Mitochondrial DNA "common deletion" in post-fine needle aspiration infarcted oncocytic thyroid tumors

Sonography Could Predict Complete/Near-Complete Necrosis of Thyroid Tumors After Fine-Needle Biopsy: Necrotic Ring Sign

OBJECTIVE

Necrosis, a rare histologic alteration caused by the fine-needle aspiration biopsy (FNA) of thyroid tumors, could be problematic in the clinical management of such cases due to the difficulty in making a definitive histopathologic diagnosis. The aim of this study was to define US features that may help to predict FNA-induced necrosis which is more common in oncocytic tumors.

MATERIALS AND METHODS

This cross-sectional study consists of all patients who underwent thyroid surgery in our center between January 2011 and December 2023. All nodules, which complete/near-complete necrosis reported on final histopathology, were included into study when full video record of preoperative US exam also available for re-evaluation. US findings of necrotic tumors compared with control group consisting of similar histopathology without necrosis.

RESULTS

A total of 11 patients have met the inclusion criteria during study period. Of these cases, seven were papillary thyroid carcinomas and four were follicular adenomas. Among the evaluated distinctive US features, necrotic ring offers the best accuracy (88%) and area under the curve (0.91) values in detecting necrosis after FNA and most valuable when combined with lack of intranodular vascularity.

CONCLUSION

In the preoperative evaluation of surgical candidates, necrotic ring, refractive edge shadowing, posterior acoustic enhancement, and lack of vascular signal may predict complete/near-complete necrosis with great accuracy.

Probability of malignancy and molecular alterations as determined by ThyroSeq v3 genomic classifier in Bethesda Category IV

BACKGROUND

ThyroSeq molecular testing assesses the probability of malignancy (POM) in thyroid fine-needle aspiration cytology (FNAC) with indeterminate cytology. The aim was to investigate whether Bethesda category IV (BIV) subcategories are associated with specific molecular alterations, molecular-derived risk of malignancy (MDROM), and risk of malignancy (ROM).

METHODS

FNAC slides, associated ThyroSeq, version 3, Genomic Classifier results, and surgical follow-up were retrieved for BIV nodules. Nodules were subcategorized as follicular neoplasm (FN) with or without cytologic atypia or oncocytic follicular neoplasm (OFN). The MDROM, ROM, and frequency of molecular alterations in FN and OFN were analyzed. p < .05 was considered significant.

RESULTS

A total of 92 FNAC were identified and subcategorized into 46 FN (15 with and 31 without cytologic atypia) and 46 OFN. The benign call rate and the positive call rate were 49% and 51%, respectively. The MDROM in BIV was 34.3%, trending lower in OFN than in FN. RAS mutations were significantly more frequent in FN when compared to OFN (p = .02). Chromosomal copy number alterations were more often present in OFN than in FN (p < .01). On histologic follow-up, ROM in OFN was trending lower than in FN (p = .1). The most common diagnosis in OFN was oncocytic adenoma, whereas follicular variant papillary thyroid carcinoma was most common in FN.

CONCLUSIONS

The MDROM and ROM were trending lower in OFN compared with FN, and the molecular alterations differed between OFN and FN subcategories.

Probability of malignancy as determined by ThyroSeq v3 genomic classifier varies according to the subtype of atypia

BACKGROUND

ThyroSeq assesses the probability of malignancy (POM) in thyroid fine-needle aspiration cytology specimens diagnosed as atypia of undetermined significance (AUS). The authors investigated whether defined AUS subcategories are associated with specific molecular alterations, the molecular-derived risk of malignancy (MDROM), and the risk of malignancy (ROM).

METHODS

Fine-needle aspiration cytology reports of AUS and corresponding results from the ThyroSeq version 3 genomic classifier results were retrieved and subcategorized as follicular cells with either cytologic atypia (FC-C), architectural atypia (FC-A), both cytologic and architectural atypia (FC-CA), or a predominance of Hurthle cells (PHC). The MDROM, ROM, and frequency of molecular alterations by subcategory were computed and analyzed, and p < .05 was considered significant.

