Abstract
ObjectivesDifferentiating thyroid nodules with a cytological diagnosis of follicular neoplasm remains an issue. The goal of this study was to determine whether ultrasonographic (US) findings obtained preoperatively from the computer-aided detection (CAD) system are sufficient to further stratify the risk of malignancy for this diagnostic cytological category.MethodsFrom September 2016 to September 2018 in our hospital, patients diagnosed with Bethesda category IV (follicular neoplasm or suspicion of follicular neoplasm) thyroid nodules and underwent surgical excisions were include in the study. Quantification and analysis of tumor features were performed using CAD software. The US findings of the region of interest, including index of composition, margin, echogenicity, texture, echogenic dots indicative of calcifications, tall and wide orientation, and margin were calculated into computerized values. The nodules were further classified into American Thyroid Association (ATA) and American College of Radiology Thyroid Imaging Reporting & Data System (TI-RADS) categories.Results92 (10.1%) of 913 patients were diagnosed with Bethesda category IV thyroid nodules. In 65 patients, the histological type of the nodule was identified. The quantitative features between patients with benign and malignant conditions differed significantly. The presence of heterogeneous echotexture, blurred margins, or irregular margins was shown to have the highest diagnostic value. The risks of malignancy for nodules classified as having very low to intermediate suspicion ATA, non-ATA, and high suspicion ATA patterns were 9%, 35.7%, and 51.7%, respectively. Meanwhile, the risks of malignancy were 12.5%, 26.1%, and 53.8% for nodules classified as TIRADS 3, 4, and 5, respectively. When compared to human observers, among whom poor agreement was noticeable, the CAD software has shown a higher average accuracy.ConclusionsFor patients with nodules diagnosed as Bethesda category IV, the software-based characterizations of US features, along with the associated ATA patterns and TIRADS system, were shown helpful in the risk stratification of malignancy.
Highlights
Thyroid nodular disease is one of the most common endocrine disorders
The preoperative diagnosis of thyroid nodule is commonly based on fine-needle aspiration cytology (FNAC) results [1]
Because further differentiation of Bethesda category IV lesions is challenging, we aimed to examine whether the information provided by commercial computer-aided detection (CAD) to surgeons is sufficient to predict thyroid cancer
Summary
The preoperative diagnosis of thyroid nodule is commonly based on fine-needle aspiration cytology (FNAC) results [1]. The Bethesda system of thyroid cytopathology defines follicular neoplasm (FN) or suspicion of follicular neoplasm (SFN) as Bethesda category IV, with the goal of increasing the probability of detecting follicular thyroid cancers [6]. It reports a 10% to 40% association with malignancy risk, and diagnostic thyroidectomy is often recommended for making a diagnosis [7,8,9]. These methods are expensive and currently are not widely available
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