Alberto Revelant
Papers
1
Total Citations
34
H-Index
1
About
Alberto Revelant is a leading researcher in the intersection of radiation oncology, medical imaging, and artificial intelligence. His primary research focuses on enhancing the precision of cancer treatment through radiomics and deep learning, particularly for lung cancer patients undergoing stereotactic body radiation therapy (SBRT). In his most cited work, "Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy" (2021, 34 citations), Revelant demonstrated that integrating pre-treatment CT image features with biologically effective dose data significantly boosts machine learning models' ability to predict tumor response in non-small cell lung cancer. This contribution is pivotal for personalizing SBRT, potentially reducing unnecessary toxicity and improving outcomes. His work bridges computational methods with clinical oncology, offering a pathway to more adaptive, data-driven treatment planning. Revelant’s research has been recognized for its translational impact, earning citations from peers in both medical physics and radiation oncology. For students and researchers, his studies exemplify how multimodal data fusion can unlock new predictive capabilities in cancer care.
Research Focus
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Top Papers
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