A Deep Dive into the RAISE Project Methodology

This animation explains the methodology used in the Research for Artificial Intelligence-Based Surrogate Endpoint (RAISE) project, presented in two manuscripts recently published in Future Oncology. This exploratory work assessed whether deep learning techniques could improve early progression-free survival (PFS) in patients with neuroendocrine tumors (NETs), using features extracted from computerized tomography scans from patients in the CLARINET phase 3 trial. As part of the RAISE project, response heterogeneity (the coexistence of responding and non-responding lesions in the same patient) was also investigated to assess whether it could be used as a biomarker to allow earlier prediction of PFS in patients with NETs. Previous definitions of response heterogeneity are not suitable for assessing slow-growing tumors, such as NETs, so the definition of response heterogeneity was adapted in this study and is described in this animation.