Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules

Role of advanced MRI sequences for thyroid lesions assessment. A narrative review

Despite not being the first imaging modality for thyroid gland assessment, Magnetic Resonance Imaging (MRI), thanks to its optimal tissue contrast and spatial resolution, has provided some advancements in detecting and characterizing thyroid abnormalities. Recent research has been focused on improving MRI sequences and employing advanced techniques for a more comprehensive understanding of thyroid pathology. Although not yet standard practice, advanced MRI sequences have shown high accuracy in preliminary studies, correlating well with histopathological results. They particularly show promise in determining malignancy risk in thyroid lesions, which may reduce the need for invasive procedures like biopsies. In this line, functional MRI sequences like Diffusion Weighted Imaging (DWI), Dynamic Contrast-Enhanced MRI (DCE-MRI), and Arterial Spin Labeling (ASL) have demonstrated their potential usefulness in evaluating both diffuse thyroid conditions and focal lesions. Multicompartmental DWI models, such as Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), and novel methods like Amide Proton Transfer (APT) imaging or artificial intelligence (AI)-based analyses are being explored for their potential valuable insights into thyroid diseases. This manuscript reviews the critical physical principles and technical requirements for optimal functional MRI sequences of the thyroid and assesses the clinical utility of each technique. It also considers future prospects in the context of advanced MR thyroid imaging and analyzes the current role of advanced MRI sequences in routine practice.

Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI

The study aims to evaluate multiparametric magnetic resonance imaging (MRI) for differentiating Follicular thyroid neoplasm (FTN) from non-FTN and malignant FTN (MFTN) from benign FTN (BFTN). We retrospectively analyzed 702 postoperatively confirmed thyroid nodules, and divided them into training (n = 482) and validation (n = 220) cohorts. The 133 FTNs were further split into BFTN (n = 116) and MFTN (n = 17) groups. Employing univariate and multivariate logistic regression, we identified independent predictors of FTN and MFTN, and subsequently develop a nomogram for FTN and a risk score system (RSS) for MFTN prediction. We assessed performance of nomogram through its discrimination, calibration, and clinical utility. The diagnostic performance of the RSS for MFTN was further compared with the performance of the Thyroid Imaging Reporting and Data System (TIRADS). The nomogram, integrating independent predictors, demonstrated robust discrimination and calibration in differentiating FTN from non-FTN in both training cohort (AUC = 0.947, Hosmer-Lemeshow P = 0.698) and validation cohort (AUC = 0.927, Hosmer-Lemeshow P = 0.088). Key risk factors for differentiating MFTN from BFTN included tumor size, restricted diffusion, and cystic degeneration. The AUC of the RSS for MFTN prediction was 0.902 (95% CI 0.798-0.971), outperforming five TIRADS with a sensitivity of 73.3%, specificity of 95.1%, accuracy of 92.4%, and positive and negative predictive values of 68.8% and 96.1%, respectively, at the optimal cutoff. MRI-based models demonstrate excellent diagnostic performance for preoperative predicting of FTN and MFTN, potentially guiding clinicians in optimizing therapeutic decision-making.

Multiplexed sensitivity-encoding versus single-shot echo-planar imaging: a comparative study for diffusion-weighted imaging of the thyroid lesions

PURPOSE

To compare multiplexed sensitivity-encoding diffusion-weighted magnetic resonance imaging (MUSE-DWI) and conventional DWI (cDWI) techniques in thyroid MRI.

MATERIALS AND METHODS

Nineteen patients who underwent thyroid MRI using both MUSE-DWI and cDWI at a 3.0 T MRI system were enrolled. Qualitative parameters (image quality, thyroid contour, and lesion conspicuity) and quantitative parameters (signal-to-noise ratio (SNR), lesion-to-thyroid contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC)) were compared between the two sequences. In addition, ADC values derived from MUSE-DWI and cDWI were separately compared between benign and malignant lesions.

RESULTS

MUSE-DWI outperformed cDWI in terms of image quality, thyroid contour, and lesion conspicuity. Significantly, higher signal-to-noise ratio (SNR) in both the thyroid and its lesion were found in MUSE-DWI than those in cDWI (both P < 0.05). The lesion-to-thyroid contrast-to-noise ratio (CNR) values were also significantly higher in MUSE-DWI than those in cDWI (P < 0.05). The apparent diffusion coefficient (ADC) of the thyroid in MUSE-DWI was significantly lower than that in cDWI (P < 0.05). The ADC of the lesion in MUSE-DWI was also significantly lower than that in cDWI (P < 0.05). In addition, ADC values derived from MUSE-DWI and cDWI were significantly higher in benign lesions than malignant lesions (P < 0.05).

