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

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.