Clustering of lung adenocarcinomas classes using automated texture analysis on CT images

Antonio Pires; Henry Rusinek; James Suh; David P. Naidich; Harvey Pass; Jane P. Ko

doi:10.1117/12.2007154

13 March 2013 Clustering of lung adenocarcinomas classes using automated texture analysis on CT images

Antonio Pires, Henry Rusinek, James Suh, David P. Naidich, Harvey Pass, Jane P. Ko

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866925 (2013) https://doi.org/10.1117/12.2007154
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Purpose: To assess whether automated texture analysis of CT images enables discrimination among pathologic classes of lung adenocarcinomas, and thus serves as an in vivo biomarker of lung cancer prognosis. Materials and Methods: Chest CTs of 30 nodules in 30 patients with resected adenocarcinomas were evaluated by a pulmonary pathologist who classified each resected cancer according to the International Association for the Study of Lung Cancer (IASLC) system. The categories included adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), lepidic-predominant adenocarcinoma (LPA), and other invasive adenocarcinomas (INV). 3D volumes of interest (VOIs) and 2D regions of interest (ROIs) were then constructed for each nodule. A comprehensive set of N=279 texture parameters were computed for both 3D and 2D regions. Clustering and classification of these parameters were performed with linear discriminant analysis (LDA) using features determined by optimal subsets. Results: Of the 30 adenocarcinomas, there were 13 INV, 11 LPA, 3 MIA, and 3 AIS. AIS and MIA groups were analyzed together. With all 3 classes, LDA classified 17 of 30 nodules correctly using the nearest neighbor (k=1) method. When only the two largest classes (INV and LPA) were used, 21 of 24 nodules were classified correctly. With 3 classes and 2D texture analysis, and when using only the two largest groups, LDA was able to correctly classify all nodules. Conclusion: CT texture parameters determined by optimal subsets allows for effective clustering of adenocarcinoma classes. These results suggest the potential use of automated (or computer-assisted) CT image analysis to predict the invasive pathologic character of lung nodules. Our approach overcomes the limitations of current radiologic interpretation, such as subjectivity, inter- and intra-observer variability, and the effect of reader experience.

Citation Download Citation

Antonio Pires, Henry Rusinek, James Suh, David P. Naidich, Harvey Pass, and Jane P. Ko "Clustering of lung adenocarcinomas classes using automated texture analysis on CT images", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866925 (13 March 2013); https://doi.org/10.1117/12.2007154

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