CONCLUSION

Compared with cDWI, MUSE-DWI can improve the image quality, thyroid contour sharpness, lesion conspicuity, SNR in both the thyroid and its lesions, and enhancing the CNR between lesions and thyroid.

Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules

BACKGROUND

The low specificity of Thyroid Imaging Reporting and Data System (TI-RADS) for preoperative benign-malignant diagnosis leads to a large number of unnecessary biopsies. This study developed and validated a predictive model based on MRI morphological features to improve the specificity.

METHODS

A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression were used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in the validation cohort. The discrimination, calibration, and decision curve analysis of the nomogram were performed. The diagnosis efficacy, area under the curve (AUC) and net reclassification index (NRI) were calculated and compared with TI-RADS.

RESULTS

572 thyroid nodules were included (training cohort: n = 397, validation cohort: n = 175). Age, low signal intensity on T2WI, restricted diffusion, reversed halo sign in delay phase, cystic degeneration and wash-out pattern were independent predictors of malignancy. The nomogram demonstrated good discrimination and calibration both in the training cohort (AUC = 0.972) and the validation cohort (AUC = 0.968). The accuracy, sensitivity, specificity, PPV, NPV and AUC of MRI-based prediction were 94.4%, 96.0%, 93.4%, 89.9%, 96.5% and 0.947, respectively. The MRI-based prediction model exhibited enhanced accuracy (NRI>0) in comparison to TI-RADSs.

CONCLUSIONS

The prediction model for diagnosis of benign and malignant thyroid nodules demonstrated a more notable diagnostic efficacy than TI-RADS. Compared with the TI-RADSs, predictive model had better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.

Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm

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.

Tongue texture may contribute to the assessment of malignant risk of thyroid nodules

In the present study, it was aimed to evaluate whether there is an objective tongue image indicator that could be used to evaluate malignant risk of thyroid nodules through a cross sectional study. From December 2018 to December 2020, the TFDA-1 digital tongue-face diagnostic instrument was used to collect the tongue images. TDAS 2.0 software was used for tongue image analysis. A standardized database was constructed by combining patient physical examination results and tongue image analysis results. The relationship between tongue image index and TI-RADS classification of thyroid nodules was tested. A total of 5,900 cases were collected and 4,615 cases were included in the present study after excluding 154 cases due to incomplete information, 1,221 cases with thyroid nodules were separated into 417 cases TI-RADS 2 group, 693 cases in TI-RADS 3 group and 111 cases in TI-RADS 4 group. Without considering confounding factors, tongue image indexes zhiCon, zhiASM, zhiENT, zhiMEAN, zhiClrB, zhiClrR, zhiClrG, zhiClrI, zhiClrL and zhiClrY were significantly different among the three groups (P<0.05). Excluding the influence of age, sex, body mass index, smoking and drinking, the results of one-way variance linear trend analysis showed that the values of zhiCon, zhiENT and zhiMEAN increased with the increasing TI-RADS category, while the values of zhiASM decreased with the increase of TI-RADS category. Tongue texture index may be helpful for differentiating the benign and malignant of thyroid nodules.

Diagnosis of benign and malignant thyroid nodules by a dual-layer spectral detector CT-based nomogram

Introduction

Preoperative diagnosis of benign and malignant thyroid nodules is crucial for appropriate clinical treatment and individual patient management. In this study, a double-layer spectral detector computed tomography (DLCT)-based nomogram for the preoperative classification of benign and malignant thyroid nodules was developed and tested.

Methods

A total of 405 patients with pathological findings of thyroid nodules who underwent DLCT preoperatively were retrospectively recruited. They were randomized into a training cohort (n=283) and a test cohort (n=122). Information on clinical features, qualitative imaging features and quantitative DLCT parameters was collected. Univariate and multifactorial logistic regression analyses were used to screen independent predictors of benign and malignant nodules. A nomogram model based on the independent predictors was developed to make individualized predictions of benign and malignant thyroid nodules. Model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis(DCA).

Results

Standardized iodine concentration in the arterial phase, the slope of the spectral hounsfield unit(HU) curves in the arterial phase, and cystic degeneration were identified as independent predictors of benign and malignant thyroid nodules. After combining these three metrics, the proposed nomogram was diagnostically effective, with AUC values of 0.880 for the training cohort and 0.884 for the test cohort. The nomogram showed a better fit (all p &gt; 0.05 by Hosmer−Lemeshow test) and provided a greater net benefit than the simple standard strategy within a large range of threshold probabilities in both cohorts.

