Diagnostics of a rare lymphoma through integrative AI – Prof. Davide Rossi (IOR-USI), Prof. Luca Mazzucchelli (EOC) and Prof. Alessandro Giusti (SUPSI)

Transforming the Diagnostics of Nodal Marginal Zone Lymphomas by Integrating Digital Pathology, Molecular Biology, and Artificial Intelligence

Nodal marginal zone lymphoma (NMZL) is a rare and difficult to diagnose B-cell malignancy that is often wrongly diagnosed. The applicants propose a digital pathology approach that they hope surpasses expert pathologists in correctly diagnosing NMZL. They will use deep learning to integrate imaging, clinical and molecular data, generating a classifier that will allow clinicians to upload their whole slide scans and get a correct NMZL diagnosis. Based on 900 cases, samples and data obtained across Europe, they hope to provide a clinically applicable, yet technically sophisticated way to identify a rare lymphoma subtype.