About the Lab
The Sun Lab in the Department of Radiation Oncology at Baylor College of Medicine develops artificial intelligence and automation tools that make radiation therapy safer, faster, and more accessible. Led by Baozhou Sun, Ph.D., MBA, FAAPM, DABR, Professor, and Director of Medical Physics, the lab connects clinical medical physics with modern machine learning. Our work spans deep learning auto-segmentation, automated and AI-driven treatment planning, and AI agents built on large language models that support clinical decisions, physics plan review, and quality assurance.
Our research is grounded in daily clinical practice. We develop and test tools inside the same workflows our clinical team uses, including the treatment planning system, the oncology information system, and the electronic health record. We aim to move validated tools into routine care. We also have a strong commitment to global health. Through partnerships with cancer centers in Mongolia and Uganda, we work to bring automation and AI to clinics with limited staff and resources.
Computing Resources: The lab runs a dedicated on-premises GPU computing cluster for model training, inference, and local large language model deployment. It includes two NVIDIA RTX PRO 6000 workstations, two NVIDIA DGX Spark systems, and additional GPU workstations with RTX 5090, RTX 5080, and RTX 4090 GPUs.
Lab Projects
AI-Based Auto-Segmentation
We develop, validate, and deploy deep learning models that contour organs at risk and targets, including brain metastases. Our work also includes clinical evaluation of commercial AI contouring and adaptive tools. We study how accurate these tools are, how much time they save, and how to use them safely in clinical practice.
Simulation-Free Radiotherapy and 3D Printing
We are building workflows that use diagnostic imaging, AI-generated synthetic CT, and 3D-printed immobilization. The goal is to shorten the time from consultation to treatment.
Automated Treatment Planning
We build automated and knowledge-based planning methods for external beam radiotherapy and brachytherapy. These include deep learning 3D dose prediction from prior patients and AI-based auto-planning for cervical cancer brachytherapy, developed with partner clinics in low- and middle-income countries.
Recent Publications
Yiding Han, Enze Zhu, Mhd Hasan AI Mekdash, Omar Awad, Piyush Pathak, Shixiao Liang, Xizhe Zhang, Daniel Allan Hamstra, Zaid Ali Siddiqui, Baozhou Sun (Senior Author), Blinded, Bias-Controlled Multi-Rater Evaluation of Human-versus-AI Brain Metastasis Segmentation Using a Hybrid Foundation-Model Framework, 2026 Jul;53(7):e70538.doi: 10.1002/mp.70538.
Yiding Han, Zaid Ali Siddiqui , Alexander Nicola Hanania , Abdallah Sherif Radwan Mohamed , Piyush Pathak, Ugarte Vincent , Omar Awad , Daniel Allan Hamstra, Baozhou Sun (Senior Author), Simulation-Free Spine Palliative Radiotherapy Enabled by AI-Adapted Diagnostic CT, Med Phys. 2026 Mar;53(3):e70366.
Yiding Han, Piyush Pathak, Omar Awad, Abdallah S R Mohamed, Vincent Ugarte, Boran Zhou, Daniel Allen Hamstra, Alfredo Enrique Echeverria, Hasan Al Mekdash, Zaid Ali Siddiqui, Baozhou Sun (Senior Author), et, al, Optimizing and Evaluating Robustness of AI for Brain Metastasis Detection and Segmentation via Loss Functions and Multi-dataset Training, Biomedical Physics & Engineering Express, 2026 Jan 6;12(1)
Lessons Learned from an Epic Transformation of a Radiation Oncology Department in Guatemala: Keys to Success, E. A. Ruiz Furlan, H.A. Gay, M. Ixquiac, A. Velarde, Baozhou Sun, J.L. Garcia-Ramirez, E. Laugeman, B. Li, C. Cardenas6, L.E. Court, J. Maas, J.M. Michalski, V. De Falla1, G.D. Hugo, J. van Rheenen, Advances in Radiation Oncology (2026) 11, 101928
Rui Zhang, Chavana Nicole, Boran Zhou, Vincent Ugarte, Beibei Guo, Baozhou Sun, Alfredo Enrique, Michelle, Ludwig, Amir Owrangi, Dosimetric comparison of high dose rate interstitial brachytherapy and stereotactic body radiotherapy as alternative treatment methods for advanced-stage cervical cancer, Biomed. Phys. Eng. Express.12 035084.
Lab Members
