years.…”

Section: Discussionmentioning

confidence: 99%

Machine learning predictive model for severe COVID-19

Kang

Chen

Luo

et al. 2021

Infection, Genetics and Evolution

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Section: Discussionmentioning

confidence: 99%

Machine learning predictive model for severe COVID-19

Kang

Chen

Luo

et al. 2021

Infection, Genetics and Evolution

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“…Nineteen papers developed models for the prognosis of patients with COVID-19 51,[63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] , fifteen using CT and four using CXR. These models were developed for predicting severity of outcomes including: death or need for ventilation 72,78,79 , a need for intensive care unit (ICU) admission 63,73,[77][78][79] , progression to acute respiratory distress syndrome 80 , the length of hospital stay 51,74 , likelihood of conversion to severe disease 64,65,75 and the extent of lung infection 76 . Most papers used models based on a multivariable Cox proportional hazards model 51,72,78,79 , logistic regression 65,[73][74][75]80 , linear regression 75,76 , random forest 74,7...…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…These models were developed for predicting severity of outcomes including: death or need for ventilation 72,78,79 , a need for intensive care unit (ICU) admission 63,73,[77][78][79] , progression to acute respiratory distress syndrome 80 , the length of hospital stay 51,74 , likelihood of conversion to severe disease 64,65,75 and the extent of lung infection 76 . Most papers used models based on a multivariable Cox proportional hazards model 51,72,78,79 , logistic regression 65,[73][74][75]80 , linear regression 75,76 , random forest 74,77 or compare a huge variety of machine learning models such as tree-based methods, support vector machines, neural networks and nearestneighbour clustering 63,64 .…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…Predictors from radiological data were extracted using either handcrafted radiomic features 63,64,[68][69][70]72,74,75,[77][78][79][80] or deep learning 51,66,70,71,73,76 . Clinical data included basic observations, serology and comorbidities.…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…Clinical data included basic observations, serology and comorbidities. Only eight models integrated both radiological and clinical data 62,63,69,72,73,[77][78][79] .…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

See 2 more Smart Citations

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

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Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study

Hoz

Kuo

et al. 2022

J Digit Imaging

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The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83–0.87), 0.76 (0.70–0.82), and 0.95 (0.92–0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

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Quantitative CT Extent of Lung Damage in COVID-19 Pneumonia Is an Independent Risk Factor for Inpatient Mortality in a Population of Cancer Patients: A Prospective Study

Cited by 13 publications

References 30 publications

Machine learning predictive model for severe COVID-19

Machine learning predictive model for severe COVID-19

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study

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