Classification Of TI-RADS Class-4 Thyroid Nodules Via Ultrasound-Based Radiomics And Multi-Kernel Learning

Abstract

The diagnosis of thyroid nodules is mainly based on the thyroid imaging reporting and data system (TI-RADS). Among different classes, class-4 thyroid nodules are of great uncertainty with malignant risk ranging from 5% to 80%. Class-4 thyroid nodules have three sub-classes, namely 4a, 4b and 4c. Accurate classification of these three sub-classes is of clinical importance. Therefore, we aim to classify TI-RADS class-4 thyroid nodules based on radiomics features from ultrasound images and multi-kernel learning algorithm. The study cohort consisted of 59 patients with TI-RADS class-4 thyroid nodules. Patients were classified into TI-RADS class-4a, 4b and 4c according to ultrasound-guided fine-needle aspiration biopsy results. Two experienced doctors drew the region-of-interest for each nodule on the ultrasound images. Pyradiomics was used to extract shape features, texture features, Laplacian of Gaussian features, wavelet features, square features, square root features, logarithm features, exponential features, gradient features and local binary pattern features from original ultrasound images. The selected features were normalized to a range from -1 to 1. Sequential backward elimination approach was used to select features from different categories. A multi-kernel support vector machine (SVM) classifier was configured with 10 linear kernels to combine features from different categories for classification. Centered alignment method was used to calculate the coefficient of each kernel for kernel combination. Accuracy, sensitivity, specificity, area under the curve (AUC) were applied to evaluate the performance of the classifier for each sub-class, and permutation test was used to test whether the AUC acquired from the proposed multi-kernel classifier was greater than random guess. Pyradiomics extracted 1493 radiomic features from the ultrasound images of thyroid nodules. After feature selection, there remained 332 features. Classification results demonstrated that the configured multi-kernel SVM could accurately classify class-4 thyroid nodules with a total classification accuracy of 86.44%. The accuracy for each of the sub-class was 94.44%, 73.33% and 75%, respectively. The detailed result is shown in the Table below. The radiomic features in combination with fine-tuned machine learning models showed high accuracy in classification of TI-RADS class-4 thyroid nodules. The results demonstrated potential clinical application of radiomic features and multi-kernel learning in the diagnosis of thyroid nodules.Abstract 3978; TableTypeAccuracySensitivitySpecificityAUCp-valuenumber of featuresTI-RADS class-4a94.44%0.94440.82610.9263<0.001332TI-RADS class-4b73.33%0.63640.86670.7667<0.001332TI-RADS class-4c75%0.58820.87500.69120.039332 Open table in a new tab