AI and Precision Medicine Transforming Cancer Treatment Landscape

In a recent interview with MedCity News, Dr. Prajnan Das, professor and department chair of Gastrointestinal Radiation Oncology at MD Anderson Cancer Center, discussed the evolving landscape of precision medicine and the role of artificial intelligence in cancer treatment.

Reflecting on the comments made by former FDA Commissioner Robert Califf, who expressed disappointment in the progress of precision medicine, Dr. Das offered a different perspective. He highlighted advancements in radiation therapy, noting that 50 to 70 percent of cancer patients will receive radiation therapy at some point in their treatment.

Innovations in treating oligometastatic disease, where cancer has spread to limited areas, have shown that targeted radiation can improve disease-free survival and allow patients to avoid systemic treatments for longer periods.

For instance, a study by his colleague Chad Tang demonstrated that radiation treatment for metastatic renal cell cancer can help patients maintain a longer time without traditional systemic treatments, which often come with side effects.

In pancreatic cancer, research led by Ethan Ludmir has shown that treating metastasis with radiation or surgery can extend disease-free survival, challenging the previous notion that chemotherapy was the only option for metastatic cases.

Dr. Das also emphasized the importance of genomic testing, which helps identify molecular subtypes of cancer, thereby allowing clinicians to determine which patients will benefit from radiation and which will not.

For example, patients with microsatellite unstable rectal cancer can achieve a 100 percent response rate to immunotherapy without the need for radiation or chemotherapy. He pointed out that while significant knowledge exists today, it often takes years for this understanding to translate into effective treatments, suggesting that there is still much to realize in precision medicine.

Regarding AI, Dr. Das noted that it has the potential to revolutionize radiation oncology by streamlining the process of radiation planning. AI can replace the manual work currently done by physicians and dosimetrists, significantly reducing the time taken to design radiation plans.

He collaborated with Lawrence Court to develop an end-to-end AI program for rectal cancer radiation planning that can perform the task in minutes instead of hours, leading to more standardized and accessible care.

This AI tool is also being utilized globally, even in low-resource settings, providing high-quality treatment plans based on MD Anderson standards. Dr. Das expressed optimism that as AI tools become more integrated into clinical practice, they will help identify unique signals within patient data that could lead to innovative treatment opportunities.

He concluded by noting that AI could play a crucial role in bridging the gap between molecular testing and practical applications in treatment, potentially leading to more personalized therapies for cancer patients.