Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques

Study on diagnosis of thyroid nodules based on convolutional neural network

OBJECTIVE

An artificial intelligence (AI) algorithm based on convolutional neural networks was used in ultrasound diagnosis in order to evaluate its performance in judging the nature of thyroid nodules and nodule classification.

METHODS

A total of 105 patients with thyroid nodules confirmed by surgery or biopsy were retrospectively analyzed. The properties, characteristics, and classification of thyroid nodules were evaluated by sonographers and by AI to obtain combined diagnoses. Receiver operating characteristic curves were generated to evaluate the performance of AI, the sonographer, and their combined effort in diagnosing the nature of thyroid nodules and classifying their characteristics. In the diagnosis of thyroid nodules with solid components, hypoechoic appearance, indistinct borders, Anteroposterior/transverse diameter ratio > 1(A/T > 1), and calcification performed by sonographers and by AI, the properties exhibited statistically significant differences.

RESULTS

Sonographers had a sensitivity of 80.7%, specificity of 73.7%, accuracy of 79.0%, and area under the curve (AUC) of 0.751 in the diagnosis of benign and malignant thyroid nodules. AI had a sensitivity of 84.5%, specificity of 81.0%, accuracy of 84.7%, and AUC of 0.803. The combined AI and sonographer diagnosis had a sensitivity of 92.1%, specificity of 86.3%, accuracy of 91.7%, and AUC of 0.910.

CONCLUSION

The efficacy of a combined diagnosis for benign and malignant thyroid nodules is higher than that of an AI-based diagnosis alone or a sonographer-based diagnosis alone. The combined diagnosis can reduce unnecessary fine-needle aspiration biopsy procedures and better evaluate the necessity of surgery in clinical practice.

The Spectrum of Thyroid Nodules at Kinshasa University Hospital, Democratic Republic of Congo: A Cross-Sectional Study

We analyzed the spectrum of thyroid nodules in patients attending the endocrinology unit care of the Kinshasa University Hospital and assessed their associated factors. We conducted a cross-sectional study, performing descriptive statistics and logistic regression. From the 888 enrolled patients, thyroid nodules were detected in 658 patients (74.1%), as mononodules in 22.5% and multiple nodules in 77.5%. Thyroid function was normal in 71.3% cases, while hyperthyroidism and hypothyroidism were found in 26.1% and 2.6% of cases, respectively. Women were more affected than men (75.1% vs. 63.6%; p = 0.03). Patients with thyroid nodules were older (44 ± 12 vs. 38 ± 12 years; p < 0.001), with a family history of goiter (38.3% vs. 27.4%; p = 0.003) and residence in the iodine-deficient region (51.7% vs. 38.8%; p = 0.012); they had a higher proportion of longer delays to consultation (47% vs. 20%; p < 0.001), but a higher rate of normal thyroid function (85.5% vs. 3 1.3%; p < 0.001). Thyroid nodules were associated with the delay to consultation (for duration ≥ three years, OR: 6.560 [95% CI: 3.525−12.208)], multiparity (present vs. absent: 2.863 [1.475−5.557]) and family history of goiter (present vs. absent: 2.086 [95% CI:1.231−3.534]) in female patients alone. The high frequency of thyroid nodules observed requires measures aimed at early detection in the population, the training of doctors involved in the management and the strengthening of technical platforms in our hospitals.

Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data

BACKGROUND

This study aims to show the impact of imbalanced data and the typical evaluation methods in developing and misleading assessments of machine learning-based models for preoperative thyroid nodules screening.

STUDY DESIGN

A retrospective study.

METHODS

The ultrasonography features for 431 thyroid nodules cases were extracted from medical records of 313 patients in Babol, Iran. Since thyroid nodules are commonly benign, the relevant data are usually unbalanced in classes. It can lead to the bias of learning models toward the majority class. To solve it, a hybrid resampling method called the Smote-was used to creating balance data. Following that, the support vector classification (SVC) algorithm was trained by balance and unbalanced datasets as Models 2 and 3, respectively, in Python language programming. Their performance was then compared with the logistic regression model as Model 1 that fitted traditionally.

