Document Detail

Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography.
MedLine Citation:
PMID:  23325122     Owner:  NLM     Status:  MEDLINE    
The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography.
Haifeng Wu; Tao Sun; Jingjing Wang; Xia Li; Wei Wang; Da Huo; Pingxin Lv; Wen He; Keyang Wang; Xiuhua Guo
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of digital imaging     Volume:  26     ISSN:  1618-727X     ISO Abbreviation:  J Digit Imaging     Publication Date:  2013 Aug 
Date Detail:
Created Date:  2013-07-09     Completed Date:  2014-03-07     Revised Date:  2014-08-05    
Medline Journal Info:
Nlm Unique ID:  9100529     Medline TA:  J Digit Imaging     Country:  United States    
Other Details:
Languages:  eng     Pagination:  797-802     Citation Subset:  IM    
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MeSH Terms
Area Under Curve
Diagnosis, Differential
Lung Neoplasms / radiography*
ROC Curve
Radiographic Image Interpretation, Computer-Assisted / methods*
Solitary Pulmonary Nodule / radiography*
Tomography, Spiral Computed / methods
Tomography, X-Ray Computed / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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