Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features.

Elmer Jeto Gomes Ataide,Simone Schenke,Alfredo Illanes,Michael Kreissl,Nikhila Ponugoti,Michael Friebe

doi:10.3390/s20216110

Elmer Jeto Gomes Ataide, Simone Schenke + Show 4 more

Open Access

https://doi.org/10.3390/s20216110

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Abstract

The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.

Highlights

The thyroid is one of the largest endocrine glands located below the epiglottis
This study focused on the geometric and morphological feature extraction techniques for the classification of thyroid nodules from US images
A total of 27 geometric and morphological features were defined and extracted from these images and the 11 most significant features selected in accordance with the Thyroid Imaging Reporting and Data System (TIRADS)-based visual features and labeled based on their class (0 = benign, 1 = malignant)

Summary

Introduction

The thyroid is one of the largest endocrine glands located below the epiglottis. It is responsible for several physiological functions such as the production of hormones, regulation of brain and nerve cells, and development and functioning of organs like the heart, eyes, hair, skin, and intestines [1].Irregularities and/or deformations of the thyroid lead to its inability to efficiently carry out these functions. The thyroid is one of the largest endocrine glands located below the epiglottis. It is responsible for several physiological functions such as the production of hormones, regulation of brain and nerve cells, and development and functioning of organs like the heart, eyes, hair, skin, and intestines [1]. Ultrasound (US) imaging is typically used as the first point of diagnosis and for the evaluation of the thyroid nodules, as it effectively images and visualizes soft tissue structures. It is free of ionizing radiation and is the most widely available and affordable imaging modality [2]

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