Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients
Oscar Brück, Susanna Lallukka-Brück, Helena Hohtari, Aleksandr Ianevski, Freja Ebeling, Panu Kovanen, Soili Kytölä, Tero Aittokallio, Pedro Marques Ramos, Kimmo Porkka, Satu Mustjoki
Received Date: 29th April 20
In myelodysplastic syndrome (MDS), bone marrow (BM) histopathology is visually assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, many morphological findings elude the human eye. Here, we extracted visual features of 236 MDS, 87 MDS/MPN, and 10 control BM biopsies with convolutional neural networks. Unsupervised analysis distinguished underlying correlations between tissue composition, leukocyte metrics, and clinical characteristics. We applied morphological features in elastic net-regularized regression models to predict genetic and cytogenetic aberrations, prognosis, and clinical variables. By parallelizing tile, pixel, and leukocyte-level image analysis, we deconvoluted each model to texture and cellular composition to dissect their pathobiological context. Model-based mutation predictions correlated with variant allele frequency and number of affected genes per pathway, demonstrating the models’ ability to identify relevant visual patterns. In summary, this study highlights the potential of deep histopathology in hematology by unveiling the fundamental association of BM morphology with genetic and clinical determinants.
Read in full at bioRxiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.