Assessment of perinodular stiffness in differentiating malignant from benign thyroid nodules

Modified TI-RADS Coupled with BRAFV600E Enhances Diagnostic Efficiency in Papillary Thyroid Carcinoma: Prospective Study

Background

Thyroid disorders, relatively common diseases of the endocrine system, have risen gradually in recent years. Early detection and accurate diagnosis of thyroid cancer hold exceptional importance. This study aimed to determine the efficacy of a modified TI-RADS and BRAFV600E mutation testing for thyroid cancer (PTC) diagnosis.

Methods

Ninety five thyroid nodules (48 benign and 47 malignant) from 81 patients were examined using Kwak Thyroid Imaging Reporting and Data System (TI-RADS) were subjected to shear wave elasticity (SWE), BRAFV600E genotyping and fine needle aspiration (FNA) cytology.

Results

The modified TI-RADS exhibited superior diagnostic accuracy compared to TI-RADS in differentiating benign nodules from malignant thyroid nodules. Moreover, the AUC of modified TI-RADS in conjunction with BRAFV600E was the highest at 95% CI (0.898-0.992, p=0.003), surpassing other diagnostic methods in enhanced sensitivity and maintaining high specificity.

Conclusion

The diagnostic efficiency of this combination surpassed that of individual diagnostic methods.

Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

Objective

Our aim was to develop dual-modal CNN models based on combining conventional ultrasound (US) images and shear-wave elastography (SWE) of peritumoral region to improve prediction of breast cancer.

Method

We retrospectively collected US images and SWE data of 1271 ACR- BIRADS 4 breast lesions from 1116 female patients (mean age ± standard deviation, 45.40 ± 9.65 years). The lesions were divided into three subgroups based on the maximum diameter (MD): ≤15 mm; >15 mm and ≤25 mm; >25 mm. We recorded lesion stiffness (SWV1) and 5-point average stiffness of the peritumoral tissue (SWV5). The CNN models were built based on the segmentation of different widths of peritumoral tissue (0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm) and internal SWE image of the lesions. All single-parameter CNN models, dual-modal CNN models, and quantitative SWE parameters in the training cohort (971 lesions) and the validation cohort (300 lesions) were assessed by receiver operating characteristic (ROC) curve.

Results

The US + 1.0 mm SWE model achieved the highest area under the ROC curve (AUC) in the subgroup of lesions with MD ≤15 mm in both the training (0.94) and the validation cohorts (0.91). In the subgroups with MD between15 and 25 mm and above 25 mm, the US + 2.0 mm SWE model achieved the highest AUCs in both the training cohort (0.96 and 0.95, respectively) and the validation cohort (0.93 and 0.91, respectively).

Conclusion

The dual-modal CNN models based on the combination of US and peritumoral region SWE images allow accurate prediction of breast cancer.

Convolutional Neural Network for Predicting Thyroid Cancer Based on Ultrasound Elastography Image of Perinodular Region

We aimed to develop deep learning models based on perinodular regions' shear-wave elastography (SWE) images and ultrasound (US) images of thyroid nodules (TNs) and determine their performances in predicting thyroid cancer. A total of 1747 American College of Radiology Thyroid Imaging Reporting & Data System 4 (TR4) thyroid nodules (TNs) in 1582 patients were included in this retrospective study. US images, SWE images, and 2 quantitative SWE parameters (maximum elasticity of TNs; 5-point average maximum elasticity of TNs) were obtained. Based on US and SWE images of TNs and perinodular tissue, respectively, 7 single-image convolutional neural networks (CNN) models [US, internal SWE, 0.5 mm SWE, 1.0 mm SWE, 1.5 mm SWE, 2.0 mm SWE of perinodular tissue, and whole SWE region of interest (ROI) image] and another 6 fusional-image CNN models (US + internal SWE, US + 0.5 mm SWE, US + 1.0 mm SWE, US + 1.5 mm SWE, US + 2.0 mm SWE, US + ROI SWE) were established using RestNet18. All of the CNN models and quantitative SWE parameters were built on a training cohort (1247 TNs) and evaluated on a validation cohort (500 TNs). In predicting thyroid cancer, US + 2.0 mm SWE image CNN model obtained the highest area under the curve in 10 mm < TNs ≤ 20 mm (0.95 for training; 0.92 for validation) and TNs > 20 mm (0.95 for training; 0.92 for validation), while US + 1.0 mm SWE image CNN model obtained the highest area under the curve in TNs ≤ 10 mm (0.95 for training; 0.92 for validation). The CNN models based on the fusion of SWE segmentation images and US images improve the radiological diagnostic accuracy of thyroid cancer.

