Abstract
IntroductionThis study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers.Methods303 patients who underwent thyroidectomy from October 2018 to July 2019 were retrospectively reviewed. The diagnostic performance of the senior radiologist, the junior radiologist, and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the size of thyroid nodules.ResultsIn total, 186 malignant thyroid nodules, and 179 benign thyroid nodules were included; 168 were papillary thyroid carcinoma (PTC), 7 were medullary thyroid carcinoma (MTC), 11 were follicular carcinoma (FTC), 127 were follicular adenoma (FA) and 52 were nodular goiters. The CAD system showed a comparable specificity as the senior radiologist (86.0% vs. 86.0%, p > 0.99), but a lower sensitivity and a lower area under the receiver operating characteristic (AUROC) curve (sensitivity: 71.5% vs. 95.2%, p < 0.001; AUROC: 0.788 vs. 0.906, p < 0.001). The CAD system improved the diagnostic sensitivities of both the senior and the junior radiologists (97.8% vs. 95.2%, p = 0.063; 88.2% vs. 75.3%, p < 0.001).ConclusionThe use of the CAD system using artificial intelligence is a potential tool to distinguish malignant thyroid nodules and is preferable to serve as a second opinion for less experienced radiologists to improve their diagnosis performance.
Highlights
This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers
When compared with the junior radiologist, the Computer-aided diagnosis (CAD) system resulted in increased specificity and similar sensitivity and accuracy in the classification of thyroid cancer (86.0% vs.78.8%, p = 0.024; 71.5% vs.75.3%, p = 0.419; 78.6% vs.77.0%, p = 0.552, respectively)
When the CAD system was used to assist the senior and junior radiologists, the diagnostic sensitivity improved (97.8% vs. 95.2%, p = 0.063; 88.2% vs. 75.3%, p < 0.001, respectively), while the specificity declined (76.0% vs. 86.0%, p < 0.001; 79.9% vs. 84.4%, p = 0.008, respectively)
Summary
This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers. As one of the most extensively applied methods in the detection of thyroid nodules, the ultrasound has the advantages of accessibility, cost-effectiveness, and non-radiation. The particular ultrasound (US) features such as microcalcifications, hypoechogenicity, and irregular margins are commonly considered to relate to malignant thyroid disease, the presence of interobserver variation is inevitable [3, 4]. The CAD detection and diagnosis methods are based on machine learning approaches that extract features based on shape, texture, and statistical values, differentiating benign and malignant nodules [5,6,7]
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