Papers
1
Total Citations
22
H-Index
1
About
Li Huo is a leading researcher in medical imaging and oncology, whose work focuses on the intersection of artificial intelligence and nuclear medicine. Their primary contributions lie in developing deep learning models to noninvasively predict genetic mutations from PET/CT images, particularly for non-small cell lung cancer (NSCLC). Huo’s most-cited paper, “Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images” (2023, 22 citations), demonstrates a groundbreaking approach to identifying EGFR mutation status—a critical biomarker for targeted therapy—without invasive biopsies. This work has the potential to significantly reduce costs and improve patient outcomes by enabling rapid, low-risk treatment decisions. Beyond this study, Huo’s research advances the integration of radiomics and neural networks to enhance diagnostic precision in cancer care. Their achievements underscore a commitment to translating computational methods into clinical practice, making them a pivotal figure in personalized oncology. With growing citation impact, Huo continues to shape how AI-driven imaging can revolutionize mutation prediction and therapeutic planning.
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Top Papers
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