US-Elastography With Different Techniques for Thyroid Nodule Characterization: Systematic Review and Meta-analysis

Background

Thyroid nodules are frequent in adult population and thyroid cancer incidence has increased dramatically over the past three decades. The aim of this systematic review and meta-analysis was to evaluate the US-Elastosonography (USE) diagnostic performance in assessing the thyroid nodules malignancy risk.

Methods

PubMed and Embase databases were searched from January 2011 to July 2021. We extracted data from selected studies and calculated the overall diagnostic accuracy of qualitative USE, semi-quantitative USE and quantitative USE. Summary receiver operating characteristic (ROC) curve was elaborated to show the results. All statistical tests were performed using Metadisc and Medcal software package.

Results

Finally 72 studies with 13,505 patients and 14,015 thyroid nodules (33% malignant) undergoing elastography were included. The pooled sensitivity, specificity and AUC were 84%, 81%, and 0.89 respectively for qualitative USE; 83%, 80%, and 0.93 for semi-quantitative USE and 78%, 81% and 0.87, for quantitative USE. The qualitative and semiquantitative USE present very similar diagnostic accuracy values and both better than the quantitative USE.

Conclusions

USE is a useful imaging tool for thyroid nodule characterization. In accordance with recent guidelines and meta-analyses, the USE could be used daily in thyroid nodule malignancy risk stratification.

Systematic Review Registration

PROSPERO: CRD42021279257.

Evaluation of Ultrasound Elastography Combined With Chi-Square Automatic Interactive Detector in Reducing Unnecessary Fine-Needle Aspiration on TIRADS 4 Thyroid Nodules

Background

Conventional ultrasound diagnosis of thyroid nodules (TNs) had a high false-positive rate, resulting in many unnecessary fine-needle aspirations (FNAs).

Objective

This study aimed to establish a simple algorithm to reduce unnecessary FNA on TIRADS 4 TNs using different quantitative parameters of ultrasonic elasticity and chi-square automatic interactive detector (CHAID) method.

Methods

From January 2020 to May 2021, 432 TNs were included in the study, which were confirmed by FNA or surgical pathology. Each TN was examined using conventional ultrasound, sound touch elastography, and Shell measurement function. The quantitative parameters E and E_shell were recorded, and the E_shell/E values were calculated for each TN. The diagnostic performance of the quantitative parameters was evaluated using the receiver operating characteristic curves. The CHAID was used to classify and analyze the quantitative parameters, and the prediction model was established.

Results

A total of 226 TNs were malignant and 206 were benign. E_shell and E_shell/E ratio were included in the classification algorithm, which showed a depth of two ramifications (E_shell/E ≤ 0.988 or 0.988–1.043 or >1.043; if E_shell/E ≤ 0.988, then E_shell ≤ 64.0 or 64.0–74.0 or >74.0; if E_shell/E = 0.988–1.043, then E_shell ≤ 66.0 or > 66.0; if E_shell/E >1.043, then E_shell ≤ 69.0 or >69.0). The unnecessary FNAs could have been avoided in 57.3% of the cases using this algorithm.

Conclusion

The prediction model using quantitative parameters had high diagnostic performance; it could quickly distinguish benign lesions and avoid subjective influence to some extent.

Clinical value of FNA puncture feeling in the diagnosis of non-diagnostic and indeterminate thyroid nodules

OBJECTIVE

The aim of this study was to explore the clinical diagnostic value of puncture feeling during fine-needle aspiration (FNA) for non-diagnostic and indeterminate thyroid nodules.

METHODS

A retrospective analysis was performed on 176 patients (196 nodules) diagnosed with Bethesda I or III by FNA cytology at the ultrasound department of our hospital between January 2017 and January 2020. Comparisons were made on the differences in puncture feeling (including stiffness and grittiness) between benign and malignant thyroid nodules, and their diagnostic performance was analyzed.

RESULTS

There were significant differences between benign and malignant nodules with respect to the puncture stiffness and puncture grittiness (P < 0.001). The presence of a hard stiffness and grittiness demonstrated comparable levels of diagnostic performance for malignant thyroid nodules, with sensitivities, specificities, accuracies, positive predictive values, and negative predicative values of 55.56% and 63.89%, 87.10% and 78.22%, 75.51% and 72.96%, 71.43% and 63.01%, and 77.14% and 78.86%, respectively. The diagnostic performance was most optimal in the presence of at least one puncture feeling (area under the receiver operating characteristic curve: 0.771), exhibiting a sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 79.17%, 75.00%, 76.53%, 64.77, and 86.11%%, respectively.

