Dataset Information

Ultrasound is helpful to differentiate Bethesda class III thyroid nodules: A PRISMA-compliant systematic review and meta-analysis.

ABSTRACT:

Background

Fine-needle aspiration (FNA) is the most dependable tool to triage thyroid nodules for medical or surgical management. However, Bethesda class III cytology, namely "follicular lesion of undetermined significance" (FLUS) or "atypia of undetermined significance" (AUS), is a major limitation of the US-FNA in assessing thyroid nodules. As the most important imaging method, ultrasound (US) has a high efficacy in diagnosing thyroid nodules. This meta-analysis aimed to assess the role of US in evaluating Bethesda class III thyroid nodules.

Methods

With keywords "Undetermined Significance," "Bethesda Category III," "Bethesda system," "Cytological Subcategory," "AUS/FLUS," "Atypia of Undetermined Significance," and "Ultrasound/US," papers in PubMed, Cochrane Library, Medline, Web of Science, Embase, and Google Scholar from inception to December 2016 were searched. A meta-analysis of these trials was then performed for evaluating the diagnostic value of thyroid ultrasound in Bethesda Category III thyroid nodules.

Results

Fourteen studies including 2405 nodules were analyzed. According to the criteria for US diagnosis of thyroid nodules in each article, with any one of suspicious features as indictors of malignancy, US had a pooled sensitivity of 0.75 (95% CI 0.72-0.78) and a pooled specificity of 0.48 (95% CI 0.45-0.50) in evaluating Bethesda Class III Nodules. The pooled diagnostic odds ratio was 10.92 (95% CI 6.04-19.74). The overall area under the curve was 0.84 and the Q* index was 0.77. With any 2 or 3 of US suspicious features as indictors of malignancy, the sensitivity and specificity were 0.77 (95% CI 0.71-0.83) and 0.54 (95% CI 0.51-0.58), 0.66 (95% CI 0.59-0.73) and 0.71 (95% CI 0.68-0.74), respectively.

Conclusions

US was helpful for differentiating benign and malignant Bethesda class III thyroid nodules, with the more suspicious features, the more likely to be malignant.

SUBMITTER: Gao LY

PROVIDER: S-EPMC5406060 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Ultrasound is helpful to differentiate Bethesda class III thyroid nodules: A PRISMA-compliant systematic review and meta-analysis.

Gao Lu-Ying LY Wang Ying Y Jiang Yu-Xin YX Yang Xiao X Liu Ru-Yu RY Xi Xue-Hua XH Zhu Shen-Ling SL Zhao Rui-Na RN Lai Xing-Jian XJ Zhang Xiao-Yan XY Zhang Bo B

Medicine 20170401 16

<h4>Background</h4>Fine-needle aspiration (FNA) is the most dependable tool to triage thyroid nodules for medical or surgical management. However, Bethesda class III cytology, namely "follicular lesion of undetermined significance" (FLUS) or "atypia of undetermined significance" (AUS), is a major limitation of the US-FNA in assessing thyroid nodules. As the most important imaging method, ultrasound (US) has a high efficacy in diagnosing thyroid nodules. This meta-analysis aimed to assess the rol ...[more]

PMID: 28422844

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Project description:Background: Analysis of plasma circulating cell-free DNA integrity (cfDI) has emerged as a promising tool in the diagnosis of malignant vs. benign tumors. There is limited data on the role of cfDI in thyroid cancer. The goal of this study was to analyze cfDI as a biomarker of malignancy in patients with cytologically indeterminate thyroid nodules. Methods: The cfDI was measured in the plasma of patients with cytologically indeterminate thyroid nodules. All patients underwent plasma collection within 24-72 h before surgical treatment for thyroid nodules. Additionally, samples were collected from seven patients via the vein draining the thyroid and peripheral vein during surgery. Quantitative real-time PCR was performed on the isolated cell-free DNA using two different primer sets (115 and 247 bp) to amplify consensus ALU sequences. The cfDI was calculated as the ratio of ALU247 to ALU115. Results: All data are given as median [25th-75th percentile]. The study group consisted of 67 patients with 100 nodules, 80.6% (54/67) women, aged 43 [33-60] years. There was no difference in cfDI between 29 patients with benign nodules (0.49 [0.41-0.59]) and 38 patients with malignant lesions (0.45 [0.36-0.57], p = 0.19). There was no difference in cfDI in the vein draining the thyroid (0.47 [0.24-1.05]) and peripheral vein (0.48 [0.36-0.56], p = 0.44). In comparison to thyroid cancer patients, patients with benign nodules were characterized by significantly higher concentrations of ALU115 (1,064 [529-2,960] vs. 411 [27-1,049] ng/ml; p = 0.002) and ALU247 (548 [276-1,894] vs. 170 [17-540] ng/ml; p = 0.0005), most likely because benign tumors were larger (3, [1.8-4.1 cm]) than malignant lesions (0.7 [0.23-1.45], p < 0.0001). Women had significantly lower cfDI (0.45 [0.27-0.54]) than men (0.56 [0.44-0.8], p = 0.011). Conclusion: The cfDI measured in the vein draining the thyroid is similar to the cfDI measured in the antecubital vein, validating cfDI measurements by peripheral liquid biopsy. Analysis of cfDI needs to be stratified by patients gender. In contrast to its diagnostic utility in aggressive cancers, cfDI has limited utility as a biomarker of malignancy in cytologically indeterminate thyroid nodules.

Project description:Objective:The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods:The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results:Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions:The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.

Dataset Information

Ultrasound is helpful to differentiate Bethesda class III thyroid nodules: A PRISMA-compliant systematic review and meta-analysis.

Background

Methods

Results

Conclusions

Publications

Ultrasound is helpful to differentiate Bethesda class III thyroid nodules: A PRISMA-compliant systematic review and meta-analysis.

Similar Datasets

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