Chenbin Liu1,2, Shanshan Chen1, Yunze Yang3, Dangdang Shao3, Wenxian Peng1,4, Yan Wang3, Yihong Chen4, Yuenan Wang2. 1. College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. 2. Radiation Oncology, Chinese Academy of Medical Science (CAMS) Shenzhen Cancer Hospital, Shenzhen 518117, China. 3. Biodesign Institute, Arizona State University, Tempe, AZ, USA. 4. Department of Radiology, Hangzhou Medical College, Hangzhou 310053, China.
Abstract
BACKGROUND: Thyroid nodules are commonly found at palpation amounting to 4-7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions. METHODS: In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition. RESULTS: In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively. CONCLUSIONS: The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.
BACKGROUND: Thyroid nodules are commonly found at palpation amounting to 4-7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions. METHODS: In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition. RESULTS: In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively. CONCLUSIONS: The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.
Entities:
Keywords:
Computed tomography (CT); computer-aided diagnosis; texture analysis; thyroid cancer
Authors: Sanjay K Shetty; Michael M Maher; Peter F Hahn; Elkan F Halpern; Suzanne L Aquino Journal: AJR Am J Roentgenol Date: 2006-11 Impact factor: 3.959