AI Article Synopsis

  • The study evaluated the effectiveness of the Thyroid Imaging Reporting and Data System (TIRADS) in predicting malignancy in thyroid nodules and how consistently different radiologists used this system.
  • A total of 238 patients with thyroid nodules were analyzed, and the positive predictive value (PPV) for cancer increased significantly across different TIRADS categories, with category 5 having a PPV of 91%.
  • The inter-observer agreement among radiologists when assessing ultrasound features was found to be substantial, making TIRADS a reliable and practical tool for thyroid nodule evaluation.

Article Abstract

Aim: To assess the positive predictive value (PPV) and inter-observer agreement of Thyroid Imaging Reporting and Data System (TIRADS) as described by Kwak et al.

Materials And Methods: This was a prospective study wherein ultrasound was performed by two radiologists on patients with thyroid nodules >1 cm. The third radiologist interpreted archived images. Ultrasound features and TIRADS category were compared with cytology and surgical histopathology. PPV was calculated for all readers' combined assessment. Inter-observer agreement was calculated using linear weighted kappa.

Results: A total of 238 patients with 272 nodules of mean size 2.9 ± 1.7 cm were included. PPV for malignancy was 6.6%, 32%, 36%, 64%, 59%, and 91% for TIRADS 2, 3, 4a, 4b, 4c, and 5 categories, respectively. Inter-observer agreement was substantial [kappa (k) = 0.61-0.80] for assessment of nodule echogenicity, margins, calcification, and shape and good (k = 0.570, P < 0.001) for assessment of composition of the thyroid nodules. Overall agreement between observers was substantial for assigning TIRADS category [multi-rater weighted kappa coefficient (wt k) = 0.721, P < 0.001].

Conclusions: TIRADS is a simple and practical method of assessing thyroid nodules with high PPV and good inter-observer agreement.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813066PMC
http://dx.doi.org/10.4103/0971-3026.178367DOI Listing

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