Dr. Jean-Baptiste Shares Clinical Research

In October 2024, Samuel Jean-Baptiste, MD, MS, joined the UF Health Proton Therapy Institute. Dr. Jean-Baptiste is actively treating patients for brain, lung and prostate cancers. We caught up with Dr. Jean-Baptiste to ask how his initial time at the Institute has been and what is next for him.

Q: How would you describe your initial experience treating patients at the Institute?

A: My experience at the UF Health Proton Therapy Institute has been phenomenal with the camaraderie and infrastructure in place to make sure patients are seen and treated in a timely manner. The integrative services addressing the significant psychosocial/financial challenges of patients and caregivers allow me to focus on delivering optimal clinical cancer care, while knowing my patients’ other critical needs are being addressed by specialized colleagues. It has truly been rewarding. Being part of a team that considers the whole person brings a deeper meaning to my work.

Q: What are your two upcoming research projects and what are the goals for these projects?

A: One of the upcoming research projects I’m working on is aimed at addressing lung cancer screening for current or former smokers who meet American Cancer Society (ACS) screening criteria. This work is particularly critical in Florida with its significant lung cancer burden. The goal would be to address the unmet need for early detection which would allow more patients to benefit from definitive, curative local treatment such as radiation or surgery alone.

The second project is aimed at using our Institutes’s patient reported outcome data going back decades in addition to clinical and dosimetric data to develop machine learning models predicting which patients are at risk for specific radiation-induced toxicities. 

As an extension of this work, I will be developing interactive tools to help patients with prostate cancer navigate treatment choices between radiation therapy and surgery. These decision support tools would present personalized outcome predictions based on the patient's specific clinical factors while comparing potential side effects between treatments. The tools would leverage our institutional outcomes database to show real-world results from similar patients going beyond generic statistics toward truly personalized decision support. 

Q: Can you briefly explain the role of AI in treating cancer, specifically as it relates to using proton therapy to treat cancer?

A: AI and machine learning tools will play a significant role in aiding in various steps of the treatment planning and delivery processes for proton therapy. Proton therapy treatment planning requires significant computational resources due to the complex calculations required to accurately model beam behavior and dose distribution in patient tissues. AI tools can help reduce dose calculation times from hours to seconds, possibly allowing for near-immediate verification during treatment sessions and could help predict outcomes by analyzing clinical and dosimetric data. The next logical step would be the development of AI tools to enhance workflow efficiency for adaptive proton therapy. There are significant workflow bottlenecks when it comes to adaptive radiation planning of any sort. AI integration would address this and enable more frequent adaptive proton therapy during a treatment course which would improve the therapeutic outcomes by better accounting for anatomical changes.