Integrative analysis for COVID-19 patient outcome prediction.
Med Image Anal
; 67: 101844, 2021 01.
Article
in English
| MEDLINE | ID: covidwho-965958
ABSTRACT
While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https//github.com/DIAL-RPI/COVID19-ICUPrediction.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Patient Admission
/
Pneumonia, Viral
/
COVID-19
/
Intensive Care Units
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Country/Region as subject:
North America
/
Asia
/
Europa
Language:
English
Journal:
Med Image Anal
Journal subject:
Diagnostic Imaging
Year:
2021
Document Type:
Article
Affiliation country:
J.media.2020.101844
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