Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial

Qian Li; Yoganand Balagurunathan; Ying Liu; Matthew Schabath; Robert J. Gillies

doi:10.1117/12.2216948

24 March 2016 Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial

Qian Li, Yoganand Balagurunathan, Ying Liu, Matthew Schabath, Robert J. Gillies

Author Affiliations +

Proceedings Volume 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment; 97870H (2016) https://doi.org/10.1117/12.2216948
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Background: Lung-RADS is the new oncology classification guideline proposed by American College of Radiology (ACR), which provides recommendation for further follow up in lung cancer screening. However, only two features (solidity and size) are included in this system. We hypothesize that additional sematic features can be used to better characterize lung nodules and diagnose cancer. Objective: We propose to develop and characterize a systematic methodology based on semantic image traits to more accurately predict occurrence of cancerous nodules. Methods: 24 radiological image traits were systematically scored on a point scale (up to 5) by a trained radiologist, and lung-RADS was independently scored. A linear discriminant model was used on the semantic features to access their performance in predicting cancer status. The semantic predictors were then compared to lung-RADS classification in 199 patients (60 cancers, 139 normal controls) obtained from the National Lung Screening Trial. Result: There were different combinations of semantic features that were strong predictors of cancer status. Of these, contour, border definition, size, solidity, focal emphysema, focal fibrosis and location emerged as top candidates. The performance of two semantic features (short axial diameter and contour) had an AUC of 0.945, and was comparable to that of lung-RADS (AUC: 0.871). Conclusion: We propose that a semantics-based discrimination approach may act as a complement to the lung-RADS to predict cancer status.

Citation Download Citation

Qian Li, Yoganand Balagurunathan, Ying Liu, Matthew Schabath, and Robert J. Gillies "Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial", Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870H (24 March 2016); https://doi.org/10.1117/12.2216948

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Lung cancer

Cancer

Lung

Computed tomography

Radiology

Emphysema

Chronic obstructive pulmonary disease

Show All Keywords

Keywords/Phrases

Search In:

Publication Years