CONCLUSIONS

Puncture feeling adds clinical value in the diagnosis of thyroid nodules with indeterminate FNA findings.

Diagnostic performance of C-TIRADS in malignancy risk stratification of thyroid nodules: A systematic review and meta-analysis

BACKGROUND

Chinese thyroid imaging reports and data systems (C-TIRADS) is a novel malignancy risk stratification used for thyroid nodule diagnosis and guiding thyroid fine needle aspiration (FNA). In this review, we aim to assess the performance of C-TIRADS in malignancy risk stratification of thyroid nodules.

METHODS

PubMed, Medline, Web of Science, Embase, CNKI, and Wanfang databases were searched until 1 April 2022. Original articles reporting data about C-TIRADS and setting FNA or histology as reference standards were included. C-TIRADS 4A, 4B, and 4C were set as thresholds, respectively, to obtain pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and the area under the curve (AUC). Integrated nested Laplace approximation was used for Bayesian bivariate meta-analysis of diagnostic tests.

RESULTS

Sixteen studies were included, evaluating 11,506 thyroid nodules. The rate of malignancy in each risk classification is comparable with that in C-TIRADS. C-TIRADS 4B appeared to have better diagnostic performance than C-TIRADS 4A and 4C. The pooled sensitivity, specificity, LR+, LR-, and DOR of C-TI-RADS 4B were 0.94 (95% CI: 0.89-0.97), 0.70 (95% CI: 0.60-0.79), 3.20 (95% CI: 2.28-4.39), 0.09 (95% CI: 0.05-0.15), and 33.71 (95% CI: 25.51-42.40), respectively. The area under the summary ROC curve was 0.94 (95% CI: 0.90-0.96).

CONCLUSION

C-TIRADS performed well in malignancy risk stratification of thyroid nodules. C-TIRADS 4B showed strong evidence of detecting malignancy.

Explore the Diagnostic Efficiency of Chinese Thyroid Imaging Reporting and Data Systems by Comparing With the Other Four Systems (ACR TI-RADS, Kwak-TIRADS, KSThR-TIRADS, and EU-TIRADS): A Single-Center Study

PURPOSE

To explore the characteristics of C-TIRADS by comparing it with ACR-TIRADS, Kwak-TIRADS, KSThR-TIRADS and EU-TIRADS.

METHODS

A total of 1096 nodules were collected from 884 patients undergoing thyroidectomy in our center between May 2018 and December 2020. Divided the nodules into two groups: ">10mm" and "≤10mm". Ultrasound characteristics of each nodule were observed and recorded by 2 doctors, then classified based on ACR-TIRADS, Kwak-TIRADS, KSThR-TIRADS, EU-TIRADS, and C-TIRADS.

RESULTS

A total of 682 benign nodules cases (62.23%) and 414 malignant nodules cases (37.77%) were identified. The ICC value of each guideline was:0.937(ACR-TIRADS), 0.858(EU-IRADS), 0.811(Kwak-TIRADS), 0.835(KTA/KSThR-TIRADS) and 0.854(C-TIRADS). The nodule malignancy rates in the groups(Kwak-TIRADS 4B, C-TIRADS 4B、4C) of two sizes were significantly different (all p<0.05). There was no statistical difference in the other grades of two sizes (all p>0.05). Unnecessary biopsy rates were the lowest in C-TIRADS (49.02% p<0.001). Furthermore, Kwak-TIRADS had the highest sensitivity and NPV (89.9%, 91.0%, all p<0.05), while C-TIRADS had the highest specificity and PPV (82.3%, 69.2%, all p<0.05). C-TIRADS and Kwak-TIRADS had the highest accuracy (76.0%, 72.5%, P=0.071). The AUCs of the 5 guidelines were C-TIRADS(0.816, P<0.05), Kwak-TIRADS(0.789, P<0.05) KTA/KSThR-TIRADS and ACR-TIRADS(0.773, 0.763, P=0.305), EU-TIRADS(0.734, P<0.05). The AUCs of the five guidelines were not statistically different between "nodules>10mm" and "nodules ≤ 10mm" (all P>0.05).

CONCLUSIONS

All five guides showed excellent interobserver agreement. C-TIRADS was slightly efficient than Kwak-IRADS, KTA/KSThR-TIRADS and ACR-TIRADS, and had greater advantages than EU-TIRADS. The diagnostic abilities of the five guidelines for "nodules ≤ 10mm" were not inferior to that of "nodules> 10mm". C-TIRADS is simple and easy to implement and can provide effective thyroid tumor risk stratification for thyroid nodule diagnosis, especially in China.