Examination of Malignant Findings of Thyroid Nodules Using Thyroid Ultrasonography

Role of echogenic foci in ultrasonographic risk stratification of thyroid nodules: Echogenic focus scoring in the American College of Radiology Thyroid Imaging Reporting and Data System

Background

Although echogenic foci may raise malignancy rates in thyroid nodules, the association between peripheral calcification or macrocalcification and thyroid carcinoma is controversial. We evaluated the malignancy probability of various echogenic foci and explored whether the method of determining a thyroid nodule’s point score in the echogenic focus category of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) is reasonable.

Methods

We retrospectively evaluated 819 patients with 852 nodules. The patterns of echogenic foci on ultrasonography were classified into the following four categories: punctate echogenic foci, macrocalcification, peripheral calcification, and multiple different types of echogenic foci. The core needle biopsy results were divided into two groups: benign and malignant or suspicious for malignancy.

Results

Among the 852 nodules, 471 (55.3%) had echogenic foci on ultrasonography. Of these nodules, there was no significant statistical difference in the malignant or suspicious for malignancy rate between nodules with peripheral calcification and those with macrocalcification [40.0% (8/20) vs. 30.6% (11/36), respectively; p = 0.474]. The incidence of malignancy or suspicious for malignancy for nodules with peripheral calcification, macrocalcification, or multiple different types of echogenic foci was significantly lower than the incidence for punctate echogenic foci alone, with odds ratios of 0.265 [95% confidence interval (CI): 0.105–0.667; p = 0.005], 0.175 (95% CI: 0.083–0.368; p = 0.000), and 0.256 (95% CI: 0.136–0.482; p = 0.000), respectively.

Conclusion

We found no significant statistical difference in the risk of malignancy or suspicious for malignancy rate between peripheral calcification and macrocalcification in thyroid nodules. We observed that nodules with multiple different types of echogenic foci were not associated with higher malignant or suspicious for malignancy rates compared with nodules with punctate echogenic foci alone.

Improving the diagnosis of thyroid cancer by machine learning and clinical data

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine learning framework to predict thyroid nodule malignancy based on our collected novel clinical dataset. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer.

Assessment of Papillary Thyroid Carcinoma with Ultrasound Examination

Ultrasound examination of the thyroid is useful for preoperative assessment of thyroid nodules including papillary thyroid carcinoma. The examination mainly is to determine the malignant potential of thyroid nodule(s). There are different systems to predict malignant potential in the thyroid nodules and cervical lymph nodes by ultrasound. Ultrasound is used in conjunction with fine-needle aspiration to diagnosis papillary thyroid carcinoma. It is used as guidance to locate the sites to obtain the samples for diagnosis and research in papillary thyroid carcinoma.

Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis

BACKGROUND

Ultrasonography (US) is the most common method of identifying thyroid nodules, but US images require an experienced surgeon for identification. Many artificial intelligence (AI) techniques such as computer-aided diagnostic systems (CAD), deep learning (DL), and machine learning (ML) have been used to assist in the diagnosis of thyroid nodules, but whether AI techniques can improve the diagnostic accuracy of thyroid nodules still needs to be explored.

OBJECTIVE

To clarify the accuracy of AI-based thyroid nodule US images for differentiating benign and malignant thyroid nodules.

METHODS

A search strategy of "subject terms + key words" was used to search PubMed, Cochrane Library, Embase, Web of Science, China Biology Medicine (CBM), and China National Knowledge Infrastructure (CNKI) for studies on AI-assisted diagnosis of thyroid nodules based on US images. The summarized receiver operating characteristic (SROC) curve and the pooled sensitivity and specificity were used to assess the performance of the diagnostic tests. The quality assessment of diagnostics accuracy studies-2 (QUADAS-2) tool was used to assess the methodological quality of the included studies. The Review Manager 5.3 and Stata 15 were used to process the data. Subgroup analysis was based on the integrity of data collection.

RESULTS

A total of 25 studies with 17,429 US images of thyroid nodules were included. AI-assisted diagnostic techniques had better diagnostic efficacy in the diagnosis of benign and malignant thyroid nodules: sensitivity 0.88 (95% CI: (0.85-0.90)), specificity 0.81 (95% CI: 0.74-0.86), diagnostic odds ratio (DOR) 30 (95% CI: 19-46). The SROC curve indicated that the area under the curve (AUC) was 0.92 (95% CI: 0.89-0.94). Threshold effect analysis showed a Spearman correlation coefficient: 0.17 < 0.5, suggesting no threshold effect for the included studies. After a meta-regression analysis of 4 different subgroups, the results showed a statistically significant effect of mean age ≥50 years on heterogeneity. Compared with studies with an average age of ≥50 years, AI-assisted diagnostic techniques had higher diagnostic performance in studies with an average age of <50 years (0.89 (95% CI: 0.87-0.92) vs. 0.80 (95% CI: 0.73-0.88)), (0.83 (95% CI: 0.77-0.88) vs. 0.73 (95% CI: 0.60-0.87)).

CONCLUSIONS

AI-assisted diagnostic techniques had good diagnostic efficacy for thyroid nodules. For the diagnosis of <50 year olds, AI-assisted diagnostic technology was more effective in diagnosis.

Can sonographic features of microcalcification predict thyroid nodule malignancy? a prospective observational study

Background

The aim of this study was to investigate the diagnostic accuracy of microcalcification, as well as its associated sonographic features, for prediction of thyroid nodule malignancy.

We prospectively assessed the patients with thyroid nodule, who underwent ultrasound-guided fine-needle aspiration during 2017–2020 in Babol, northern Iran. The ultrasonographic characteristics of the nodules, as well as their cytological results, were recorded. We used regression analysis to evaluate the relation between sonographic findings and nodule malignancy. A receiver operator characteristics (ROC) analysis was also used to estimate the ability of ultrasound to predict the characteristic features of malignancy, as estimated by the area under the curve (AUC).

Results

Overall, 1129 thyroid nodules were finally included in the study, of which 452 (40%) had microcalcification. A significant positive association was found between nodule malignancy and microcalcification in both univariate (OR=3.626, 95% CI 2.258–5.822) and multivariable regression analyses (OR=1.878, 95% CI 1.095–3.219). In the nodules with microcalcification, significant positive relations were seen between malignancy and hypoechogenicity (OR=3.833, 95% CI 1.032–14.238), >5 microcalcification number (OR=3.045, 95% CI 1.328–6.982), irregular margin (OR=3.341, 95% CI 1.078–10.352), and lobulated margin (OR=5.727, 95% CI 1.934–16.959). The ROC analysis indicated that AUC for hypoechogenicity, >5 microcalcification number, irregular margin, and lobulated margin were 60%, 62%, 55%, and 60%, respectively, in predicting malignant thyroid nodules.

Conclusion

The findings indicated that microcalcification can be a potential predictor of thyroid nodule malignancy. Also, the presence of irregular or lobulated margins, multiple intranodular microcalcification (>5 microcalcifications), and/or hypoechogenicity can improve the ability of microcalcification in distinguishing malignant from benign nodules.