Observation Variation in Ultrasonography Assessment of Thyroid Nodules.

医学 甲状腺 放射科 超声科 核医学 甲状腺机能正常 甲状腺肿 甲状腺癌 内科学 超声波 甲状腺结节 甲状腺癌
作者
Yasaman Sharifi,Susan Shafiei,Hamed Tabesh,Behzad Aminzadeh,Parvaneh Layegh,Abolfazl Mahmoodzadeh,S. Rasoul Zakavi,Saeid Eslami
出处
期刊:Asia Oceania journal of nuclear medicine & biology 卷期号:10 (1): 28-35
标识
DOI:10.22038/aojnmb.2021.59283.1411
摘要

Accurate detection and competent management of thyroid nodules, as a common disease, basically depends on the reliability of the ultrasonography (US) report. In this research, we evaluated inter and intra-observer variation among ultrasonography reporters, based on ACR-TIRADS.In this retrospective study, 345 thyroid US images of 150 patients were reviewed. Three clinicians with at least 6-year experience in thyroid US reviewed the images twice at 6-8 weeks' intervals. Composition, echogenicity, shape, margin, and echogenic foci based on ACR-TIRADS were reported, independently. Inter and intra-observer variations were calculated based on Cohen's Kappa statistics.345 ultrasonography images of 150 patients with thyroid nodules (83 women and 67 men) with a mean age of 65 years were reviewed. Moderate to the substantial intra-observer agreement was achieved with the highest Kapa value in the category of shape (k=0.61-0.77). For TIRADS level, the moderate intra-observer agreement was observed (k=0.42-0.46). Inter-observer agreement for the US category of thyroid nodules was obtained slightly to moderate. Composition (k=0.42 and 0.51) and echogenicity (k=0.45 and 0.46) showed the highest overall agreement and margin showed the lowest overall agreement (k=0.18 and 0.19). In assessing TIRADS level of nodules, a fair agreement was obtained (k=0.23 and 0.29).Moderate to substantial intra-observer agreement and slight to moderate inter-observer variation for evaluation of thyroid nodules; shows the need for a computer-aided diagnosis system based on artificial intelligence to assist our physicians in differentiating thyroid nodule characteristics based on explicit image features. An additional training course based on ACR-TIRADS for physicians can be another useful recommendation.
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