Deep Learning Predicts Therapy-Relevant Genetics in Acute Myeloid Leukemia from Pappenheim-stained Bone Marrow Smears.

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Abstract

The detection of genetic aberrations is crucial for early therapy decisions in acute myeloid leukemia (AML) and is recommended for all patients. Since genetic testing is expensive and time-consuming, a need remains for cost-effective, fast, and broadly accessible tests to predict these aberrations in this aggressive malignancy. Here, we developed a novel fully automated end-to-end deep learning pipeline to predict genetic aberrations directly from single-cell images from scans of conventionally stained bone marrow smears already on the day of diagnosis. We used this pipeline to compile a multi-terabyte dataset of over 2,000,000 single-cell images from diagnostic samples of 408 AML patients. These images were then used to train convolutional neural networks for the prediction of various therapy-relevant genetic alterations. We show that the models from our pipeline can significantly predict these subgroups with high AUROCs. Potential genotype-phenotype links were visualized with two different strategies. Our pipeline holds the potential to be used as a fast and inexpensive automated tool to screen AML patients for therapy-relevant genetic aberrations directly from routine, conventionally stained bone marrow smears already on the day of diagnosis. It also creates a foundation to develop similar approaches for other bone marrow disorders in the future.Copyright © 2023 American Society of Hematology.