Application of a Convolutional Neural Network to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma

Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) are entities that can present a diagnostic challenge due to overlapping morphological features and often require exhaustive phenotypic and often expensive ancillary testing to yield a final diagnosis. On occasion, even with extensive testing and depending on the cytogenetic or molecular findings, the final diagnosis can be challenging. Convolutional neural networks (CNNs) are a popular machine-learning method for object recognition. The objective of this study was to evaluate if a CNN could reliably differentiate between images of BL and DLBCL. Two hundred images (×20) from a single BL case and 200 images (×20) from a single DLBCL case were captured using Aperio ImageScope (Leica Biosystems, Buffalo Grove, IL). Based on previously published work, a deep and densely connected CNN (DenseNet) was developed and trained over the images and tested on two holdout subsets, one of DLBCL images (n = 10) and another of 10 randomly selected (n = 5 DLBCL and n = 5 BL) unknown images from the original lymphoma cases. The 121-layered network was built by optimizing cross-entropy loss during mini-batch training. An element-wise sigmoid nonlinearity function was applied to the outputs of the final, fully connected layer. The resulting output was the predicted probability of each lymphoma class for the image. The CNN predicted with 100% accuracy both the subset of DLBCL images (10/10) as well as the lymphoma unknown images (DLBCL = 5/5; BL = 5/5) all with >99.9% probability. CNNs hold promise for visual recognition of lymphoma subtypes; however, more research is needed to prove this concept, and an efficient process for generating extremely large amounts of high-quality magnified images of annotated slides will be necessary to fully test this concept and apply CNNs in more nuanced lymphoma cases and in a broader scope.