Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma.

Area of Science:

• Veterinary Pathology
• Computational Pathology
• Artificial Intelligence in Medicine

Background:

• Diffuse large B-cell lymphoma (DLBCL) is a common cancer in humans and dogs.
• Canine DLBCL (cDLBCL) is aggressive, and current diagnosis relies on time-consuming histopathology.
• There's a need for faster, more accurate diagnostic tools in veterinary medicine.

Purpose of the Study:

• To develop and evaluate a convolutional neural network (CNN) for differentiating cDLBCL from reactive lymphoid hyperplasia (RLH) in canine lymph node biopsies.
• To introduce HawksheadNet, a novel CNN architecture for cancer image classification.
• To assess the impact of stain normalization on CNN performance.

Main Methods:

• Whole slide images (WSIs) of H&E stained canine lymph nodes were digitized.
• A modified Aachen protocol was used for image pre-processing.
• HawksheadNet, a lightweight CNN, was trained and fine-tuned on a dataset split into training, validation, and testing sets.
• Stain normalization using StainNet was applied.

Main Results:

• HawksheadNet achieved a high area under the receiver operating characteristic (AUROC) of 0.9691 for differentiating cDLBCL from RLH on StainNet-normalized images.
• The CNN outperformed other pre-trained models like EfficientNet, Inception, and MobileNet.
• WSI segmentation using tile-wise predictions provided visual diagnostic aids aligned with pathologist interpretations.

Conclusions:

• Convolutional neural networks, particularly HawksheadNet, show significant potential for accurate cDLBCL diagnosis from WSIs.
• Stain normalization is crucial for optimizing CNN performance in veterinary cancer image analysis.
• This AI-driven approach can enhance veterinary diagnostic workflows, potentially improving patient care and prognostication.