Ms. Anna Marie Gann Profile

Anna Marie Gann

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

An analysis of radiomics features in lung lesions in COVID-19

Anna Marie Gann, Ehsan Abadi, Jocelyn Hoye, Thomas Sauer, W. Paul Segars, Hamid Chalian, Ehsan Samei

Proceedings Volume 11595, 115953K (2021) https://doi.org/10.1117/12.2582296

KEYWORDS: Lung, Image segmentation, Computed tomography, Statistical analysis, Chest

Read Abstract +

Radiomic features extracted from CT imaging can be used to quantitively assess COVID-19. The objective of this work was to extract and analyze radiomics features in RT-PRC confirmed COVID-19 cases to identify relevant characteristics for COVID-19 diagnosis, prognosis, and treatment. We measured 29 morphology and second-order statistical-based radiomics features from 310 lung lesions extracted from 48 chest CT cases. Features were evaluated according to their coefficient of variation (CV). We calculated the CV for each feature under two statistical conditions: one with all lesions weighted equally and one with all cases weighted equally. In analyzing the patient data, there were 6.46 lesions-per-case and for 81.25% of cases, the lesions presented with bilateral lung involvement. For all radiomic features examined except ‘energy’, the CV was higher in the lesion distribution than the case distribution. The CV for morphological features were larger than second-order in both distributions, 181% and 85% versus 50% and 42%, respectively. The most variable features were ‘surface area’, ‘ellipsoid volume’, ‘ellipsoid surface area’, ‘volume’, and ‘approximate volume’, which deviated from the mean 173-255% in the lesion distribution and 119-176% in the case distribution. The features with the lowest CV were ‘homogeneity’, ‘discrete compactness’, ‘texture entropy’, ‘sum average’, and ‘elongation’, which deviated less than 31% by case and less than 25% by lesion. Future work will investigate integrating this data with similar studies and other diagnostic and prognostic criterion enhancing the role of CT in detecting and managing COVID-19.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years