Discussion

The DLCT-based nomogram has great potential for the preoperative prediction of benign and malignant thyroid nodules. This nomogram can be used as a simple, noninvasive, and effective tool for the individualized risk assessment of benign and malignant thyroid nodules, helping clinicians make appropriate treatment decisions.

MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma

Purpose

BRAF V600E mutation can compensate for the low detection rate by fine-needle aspiration (FNA) and is related to aggressiveness and lymph node metastasis. This study aimed to investigate the relationship between texture analysis features based on magnetic resonance imaging (MRI) and mutations.

Methods

Retrospective analysis was performed on patients with postoperative pathology confirmed papillary thyroid carcinoma (PTC) from 2017 to 2021. One thousand one hundred and thirty-two texture features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) separately by outlining the tumor volume of interest (VOI). Univariate, minimum redundancy maximum relevance (mRMR), and multivariate analyses were used for feature selection to construct 3 models (T2WI, CE-T1WI, and combined model) to predict mutation. The reproducibility between observers was evaluated by intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) analysis was used to assess the performance of models. The diagnostic performance of the optimal cut-off value of models were calculated and validated by 10-fold cross-validation.

Results

A total of 80 PTCs (22 BRAF V600E wild-type and 58 BRAF V600E mutant) were included in our study. Good interobserver agreement was found on texture features we selected (all ICCs >0.75). The area under the ROC curves (AUCs) for the T2WI model, CE-T1WI model, and combined model were 0.83 (95% CI: 0.75-0.91), 0.83 (95% CI: 0.73-0.90), and 0.88 (95% CI: 0.81-0.94), respectively. The accuracy, sensitivity, specificity, PPV, and NPV were 0.776, 0.679, 0.905, 0.905, and 0.679 for the T2WI model at a cut-off value of 0.674; 0.755, 0.750, 0.762, 0.808, and 0.696 for the CE-T1WI model at a cut-off value of 0.573; 0.816, 0.893, 0.714, 0.806, and 0.833 for the combined model at a cut-off value of 0.420.

Conclusion

MRI-based texture analysis could be a potential method for predicting BRAF V600E mutation in PTC preoperatively.

Diagnostic Accuracy of Multiple MRI Parameters in Dealing with Incidental Thyroid Nodules

Background

Different MRI parameters have been studied for evaluating thyroid nodules. Diffusion-weighted imaging (DWI) and T2 imaging sequences with considerable efficacy in evaluating soft tissue tumors merit further assessment for thyroid nodule investigation.

Method

We evaluated incidental thyroid nodules (ITNs) reported on head and neck MRI studies. The T2 signal intensity (SI), T2 signal intensity ratio (SIR), Z value, and apparent diffusion coefficient (ADC) values of the thyroid nodule were obtained for every patient. The patients were referred to the radiology department for the thyroid nodule ultrasound study. Finally, 33 participants (37 thyroid nodules) who were scheduled for fine needle aspiration and cytology (FNAC) were enrolled. Regarding the FNAC results, the nodules were divided into malignant and benign groups. The two groups’ MRI parameters were compared using a two samples independent t test, and the cutoff values were estimated by analyzing the receiver operating characteristics plot.

Results

The T2 signal intensities, SIR, Z values, and ADC values were significantly higher in the benign group than malignant. The cutoff points of 230 (AUC = 0.759), 3.38 (AUC = 0.754), 37 (AUC = 0.759), and 1.73 (AUC = .690) were obtained for T2 values, SIR, Z values, and ADC values, respectively.

Conclusion

T2, SIR, Z, and ADC values are reliable for discriminating benign from malignant ITNs. However, further studies with a larger sample size are needed to provide more accurate mean values, identify outliers, and reduce confounding factors and bias.

Diagnostic accuracy of B-mode ultrasound, ultrasound elastography and diffusion weighted MRI in differentiation of thyroid nodules (prospective study)

Background

The incidence of the thyroid nodules and its detection is increasing rapidly. The most precise method for diagnosis of the nodules of the thyroid is FNAC. But, about 10–20% of specimens of FNAC are indeterminate and non-diagnostic. Therefore, there is a demand for another diagnostic method for evaluating thyroid nodules. Thyroid ultrasound elastography may improve the ability to differentiate malignant from benign thyroid nodules. Few articles were published about the results of DW MRI in thyroid nodules, with its results confirmed that malignant nodules have lower mean ADC values than benign nodules. This study aims to investigate and compare the accuracy of B-mode ultrasound, ultrasound elastography and diffusion-weighted MRI in characterization of the nodules of the thyroid.

