Computer-aided diagnosis of liver tumors on computed tomography images

Chin-Chen Chang; Hong-Hao Chen; Yeun-Chung Chang; Ming-Yang Yang; Chung-Ming Lo; Wei-Chun Ko; Yee-Fan Lee; Kao-Lang Liu; Ruey-Feng Chang

doi:10.1016/j.cmpb.2017.04.008

Computer-aided diagnosis of liver tumors on computed tomography images

Comput Methods Programs Biomed. 2017 Jul:145:45-51. doi: 10.1016/j.cmpb.2017.04.008. Epub 2017 Apr 13.

Authors

Chin-Chen Chang¹, Hong-Hao Chen², Yeun-Chung Chang¹, Ming-Yang Yang², Chung-Ming Lo³, Wei-Chun Ko¹, Yee-Fan Lee¹, Kao-Lang Liu⁴, Ruey-Feng Chang⁵

Affiliations

¹ Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
² Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
³ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
⁴ Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. Electronic address: lkl@ntu.edu.tw.
⁵ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.

PMID: 28552125
DOI: 10.1016/j.cmpb.2017.04.008

Abstract

Background and objective: Liver cancer is the tenth most common cancer in the USA, and its incidence has been increasing for several decades. Early detection, diagnosis, and treatment of the disease are very important. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver cancer. CT scanners can provide multiple-phase sequential scans of the whole liver. In this study, we proposed a computer-aided diagnosis (CAD) system to diagnose liver cancer using the features of tumors obtained from multiphase CT images.

Methods: A total of 71 histologically-proven liver tumors including 49 benign and 22 malignant lesions were evaluated with the proposed CAD system to evaluate its performance. Tumors were identified by the user and then segmented using a region growing algorithm. After tumor segmentation, three kinds of features were obtained for each tumor, including texture, shape, and kinetic curve. The texture was quantified using 3 dimensional (3-D) texture data of the tumor based on the grey level co-occurrence matrix (GLCM). Compactness, margin, and an elliptic model were used to describe the 3-D shape of the tumor. The kinetic curve was established from each phase of tumor and represented as variations in density between each phase. Backward elimination was used to select the best combination of features, and binary logistic regression analysis was used to classify the tumors with leave-one-out cross validation.

Results: The accuracy and sensitivity for the texture were 71.82% and 68.18%, respectively, which were better than for the shape and kinetic curve under closed specificity. Combining all of the features achieved the highest accuracy (58/71, 81.69%), sensitivity (18/22, 81.82%), and specificity (40/49, 81.63%). The Az value of combining all features was 0.8713.

Conclusions: Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using our proposed CAD system.

Keywords: Computed tomography; Diagnosis; Elliptic model; Grey level co-occurrence matrix; Kinetic curve; Liver.

MeSH terms

Algorithms
Diagnosis, Computer-Assisted*
Humans
Liver Neoplasms / diagnostic imaging*
Sensitivity and Specificity
Tomography, X-Ray Computed*