Name
Abstract | ||
|---|---|---|
Nonsmall cell lung cancer is a prevalent disease. It is diagnosed and treated with the help of computed tomography (CT) scans. In this paper, we apply radiomics to select 3-D features from CT images of the lung toward providing prognostic information. Focusing on cases of the adenocarcinoma nonsmall cell lung cancer tumor subtype from a larger data set, we show that classifiers can be built to predict survival time. This is the first known result to make such predictions from CT scans of lung cancer. We compare classifiers and feature selection approaches. The best accuracy when predicting survival was 77.5% using a decision tree in a leave-one-out cross validation and was obtained after selecting five features per fold from 219. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ACCESS.2014.2373335 | Access, IEEE |
Keywords | Field | DocType |
cancer,cellular biophysics,computerised tomography,diseases,feature selection,image classification,lung,medical image processing,3D feature selection,CT image features,adenocarcinoma nonsmall cell lung cancer tumor subtype,computed tomography,decision tree,disease,prognostic information,radiomics,CT 3D texture features,Computed tomography,Naive Bayes,decision tree,support vector machine | Lung cancer,Decision tree,Feature selection,Lung,Computer science,Feature (computer vision),Computed tomography,Adenocarcinoma,Radiology,Cross-validation,Distributed computing | Journal |
Volume | ISSN | Citations |
2 | 2169-3536 | 3 |
PageRank | References | Authors |
0.40 | 11 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel H. Hawkins | 1 | 5 | 1.81 |
| John N. Korecki | 2 | 7 | 1.13 |
| Yoganand Balagurunathan | 3 | 13 | 5.16 |
| Yuhua Gu | 4 | 81 | 8.57 |
| Virendra Kumar | 5 | 22 | 1.47 |
| Satrajit Basu | 6 | 3 | 1.07 |
| Lawrence O. Hall | 7 | 5543 | 335.87 |
| Dmitry B. Goldgof | 8 | 2021 | 198.90 |
| ROBERT A. GATENBY | 9 | 49 | 12.92 |
| Robert J Gillies | 10 | 47 | 12.10 |