Giovanni Valbusa

Università Campus Bio-Medico, Centro Diagnostico Italiano

Papers

Total Citations

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About

Giovanni Valbusa’s research centers on the intersection of medical imaging and machine learning, with a specific focus on improving the clinical management of acoustic neuroma (vestibular schwannoma), a benign intracranial tumor. His major contributions lie in pioneering the application of radiomics—a method that extracts quantitative features from medical images—to predict tumor response to stereotactic radiosurgery, particularly using the CyberKnife robotic system. Valbusa’s work directly addresses the challenge of treatment planning for this slow-growing but potentially serious tumor, aiming to identify which patients will benefit most from radiation therapy. His most-cited paper, “Tackling imbalance radiomics in acoustic neuroma” (2019, 8 citations), tackles the critical issue of class imbalance in radiomics datasets, a methodological hurdle that can skew predictive models. His pilot studies (2018, 4 citations each) provided early evidence that radiomic signatures could forecast CyberKnife response, laying groundwork for personalized treatment strategies. Though his citation counts are modest, Valbusa’s research is notable for its translational focus—bridging computational analysis and clinical decision-making—and for addressing a niche but impactful problem in neuro-oncology. His work represents an important step toward non-invasive, image-based biomarkers for tumor control.