Abstract
Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.
Highlights
The aim of this research was to build a Computer aided diagnosis (CAD) model that would predict whether thyroid nodules are benign or malignant
The value indicates highclassification performance and the results demonstrated that support vector machine (SVM) outperformed artificial neural network (ANN)
The value indicates high-classification performance and the results demonstrated that SVM outperformed ANN
Summary
Thyroid nodules are frequently detected incidentally during the diagnostic imaging of the neck [1] They may be found in 42–76% of people, becoming more prevalent with increasing age [2]. Some older patients with comorbidities may not suffer adverse outcomes within their lifetime from low-risk thyroid cancers detected after the FNAB of a thyroid nodule [9]. As an alternative to invasive FNAB, a conservative strategy such as the active ultrasound surveillance of thyroid nodules may be opted for in carefully selected patients, but this strategy risks missing clinically relevant cancers [11]. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features These data were divided into 87% training and 13% validation sets. Further testing with external data is required before our classification model can be employed in practice
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