Risk for malignancy of thyroid nodules: Comparative study between TIRADS and US based classification system

Performance of Thyroid Fine-Needle Aspiration Biopsy in a Low- and Middle-Income Country

INTRODUCTION

The 6 categories of the Bethesda System for Reporting Thyroid Cytology (TBSRTC) with associated risk of malignancy (ROM) provide evidence-based clinical management guidelines. This study aimed to determine the ROM and accuracy of FNAB in South Africa (SA).

METHODS

Thyroid specimens from 3 pathology laboratories registered between January 2015 and December 2019 were considered for inclusion. ROM was obtained per TBSRTC category by cytohistological correlation and dividing the total number of specimens with malignant histology by the total number of cases operated. Accuracy was calculated based on the Bethesda category and eventual malignant histology.

RESULTS

Seventeen thousand seven hundred and seventy-three histology and 4,791 cytology cases were identified. Of the 4,791 cytology cases, 931 (19%) underwent surgery. More than a third (333, 35.8%) of cases were confirmed as malignant following histological assessment, with the majority being benign (584, 62.7%). The ROM for the nondiagnostic and benign categories was 24.3% and 20.5%. The highest ROM was for category VI (91.5%), followed by categories V (69.5%), IV (51.9%), and III (38.8%). Thyroid FNAB had a sensitivity of 73%, specificity of 74%, and overall accuracy of 74%.

CONCLUSION

Bethesda categories II and IV have a relatively higher ROM in SA compared to findings from other developed countries. The diagnostic accuracy of thyroid FNAB in SA and the high rate of nondiagnostic diagnoses (38%) require further investigation. A national thyroid registry could provide location-specific data to aid the implementation of appropriate local policies and national guidelines for practicing thyroid surgeons.

Multi-Omics and Management of Follicular Carcinoma of the Thyroid

Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland, accounting for up to 20% of all primary malignant tumors in iodine-replete areas. The diagnostic work-up, staging, risk stratification, management, and follow-up strategies in patients who have FTC are modeled after those of papillary thyroid carcinoma (PTC), even though FTC is more aggressive. FTC has a greater propensity for haematogenous metastasis than PTC. Furthermore, FTC is a phenotypically and genotypically heterogeneous disease. The diagnosis and identification of markers of an aggressive FTC depend on the expertise and thoroughness of pathologists during histopathological analysis. An untreated or metastatic FTC is likely to de-differentiate and become poorly differentiated or undifferentiated and resistant to standard treatment. While thyroid lobectomy is adequate for the treatment of selected patients who have low-risk FTC, it is not advisable for patients whose tumor is larger than 4 cm in diameter or has extensive extra-thyroidal extension. Lobectomy is also not adequate for tumors that have aggressive mutations. Although the prognosis for over 80% of PTC and FTC is good, nearly 20% of the tumors behave aggressively. The introduction of radiomics, pathomics, genomics, transcriptomics, metabolomics, and liquid biopsy have led to improvements in the understanding of tumorigenesis, progression, treatment response, and prognostication of thyroid cancer. The article reviews the challenges that are encountered during the diagnostic work-up, staging, risk stratification, management, and follow-up of patients who have FTC. How the application of multi-omics can strengthen decision-making during the management of follicular carcinoma is also discussed.

The impact of the ultrasound classification on the rate of thyroid surgery indications: a 577 cases series

OBJECTIVES

Nodules of the thyroid gland are common but rarely malignant. Their management can range from simple monitoring to surgery. The use of ultrasound and fine needle aspiration can reduce the rate of unnecessary surgeries. However, there is a risk of false positives and false negatives of malignancy that only pathology can avoid. The objective of this study is to assess the impact of ultrasound classification on the rate of surgical indications.

MATERIAL AND METHODS

Between 2013 and 2017, the ultrasound classification was gradually adopted in our daily practice to become now routine. During this period, we conducted a retrospective study of all the patients who presented to our department for one or more thyroid nodules.

RESULTS

A total of 577 patients were included in the study. We compared two groups, a first where the ultrasound classification was used and a second where this classification was not used. In the end, we found that this classification significantly reduced the surgical indication by 19% while increasing the malignancy detection rate in operated patients by 21%.

CONCLUSIONS

The use of ultrasound classification reduces the indications for surgery while increasing the rate of malignancy in operated patients. The generalization of the use of the ultrasound classification score is strongly recommended in daily practice.

Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips Using Recurrent Fully Convolutional Networks

Medical segmentation is an important but challenging task with applications in standardized report generation, remote medicine and reducing medical exam costs by assisting experts. In this paper, we exploit time sequence information using a novel spatio-temporal recurrent deep learning network to automatically segment the thyroid gland in ultrasound cineclips. We train a DeepLabv3+ based convolutional LSTM model in four stages to perform semantic segmentation by exploiting spatial context from ultrasound cineclips. The backbone DeepLabv3+ model is replicated six times and the output layers are replaced with convolutional LSTM layers in an atrous spatial pyramid pooling configuration. Our proposed model achieves mean intersection over union scores of 0.427 for cysts, 0.533 for nodules and 0.739 for thyroid. We demonstrate the potential application of convolutional LSTM models for thyroid ultrasound segmentation.