(Tuesday, 7/16/2019) 10:30 AM - 11:00 AM
Room: Exhibit Hall | Forum 6
Purpose: Thyroid nodules are commonly found at palpation with a proportion about 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 nodules.
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. First-order statistic and gray-level co-occurrence matrix based 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 process. Leave-one-out cross validation was used to evaluate the performance of the thyroid cancer recognition.
Results: Based on the thyroid cancer identification results using computed tomography image, we found the strategy using 17 texture features and support vector machine performed well. The accuracy, area under 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.
Conclusion: The proposed computer-aided diagnosis system provides a good assessment of the malignancy risk of the thyroid nodules, which may help radiologists improve the accuracy and efficiency of thyroid diagnosis.
Keywords
Taxonomy
IM- CT: Quantitative imaging/analysis
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