Non-Marked Hypoechogenic Nodules: Multicenter Study on the Thyroid Malignancy Risk Stratification and Accuracy Based on TIRADS Systems Comparison

Correlation of pN Stage and Hypoechogenicity with Tumour Encapsulation and Vascular Invasion in Thyroid Cancer (TC): A Comprehensive Analysis and Clinical Outcomes

In this retrospective study, the relationship between the pN stage of TC and the ultrasound hypoechogenicity of tumour encapsulation and vascular invasion was investigated. The data of a total of 678 TC patients were analysed. The goal of this study was to assess the significance of the pTNM score and preoperative ultrasound features in predicting cancer prognosis and guiding therapeutic decisions in patients with TC. The main research methods included a retrospective analysis of patient data, mainly the pTNM score and presence of tumour encapsulation and vascular invasion obtained from histopathological results and preoperative ultrasound imaging. Patients with well-differentiated TCs (papillary and follicular) were extracted from TC patients to better unify the results because of similar clinical strategies for these TCs. Significant associations were observed between advanced pN stage and the presence of encapsulation and vessel invasion. The majority of pN1a patients exhibited encapsulation (77.71%; p < 0.0001) and vascular invasion (75.30%; p < 0.0001), as did the majority of pN1b patients (100%; p < 0.0001 and 100%; p < 0.0001, respectively). Less than half of the patients with hypoeghogenic patterns presented with encapsulation (43.30%; p < 0.0001) and vascular invasion (43.52%; p < 0.0001), while the vast majority of patients without hypoechogenicity did not present with encapsulation (90.97%; p < 0.0001) or vascular invasion (90.97%; p < 0.0001). Hypoechogenicity was found to be indicative of aggressive tumour behaviour. The results of this study underscore the importance of accurate N staging in TC and suggests the potential use of ultrasound features in predicting tumour behaviour. Further research is needed to confirm these findings and explore additional prognostic markers to streamline TC management strategies and improve patient outcomes.

Correlation analysis between BRAFV600E mutation and ultrasonic and clinical features of papillary thyroid cancer

Purpose

The study investigates the value of the BRAFV600E mutation in determining the aggressiveness of papillary thyroid cancer (PTC) and its correlation with ultrasound features.

Methods

The study selected 176 patients with BRAFV600E mutation and 80 without the mutation who underwent surgery at Guangxi Medical University Cancer Hospital. Clinical and pathological data were collected, focusing on BRAFV600E mutations and associated ultrasonic features. Correlation analysis, as well as univariate and multivariate logistic regression analysis, were conducted to identify independent risk factors for BRAFV600E mutation. The results were verified using a nomogram model.

Results

The analysis results indicate that the BRAFV600E mutation correlates with tumor size, nodule size, taller-than-wide shape, margin, and shape of papillary thyroid cancer. The receiver operating characteristic curve was used to analyze the diagnostic effect of these features on BRAFV600E mutation. The results showed that nodule size had the most significant area under the curve (AUC = 0.665). Univariate and multivariate logistic regression analyses revealed that taller-than-wide shape ≥1, ill-defined margin, irregular shape, nodule size (≤1.40 cm), TT4 (>98.67 nmol/L), and FT3 (<4.14 pmol/L) were independent risk factors for BRAFV600E mutation. While considering all these factors in the nomogram, the Concordance index (C-index) remained high at 0.764. This suggests that the model has a good predictive effect.

Conclusion

Ultrasound features including nodule size, taller-than-wide shape ≥1, ill-defined margins, irregular shape, higher TT4 levels, and lower FT3 levels were associated with papillary thyroid cancer aggressiveness and BRAFV600E mutation.

Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

Simple Summary

In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes.

Abstract

Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.