G. Pirrone
Papers
1
Total Citations
34
H-Index
1
About
G. Pirrone is a leading researcher in the intersection of medical physics, radiomics, and artificial intelligence for precision oncology. Their primary contributions lie in developing advanced machine learning models that integrate multimodal imaging and dosimetric data to predict tumor response to radiation therapy. In their landmark 2021 study, Pirrone demonstrated that combining pre-treatment computed tomography (CT) features with biologically effective dose (BED) information significantly improves the prediction of non-small cell lung cancer (NSCLC) response to robotic lung stereotactic body radiation therapy (SBRT). This work, which has garnered 34 citations, showcases their innovative approach of fusing radiomics and deep learning to enhance clinical decision-making. By bridging the gap between quantitative imaging biomarkers and treatment planning, Pirrone's research has the potential to personalize radiotherapy, reducing unnecessary toxicity while maximizing tumor control. Their work is highly regarded for its methodological rigor and translational impact, positioning them as a key figure in the emerging field of radiomics-guided radiation oncology.
Research Focus
Key Achievements
Top Papers
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