RESULTS

The final analysis included 541 cases subdivided into 233 with FC-A, 104 with FC-C, 116 with FC-CA, and 88 with PHC. The benign call rate and positive call rate for the AUS category were 72% and 28%, respectively, which varied between AUS subcategories. The MDROM by subcategory was 15.9% FC-A, 20.5% FC-C, 33.8% FC-CA, and 14.4% PHC. Histologic follow-up was available for 155 (28%) AUS cases with a follow-up period ≥12 months. The 95% confidence intervals of the MDROMs overlapped with the ROMs. The highest MDROM and ROM were in the FC-CA subcategory. RAS mutations were present in all subcategories. BRAF V600E mutations and papillary thyroid carcinoma were most frequent in the FC-CA subcategory. Noninvasive follicular thyroid neoplasm with papillary-like nuclear features was significantly more frequent in the FC-C subcategory.

CONCLUSIONS

The current results demonstrated that AUS subcategories are associated with specific genetic alterations, the MDROM, and the ROM. Molecular results and an awareness of various cancer probabilities within AUS subcategories can allow for a more tailored management.

Thyroid Hürthle Cell Carcinoma: Clinical, Pathological, and Molecular Features

Simple Summary

Hürthle cell carcinoma (HCC) represents 3–4% of thyroid carcinoma cases. It is characterized by its large, granular and eosinophilic cytoplasm, due to an excessive number of mitochondria. Hürthle cells can be identified only after fine needle aspiration cytology biopsy or by histological diagnosis after the surgical operation. Published studies on HCC indicate its putative high aggressiveness. In this article, current knowledge of HCC focusing on clinical features, cytopathological features, genetic changes, as well as pitfalls in diagnosis are reviewed in order to improve clinical management.

Abstract

Hürthle cell carcinoma (HCC) represents 3–4% of thyroid carcinoma cases. It is considered to be more aggressive than non-oncocytic thyroid carcinomas. However, due to its rarity, the pathological characteristics and biological behavior of HCC remain to be elucidated. The Hürthle cell is characterized cytologically as a large cell with abundant eosinophilic, granular cytoplasm, and a large hyperchromatic nucleus with a prominent nucleolus. Cytoplasmic granularity is due to the presence of numerous mitochondria. These mitochondria display packed stacking cristae and are arranged in the center. HCC is more often observed in females in their 50–60s. Preoperative diagnosis is challenging, but indicators of malignancy are male, older age, tumor size > 4 cm, a solid nodule with an irregular border, or the presence of psammoma calcifications according to ultrasound. Thyroid lobectomy alone is sufficient treatment for small, unifocal, intrathyroidal carcinomas, or clinically detectable cervical nodal metastases, but total thyroidectomy is recommended for tumors larger than 4 cm. The effectiveness of radioactive iodine is still debated. Molecular changes involve cellular signaling pathways and mitochondria-related DNA. Current knowledge of Hürthle cell carcinoma, including clinical, pathological, and molecular features, with the aim of improving clinical management, is reviewed.

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms

BACKGROUND

Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obtain a diagnosis despite most being non-cancerous. It is highly desirable to avoid surgery and to provide accurate classification of benignity versus malignancy from FNAB preoperatively. In our first-generation algorithm, Gene Expression Classifier (GEC), we achieved this goal by using machine learning (ML) on gene expression features. The classifier is sensitive, but not specific due in part to the presence of non-neoplastic benign Hürthle cells in many FNAB.

RESULTS

We sought to overcome this low-specificity limitation by expanding the feature set for ML using next-generation whole transcriptome RNA sequencing and called the improved algorithm the Genomic Sequencing Classifier (GSC). The Hürthle identification leverages mitochondrial expression and we developed novel feature extraction mechanisms to measure chromosomal and genomic level loss-of-heterozygosity (LOH) for the algorithm. Additionally, we developed a multi-layered system of cascading classifiers to sequentially triage Hürthle cell-containing FNAB, including: 1. presence of Hürthle cells, 2. presence of neoplastic Hürthle cells, and 3. presence of benign Hürthle cells. The final Hürthle cell Index utilizes 1048 nuclear and mitochondrial genes; and Hürthle cell Neoplasm Index leverages LOH features as well as 2041 genes. Both indices are Support Vector Machine (SVM) based. The third classifier, the GSC Benign/Suspicious classifier, utilizes 1115 core genes and is an ensemble classifier incorporating 12 individual models.

CONCLUSIONS

The accurate algorithmic depiction of this complex biological system among Hürthle subtypes results in a dramatic improvement of classification performance; specificity among Hürthle cell neoplasms increases from 11.8% with the GEC to 58.8% with the GSC, while maintaining the same sensitivity of 89%.