Akademik Veri Yönetim Sistemi
Turkish Journal of Ear Nose and Throat, cilt.34, sa.2, ss.42-50, 2024 (Scopus)
Objective: Computed aided detection (CAD) systems can be developed to help radiologists in the accurate interpretation of computed tomography (CT) images. The recently popularised texture analysis method allows for qualitative and quantitative evaluation by analysing the grey-level distribution and relationships within an image. We aimed to compare the ratios of texture analysis data in the differentiation of benign-malignant nodules with the proportions of radiologists in the distinction between benign and malignant nodules and to compare the results. Materials and Methods: Retrospectively, the data of 80 patients who underwent thyroidectomy and had contrast-enhanced neck CT preoperatively were analysed. Two radiologists, experienced in head and neck radiology, blinded to the patients’ data evaluated neck CT images. Manual marking was performed and scanned to take tissue sections from the nodule area in transverse contrast-enhanced CT images, and the size of the nodule in the contralateral normal thyroid parenchyma was almost equal. Results: The computed tomography texture analysis (CTTA) model achieved the highest sensitivity of 81.4%, followed by the first radiologist at 51.2% and the second radiologist at 55.8%. Additionally, the CTTA model achieved the highest accuracy at 61.3%, followed by the first radiologist at 41.3% and second radiologist at 47.5%. On average, the CTTA model performed significantly better than the two radiologists, especially with regard to sensitivity. Conclusion: The CTTA model was superior to both radiologists in differentiating between benign and malignant thyroid nodules. Medical experts can benefit from CTTA-based solutions to extend their understanding of thyroid nodules in their routine practise.