Lab technique

Recent Advances in Fine Needle Aspiration Cytology (FNAC) for Diagnosis in Veterinary Sciences

August 17, 2025

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Recent Advances in Fine Needle Aspiration Cytology (FNAC) for Diagnosis in Veterinary Sciences

Dipesh Kumar Dabi, Pranesh M and Geeta Devi Leishangthem

Department of Veterinary Pathology, College of Veterinary Science, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India.

*Corresponding Author: dipesh692001@gmail.com

Abstract

Fine Needle Aspiration Cytology (FNAC) has long been an essential tool in veterinary diagnostics due to its simplicity, cost-effectiveness, and minimally invasive nature. However, recent scientific and technological innovations have transformed FNAC into a highly sophisticated diagnostic approach. This review highlights the recent advances in FNAC techniques in veterinary science, examining each development’s advantages and disadvantages. From digital cytology, artificial intelligence (AI), and machine learning applications to molecular diagnostics, imaging-guided FNAC, and the emergence of standardized protocols, this article presents a comprehensive overview of how FNAC is evolving into a more reliable, precise, and informative diagnostic modality.

Introduction

FNAC is a quick, minimally invasive procedure used to assess lumps or masses by aspirating cells through a fine needle, providing cytological diagnosis without the need for surgery. In veterinary medicine, accurate and rapid diagnosis of tumors and other lesions is crucial for effective treatment planning. It offers a minimally invasive method to collect cellular material for microscopic evaluation. Whether for diagnosing a suspicious lump or staging a tumor, FNAC helps to deliver rapid answers without surgical intervention. Despite its early success, FNAC traditionally faced challenges such as limited sampling accuracy and subjective interpretation. Recent advances aim to overcome these hurdles, enhancing diagnostic yield, reliability, and clinical utility. This article explores these innovations, emphasizing their practical implications in veterinary science.

Digital Cytology and Artificial Intelligence

Digital cytology offers real-time analysis with high-resolution images of FNAC smears, enabling further tele-analysis and even pattern recognition through AI within seconds and can tell minutiae differences. Conventional FNAC vs. Digital Cytology as a novel approach, digital cytology and AI in FNAC have the potential to revolutionize the field of cytopathology through improved accessibility, efficiency, training, and diagnostic performance.

Advantages:

Enables remote consultations (telecytology)

Reduces human error

Accelerates diagnosis

Disadvantages:

Requires expensive equipment

Needs stable internet

Initial setup cost is high

Machine Learning Algorithms in Cytology Interpretation

Machine learning (M L) models

Machine learning models are trained on thousands of annotated cytology images to recognize malignancy patterns and cellular features for accurate diagnosis.

Advantages:

Improves diagnostic accuracy

Reduces pathologist workload

Learns continuously from new data

Disadvantages:

Requires vast datasets

Cannot fully replace human judgment

Risk of bias if poorly trained

Integration of FNAC with Liquid Biopsy Techniques

Liquid biopsy analyzes biomarkers from blood or body fluids, complementing FNAC findings. It serves as a valuable complement to FNAC by providing molecular-level insights, especially for tumors that are difficult to access.

Advantages:

Provides molecular and cytological data

Useful for inaccessible tumors

Monitors tumor recurrence

Disadvantages:

Specialized lab required

Risk of false positives/negatives

Costly procedure

Molecular Diagnostics from FNAC Samples

Material from FNAC can undergo PCR and NGS for detecting genetic abnormalities.

Advantages:

Enables precise tumor typing

Supports personalized treatments

Detects minimal residual disease

Disadvantages:

High technical complexity

Costly

Not feasible everywhere

Imaging-Guided FNAC (Ultrasound and CT Assistance)

Ultrasound or CT imaging helps guide needle placement precisely during FNAC. This technique is becoming very popular in well-developed clinics as it allows accurate sampling of deep-seated or small lesions, minimizes complications, and improves diagnostic yield, especially in challenging veterinary cases.

Advantages:

Targets accurate lesion areas

Minimizes sampling errors

Reduces complications

Disadvantages:

Equipment expensive

Trained personnel needed

May not be available in rural setups

3D Cytology Imaging and Predictive AI Models

Emerging era of 3D cytology imaging and AI models can predict tumor behavior and progression in a very precise and non-invasive manner, significantly enhancing early diagnosis and treatment strategies in veterinary oncology

Advantages:

Better visualization of cells

Early tumor prediction

Highly individualized therapy

Disadvantages:

Still experimental

Needs heavy computational resources

Standardization of FNAC Protocols and Training Programs

Standardized protocols and training ensure consistency in FNAC practices for that a guidelines should be issued with detail description of all the important veterinary diseases and tumors so it will be easy for veterinarians to easily diagnose the anomaly.

Advantages:

Reduces observer variability

Improves diagnostic quality

Boosts training outcomes

Disadvantages:

Implementation requires time

Resistance from traditionally trained practitioners

Table 1. Comparison of FNAC Technological Advancements

Innovation	Advantages	Disadvantages
Digital Cytology	Remote access, fast diagnosis	Expensive equipment, internet-dependent
Machine Learning	High accuracy, reduces workload	Needs large datasets, bias risk
Liquid Biopsy	Molecular + cytology data	Costly, risk of errors
Molecular Diagnostics	Precise typing, personalized therapy	Technically complex
Imaging-Guided FNAC	Precise targeting, fewer errors	Needs expensive tools
3D Imaging + AI	Early prediction, better views	Still experimental
Standardized Protocols	Consistency, better training	Slow adoption

Conclusions:

Recent advances in Fine Needle Aspiration Cytology have significantly expanded its diagnostic potential in veterinary science. Innovations like digital cytology, AI, machine learning, liquid biopsy integration, molecular diagnostics, and imaging guidance have transformed FNAC from a basic tool into a sophisticated, multi-parametric diagnostic modality. Each of the advancement brings clear advantages but also poses challenges that require careful adaptation and continuous training. By integrating these advancements, veterinarians can ensure earlier, more accurate diagnoses and optimized therapeutic interventions, improving the overall health outcomes for animal patients.