Every cancer patient deserves their own equation: Kristin Swanson at TEDxUChicago 2014

Summary

In this talk, a mathematician shares a personal story about losing several family members, including their father, to cancer. This personal experience inspired them to apply mathematics to cancer treatment, specifically focusing on improving clinical decisions for individual patients. The speaker highlights how mathematical models can help tailor treatments to unique patients, rather than relying on generalized clinical trial results. They illustrate the potential of these models to better predict tumor growth, treatment success, and improve patient outcomes, advocating for more personalized and precise cancer care.

Takeaways

• 🧑‍⚕️ The speaker shares personal experiences of losing family members, including their father, to cancer, highlighting the widespread impact of the disease.
• 📊 The speaker emphasizes that cancer is a significant global issue, and everyone can likely relate to knowing someone affected by it.
• ➕ The speaker believes math can contribute to finding solutions for cancer, despite the complexity of the disease.
• 👨‍👧‍👦 The speaker's love for math began at a young age, influenced by their father, who played math games with them during long road trips.
• 💔 The speaker's father was diagnosed with lung cancer at age 62, which deeply impacted the speaker and led them to focus on cancer research.
• 📚 The speaker pursued a PhD in applied mathematics, studying under Jim Murray, a pioneer in mathematical biology, to explore the intersection of math and biology.
• 👩‍🔬 Attending tumor boards, the speaker learned how physicians make complex treatment decisions based on clinical trials and patient data.
• 🏥 Clinical trials typically focus on the 'median patient,' but the speaker argues that every patient is unique and should not be treated as a statistic.
• 🔬 The speaker's research focuses on glioblastoma, an aggressive brain cancer, and how math models can help predict tumor behavior and improve treatment plans.
• 💡 The speaker advocates for using mathematical models to better individualize cancer treatments, potentially improving patient outcomes and avoiding unnecessary suffering.

Q & A

What personal experience did the speaker have with cancer?

The speaker’s father was diagnosed with lung cancer when the speaker was 20 years old. The speaker was heavily involved in his care during the last seven months of his life, which motivated the speaker to study the intersection of math and biology to contribute to cancer research.

How did the speaker’s background in mathematics influence their approach to cancer research?

The speaker’s background in mathematics, cultivated by their father during childhood, gave them the tools to approach cancer as a complex system. This mathematical approach became the foundation for developing models to understand and improve cancer treatments.

What is the role of mathematical biology in cancer research, according to the speaker?

Mathematical biology helps understand the complexity of cancer as a system. It allows researchers to model tumor growth, cell behavior, and treatment responses, providing more personalized and effective strategies for cancer treatment.

What frustrations did the speaker experience while dealing with their father's cancer treatment?

The speaker felt powerless and frustrated by their inability to contribute to medical decisions and understand the medical conversations. Despite their mathematical background, they struggled to grasp the complexity of cancer treatment decision-making at the time.

Why does the speaker believe the solution to cancer involves math?

The speaker believes math is essential because it allows for better modeling of the complexity of cancer. Mathematical tools can simulate how tumors grow, how they respond to treatments, and predict outcomes, moving beyond generalized clinical trial results to more personalized patient care.

What is the concept of ‘treating to the median’ in cancer treatment, and what is its limitation?

‘Treating to the median’ refers to basing treatment decisions on the average outcomes from clinical trials, which may not reflect the individual patient's response. The limitation is that every patient is different, and treating them based on a median approach can overlook those who fall outside the typical response curve.

How does the speaker suggest cancer treatment should be individualized?

The speaker suggests using mathematical models to predict how a specific patient's tumor will behave and respond to treatment. This would allow doctors to tailor treatment plans more precisely to the individual patient rather than relying solely on generalized data from clinical trials.

What example does the speaker give of a case where traditional clinical trials might mislead treatment decisions?

The speaker gives an example of a 66-year-old patient with a brain tumor whose tumor grew during treatment, which would typically be considered a failure. However, the patient lived for five years, much longer than expected, suggesting that despite the tumor growth, the treatment was effective in derailing the tumor's course.

What is the role of MRI in cancer treatment decision-making, according to the speaker?

MRI scans are used to provide detailed images of tumors before and after treatment. Physicians use these images to assess the tumor's size and shape, plan surgeries, and tailor radiation doses. However, the speaker argues that MRI alone may not tell the full story of a tumor's behavior and response to therapy.

What drives the speaker’s commitment to improving cancer treatment through mathematics?

The speaker is driven by personal loss, including the death of their father and other family members to cancer. Their work is further inspired by the colleagues and friends in their lab who have faced cancer. They are motivated to ensure that every patient receives treatment tailored to their unique condition.