WP2’s integration of machine learning and digital pathology into sarcoma research is a promising step forward in the fight against cancer. By improving the prediction of TLS status and treatment response, WP2 aims to enhance the accuracy of treatment decisions, leading to more personalized and effective therapies for sarcoma patients. As technology continues to evolve, the future of cancer treatment looks brighter, and these innovations are paving the way for new breakthroughs in oncology.

In sarcoma, predicting TLS status and understanding its implications on treatment response has been a challenge due to the tumor’s heterogeneity and complex immune interactions.

WP2 aims to bridge this gap by utilizing state-of-the-art ML strategies and digital pathology by analyzing large datasets of pathological images and clinical data.

The ultimate goal of WP2’s efforts is to predict how a sarcoma patient will respond to immunotherapy.