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Meet Our Researchers

Leveraging AI for Radiation Oncology

 
Allen Mo, MD, PhD

Chaitra Badve, MD, Serah Choi, MD, PhD, Dan Ma, PhD, and Yong Chen, PhD

Dr. Badve, Dr. Choi, Dr. Ma and Dr. Chen are combining Magnetic Resonance Fingerprinting (MRF) with artificial intelligence (AI) based neural networks to improve target delineation in post-operative radiotherapy for glioblastoma. Radiation helps control local recurrence but carries the risk of neurological deterioration. The team aims to map tumor infiltration more accurately for each patient to create personalized radiation treatment strategies that better target tumor cells and reduce neurological deterioration.

Allen Mo, MD, PhD

Ming Chao, PhD, and Jose Penagaricano, MD

Dr. Chao, Dr. Penagaricano and their team are testing their unique spatial cluster model to predict xerostomia in head and neck cancer. In prior research, they have shown that incorporating spatial dose distribution into the cluster model improves the prediction of xerostomia over existing methods. They will use big data analytics to optimize the accuracy and sensitivity of the cluster model. Their goal is to develop a better decision-making tool for treatment planning in head and neck cancer that can help improve patient quality of life by reducing or preventing xerostomia.

Allen Mo, MD, PhD

Tafadzwa Chaunzwa, MD, MHS

Dr. Chaunzwa and his team will use deep learning radiomics to develop imaging-based biomarkers that identify patients with advanced non-small cell lung cancer who are unlikely to respond to immunotherapy and will need intensified treatment with stereotactic ablative radiotherapy (SABR) or chemotherapy. They aim to develop an advanced and inexpensive AI software that can improve treatment selection and outcomes for patients with lung cancer.

Allen Mo, MD, PhD

Allen Mo, MD, PhD

Dr. Mo and his team are using machine learning algorithms to aid clinical decision making for optimal treatment selection for patients with hepatocellular carcinoma (HCC). Building on prior work analyzing clinical, demographic, pathologic and radiographic features in their database of over 900 patients with HCC, Dr. Mo and mentor Rafi Kabarriti, MD, are developing decision support tools to evaluate progression free survival and risk of toxicity for patients in both the newly diagnosed and previously treated settings.