Enzo Grossi

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About

Enzo Grossi is a pioneering researcher in the field of radiomics, with a focused expertise in the non-invasive characterization and treatment prediction of acoustic neuromas (vestibular schwannomas). His major contributions center on developing and validating radiomic models that extract quantitative imaging features from standard MRI scans to predict tumor response to stereotactic radiosurgery, particularly using the CyberKnife robotic system. Grossi’s work directly addresses the clinical challenge of managing these benign but potentially debilitating intracranial tumors, aiming to personalize therapy by forecasting which patients will benefit most from radiosurgery. His most cited paper, “Tackling imbalance radiomics in acoustic neuroma” (2019, 8 citations), tackles the critical issue of class imbalance in small medical datasets, improving the robustness of predictive models. Through his early pilot studies (2018, 4 citations each), he laid the groundwork for translating radiomics from a research concept into a practical clinical tool. Grossi’s research not only advances the precision of radiosurgery planning but also demonstrates how machine learning can overcome data limitations in oncology, marking him as a key figure in the evolution of radiomics for neuro-oncology.