Integrative analysis for COVID-19 patient outcome prediction

Chao, Hanqing; Fang, Xi; Zhang, Jiajin; Homayounieh, Fatemeh; Arru, Chiara; Digumarthy, Subba R.; Babaei, Rosa; Mobin, Hadi Karimi; Mohseni, Iman; Saba, Luca; Carriero, Alessandro; Falaschi, Zeno; Paschè, Alessio; Wang, Ge; Kalra, Mannudeep K.; Yan, Pingkun

doi:10.1016/j.media.2020.101844

Cited by 71 publications

(72 citation statements)

References 27 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the scores estimated by radiologists and out AI methods, we also include another two groups of scores, i.e., threshold-based scores, and the mixture of radiologists’ scores and the AI-based scores. As HU corresponds to GGO regions [ 2 , 11 ], the method regards voxels within this threshold as pulmonary opacities and further calculates the severity scores. To investigate whether the scores of our AI method are complementary with those of the radiologists on patient outcome prediction, we merged their scores (denoted as AI + Radiologists).…”

Section: Ai-assisted Severity Assessmentmentioning

confidence: 99%

Association of AI quantified COVID-19 chest CT and patient outcome

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purpose Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. Methods We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). Results AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, p < 0.001). Using AI-based scores produced significantly higher ( p < 0.05) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. Conclusions Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

show abstract

Section: Ai-assisted Severity Assessmentmentioning

confidence: 99%

Association of AI quantified COVID-19 chest CT and patient outcome

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A number of studies applied deep or machine learning algorithms for COVID-19 outbreak prediction, detection/segmentation of infected pneumonia regions from radiologic images, as well as new drug development and disease screening [ [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] ]. In diagnostic studies, artificial intelligence approaches have been applied to various medical imaging modalities, including radiography, ultrasound, and CT to build more accurate detection/diagnostic models [ 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Shiri

Sorouri

Geramifar

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

Objective To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Methods Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. Results For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). Conclusion Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

show abstract

“…Pinkung et al did an integrative analysis for COVID-19 patient outcome prediction showing that radiomics features describing texture and change of pulmonary opacities in combination with laboratory and demographic data can significantly increase the performance of prediction for a need of ICU admission by an AUC up to 0,884 and sensitivity of 96,1% [34] . We found several correlations between image-based severity measures (e.g.…”

Section: Discussionmentioning

confidence: 99%

Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

et al. 2021

View full text Add to dashboard Cite

Objectives : To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data. Methods : We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into subcohorts with or without necessity of intensive care unit (ICU) treatment. Subcohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test. Results : We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all P<0.001 if not otherwise stated) with occurrence of ICU stay (R=0.74), length of ICU stay (R=0.81), lethal outcome (R=0.56) and length of hospital stay (R=0.33, P<0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R=0.60), lactate dehydrogenase (LDH) (R=0.60), troponin (TNTHS) (R=0.55) and c-reactive protein (CRP) (R=0.51). Differences (P<0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher Opacityscore (12.50 vs. 4.96) and severity of laboratory data changes such as CRP (11.64 vs. 5.07 mg/dl, P<0.01). Conclusions : Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.

show abstract

Integrative analysis for COVID-19 patient outcome prediction

Cited by 71 publications

References 27 publications

Association of AI quantified COVID-19 chest CT and patient outcome

Association of AI quantified COVID-19 chest CT and patient outcome

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

Contact Info

Product

Resources

About