Expanding cancer diagnostics through an analysis of lymph nodes – Prof. Inti Zlobec (UNIBE), Dr. Bastian Dislich (UNIBE), Dr. Amjad Khan (UNIBE) and Prof. Martin D. Berger (INSEL)

Clinical Deployment and Validation of a Multi-Cancer Deep Learning Algorithm for Lymph Node Metastasis Detection Based on Foundation Models

This study is based on a tight collaboration between computer science and clinical application. The detection of cancer cells in a patient’s lymph nodes is key to the clinical management of cancer. Analyzing the status of lymph nodes requires a pathologist’s skill, yet it is labor-intensive and time-consuming. To support pathologists in this task, a computer-assisted diagnostic tool that uses deep learning for the detection of colorectal lymph node metastases was developed. Now the scientists will extend the training of this algorithm to lymph nodes from 10 cancer types to expand its utility for daily use in hospital oncology.