Author:
Zheng Tingting,Xie Xiaoli,Ni Zhaoxian,Tang Lang,Wu Pu-Yeh,Song Bin
Abstract
Abstract
Purpose
Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis.
Methods
Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation (DWISD and ADCSD), DWI and ADC signal intensity ratio (DWISIR and ADCSIR), mean ADC and minimum ADC value (ADCmean and ADCmin) and ADC value standard deviation (ADCVSD). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result.
Results
Two independent predictors of malignant nodules were identified by multivariate analysis: DWISIR (P = 0.007) and ADCmin (P < 0.001). The AUCs for multivariate prediction model, combined DWISIR and ADCmin thresholds model, combined DWISIR and ADCSIR thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined DWISIR and ADCmin threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS.
Conclusion
Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined DWISIR and ADCmin thresholds model significantly reduced the unnecessary biopsy rate.
Funder
Science and Technology Commission of Minhang District, Shanghai
Nature Science Foundation of Shanghai
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
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