Identification of a radiomic signature to distinguish recurrence from radiation-induced necrosis in treated glioblastomas using machine learning methods on dual-point 18F-FDOPA PET images

Conclusions: In glioblastomas, we demonstrated that, thanks to a machine learning approach designed for low-sample size/high-dimensional data, it is possible to distinguish recurrence and radiation necrosis with better performance than visual assessment. The best finding was obtained based on parametric images resulting from the evolution of 18F-FDOPA uptake between 20 and 90 min post-injection. Our results should be validated on an independent cohort, but confirm that modern machine learning methods applied to medical images can improve patients’ management.

Source: Journal of Nuclear Medicine - May 20, 2019 Category: Nuclear Medicine Authors: Orlhac, F., Rollet, A.-C., Bouveyron, C., Darcourt, J., Ayache, N., Humbert, O. Tags: Radiomics and Classification Source Type: research