The project focuses on nodal marginal zone lymphoma (NMZL), a rare B-cell neoplasm characterized by high diagnostic complexity and frequent misidentification.

To address this challenge, the research team is developing an innovative approach that integrates digital pathology, molecular biology, and artificial intelligence.

Using advanced deep learning techniques, a classifier will be developed capable of jointly analyzing histological images, clinical data, and molecular profiles. The goal is to provide clinicians with a simple and effective tool that allows them to upload digital scans of histological slides and quickly obtain an accurate diagnosis of NMZL.

The model will be trained on 900 cases collected from various European centers, forming one of the most comprehensive datasets ever assembled for this rare lymphoma subtype. The proposed solution aims to support expert pathologists in diagnosis, offering an advanced aid capable of more reliably identifying a rare and complex disease.

The project is carried out by Prof. Giusti in collaboration with Prof. Davide Rossi (IOR–USI) and Prof. Luca Mazzucchelli (EOC), combining expertise in artificial intelligence, medicine, and pathology. It is funded by the ISREC Foundation for cancer research, within the TANDEM program—research initiatives designed to foster collaboration between basic research and clinical research, with the goal of transforming scientific discoveries into innovative medical solutions applicable in practice.