The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In these methods, the selection of quantitative features that describe cytological characteristics plays a key role. They are expected to add objectivity and consistency among observers to the geometric, color, or texture variables that pathologists usually interpret from visual inspection. In a recent paper in The Journal of Pathology, El Hussein, Chen et al proposed an algorithmic procedure to assist pathologists in the diagnostic evaluation of chronic lymphocytic leukemia (CLL) progression using whole-slide image analysis of tissue samples. The core of the procedure was a set of quantitative descriptors (biomarkers) calculated from the segmentation of cell nuclei, which was performed using a convolutional neural network. These biomarkers were based on clinical practice and easily calculated with reproducible tools. They were used as input to a machine learning algorithm that provided classification in one of the stages of CLL progression. Works like this can contribute to the integration into the workflow of clinical laboratories of automated diagnostic systems based on the morphological analysis of histological slides and blood smears. © 2021 The Pathological Society of Great Britain and Ireland.
Keywords: CLL progression; artificial intelligence; cell morphology; cellular biomarker; chronic lymphocytic leukemia; deep learning; explainability; large B-cell lymphoma.
© 2021 The Pathological Society of Great Britain and Ireland.