Computer-Aided Diagnosis of Soft-Tissue Tumors Using Sonographic Morphologic and Texture Features

Chen, Chih-Yen; Chiou, Hong-Jen; Chou, Szu-Yuan; Chiou, See-Ying; Wang, Hsin-Kai; Chou, Yi-Hong; Chiang, Huihua Kenny

doi:10.1016/j.acra.2009.07.024

« Previous Next »Academic Radiology
Volume 16, Issue 12 , Pages 1531-1538, December 2009

Computer-Aided Diagnosis of Soft-Tissue Tumors Using Sonographic Morphologic and Texture Features

Chih-Yen Chen, PhD
- Institute of Biomedical Engineering, National Yang-Ming University, No. 155, Sec. 2, Linong St., Beitou District, Taipei City 112, Taiwan, R.O.C
Hong-Jen Chiou, MD
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- General Hospital and National Yang Ming University, School of Medicine, Taipei, Taiwan
Szu-Yuan Chou, MD
- Department of Obstetrics and Gynecology, Taipei Medical University-Wan Fang Hospital, Taipei, Taiwan
See-Ying Chiou, MD
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
Hsin-Kai Wang, MD
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
Yi-Hong Chou, MD
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- General Hospital and National Yang Ming University, School of Medicine, Taipei, Taiwan
Huihua Kenny Chiang, PhD
- Institute of Biomedical Engineering, National Yang-Ming University, No. 155, Sec. 2, Linong St., Beitou District, Taipei City 112, Taiwan, R.O.C
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
- Address correspondence to: H.K.C.

Received 3 July 2009; accepted 27 July 2009.

Rationale and Objectives

The aim of this study was to develop a computer-aided diagnosis (CAD) system in assessing the sonographic morphologic and texture features of soft-tissue tumors.

Materials and Methods

The retrospective study involved 114 pathology proven cases including 73 benign and 41 malignant soft-tissue tumors. The tumor regions were delineated by an experienced radiologist who was unknown to the pathologic result. Then, we applied 10 morphologic features and 6 gray-level co-occurrence matrix texture features to analyze the tumor regions. To classify the tumors as benign or malignant, we used two methods, a linear discriminant analysis with stepwise feature selection and a multilayer neural network with the back-propagation algorithm as classifiers. The classification performances are evaluated by the area A_z under the receiver operating characteristic. Furthermore, four radiologists provided malignancy grades for all tumors in the comparison of the CAD system.

Results

In this analysis, the CAD system based on the combination of morphologic and texture feature sets can give the optimal CAD result by LDA with an accuracy of 89.5%, a sensitivity of 90.2%, a specificity of 89.0%, a positive predictive value (PPV) of 82.2%, negative predictive value (NPV) of 94.2%, and A_z value of 0.96, and by the multilayer perception with an accuracy of 88.6%, a sensitivity of 90.2%, a specificity of 87.5%, a positive predictive value of 80.4%, negative predictive value of 94.2%, and A_z value of 0.95. The A_z values of the four radiologists were ranged between 0.74 and 0.86, and the optimal CAD results were shown the highest A_z values than the four radiologists' rankings.

Conclusions

This study has shown that performing the CAD system with both morphologic and texture features on sonography, can successfully distinguish between benign and malignant soft-tissue tumors. Moreover, it can also provide a second opinion for the tumor diagnosis and avert unnecessary biopsy.

Key Words: soft-tissue tumors, computer-aided diagnosis (CAD), morphologic feature, texture feature, linear discriminant analysis (LDA), multilayer perception (MLP)