Results

The study included 56 patients with thyroid nodules (36 benign and 20 malignant). Thyroid ultrasound, ultrasound elastography and DWI were done for all patients. Ultrasound-guided FNA Cytological examination (as the gold standard) was done for 48 patients and surgical histopathology was done to 8 patients with non-diagnostic FNAC. The results showed: TIRADS score had sensitivity 90%, specificity 77.8% and accuracy of 82.14%. The elastography score had sensitivity 80%, specificity 88.9% and accuracy 85.7%. The use of the strain ratio had 80% sensitivity, 94.4% specificity and 89.3% accuracy. DWI and ADC value had 100% sensitivity and 94.4% specificity and the accuracy was 96.4% for differentiating malignant from benign thyroid nodules. Multi-parametric analysis by TIRADS and ADC had 100% accuracy.

Conclusion

Ultrasound elastography add valuable data over ultrasound TIRADS. But, diffusion weighted MRI and ADC value has more accuracy in differentiating malignant from benign thyroid nodules. The best performance was achieved by the combination of ACR-TIRADS and ADC value.

Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers

Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m² ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.

Bilateral thyroid adenomas in an alpaca

A 7-year-old neutered male alpaca (Vicugna pacos) was presented for evaluation of a 3-year history of large, bilateral, firm ventral cervical masses causing esophageal and tracheal impingement. Ultrasound examination, radiographic evaluation, histopathological findings, and magnetic resonance imaging confirmed the masses to be bilateral thyroid adenomas. Conservative medical treatment by unilateral chemical ablation, using 10% formalin by aspiration technique, was performed on the left mass. Chemical ablation proved to be effective in decreasing the size of the mass, with no apparent adverse effects. To our knowledge, this case is the first known report of bilateral thyroid adenomas in an alpaca, a condition previously described in humans, horses, dogs, and cats.

Discrimination between malignant and benign thyroid tumors by diffusion-weighted imaging - A systematic review and meta analysis

PURPOSE

Magnetic resonance imaging is used to stage thyroid tumors. Diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Our aim was to compare ADC values of malignant and benign thyroid lesions based on a large sample.

METHODS

MEDLINE library, EMBASE and SCOPUS databases were screened for the associations between ADC values and thyroid lesions up to August 2021. The primary endpoint of the systematic review were ADC values of benign and malignant thyroid lesions. In total, 29 studies were suitable for the analysis and were included into the present study.

RESULTS

The included studies comprised a total of 2137 lesions, 1118 (52.3%) benign and 1019 (47.7%) malignant lesions. The pooled mean ADC value of the benign thyroid lesions was 1.88 × 10^-3 mm²/s [95% CI 1.77-2.0] and the pooled mean ADC value of malignant thyroid lesions was 1.15 × 10^-3 mm²/s [95% CI 1.04-1.25].

CONCLUSIONS

ADC can well discriminate benign and malignant thyroid tumors. Therefore, DWI should be implemented into the presurgical diagnostic work-up in clinical routine.

Diagnostic value of thyroid micronodules with high b-value diffusion weighted imaging: Comparative study with high-resolution ultrasound

PURPOSE

This study aims to compare the diagnostic performance of two imaging methods for thyroid nodules ≤1.0 cm and reduce unnecessary overdiagnosis.

METHODS

A retrospective study was conducted on 80 patients with pathologically confirmed solitary thyroid micronodules underwent both high-resolution ultrasound (HRUS) and High b-value (2000 s/mm²) diffusion weighted imaging (DWI). Intra- and interobserver agreement (Intraclass correlation coefficient) was followed by Kruskal-Wallis test to detect whether the quantitative apparent diffusion coefficient (ADC) and thyroid nodule subgroups were related. Cohen's kappa analysis was applied to assess the interobserver consistency of DWI and HRUS characteristics. The receiver operating characteristic curves were adopted for evaluating the diagnostic performance of thyroid malignancy. The sensitivity, specificity, and accuracy of the two imaging methods were compared using the McNemar's test and Kappa test.

RESULTS

A total of 80 patients were included, consisting of 43 malignant and 37 benign micronodules. The sensitivity, specificity and accuracy of DWI combined with rADC (ADCmin to ADCn ratio) for the diagnosis of thyroid micronodules were 83.7%, 89.2% and 86.3%, respectively. The area under the curve (AUC) was 0.91 (95% confidence interval [CI]: 0.84-0.97). The sensitivity, specificity and accuracy of HRUS diagnosis were 100%, 62.16% and 82.5%, respectively.

