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Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography

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Abstract

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.

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Acknowledgments

The program of Natural Science Fund of China (Serial Number: 81172772 and 30972550); the program of Natural Science Fund of Beijing (Serial Number: 4112015); the Program of Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (Serail Number: PHR201007112).

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Authors and Affiliations

  1. School of Public Health and Family Medicine, Capital Medical University, Beijing, 100069, China

    Haifeng Wu, Tao Sun, Jingjing Wang, Xia Li, Wei Wang & Da Huo

  2. Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China

    Xia Li & Xiuhua Guo

  3. Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China

    Pingxin Lv

  4. Department of Radiology, Friendship Hospital, Capital Medical University, Beijing, 100053, China

    Wen He & Keyang Wang

  5. Department of Epidemiology and Health Statistics, School of Public Health and Family Medicine, Capital Medical University, Beijing, 100069, China

    Xiuhua Guo

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  1. Haifeng Wu
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  2. Tao Sun
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  3. Jingjing Wang
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  4. Xia Li
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  5. Wei Wang
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  6. Da Huo
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  7. Pingxin Lv
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  8. Wen He
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  9. Keyang Wang
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  10. Xiuhua Guo
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Corresponding author

Correspondence to Xiuhua Guo.

Additional information

Haifeng Wu and Tao Sun contributed equally to this work

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Wu, H., Sun, T., Wang, J. et al. Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography. J Digit Imaging 26, 797–802 (2013). https://doi.org/10.1007/s10278-012-9547-6

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  • DOI: https://doi.org/10.1007/s10278-012-9547-6

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