Diagnostic Accuracy of Diffusion-Weighted MRI for Differentiating Benign and Malignant Thyroid Nodules: Systematic Review and Meta-Analysis

弥散加权磁共振成像鉴别甲状腺良恶性结节的诊断准确性:系统评价和荟萃分析

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Abstract

Background: Thyroid nodules are highly prevalent, affecting up to 75% of the population, yet most are benign. The limited specificity of ultrasound-based workup leads to substantial overdiagnosis and overtreatment, underscoring the need for improved imaging-based classification. Diffusion-weighted MRI (DWI), quantified via the apparent diffusion coefficient (ADC), has emerged as a promising imaging biomarker. This meta-analysis updates pooled diagnostic performance metrics and systematically evaluates which DWI acquisition techniques, imaging parameters, and combinations with other MRI modalities are most promising for clinical translation. Methods: PubMed, Web of Science, Scopus, and ProQuest were systematically searched. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using bivariate random-effects models. The effects of b-value, magnetic field strength, echo time, and diffusion model on diagnostic accuracy and ADC values were examined through subgroup and meta-regression analyses. Results: Forty-six studies (3003 nodules) were included. Pooled sensitivity and specificity were 0.84 (95% CI: 0.81-0.86) and 0.88 (95% CI: 0.85-0.90), with an AUC of 0.912. Intravoxel incoherent motion and diffusion kurtosis imaging showed no added value over the mono-exponential model. For the mono-exponential model, a negative association between b-values and reported ADCs was observed, whereas no association was found between b-values and diagnostic accuracy. Magnetic field strength and echo time did not affect ADCs. Combining DWI with morphological imaging showed the potential to further enhance diagnostic performance. Conclusions: DWI holds strong potential to improve the diagnostic workup of thyroid nodules. Technical standardization, particularly of key acquisition parameters, should be pursued to enable clinical implementation.

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