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Published on: August 7, 2019
AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis
Irfan Ahmed1,2, Wei Zhang3, Pikting Cheung1
1Department of Physics, City University of Hong Kong, Hong Kong SAR, China.
Insights
An AI-powered virtual immunocytochemistry (ICC) platform rapidly analyzes cell morphology from whole slide images. This AI tool significantly reduces diagnostic time and cost for pathologists evaluating canine lymphoma samples.
Area of Science:
- Veterinary Pathology
- Computational Pathology
- Digital Pathology
Background:
- Traditional immunocytochemistry (ICC) requires extensive time (hours to days), specialized equipment, and skilled personnel for staining biopsy samples.
- Accurate diagnosis of canine lymphomas relies on precise identification of cell types, often requiring ICC.
Purpose of the Study:
- To develop and validate an Artificial Intelligence (AI)-based virtual ICC platform for rapid and accurate cell labeling.
- To assess the platform's performance in diagnosing canine T-cell and B-cell lymph node lymphomas using Fine Needle Aspiration (FNA) samples.
Main Methods:
- Cytopathology slides from 100 canine lymphoma cases were stained with Wright-Giemsa (WG) and ICC reagents (anti-CD3 or anti-PAX5).
- Digital whole slide images underwent pre-processing for stain separation and nuclei segmentation.
- AI model trained on geometrical cell features from 8.48 million segmented cells to predict immuno-positive/negative labels.
Main Results:
- The AI virtual ICC platform achieved high accuracy, with sensitivity and specificity of 0.98 and 0.97 for CD3, and 0.94 and 0.99 for PAX5.
- The platform demonstrated capabilities in cell counting, spatial distribution analysis, segmentation, and classification.
- Virtual ICC analysis completed in minutes, offering significant time and cost savings compared to traditional methods.
Conclusions:
- AI-based virtual ICC provides a rapid, accurate, and precise method for evaluating FNA samples in veterinary diagnostics.
- The platform has the potential to enhance diagnostic cellular and molecular pathology capabilities, particularly for lymphomas.
- This technology offers a valuable alternative to conventional ICC, improving workflow efficiency for pathologists.
Abstract:
Presently, pathologists need to stain biopsy samples with standard and antibody-based immunocytochemistry (ICC) reagents for final diagnosis. Antibody reagents take hours to days to perform staining, along with requiring specialized equipment and technical skills. We have developed an AI-based virtual ICC platform that measures individual cell morphological features in whole slide images and labels the cells as immuno-positive or negative. The platform runs on the cloud in minutes, saving pathologists significant time and cost. For this purpose, cytopathology slides were obtained from N = 100 suspected cases of canine T-cell and B-cell lymph node lymphomas through Fine Needle Aspiration (FNA). Cytopathology slides were initially stained with the standard Wright-Giemsa (WG) and then re-stained with ICC reagents, anti-CD3 or anti-PAX5 antibodies, resulting in a pair of stained slides (WG-CD3 or WG-PAX5). Prior to AI training, cytopathology slides were digitally scanned, and the resulting images underwent a comprehensive pre-processing protocol to separate stains of interest for nuclei segmentation in WG and CD3 or PAX5. Following nuclei segmentation, the cell features from processed image pairs were translated into a structured tabular features format with immuno-positive and negative labeled classes. In total, the geometrical features of 8.48 million segmented cells (4.24 million pairs) were translated into a tabular format and paired based on the Euclidean cell matching algorithm. This approach facilitated the prediction of cell labels, achieving sensitivity and specificity of 0.98 and 0.97 (0.94 and 0.99), respectively for CD3 (PAX5). Additionally, the AI-based virtual ICC has demonstrated capabilities in cell counting, cell spatial distribution, cell segmentation, and classification. It offers a rapid, accurate, and precise evaluation of FNA samples and has the potential to help advance diagnostic cellular and molecular pathology capabilities.
