Dr. Xiaofeng Yang is Professor, Vice Chair, and Chief of Medical Physics in the Department of Radiation and Cellular Oncology at the University of Chicago. He is also a board-certified therapeutic medical physicist. His research focuses on artificial intelligence, quantitative medical imaging, and image-guided radiation therapy, with the overarching goal of improving the precision, efficiency, and personalization of cancer care. His Deep Biomedical Imaging Laboratory develops machine learning, deep learning, and foundation models for fast, low-dose, and high-quality medical imaging; image segmentation, synthesis, and registration; real-time tumor tracking and motion management; and quantitative imaging for disease diagnosis and treatment response assessment. A major focus of his research is translating these computational and imaging innovations into clinical applications, particularly AI-enabled image-guided and adaptive radiotherapy across photon therapy, proton therapy, and brachytherapy.
Cardiac Substructure Dose Quantification and Optimization in Left-Sided Lung Cancer Radiotherapy.
Cardiac Substructure Dose Quantification and Optimization in Left-Sided Lung Cancer Radiotherapy. Pract Radiat Oncol. 2026 Sep 11.
PMID: 42727773
Early detection of invasive lung cancer and multiorgan metastasis by a collagen-targeted protein MRI contrast agent.
Early detection of invasive lung cancer and multiorgan metastasis by a collagen-targeted protein MRI contrast agent. Sci Adv. 2026 Sep 11; 12(37):eadz6815.
PMID: 42715331
Dosimetric impact of aperture-restricted VMAT on heart dose in breast cancer treated with regional nodal irradiation.
Dosimetric impact of aperture-restricted VMAT on heart dose in breast cancer treated with regional nodal irradiation. J Appl Clin Med Phys. 2026 Sep; 27(9):e70775.
PMID: 42643153
Beam geometry optimization and convergence mode evaluation for knowledge-based planning of hippocampal avoidance whole-brain radiotherapy: A cross-platform study with quantitative scorecard assessment.
Beam geometry optimization and convergence mode evaluation for knowledge-based planning of hippocampal avoidance whole-brain radiotherapy: A cross-platform study with quantitative scorecard assessment. J Appl Clin Med Phys. 2026 Sep; 27(9):e70771.
PMID: 42638463
Foundation models in medical image analysis: A systematic review and quantitative analysis.
Foundation models in medical image analysis: A systematic review and quantitative analysis. Med Image Anal. 2026 Aug 27; 115:104293.
PMID: 42696936
Automatic treatment planning using reinforcement learning for high-dose-rate prostate brachytherapy.
Automatic treatment planning using reinforcement learning for high-dose-rate prostate brachytherapy. Phys Med Biol. 2026 Aug 19; 71(16).
PMID: 42562017
Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.
Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions. Med Phys. 2026 Aug; 53(8):e70616.
PMID: 42563397
Low-dose CT imaging using a regularization-enhanced efficient diffusion probabilistic model.
Low-dose CT imaging using a regularization-enhanced efficient diffusion probabilistic model. Med Phys. 2026 Aug; 53(8):e70626.
PMID: 42563368
HELZ is a RNA-DNA helicase that resolves R loops to facilitate homologous recombination repair.
HELZ is a RNA-DNA helicase that resolves R loops to facilitate homologous recombination repair. Nat Commun. 2026 07 23; 17(1).
PMID: 42493500
One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.
One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy. ArXiv. 2026 Jul 16.
PMID: 42523498