  The accurate classification of lymphoma subtypes using hematoxylin and eosin
(H&E)-stained tissue is complicated by the wide range of morphological features
these cancers can exhibit. We present LymphoML - an interpretable machine
learning method that identifies morphologic features that correlate with
lymphoma subtypes. Our method applies steps to process H&E-stained tissue
microarray cores, segment nuclei and cells, compute features encompassing
morphology, texture, and architecture, and train gradient-boosted models to
make diagnostic predictions. LymphoML's interpretable models, developed on a
limited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracy
to pathologists using whole-slide images and outperform black box deep-learning
on a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. Using
SHapley Additive exPlanation (SHAP) analysis, we assess the impact of each
feature on model prediction and find that nuclear shape features are most
discriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma
(F1-score: 74.5%). Finally, we provide the first demonstration that a model
combining features from H&E-stained tissue with features from a standardized
panel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a
46-stain panel (86.1%).
