Share Email Print
cover

Proceedings Paper

Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial
Author(s): Qian Li; Yoganand Balagurunathan; Ying Liu; Matthew Schabath; Robert J. Gillies
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870H (24 March 2016); doi: 10.1117/12.2216948
Show Author Affiliations
Qian Li, Tianjin Medical Univ. (China)
H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Yoganand Balagurunathan, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Ying Liu, Tianjin Medical Univ. (China)
H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Matthew Schabath, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert J. Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

© SPIE. Terms of Use
Back to Top