Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

Lee, Michael C.; Boroczky, Lilla; Sungur-Stasik, Kivilcim; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

doi:10.1016/j.artmed.2010.04.011

« Previous Next »Artificial Intelligence in Medicine
Volume 50, Issue 1 , Pages 43-53, September 2010

Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

Michael C. Lee
- Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510-2099, USA
- Corresponding author. Tel.: +1 914 945 6047; fax: +1 914 945 6580.
Lilla Boroczky
- Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510-2099, USA
Kivilcim Sungur-Stasik
- College of Physicians and Surgeons, Columbia University, 630 West 168th Street, P&S 8, Room 503, New York, NY 10032, USA
Aaron D. Cann
- College of Physicians and Surgeons, Columbia University, 630 West 168th Street, P&S 8, Room 503, New York, NY 10032, USA
Alain C. Borczuk
- College of Physicians and Surgeons, Columbia University, 630 West 168th Street, P&S 8, Room 503, New York, NY 10032, USA
Steven M. Kawut
- College of Physicians and Surgeons, Columbia University, 630 West 168th Street, P&S 8, Room 503, New York, NY 10032, USA
Charles A. Powell
- College of Physicians and Surgeons, Columbia University, 630 West 168th Street, P&S 8, Room 503, New York, NY 10032, USA

Received 19 September 2008; received in revised form 4 April 2010; accepted 4 April 2010.

Abstract

Objective

Accurate classification methods are critical in computer-aided diagnosis (CADx) and other clinical decision support systems. Previous research has reported on methods for combining genetic algorithm (GA) feature selection with ensemble classifier systems in an effort to increase classification accuracy. In this study, we describe a CADx system for pulmonary nodules using a two-step supervised learning system combining a GA with the random subspace method (RSM), with the aim of exploring algorithm design parameters and demonstrating improved classification performance over either the GA or RSM-based ensembles alone.

Methods and materials

We used a retrospective database of 125 pulmonary nodules (63 benign; 62 malignant) with CT volumes and clinical history. A total of 216 features were derived from the segmented image data and clinical history. Ensemble classifiers using RSM or GA-based feature selection were constructed and tested via leave-one-out validation with feature selection and classifier training executed within each iteration. We further tested a two-step approach using a GA ensemble to first assess the relevance of the features, and then using this information to control feature selection during a subsequent RSM step. The base classification was performed using linear discriminant analysis (LDA).

Results

The RSM classifier alone achieved a maximum leave-one-out Az of 0.866 (95% confidence interval: 0.794–0.919) at a subset size of s=36 features. The GA ensemble yielded an Az of 0.851 (0.775–0.907). The proposed two-step algorithm produced a maximum Az value of 0.889 (0.823–0.936) when the GA ensemble was used to completely remove less relevant features from the second RSM step, with similar results obtained when the GA-LDA results were used to reduce but not eliminate the occurrence of certain features. After accounting for correlations in the data, the leave-one-out Az in the two-step method was significantly higher than in the RSM and the GA-LDA.

Conclusions

We have developed a CADx system for evaluation of pulmonary nodule based on a two-step feature selection and ensemble classifier algorithm. We have shown that by combining classifier ensemble algorithms in this two-step manner, it is possible to predict the malignancy for solitary pulmonary nodules with a performance exceeding that of either of the individual steps.

Keywords: Genetic algorithms, Linear discriminant analysis, Feature selection, Random subspace, Computer-aided diagnosis, Pulmonary nodules