Nydia Burgos1, Jing Zhao2, Juan P Brito3,4, Jenny K Hoang5, Fabian Pitoia6, Spyridoula Maraka4,7,8, M Regina Castro3, Ji-Hyun Lee2,9, Naykky Singh Ospina10. 1. Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico. 2. Division of Quantitative Sciences, University of Florida Health Cancer Center, University of Florida, Gainesville, FL, USA. 3. Division of Endocrinology and Metabolism, Mayo Clinic, Rochester, MN, USA. 4. Knowledge and Evaluation Research Unit, Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, MN, USA. 5. Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, Baltimore, MD, USA. 6. Division of Endocrinology, University of Buenos Aires, Buenos Aires, Argentina. 7. Division of Endocrinology and Metabolism, University of Arkansas for Medical Sciences, Little Rock, AR, USA. 8. Central Arkansas Veterans Healthcare System, Little Rock, AR, USA. 9. Department of Biostatistics, University of Florida, Gainesville, FL, USA. 10. Division of Endocrinology, Department of Medicine, University of Florida, Gainesville, FL, USA.
Abstract
CONTEXT: Thyroid nodule risk stratification allows clinicians to standardize the evaluation of thyroid cancer risk according to ultrasound features. OBJECTIVE: To evaluate interrater agreement among clinicians assessing thyroid nodules ultrasound features and thyroid cancer risk categories. DESIGN, SETTING, AND PARTICIPANTS: We surveyed Endocrine Society and Latin American Thyroid Society members to assess their interpretation of composition, echogenicity, shape, margins, and presence of echogenic foci of 10 thyroid nodule cases. The risk category for thyroid cancer was calculated following the American College of Radiology-Thyroid Imaging Reporting & Data System (ACR-TIRADS) framework from individual responses. MAIN OUTCOMES AND MEASURES: We used descriptive statistics and Gwet's agreement coefficient (AC1) to assess the primary outcome of interrater agreement for ACR-TIRADS risk category. As secondary outcomes, the interrater agreement for individual features and a subgroup analysis of interrater agreement for the ACR-TIRADS category were performed (ultrasound reporting system, type of practice, and number of monthly appraisals). RESULTS: A total of 144 participants were included, mostly endocrinologists. There was moderate level of agreement for the absence of echogenic foci (AC1 0.53, 95% CI 0.24-0.81) and composition (AC1 0.54, 95% CI 0.36-0.71). The agreement for margins (AC1 0.24, 95% CI 0.15-0.33), echogenicity (AC1 0.34, 95% CI 0.22-0.46), and shape assessment (AC1 0.42, 95% CI 0.13-0.70) was lower. The overall agreement for ACR-TIRADS assessment was AC1 0.29, (95% CI 0.13-0.45). The AC1 of ACR-TIRADS among subgroups was similar. CONCLUSIONS: This study found high variation of judgments about ACR-TIRADS risk category and individual features, which poses a potential challenge for the widescale implementation of thyroid nodule risk stratification.
CONTEXT: Thyroid nodule risk stratification allows clinicians to standardize the evaluation of thyroid cancer risk according to ultrasound features. OBJECTIVE: To evaluate interrater agreement among clinicians assessing thyroid nodules ultrasound features and thyroid cancer risk categories. DESIGN, SETTING, AND PARTICIPANTS: We surveyed Endocrine Society and Latin American Thyroid Society members to assess their interpretation of composition, echogenicity, shape, margins, and presence of echogenic foci of 10 thyroid nodule cases. The risk category for thyroid cancer was calculated following the American College of Radiology-Thyroid Imaging Reporting & Data System (ACR-TIRADS) framework from individual responses. MAIN OUTCOMES AND MEASURES: We used descriptive statistics and Gwet's agreement coefficient (AC1) to assess the primary outcome of interrater agreement for ACR-TIRADS risk category. As secondary outcomes, the interrater agreement for individual features and a subgroup analysis of interrater agreement for the ACR-TIRADS category were performed (ultrasound reporting system, type of practice, and number of monthly appraisals). RESULTS: A total of 144 participants were included, mostly endocrinologists. There was moderate level of agreement for the absence of echogenic foci (AC1 0.53, 95% CI 0.24-0.81) and composition (AC1 0.54, 95% CI 0.36-0.71). The agreement for margins (AC1 0.24, 95% CI 0.15-0.33), echogenicity (AC1 0.34, 95% CI 0.22-0.46), and shape assessment (AC1 0.42, 95% CI 0.13-0.70) was lower. The overall agreement for ACR-TIRADS assessment was AC1 0.29, (95% CI 0.13-0.45). The AC1 of ACR-TIRADS among subgroups was similar. CONCLUSIONS: This study found high variation of judgments about ACR-TIRADS risk category and individual features, which poses a potential challenge for the widescale implementation of thyroid nodule risk stratification.
Authors: Jenny K Hoang; William D Middleton; Alfredo E Farjat; Sharlene A Teefey; Nicole Abinanti; Fernando J Boschini; Abraham J Bronner; Nirvikar Dahiya; Barbara S Hertzberg; Justin R Newman; Daniel Scanga; Robert C Vogler; Franklin N Tessler Journal: AJR Am J Roentgenol Date: 2018-04-27 Impact factor: 3.959
Authors: Cesar A Lam; Melissa J McGettigan; Zachary J Thompson; Laila Khazai; Christine H Chung; Barbara A Centeno; Bryan McIver; Pablo Valderrabano Journal: Endocrine Date: 2019-07-12 Impact factor: 3.633
Authors: Amr F Hamour; Weining Yang; John J W Lee; Vincent Wu; Hedyeh Ziai; Praby Singh; Antoine Eskander; Axel Sahovaler; Kevin Higgins; Ian J Witterick; Allan Vescan; Jeremy Freeman; John R de Almeida; David Goldstein; Ralph Gilbert; Douglas Chepeha; Jonathan Irish; Danny Enepekides; Eric Monteiro Journal: JAMA Otolaryngol Head Neck Surg Date: 2021-04-01 Impact factor: 6.223