**1. Introduction**

In the international scenario, different alternative ultrasound (US) algorithms have been proposed for characterizing thyroid nodules [1]. The American College of Radiology (ACR) Thyroid Imaging Reporting and Data Systems (TIRADS) consists of a scale with increasing scores for specific US features of thyroid nodules and is able to stratify lesions with a progressively higher risk of malignancy (ROM) [2–4]. On this side of the Atlantic ocean, the European Thyroid Association (EU-TIRADS) proposed in 2017 a US pattern recognition method that combines high-risk criteria, such as nodule composition, echogenicity, margins, shape, and calcifications [5]. The main purpose of these scoring systems is the reduction in inappropriate fine-needle aspirations (FNAs), triaging thyroid nodules at their best. However, the final FNA results do not always match the pre-biopsy risk class assigned by these two systems. Indeed, the indication of a biopsy is influenced by subjectivity in US interpretation, different ROMs in the screened populations, and personalized clinical decisions [6,7]. On the other hand, defensive medicine, excessive scrupulousness, or limited experience could induce clinicians to perform FNA regardless of the TIRADS score, especially in large nodules, still depending on cytology for the final answer regarding the nature of a thyroid lesion and thus reducing the practical utility of TIRADS [8]. Although the most recent reports in the literature stress the recommendation to implement these algorithms in the clinical setting, the selection of more impactful radiological criteria may improve the contribution of these systems to risk assessments [6,9–11]. In this work, we present a comparative analysis of the most widely employed US classifications to support their systematic application in a first-level general hospital for the bioptic triage of thyroid nodules [8]. Moreover, the present paper supports the hypothesis that specific US features, coupled with cytological classes, might be better able predict the final malignant nature of a thyroid nodule.
