MENU CLOSE
About Us

Who We Are

Our Social Responsibility

Events

Exhibitions

Activeties

Members

Hospitals

Individuals

Companies

Member Benefits

 
Bio-medical projectsi

State Key Laboratory of Biotherapy

Cooperation

Achievement Exhibition

Scimea Journals

Signal Transduction and Targeted Therapy

News

News Information

 
Home   >  News
13 Aug 2020
356
MedComm | Radiomics‐based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS‐CoV‐2) and influenza A infected pneumonia
Scimea

The spread of coronavirus disease 2019 (COVID‐19) has become a global pandemic and public health problem. Although unprecedented efforts had been concentrated to identify and isolate individuals with risk of SARS‐CoV‐2 infection, clinicians are facing tremendous difficulties in efficiently and quickly diagnosing these patients due to massive volume of suspected cases. Herein, Prof. Minghua Zheng et al developed a machine‐learning algorithm based on radiomics to assist clinical diagnosis.


1601359060(1).png


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 infected pneumonia (IAP), a seasonal disease that coincided with the outbreak. Thus, the authors aim to develop a machine‐learning algorithm based on radiomics to distinguish NCP from IAP by texture analysis based on computed tomography (CT) imaging (Fig. 1).

 

Forty‐one NCP and 37 IAP patients admitted from January to February 6, 2019 admitted to two hospitals in Wenzhou, China. All patients had undergone chest CT examination and blood routine tests prior to receiving medical treatment. NCP was diagnosed by real‐time RT‐PCR assays. Eight of 56 radiomic features extracted by LIFEx were selected by least absolute shrinkage and selection operator regression to develop a radiomics score and subsequently constructed into a nomogram to predict NCP with area under the operating characteristics curve of 0.87 (95% confidence interval: 0.77‐0.93). The nomogram also showed excellent calibration with Hosmer‐Lemeshow test yielding a nonsignificant statistic (P = .904). The novel nomogram may efficiently distinguish between NCP and IAP patients. The nomogram may be incorporated to existing diagnostic algorithm to effectively stratify suspected patients for SARS‐CoV‐2 pneumonia.


image.png


Fig. 1 Radiomics‐based machine learning workflow



Article Access: https://onlinelibrary.wiley.com/doi/10.1002/mco2.14

 

 

                                                                                                                            

Website for MedCommhttps://onlinelibrary.wiley.com/journal/26882663

Looking forward to your contributions.


MedComm | Enhancing anticancer activity of checkpoint immunotherapy by targeting RAS
Expert View丨Song Bin, Director of Imaging Department of West China Hospital, Staged on the "Experts Opinions on Epidemic Combat"
Review of Academic Reports at the State Key Laboratory of Biotherapy, Sichuan University
Breaking News! MedComm Launched Awaiting Your Paper Submission!
Molecular Biomedicine | Progress in exosome associated tumor markers and their detection methods
Latest Events Journals News Members About Us Home
Contact Us

Address: No. 1103-1105, Building 6, S2, Global Center, High-tech Zone, Chengdu

Email: scimea@163.com 

Tel: (0086-)028-63859818   

Fax: (0086-)028-63859818   

Contact: (0086-)19113901604 (wechat:19113901604)


Follow Us
Copyright © 2009-2019 SCIMEA. All rights reserved 蜀ICP备19011649号-1