Barbara Avuzzi

Prospectively collected long-term adverse event data with pre-radiotherapy assessment and regular follow-up reflecting 'real world settings' are still limited. Here, we present the available data on early and long-term outcomes up to eight years after radiotherapy in men with prostate cancer who participated in the international REQUITE cohort study, stratified by treatment modalities and patient characteristics.

Abstract While considerable effort has been devoted to examining how variations in study protocols, acquisition settings, and annotations influence radiomics features, the role of feature selection (FS) methods largely remains unexplored. This study investigates a self-supervised deep sparse autoencoder ensemble (ensembleAE) and a novel Bayesian variant (bayesianAE) for radiomics FS in a small, class-imbalanced framework.

Summary The search for factors beyond the radiotherapy dose that could identify patients more at risk of developing radio-induced toxicity is essential to establish personalised treatment protocols for improving the quality-of-life of survivors.