SynthesisBMJ open2016
Diffusion-weighted MRI in differentiating malignant from benign thyroid nodules: a meta-analysis.
Synthesis in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Impact of Diffusion-Weighted Magnetic Resonance Imaging Parameters on Diagnostic Accuracy for Thyroid Nodules: A Systematic Review and Meta-Analysis.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- The evolving role of MRI in the detection of extrathyroidal extension of papillary thyroid carcinoma: a systematic review and meta-analysis.BMC cancer · 2024Pooled it
- Review
- Enhancing diagnostic accuracy of American College of Radiology TI-RADS 4 nodules: nomogram models based on MRI morphological features.Quantitative imaging in medicine and surgery · 2025Article
- Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI.Journal of imaging informatics in medicine · 2024Article
- Utility of diffusion tensor imaging in differentiating benign from malignant thyroid nodules.The neuroradiology journal · 2024Article
- Effectiveness of diffusion-weighted magnetic resonance imaging (DW-MRI) in the differentiation of thyroid nodules.Thyroid research · 2024Article
- Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules.BMC cancer · 2024Article
- Comparative analysis of the image quality and diagnostic performance of the zooming technique with diffusion-weighted imaging using different b-values for thyroid papillary carcinomas and benign nodules.Frontiers in oncology · 2024Article
- Article
- Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer.BMC medical imaging · 2022Article
- Diagnostic Accuracy of Multiple MRI Parameters in Dealing with Incidental Thyroid Nodules.SN comprehensive clinical medicine · 2022Article
- MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma.Gland surgery · 2020Article
- Intravoxel incoherent motion and ADC measurements for differentiating benign from malignant thyroid nodules: utilizing the most repeatable region of interest delineation at 3.0 T.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Article
- Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications.The British journal of radiology · 2019Review
- Value of Diffusion Weighted MRI with Quantitative ADC Map in Diagnosis of Malignant Thyroid Disease.Diagnostics (Basel, Switzerland) · 2019Article
- Effect of region of interest on ADC and interobserver variability in thyroid nodules.BMC medical imaging · 2019Article
- Article
- Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules.BMC medical imaging · 2018Article
- Efficacy of apparent diffusion coefficient in predicting aggressive histological features of papillary thyroid carcinoma.Diagnostic and interventional radiology (Ankara, Turkey) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo perform a meta-analysis to evaluate the diagnostic efficacy of diffusion-weighted imaging (DWI) in differentiating malignant from benign thyroid nodules.
designA meta-analysis. DATA SOURCES AND STUDY SELECTION: Medical and scientific literature databases were searched for original articles published up to August 2015. Studies were selected if they (1) included diagnostic DWI for differentiating malignant from benign thyroid lesions, (2) included patients who later underwent biopsy and (3) presented sufficient data to enable the construction of contingency tables. DATA SYNTHESIS: For each study, the true-positive, false-positive, true-negative and false-negative values were extracted or derived, and 2×2 contingency tables were constructed. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) instrument. The heterogeneity test, threshold effect test, subgroup analyses and publication bias analyses were performed.
resultsFrom the 113 identified search results, 15 studies, representing a total of 765 lesions, were included in the meta-analysis. We detected heterogeneity between studies but found no evidence of publication bias. The methodological quality was moderate. The pooled weighted sensitivity was 0.90 (95% CI 0.85 to 0.93); the specificity was 0.95 (95% CI 0.88 to 0.98); the positive likelihood ratio was 16.49 (95% CI 7.37 to 36.86); the negative likelihood ratio was 0.11 (95% CI 0.08 to 0.16); and the diagnostic OR was 150.73 (95% CI 64.96 to 349.75). The area under the receiver operator characteristic curve was 0.95 (95% CI 0.93 to 0.97).
conclusionsQuantitative DWI may be a non-invasive, non-radiative and accurate method of distinguishing malignant from benign thyroid nodules. Nevertheless, large-scale trials are necessary to assess its clinical value and to establish standards regarding b values and cut-off values for DWI-based diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.