Podcast: Cancer and artificial intelligence

What’s cancer got to do with crabs, artist Jackson Pollock, and artificial intelligence? It’s not a riddle; these are some of the things we’ll explore with surgeon Grant Stewart, computer scientist Mateja Jamnik and radiologist Evis Sala from the Mark Foundation Institute for Integrated Cancer Medicine.
In this episode, we’ll discover how artificial intelligence is making it easier for doctors to diagnose and treat cancer and we’ll share some cancer facts that are both amazing and disturbing. We also learn about the WIRE clinical trial for kidney cancer. WIRE evaluates the effectiveness of giving a short course of drug treatment to patients in the one-month “window of opportunity” between diagnosis and surgery. Patients on the WIRE trial also undergo a suite of new imaging techniques that have been brought together for the first time globally in this clinical trial.
This episode was produced by Nick Saffell, James Dolan, Naomi Clements-Brod and Annie Thwaite.
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Timestamps:
[00:00] - Introductions
[01:15] - A bit about the guests’ research
[02:45] - The origins of cancer. Why Hippocrates is known as the father of medicine.
[04:00] - How cancer starts
[05:05] - How many types of cancer are there? What are the most common types of cancer?
[06:10] - How do cancers develop? The lifecycle of cancer.
[09:00] - How early in the lifecycle can we see or detect cancer? What size does the cancer cell need to be for us to see it?
[10:15] - What improved machines and AI help with detection and characterisation?
[11:00] - Can we turn imaging into a virtual biopsy?
[12:20] - Defining Artificial Integilence (AI)
[14:20] - AI and machine learning and how they interlink.
[15:10] - Deep learning and statistical learning.
[16:10] - Origins of AI in medicine and healthcare.
[17:15] - Intro to AI and cancer imagery
[18:30] - How the AI algorithm assists the radiologist
[20:10] - AI and prepared models. How the data is trained to understand what cancer looks like.
[21:20] - The importance of sharing the data set.
[22:00] - Time for a recap!
[28:40] - Ai and surgical robots
[29:30] - AI and screening kidney cancer. Grant’s and Evis’s work using models, imagery, automation to screen for kidney cancer
[31:50] - Explaining the types of imaging in oncology
[33:10] - How Evis uses AI in her imagery
[34:10] - How to scan for ovarian cancer
[35:20] - Comparing images of tumours to paintings. Comparing Jackson to a Mark Rothko painting. Homogeneous or heterogeneous
[37:40] - Describing what the images actually look like from a non-radiologist perspective. Grades of grey. What CT scans and MRI scans look like.
[41:10] - How AI is used throughout the imagery process, not just for clarification.
[42:30] - Comparing the AI in oncology imagery to an Instagram filter. Do we lose any information when we use AI?
[43:15] - Time for another recap!
[48:15] - How do we create and ensure a high quality of data in a healthcare context?
[50:50] - Is there any governance for introducing AI into clinical practice. GDPR and how it impacts AI decisions around the care of a human being. A huge area of research around explainability.
[53:20] - The typical process (modality of data) What Evis, Grant and Matejia are doing with Integrated Cancer Medicine. The techniques
[56:30] - The time has come for integrated care and shared streams of data. Increase the involvement of the patient in their care.
[58:15] - Grant explains the WIRE trial (WIndow-of-opportunity clinical trial platform for evaluation of novel treatment strategies in renal cell cancer).
[1:02:40] - Is it possible to do a holistic analysis? The goal of AI is to help clinicians with personalised medicine.
[1:03:20] - Why it is so important for patients to be involved in oncology AI-based studies.
[1:04:10] - Let's break this episode down and close this thing out.

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好想读书啊