The Gray Zone of Thyroid Nodules: Using a Nomogram to Provide Malignancy Risk Assessment and Guide Patient Management

Document Type

Article

Publication Date

4-1-2021

Publication Title

Cancer Medicine

Abstract

Background: Thyroid nodules have a low prevalence of malignancy and most proven cancers do not behave aggressively. Thus, risk-stratification of nodules is a critical step to avoid surgical overtreatment. We hypothesized that a risk management system superior to those currently in use could be created to reduce the number of clinically indeterminate nodules (i.e., the “gray zone”) by concurrently considering the malignancy risks conferred by clinical, ultrasonographic, and cytologic variables. Methods: Thyroidectomy cases were reviewed from three institutions. Their benign versus malignant outcome was used to evaluate the variables for correlation. A binary logistic regression model was trained and, using indeterminate nodules with Bethesda III and IV results, validated. A scoring nomogram was designed to demonstrate the application of the model in clinical practice. Results: One hundred thirty thyroidectomies (28% malignant) met inclusion criteria. The final logistic regression model included difficulty in swallowing, hypothyroidism, echogenicity, hypervascularity, margins, calcification, and cytology diagnosis as input parameters. The model was highly successful in determining the outcome (p value: 0.001) with a R2(Nagelkerke) score of 0.93. The area under the curve as determined by receiver operating characteristics was 0.91. The accuracy of the model on the training dataset was 93% (sensitivity and specificity 92% and 96%, respectively) and, on the validation dataset, 80% (sensitivity and specificity 91% and 67%, respectively). Conclusions: We report a model for risk assessment of thyroid nodules that has the potential to significantly reduce indeterminates and surgical overtreatment. We illustrate its application via a straightforward nomogram, which integrates clinical, ultrasonographic, and cytologic data, and can be used to create clear, evidence-based management plans for patients.

First Page

2723

Last Page

2731

PubMed ID

33763983

Volume

10

Issue

8

Publisher

Wiley Open Access

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