Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears
Jan‐Niklas Eckardt, Jan Moritz Middeke, Sebastian Riechert, Tim Schmittmann, Anas Shekh Sulaiman, Michael Krämer, Katja Sockel, Frank Kroschinsky, Ulrich S. Schuler, Johannes Schetelig, Christoph Röllig, Christian Thiede, Karsten Wendt, Martin Bornhäuser
Abstract
The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 (NPM1)-one of the most common mutations in AML-with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1-mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions.