An Automatable Method for Determining Adequacy of Thyroid Fine-Needle Aspiration Samples

Area of Science:

• Medical Imaging
• Computational Pathology
• Cytopathology

Background:

• Thyroid nodules are common, often requiring fine-needle aspiration (FNA) for diagnosis.
• Rapid on-site assessment (ROSA) of FNA adequacy is crucial but uses only part of the sample, hindering further analysis.
• ROSA is time-consuming and poorly reimbursed, necessitating alternative methods.

Purpose of the Study:

• To develop an automated, fluorescence-based image analysis system for assessing thyroid FNA adequacy.
• To utilize the entire aspirated sample for adequacy assessment, preserving material for ancillary testing.
• To create a more efficient and potentially cost-effective method for FNA adequacy evaluation.

Main Methods:

• A fluorescence-based image analysis algorithm was developed.
• Samples were fluorescently stained and imaged using a fluorescent microscope.
• An algorithm assessed image adequacy, with results compared to cytopathologist scoring of ThinPrep slides.

Main Results:

• The algorithm was trained on 12 cases and validated on 11 cases.
• It correctly identified 8 of 8 adequate samples and 2 of 3 inadequate samples in the test group.
• One inadequate case was misclassified by the algorithm.

Conclusions:

• Automated assessment of thyroid FNA adequacy using fluorescence labeling and computer image analysis is feasible.
• This approach offers a potential solution to the limitations of traditional ROSA.
• Further development could enhance diagnostic workflows for thyroid nodules.