Dataset Information

Improvement of diagnostic efficiency in distinguishing the benign and malignant thyroid nodules via conventional ultrasound combined with ultrasound contrast and elastography.

ABSTRACT: We aimed to evaluate the diagnostic values of conventional ultrasound (US), ultrasound contrast (UC) and ultrasound elastography (UE) in distinguishing the benign and malignant thyroid nodules. A total of 100 patients with thyroid nodules receiving operative treatment were selected; they underwent the conventional US, UE and UC examinations before operation, respectively. The nodules received pathological examination after operation to distinguish benign from malignant lesions. The sensitivity, specificity and diagnostic accordance rate of each diagnostic method was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC) of ROC was calculated. The manifestations of malignant thyroid nodules in conventional US examination were mostly the hypoecho, heterogeneous echo, irregular shape, unclear boundary, aspect ratio <1, microcalcification and irregular peripheral echo halo, and there were statistically significant differences compared with the benign nodules (P<0.05). UE showed that the differences between benign and malignant nodules in 2, 3 and 4 points were statistically significant (P<0.05). The manifestations of malignant nodules in UC were mostly the irregular shape, obscure boundary, no obvious enhancement, heterogeneous enhancement and visible perfusion defects, and there were statistically significant differences compared with the benign nodules (P<0.05). ROC curve showed that both sensitivity and specificity of UE and UC were superior to those of conventional US. AUC was the largest (AUC = 0.908) and the diagnostic value was the highest in the conventional US combined with UE and UC. Conventional US combined with elastography and UC can significantly improve the sensitivity, specificity and accuracy of diagnosis of benign and malignant thyroid nodules.

SUBMITTER: Liu MJ

PROVIDER: S-EPMC5494667 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundThyroid nodular disease is a common disorder. Ultrasonography has emerged as a powerful tool for the diagnosis of thyroid disease owing to its high operational simplicity, non-invasiveness and reproducibility. This study aimed to systematically evaluate the value of ultrasound elastography and conventional ultrasound for the diagnosis of thyroid nodules.MethodsThe PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, Wanfang and VIP databases were searched systematically. RevMan 5.3 software was used to draw a risk bias map, and Stata 16.0 was used to plot a sensitivity and specificity forest map. A summary receiver operating characteristics (SROC) curve was plotted, and the area under the curve (AUC) was calculated. Meta-regression was used to explore the sources of heterogeneity.ResultsThe sensitivity of conventional ultrasonography for detecting thyroid nodules was 0.55 [95% confidence interval (CI): 0.45-0.65], the specificity was 0.90 (95% CI: 0.87-0.93), the positive likelihood ratio was 5.8 (95% CI: 3.8-8.9), the negative likelihood ratio was 0.49 (95% CI: 0.39-0.6), and the AUC was 0.86 (95% CI: 0.82-0.88). The sensitivity of ultrasonic elastography for detecting thyroid nodules was 0.67 (95% CI: 0.60-0.73), the specificity was 0.93 (95% CI: 0.90-0.95), the positive likelihood ratio was 9.1 (95% CI: 6.3-13.3), the negative likelihood ratio was 0.36 (95% CI: 0.29-0.44), and the AUC was 0.89 (95% CI: 0.86-0.91). The sensitivity of conventional ultrasound combined with ultrasonic elastography for detecting thyroid nodules was 0.88 (95% CI: 0.84-0.90), the specificity of the combined approach was 0.96 (95% CI: 0.93-0.98), the positive likelihood ratio was 23.3 (95% CI: 12.4-43.6), the negative likelihood ratio was 0.13 (95% CI: 0.10-0.17), and the AUC was 0.92 (95% CI: 0.90-0.94). Age, region, research type, conventional ultrasound evaluation criteria and elastography evaluation criteria had specific effects on sensitivity and specificity when detecting thyroid nodules.ConclusionsConventional ultrasound can be used as a routine examination technique for the differential diagnosis of benign and malignant thyroid nodules, while ultrasound elastography can improve the sensitivity and specificity of the diagnosis. The diagnostic value of conventional ultrasound combined with ultrasound elastography is higher than that of a single diagnostic method.

Project description:BackgroundThe contrasted-enhanced ultrasound thyroid imaging reporting and data system (CEUS TI-RADS) is the first international risk stratification system for thyroid nodules based on conventional ultrasound (US) and CEUS. This study aimed to evaluate the diagnostic efficacy of CEUS TI-RADS for benign and malignant thyroid nodules and to assess the related interobserver agreement.MethodsThe study recruited 433 patients who underwent thyroid US and CEUS between January 2019 and June 2023 at the Affiliated Hospital of Guangdong Medical University. A retrospective analysis of 467 thyroid nodules confirmed by fine-needle aspiration (FNA) and/or surgery was performed. Further, a CEUS TI-RADS classification was assigned to each thyroid nodule based on the CEUS TI-RADS scoring criteria for the US and CEUS features of the nodule. The nodules were grouped based on their sizes as follows: size ≤1 cm, group A; size >1 and ≤4 cm, group B; and size >4 cm, group C. Multivariate logistic regression was used to analyze independent risk factors for malignant thyroid nodules. Pathological assessment was the reference standard for establishing the sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of CEUS TI-RADS in diagnosing malignant thyroid nodules. The area under the curve (AUC) in the receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of the scoring system in predicting malignancy in three groups of nodules. The intragroup correlation coefficient (ICC) was adopted to assess the interobserver agreement of the CEUS TI-RADS score.ResultsOut of the 467 thyroid nodules, 262 were malignant and 205 were benign. Logistic regression analysis revealed that the independent risk factors for malignant thyroid nodules included punctate echogenic foci (P<0.001), taller-than-wide shape (P=0.015), extrathyroidal invasion (P=0.020), irregular margins/lobulation (P=0.036), hypoechoicity on US (P=0.038), and hypoenhancement on CEUS (P<0.001). The AUC for the CEUS TI-RADS in diagnosing malignant thyroid nodules was 0.898 for all nodules, 0.795 for group A, 0.949 for group B, and 0.801 for group C, with the optimal cutoff values of the CEUS TI-RADS being 5 points, 6 points, 5 points, and 5 points, respectively. Among these groups of nodules, group B had the highest AUC, with the SEN, SPE, ACC, PPV, and NPV for diagnosing malignant nodules being 95.9%, 88.1%, 92.8%, 92.6%, and 93.2%, respectively. The ICC of the CEUS TI-RADS classification between senior and junior physicians was 0.862 (P<0.001).ConclusionsIn summary, CEUS TI-RADS demonstrated significant efficacy in distinguishing thyroid nodules. Nonetheless, there were variations in its capacity to detect malignant nodules across diverse sizes, and it demonstrate optimal performance in 1- to 4-cm nodules. These findings may serve as important insights for clinical diagnoses.

Project description:Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites. Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3). Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = -6.67, 95%CI [-15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [-3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89. Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.

Project description:PurposeComputed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.MethodsLung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign).ResultsThe inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures.ConclusionsRadiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Dataset Information

Improvement of diagnostic efficiency in distinguishing the benign and malignant thyroid nodules via conventional ultrasound combined with ultrasound contrast and elastography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets