Role of artificial intelligence in haematolymphoid diagnostics.

The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenue for extracting relevant information from complex data structures. From diagnostic assistance to powerful research tools, the potential fields of application of machine learning techniques in pathology are vast and constitute the subject of considerable research work.

Large B-cell lymphomas with CCND1 rearrangement have different immunoglobulin gene breakpoints and genomic profile than mantle cell lymphoma.

Mantle cell lymphoma (MCL) is genetically characterized by the IG::CCND1 translocation mediated by an aberrant V(D)J rearrangement. CCND1 translocations and overexpression have been identified in occasional aggressive B-cell lymphomas with unusual features for MCL. The mechanism generating CCND1 rearrangements in these tumors and their genomic profile are not known.

MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models.

Large B-cell lymphoma (LBCL) is a heterogeneous lymphoid malignancy in which MYC gene rearrangement (MYC-R) is associated with a poor prognosis, prompting the recommendation for more intensive treatment. MYC-R detection relies on fluorescence in situ hybridization method which is time consuming, expensive, and not available in all laboratories. Automating MYC-R detection on hematoxylin-and-eosin-stained whole slide images of LBCL would decrease the need for costly molecular testing and improve pathologists' productivity.