Keywords: Biology, Models, Methods, Head & Neck/ENT
Motivation: The low specificity of many Thyroid Imaging Reporting and Data Systems (TI-RADSs) lead to a large number of unnecessary biopsies.
Goal(s): This study developed and validated a predictive model based on MRI morphological features to improve the specificity.
Approach: A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression was used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in a validation cohort.
Results: Compared with the TI-RADSs, predictive models have better specificity along with a high sensitivity and can reduce unnecessary biopsies.
Impact: predictive models have better specificity along with a high sensitivity and may avoid numerous invasive needle biopsies
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