Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases

Abstract

Introduction Our objective is to improve the assistive role of a deep, densely connected convolutional neural network (CNN) to hematopathologists in differentiating histologic images of Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL). We hypothesized that for the majority of cases, a CNN would accurately differentiate BL from DLBCL and attempted to identify design and input variables to improve performance. Methods and Materials In total, 3608 images of BL were collected from 18 cases and 2,071 images of DLBCL were collected from 20 cases using Aperio Image Scope (Leica Biosystems, Buffalo Grove, IL). Cases were randomized into training and unknowns, and three separate CNNs were trained and applied to unknown images. Networks differed by either the size in pixels of the images (network 1 used 32 × 32 while networks 2 and 3 used 224 × 224) or absence of color (in network 2). Decreased numbers of training images were used to evaluate network 3. Results Network 3 performed the best with 17 of 19 (89%) cases classified correctly, 10 with 100% of images correct. Overall, network 3 had 88% accuracy, 80% precision, 81% recall, and an F1 score of 0.81. Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) of 0.89 for DLBCL and 0.88 for BL. Networks 1 and 2 performed more poorly with F1 scores of 0.63 and 0.39, respectively. Decreasing training images by one-half and one-fourth resulted in decreased F1 scores of 0.63 and 0.60, respectively. Conclusions CNNs are promising augmented human intelligence tools for differentiating DLBCL from BL cases and potentially could contribute to improved workflow in the laboratory. More training images, larger image size, and color significantly improved performance of the CNN because of the added information available to the network. More research is under way to optimize the performance of CNNs’ lymphoma application.

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia

“…Deep learning algorithms have been applied on digital pathology images to improve diagnostic accuracy and correlate with biologic subsets [1][2][3][4][5]. Limited studies have described deep learning algorithms to evaluate lymphoid proliferations, and their main focus has been to distinguish benign from malignant conditions [6][7][8][9][10][11] and different subtypes of lymphoma [9,10,12]. Most studies have adopted a patch-wise strategy for whole-slide image analysis, which entails making diagnostic predictions for patches using a convolutional neural network (CNN) followed by fusing patch predictions to render a final slide diagnosis.…”

Is the Time Right to Start Using Digital Pathology and Artificial Intelligence for the Diagnosis of Lymphoma?

“…We highlight three recent articles that have broken the mold by expanding the application of WSI to include hematopathology and reflect on our recent relevant work. [ 5 6 7 ] The first article assessed the usefulness of WSI for the diagnosis of lymphoma[ 5 ] and the other publications employed deep learning with a convolutional neural network (CNN) algorithm to build diagnostic lymphoma models. [ 6 7 ]…”

“…The third publication gives a preview of the potential value of AI in lymphoma diagnosis. [ 7 ] The authors used 10,818 images from BL ( n = 34) and DLBCL ( n = 36) cases to either train or apply different CNNs that differed by number of training images, pixels exploited, color, stain augmentation, and how many layers of the network, among other parameters. [ 7 ] The best performing optimized CNN with respect to image attributes showed a receiver operating characteristic curve analysis area under the curve of 0.92 for both BL and DLBCL.…”

“…[ 7 ] The authors used 10,818 images from BL ( n = 34) and DLBCL ( n = 36) cases to either train or apply different CNNs that differed by number of training images, pixels exploited, color, stain augmentation, and how many layers of the network, among other parameters. [ 7 ] The best performing optimized CNN with respect to image attributes showed a receiver operating characteristic curve analysis area under the curve of 0.92 for both BL and DLBCL. The results of this publication support the premise that larger number of images/cases with enough morphologic variation, as seen in DLBCL subtypes and in cases with transformation, is required to properly train deep learning algorithms and to avoid results from being overfitted.…”

“…Overfitting is a common problem in machine learning that refers to a model that learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data in real practice. It is worth highlighting that both the articles by Achi et al [ 6 ] and Mohlman et al [ 7 ] emphasized the use of AI to separate lymphoma types rather than focus on the use of WSI. However, both studies suffered from a small number of total cases to be placed into a limited number of diagnostic categories.…”