Radiomics‐based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS‐CoV‐2) and influenza A infected pneumonia
Nomogram
Viral Pneumonia
DOI:
10.1002/mco2.14
Publication Date:
2020-08-13T13:11:36Z
AUTHORS (11)
ABSTRACT
Clinicians have been faced with the challenge of differentiating between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) infected pneumonia (NCP) and influenza A (IAP), a seasonal disease that coincided outbreak. We aim to develop machine-learning algorithm based on radiomics distinguish NCP from IAP by texture analysis computed tomography (CT) imaging. Forty-one 37 patients admitted January February 6, 2019 two hospitals in Wenzhou, China. All had undergone chest CT examination blood routine tests prior receiving medical treatment. was diagnosed real-time RT-PCR assays. Eight 56 radiomic features extracted LIFEx were selected least absolute shrinkage selection operator regression score subsequently constructed into nomogram predict area under operating characteristics curve 0.87 (95% confidence interval: 0.77-0.93). The also showed excellent calibration Hosmer-Lemeshow test yielding nonsignificant statistic (P = .904). novel may efficiently patients. be incorporated existing diagnostic effectively stratify suspected for SARS-CoV-2 pneumonia.
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