CONCLUSION

High b-value DWI is superior to HRUS for evaluating the diagnostic performance of solid thyroid micronodules. DWI and its ADC quantitative analysis could be added to the evaluation of thyroid micronodules to improve the specificity of diagnosis, reduce overdiagnosis and avoid unnecessary biopsies or surgeries.

Imaging of the Thyroid: Practical Approach

Imaging evaluation of the thyroid gland spans a plethora of modalities, including ultrasound imaging, cross-sectional studies, and nuclear medicine techniques. The overlapping of clinical and imaging findings of benign and malignant thyroid disease can make interpretation a complex undertaking. We aim to review and simplify the vast current literature and provide a practical approach to the imaging of thyroid disease for application in daily practice. Our approach highlights the keys to differentiating and diagnosing common benign and malignant disease affecting the thyroid gland.

Diffusion-Weighted Imaging of the Head and Neck (Including Temporal Bone)

Diffusion techniques provide valuable information when performing head and neck imaging. This information can be used to detect the presence or absence of pathology, refine differential diagnosis, determine the location for biopsy, assess response to treatment, and prognosticate outcomes. For example, when certain technical factors are taken into consideration, diffusion techniques prove indispensable in assessing for residual cholesteatoma following middle ear surgery. In other scenarios, pretreatment apparent diffusion coefficient values may assist in prognosticating outcomes in laryngeal cancer and likelihood of response to radiation therapy. As diffusion techniques continue to advance, so too will its clinical utility.

Evaluation of vitamin D receptor gene polymorphisms in patients with differentiated thyroid carcinomas and nodular goiter

BACKGROUND

The role of vitamin D has previously been determined in autoimmune and malignant thyroid diseases. We aimed to identify the haplotype distribution of single nucleotide polymorphisms (SNPs) in the vitamin D receptor (VDR) gene, which has been suggested to play a role in the pathogenesis of differentiated thyroid cancers and benign thyroid diseases.

METHODS

Two hundred and sixteen patients, 113 with benign and 103 with differentiated thyroid cancers, together with the same number of healthy controls, were included in the study. FokI, BsmI, ApaI, and TaqI SNPs in VDR were analyzed in all participants using the PCR-RFLP method.

RESULTS

When the patients with differentiated thyroid cancers or the patients with nodular goiter and control cases were compared for BsmI, ApaI or TaqI polymorphisms, three genotype distributions (BB, Bb, bb; AA, Aa, aa; TT, Tt, tt) were found to not differ significantly. When the patients with differentiated thyroid cancers and control cases were compared for the FokI polymorphism in the VDR gene, the three genotype distributions (FF, Ff, ff) did not differ. However, in patients with nodular goiter, the FF genotype in the FokI polymorphism of the VDR gene was found to be statistically significantly higher (P=0.033).

CONCLUSIONS

This is the first study in the literature evaluating the role of VDR gene SNPs in nodular goiter. We can suggest that SNP distribution in the VDR gene is not associated with malignancy but may cause some alterations in thyrocyte morphology and functions.

A comparative analysis of diffusion-weighted imaging and ultrasound in thyroid nodules

BACKGROUND

Diffusion-weighted imaging (DWI) and ultrasound are commonly used methods to examine thyroid nodules, but their comparative value is rarely studied. We evaluated the utility of DWI and ultrasound in differentiating benign and malignant thyroid nodules.

METHODS

A total of 100 patients with 137 nodules who underwent both DWI and ultrasound before operation were enrolled. The T1 and T2 signal intensity ratio (SIR) of each thyroid nodule was calculated by measuring the mean signal intensity divided by that of paraspinal muscle. The apparent diffusion coefficient (ADC) value and the SIR of benign and malignant thyroid nodules were analyzed by two-sample independent t tests. The sensitivity, specificity, and accuracy of DWI and ultrasound were compared with chi-square tests.

RESULTS

There was no significant difference in the SIR between benign and malignant thyroid nodules. The ADC value was significantly different. At the threshold value was 1.12 × 10^- 3 mm²/s, the maximum area under the curve was 0.944. The sensitivity, specificity, and accuracy were 84.9, 92.2, and 87.6% respectively. The corresponding values of ultrasound diagnosis were 90.1, 80.4, and 86.9%.

CONCLUSIONS

Ultrasound has high sensitivity in differentiating benign and malignant thyroid nodules, and the ADC value has high specificity, but there is no statistical difference in sensitivity or specificity between the two modalities. DWI and ultrasound each have their own advantages in differentiating benign and malignant thyroid nodules.