Classification and Morphological Analysis of DLBCL Subtypes in H\&E-Stained Slides

TL;DR

This paper presents a deep learning approach for classifying DLBCL subtypes in H&E-stained slides, achieving high accuracy and providing biological insights into morphological features, aiding clinical decision-making.

Contribution

The study introduces a robust deep learning model for DLBCL subtype classification and analyzes morphological features to understand visual differences between subtypes.

Findings

Deep learning model achieved 87.4% AUC in classification.

Morphological features showed subtle differences between subtypes.

Model has potential for clinical triaging and treatment planning.

Abstract

We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essential for determining the appropriate therapeutic strategy, given their distinct molecular profiles and treatment responses. Our proposed deep learning model demonstrates robust performance, achieving an average area under the curve (AUC) of (87.4 pm 5.7)\% during cross-validation. It shows a high positive predictive value (PPV), highlighting its potential for clinical application, such as triaging for molecular testing. To gain biological insights, we performed an analysis of morphological features of ABC and GCB subtypes. We segmented cell nuclei using a pre-trained deep neural network and compared the statistics of geometric and color features for ABC and GCB. We found that the distributions of these features were not very different for the two subtypes, which suggests that the visual differences between them are more subtle. These results underscore the potential of our method to assist in more precise subtype classification and can contribute to improved treatment management and outcomes for patients of DLBCL.