BACKGROUND: Several thyroid ultrasound (TUS) findings have been associated with an increased risk for thyroid cancer; however, there is no consensus as to the format and style for reporting the results of TUS. The objective of this study was to discover the features indicative of malignancy in thyroid nodules based on TUS, generate an equation using these features that would be predictive of malignancy in thyroid nodules, and stratify the results of this equation into TUS categories reflecting the probability of malignancy. METHODS: We obtained odds ratios of TUS findings indicative of malignancy and probability of malignancy for each nodule as determined by logistic regression analysis of ultrasound (US) findings in 1694 patients who had US-guided fine-needle aspiration biopsy. We then generated an equation to predict the probability of malignancy based on TUS and developed categories ranging from lowest to highest probability of malignancy. We evaluated the reliability of this equation and the categories using cytology and histopathology information regarding malignancy in the thyroid nodules. RESULTS: We characterized 12 aspects of thyroid nodules as seen on TUS and developed an equation to predict P(us), the probability of a nodule being malignant based on these US findings. The equation was P(us) = 1/(1 + e(-z)), where e is the mathematical constant 2.71828 and z is the logit of malignant thyroid nodule. P(us) was stratified into five categories based on the probability of a nodule being malignant as indicated by the findings (TUS 1, benign; TUS 2, probably benign; TUS 3, indeterminate; TUS 4, probably malignant; TUS 5, malignant). There was a significant correlation between the cytological category and the TUS 1 through TUS 5 categories (r = 0.491, p < 0.001). CONCLUSIONS: We propose an equation to predict the probability of malignancy in thyroid nodules based on 12 features of thyroid nodules as noted on TUS. This equation, and the stratification of its results into categories, should be useful in reporting the findings of US for thyroid nodules and in guiding management decisions.
BACKGROUND: Several thyroid ultrasound (TUS) findings have been associated with an increased risk for thyroid cancer; however, there is no consensus as to the format and style for reporting the results of TUS. The objective of this study was to discover the features indicative of malignancy in thyroid nodules based on TUS, generate an equation using these features that would be predictive of malignancy in thyroid nodules, and stratify the results of this equation into TUS categories reflecting the probability of malignancy. METHODS: We obtained odds ratios of TUS findings indicative of malignancy and probability of malignancy for each nodule as determined by logistic regression analysis of ultrasound (US) findings in 1694 patients who had US-guided fine-needle aspiration biopsy. We then generated an equation to predict the probability of malignancy based on TUS and developed categories ranging from lowest to highest probability of malignancy. We evaluated the reliability of this equation and the categories using cytology and histopathology information regarding malignancy in the thyroid nodules. RESULTS: We characterized 12 aspects of thyroid nodules as seen on TUS and developed an equation to predict P(us), the probability of a nodule being malignant based on these US findings. The equation was P(us) = 1/(1 + e(-z)), where e is the mathematical constant 2.71828 and z is the logit of malignant thyroid nodule. P(us) was stratified into five categories based on the probability of a nodule being malignant as indicated by the findings (TUS 1, benign; TUS 2, probably benign; TUS 3, indeterminate; TUS 4, probably malignant; TUS 5, malignant). There was a significant correlation between the cytological category and the TUS 1 through TUS 5 categories (r = 0.491, p < 0.001). CONCLUSIONS: We propose an equation to predict the probability of malignancy in thyroid nodules based on 12 features of thyroid nodules as noted on TUS. This equation, and the stratification of its results into categories, should be useful in reporting the findings of US for thyroid nodules and in guiding management decisions.
Authors: Guilherme F Mendes; Marcio Rt Garcia; Priscila M Falsarella; Antonio Rahal; Francisco A Cavalcante Junior; Daniela R Nery; Rodrigo G Garcia Journal: Br J Radiol Date: 2017-12-22 Impact factor: 3.039
Authors: T Rago; V Cantisani; F Ianni; L Chiovato; R Garberoglio; C Durante; A Frasoldati; S Spiezia; R Farina; G Vallone; A Pontecorvi; P Vitti Journal: J Endocrinol Invest Date: 2018-10-16 Impact factor: 4.256
Authors: Francesca Ianni; Paolo Campanella; Carlo Antonio Rota; Alessandro Prete; Laura Castellino; Alfredo Pontecorvi; Salvatore Maria Corsello Journal: Endocrine Date: 2015-10-30 Impact factor: 3.633