Deep Learning-Based Morphological Classification between Classical Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma: A Proof of Concept and Literature Review

Author(s): Daniel Rivera, Kristine Ali, Rongzhen Zhang, Brenda Mai, Hanadi El Achi, Jacob Armstrong, Amer Wahed, Andy Nguyen

Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging. However, they were limited to predicting a positive or negative finding for a specific neoplasm. We attempted to use Deep Learning with a convolutional neural network algorithm to build a lymphoma diagnostic model specifically for difficult cases where two differential diagnoses are considered: classical Hodgkin lymphoma and anaplastic large-cell lymphoma. Our software was written in Python language. We obtained digital whole-slide images of hematoxylin and eosin-stained slides of 20 cases, including 10 cases for each diagnostic category. From each WSI, 60 image patches (100x100 pixels) at 20x magnification were obtained to yield 1200 image patches, from which 1079 (90%) were used for training, 108 (9%) for validation, and 120 (10%) for testing. For each test set of 5 images, the predicted diagnosis was combined from the prediction of five images. The test results showed excellent diagnostic accuracy at 100% for both image-by-image prediction and 100% for set-by-set prediction. This preliminary study provided a proof of concept for incorporating automated lymphoma diagnostic screens in specific scenarios into future pathology workflow to augment the pathologists’ productivity.

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