Improving Lymphoma diagnosis – Prof. Davide Rossi (IOR-USI), Prof. Luca Mazzucchelli (EOC) and Prof. Alessandro Giusti (SUPSI)

Diagnostics of a rare lymphoma through integrative AI

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.