RESULTS

The prevalence of malignant nodules was obtained at 14% (n = 61). In addition, 87% of the patients in this study were women. However, there was no difference in the prevalence of malignancy for gender. Furthermore, the accuracy, area under the curve, and geometric mean values were estimated at 92.1%, 93.2%, and 76.8% for Model 1, 91.3%, 93%, and 77.6% for Model 2, and finally, 91%, 92.6% and 84.2% for Model 3, respectively. Similarly, the results identified Micro calcification, Taller than wide shape, as well as lack of ISO and hyperechogenicity features as the most effective malignant variables.

CONCLUSION

Paying attention to data challenges, such as data imbalances, and using proper criteria measures can improve the performance of machine learning models for preoperative thyroid nodules screening.

Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis

Objective

The aim of this study was to evaluate the accuracy of deep learning using the convolutional neural network VGGNet model in distinguishing benign and malignant thyroid nodules based on ultrasound images.

Methods

Relevant studies were selected from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang databases, which used the deep learning-related convolutional neural network VGGNet model to classify benign and malignant thyroid nodules based on ultrasound images. Cytology and pathology were used as gold standards. Furthermore, reported eligibility and risk bias were assessed using the QUADAS-2 tool, and the diagnostic accuracy of deep learning VGGNet was analyzed with pooled sensitivity, pooled specificity, diagnostic odds ratio, and the area under the curve.

Results

A total of 11 studies were included in this meta-analysis. The overall estimates of sensitivity and specificity were 0.87 [95% CI (0.83, 0.91)] and 0.85 [95% CI (0.79, 0.90)], respectively. The diagnostic odds ratio was 38.79 [95% CI (22.49, 66.91)]. The area under the curve was 0.93 [95% CI (0.90, 0.95)]. No obvious publication bias was found.

Conclusion

Deep learning using the convolutional neural network VGGNet model based on ultrasound images performed good diagnostic efficacy in distinguishing benign and malignant thyroid nodules.

Systematic Review Registration

https://www.crd.york.ac.nk/prospero, identifier CRD42022336701.

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules

Purpose

The fully automatic AI-Sonic computer-aided design (CAD) system was employed for the detection and diagnosis of benign and malignant thyroid nodules. The aim of this study was to investigate the efficiency of the AI-Sonic CAD system with the use of a deep learning algorithm to improve the diagnostic accuracy of ultrasound-guided fine-needle aspiration (FNA).

Methods

A total of 138 thyroid nodules were collected from 124 patients and diagnosed by an expert, a novice, and the Thyroid Imaging Reporting and Data System (TI-RADS). Diagnostic efficiency and feasibility were compared among the expert, novice, and CAD system. The application of the CAD system to enhance the diagnostic efficiency of novices was assessed. Moreover, with the experience of the expert as the gold standard, the values of features detected by the CAD system were also analyzed. The efficiency of FNA was compared among the expert, novice, and CAD system to determine whether the CAD system is helpful for the management of FNA.

Result

In total, 56 malignant and 82 benign thyroid nodules were collected from the 124 patients (mean age, 46.4 ± 12.1 years; range, 12–70 years). The diagnostic area under the curve of the CAD system, expert, and novice were 0.919, 0.891, and 0.877, respectively (p < 0.05). In regard to feature detection, there was no significant differences in the margin and composition between the benign and malignant nodules (p > 0.05), while echogenicity and the existence of echogenic foci were of great significance (p < 0.05). For the recommendation of FNA, the results showed that the CAD system had better performance than the expert and novice (p < 0.05).

Conclusions

Precise diagnosis and recommendation of FNA are continuing hot topics for thyroid nodules. The CAD system based on deep learning had better accuracy and feasibility for the diagnosis of thyroid nodules, and was useful to avoid unnecessary FNA. The CAD system is potentially an effective auxiliary approach for diagnosis and asymptomatic screening, especially in developing areas.

