Cytotechnologist Performance for Screening Hürthle Cell Atypia in Indeterminate Thyroid Fine-Needle Aspirates

Challenges in Cytology Specimens With Hürthle Cells

In fine-needle aspirations (FNA) of thyroid, Hürthle cells can be found in a broad spectrum of lesions, ranging from non-neoplastic conditions to aggressive malignant tumors. Recognize them morphologically, frequently represents a challenging for an adequately diagnosis and are associated with a significant interobserver variability. Although the limitations of the morphologic diagnosis still exist, the interpretation of the context where the cells appear and the recent advances in the molecular knowledge of Hürthle cells tumors are contributing for a more precise diagnosis. This review aims to describe the cytology aspects of all Hürthle cells neoplastic and non-neoplastic thyroid lesions, focusing on the differential diagnosis and reporting according to The Bethesda System for Reporting Thyroid Cytology (TBSRTC). New entities according to the latest World Health Organization (WHO) classification are included, as well as an update of the current molecular data.

Hürthle cell lesions of the thyroid: Progress made and challenges remaining

Hürthle cell-predominant thyroid fine needle aspirations (FNA) are encountered frequently in routine practice, yet they are often challenging to diagnose accurately and are associated with significant interobserver variability. This is largely due to the ubiquity of Hürthle cells in thyroid pathology, ranging from nonneoplastic conditions to aggressive malignancies. Although limitations in cytomorphologic diagnoses likely will remain for the foreseeable future, our knowledge of the molecular pathogenesis of Hürthle cell neoplasia and application of molecular testing to cytologic material have increased dramatically within the past decade. This review provides context behind the challenges in diagnosis of Hürthle cell lesions and summarizes the more recent advances in diagnostic tools.

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%.