AI and Deep Learning Revolutionize Radiation Oncology

Hina Saeed, MD

Artificial intelligence (AI), particularly deep learning, is transforming the field of radiation oncology. This tool has shown its impact and promise by improving treatment planning, accuracy, and overall patient care.

According to Hina Saeed, MD, advancements in AI have helped in a plethora of ways. For example, treatment plans that once took days to complete can now be generated in hours, allowing for more efficient care, and experts can enhance tumor and organ segmentation, helping radiation oncologists deliver more precise therapies.

Radiomics is also becoming an increasingly key part of radiation oncology, contributing to more personalized treatment plans. Through the analysis of imaging data, radiomics can predict treatment outcomes and support decision-making, giving oncologists additional data to tailor treatment to individual patients.

As AI and radiomics continue to evolve, they are creating opportunities for predictive modeling that improves the accuracy of patient care. Still, several challenges must be addressed to fully harness the potential of AI in radiation oncology.

In an interview with Targeted Oncology^TM, Hina Saeed, MD, radiation oncologist, deputy director at Baptist Health, South Florida, discussed how AI and radiomics are changing the landscape of radiation oncology and what the future holds for these technologies.

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Targeted Oncology: How is AI, especially deep learning, changing the field of radiation oncology?

Saeed: AI, particularly deep learning, is revolutionizing radiation oncology in several ways. It is enhancing treatment planning and optimization, making it possible to create plans in hours rather than days. AI also improves the accuracy of tumor and organ segmentation, which is crucial for effective radiation therapy. Additionally, AI aids in quality control and assurance, ensuring treatment plans meet high standards. It optimizes image-guided radiation therapy by monitoring tumor movement in real time.

How do you see radiomics being integrated into daily radiation oncology practice?

Radiomics is slowly but surely becoming an integral part of radiation oncology by enhancing predictive modeling and decision support. By analyzing imaging data, radiomics can predict treatment outcomes and help tailor personalized treatment plans. It provides additional data points that assist oncologists in making more informed decisions, ultimately improving patient care.

What challenges exist in applying deep learning to radiation therapy, and how can they be addressed?

There are several challenges, such as the need for large, high-quality, annotated, and standardized datasets for training AI models. Collaborative efforts and data-sharing initiatives can help overcome this. Another challenge is the interpretability of AI models, often seen as black boxes. Developing explainable AI models that provide insights into their decision-making process is crucial. Additionally, continuous validation and updating of AI models are necessary to maintain accuracy and reliability.

How might AI and radiomics improve personalized treatment planning for patients?

AI and radiomics enable more personalized treatment planning by providing detailed insights into each patient’s unique tumor biology and response to therapy. AI can analyze vast amounts of data to identify patterns and predict outcomes, allowing us to customize radiation doses and schedules for optimal effectiveness. This personalized approach enhances treatment efficacy and minimizes adverse effects.

How do you think AI will affect the role of radiation oncologists in the future?

There may be some fear about AI among radiation oncologists, but in reality, AI will augment their role by automating routine tasks and providing advanced analytical tools. This will allow oncologists to focus more on patient care and complex decision-making. AI will serve as a valuable assistant, enhancing our capabilities and improving the overall quality of personalized care we provide.

Are there any ethical concerns that should be considered when using AI in this field?

Ethical concerns include patient privacy, data security, and informed consent, especially when using AI. It is also important to address potential biases in AI algorithms that could lead to disparities in treatment. Transparency in AI decision-making processes and continuous monitoring for fairness and accuracy are essential to maintaining trust and equity in patient care.

How can AI improve clinical research and trials in radiation oncology?

AI can streamline clinical research by analyzing large datasets to identify trends and generate hypotheses. It can also enhance patient selection for trials by predicting which subsets of patients are most likely to benefit from specific treatments. Additionally, AI can monitor trial progress in real time, ensuring adherence to protocols and improving overall research efficiency.

What upcoming advancements in the space are you most excited about?

I am very excited about advancements in real-time adaptive radiation therapy, where AI continuously adjusts treatment plan delivery based on real-time imaging and patient feedback. This approach allows us to further personalize treatments and improve outcomes. The integration of multi-omic data with radiomics to provide a comprehensive view of tumor biology is also a very promising area of research.