Prognostic analysis of recurrence in children and adolescents with differentiated thyroid cancer

Background:

The standard treatment for pediatric differentiated thyroid carcinoma (DTC) still requires consideration because of a lack of clinical evidence. The purpose of this study was to summarize the clinical experiences and explore the risk factors for post-operative recurrence through a retrospective analysis to develop better clinical strategies for pediatric DTC.

Methods:

This study retrospectively analyzed children and adolescents with DTC who were treated between January 1999 and December 2014 at the Cancer Hospital, Chinese Academy of Medical Sciences. Clinicopathological results and outcomes were collected. A log-rank test of Kaplan-Meier curves and the Cox regression model were used to determine the factors associated with recurrence.

Results:

Data of 150 patients were collected in this study. During the follow-up, there was only one disease-related death. The recurrence rates at 3, 5, and 10 years were 13.6%, 18.7%, and 28.6%, respectively. There was a significant difference in the rate of recurrence according to age (P < 0.001), extrathyroidal extension (P < 0.001), lymph node metastasis (P = 0.023), and invasion of the trachea and esophageal wall (P = 0.004). Cox regression analysis demonstrated that age (P = 0.006) and extrathyroidal extension (P = 0.013) were significant dependent factors of post-operative recurrence.

Conclusions:

The prognosis of DTC in children and adolescents is favorable. A close follow-up is recommended because of the high recurrence rate. A comparatively higher recurrence rate was observed in the younger age group, and new age-based divisions may be needed to conveniently evaluate the possibility of recurrence.

Diagnostic performance evaluation of different TI-RADS using ultrasound computer-aided diagnosis of thyroid nodules: An experience with adjusted settings

BACKGROUND

Thyroid cancer diagnosis has evolved to include computer-aided diagnosis (CAD) approaches to overcome the limitations of human ultrasound feature assessment. This study aimed to evaluate the diagnostic performance of a CAD system in thyroid nodule differentiation using varied settings.

METHODS

Ultrasound images of 205 thyroid nodules from 198 patients were analysed in this retrospective study. AmCAD-UT software was used at default settings and 3 adjusted settings to diagnose the nodules. Six risk-stratification systems in the software were used to classify the thyroid nodules: The American Thyroid Association (ATA), American College of Radiology Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), British Thyroid Association (BTA), European Union (EU-TIRADS), Kwak (2011) and the Korean Society of Thyroid Radiology (KSThR). The diagnostic performance of CAD was determined relative to the histopathology and/or cytology diagnosis of each nodule.

RESULTS

At the default setting, EU-TIRADS yielded the highest sensitivity, 82.6% and lowest specificity, 42.1% while the ATA-TIRADS yielded the highest specificity, 66.4%. Kwak had the highest AUROC (0.74) which was comparable to that of ACR, ATA, and KSThR TIRADS (0.72, 0.73, and 0.70 respectively). At a hyperechoic foci setting of 3.5 with other settings at median values; ATA had the best-balanced sensitivity, specificity and good AUROC (70.4%; 67.3% and 0.71 respectively).

CONCLUSION

The default setting achieved the best diagnostic performance with all TIRADS and was best for maximizing the sensitivity of EU-TIRADS. Adjusting the settings by only reducing the sensitivity to echogenic foci may be most helpful for improving specificity with minimal change in sensitivity.

A Review of the Pathology, Diagnosis and Management of Colloid Goitre

Colloid goitres are a common, benign lesion of the thyroid gland, that involve a diffuse or nodular pattern. An important feature of colloid goitre is the contained colloid material inside and the comet-tail artefacts, which show up on ultrasound. However, some cases of colloid goitre do not show a comet-tail sign and sometimes these artefacts need to be distinguished from microcalcifications in malignant lesions. This review discusses the epidemiology, pathophysiology, clinical manifestations, ultrasound features and management of colloid goitres.