Abstract
Bone marrow (BM) cytology and histopathology images are crucial for diagnosing and prognosticating myeloid neoplasms (MNs), but their high-dimensional data are underused. Artificial Intelligence (AI) applied to tumor morphology (digital pathology, DP) has improved the use of tumor biopsies’ data for various types of malignancies, accurately detecting patterns and converting complex image information into numerical features. Here, we explored the potential of AI-based DP to improve personalized medicine in MNs which are characterized by high heterogeneity and a significant proportion of patients with unmet clinical needs.
This project was conducted by the GenoMed4All and Synthema consortia, to build AI-based features extraction tools from BM histopathological and cytological Whole Slide Images (WSI). High-dimensional data were used to 1) assess diagnostic accuracy in MN patients; 2) elucidate the association between morphologic features, clinical variables and molecular genetics, and 3) create an innovative tool for personalized risk assessments integrating morphological features with clinical and genomic information.