The 14th of March marked the end of my redeployment to a support role in Intensive Care and the second interruption of my PhD due to the pandemic. Academic papers and lines of data were replaced by disembodied voices as I endeavoured to keep two wards worth of family members updated on their loved ones’ progress. With strict restrictions on visitation, this daily conversation was often the only insight into how their relatives were recovering, and in many cases, how they weren’t. Whether I’ll be due a third pandemic-related sabbatical is yet to be seen. In the past few weeks, I’ve personally witnessed the steady downtick of COVID-related admissions. Beds filled with ventilated patients are now replaced by those in need of ITU-level monitoring following delayed essential procedures. Things are no less busy, but the grip of COVID has loosened and at the very least, there is a measure of respite.
Today, I replace my ITU hat with the academic hat I hung up two months ago. My oncology hat continues to gather dust, awaiting its eventual turn. Being reminded of my time as a clinician, one of my motivations for delving into the world of code and computational solutions is in its ability to capture and manipulate data that is often overlooked in day-to-day practice. Medical data is costly, both in time and manpower. Request forms, going through the scanning process, having labs do bloodwork, waiting for a report to be generated, are all steps taken to produce what is often a singular data point, which subsequently is consigned to medical archives. As our technology advances, so too has the information we capture from investigations, as well as our ability to store and read it on a larger integrated scale. This could enable – the discovery of complex relationships that would otherwise not have fit onto a blackboard or spreadsheet. Pairing this with the zeitgeist that is the renewed interest in artificial intelligence, we now have the technology to realise complex manipulation of large datasets at a level previously unattainable; bursting open the barriers that previously held us back.
One venue of unused data lies in opportunistic imaging. Cross-sectional imaging such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) are commonly used in cancer care. These scans reconstruct “slices” through the scanned body which clinicians can scroll through to visualise internal structures. In cancer, the main reason to do this is to evaluate how the cancer is responding to a treatment. Simply put, if a cancer is in the lung, the focus of imaging and attention will be in the lung. But scanning the chest for lung cancer patients invariably picks up other organs as well, including the heart, bones, muscle and fatty tissue present in all of us. Unless there is an obvious abnormality in these other organs (such as a grossly enlarged heart, or cancer deposits elsewhere in the chest), these other organs are barely commented on and become unused by-products. There is information to be gained by review of these other organs, but until now there have not been the tools to attempt to fully realize them.
In a landmark 2009 study, Prado et al. measured muscle mass in obese cancer patients using CT scans obtained routinely during their cancer management. Due to its correlation with total muscle mass, the muscle area was measured at the level of the third lumbar vertebrae. This was subsequently corrected for height to a skeletal muscle index. Prado et al. found a relationship between low muscle index and survival, creating the label of sarcopenic obesity with a diagnostic cut-off determined by optimum stratification (<Male 55/Female 39 cm2/m2). Sarcopenia was a new concept in cancer care at that point, previously having been used in mainly an aging context to define frailty associated with poor muscle and mass. In the frailty literature, where there is limited access to CT or MRI imaging, assessments were usually functional (such as defined set of exercises) or involved plain X-ray imaging of limbs for practicality, cost and radiation dose purposes.
For cancer sarcopenia, assessment of muscle index has been repeated by other groups in single-centre studies across a variety of tumour types and geographic locations. Even when correcting for sex and height, there are enough other uncorrected factors that the range of cut-offs for pathological sarcopenia is too wide to be of practical utility (29.6-41 cm2/m2 in women and 36-55.4 cm2/m2 in men). Another limitation lies in that tumour sites do not always share imaging practices. For instance, in the case of brain tumours, there is less of a need to look for extracranial disease and thus, no imaging is available at the level of the third lumbar vertebrae for analysis.
In the current age of personalised medicine, being able to create individualised risk profiles based on the incidental information gained from necessary clinical imaging would add utility to scan results without adding clinical effort. For my PhD, the goal is to overcome this challenge using transfer learning from an existing detailed dataset. We’ve had the fortune to secure access to the UK Biobank, a medical compendium of half a million UK participants. The biobank includes results of biometrics, genomics, blood tests, imaging as well as medical history. Such a rich dataset is ripe for machine learning tasks.
I have therefore been working to integrate several high-dimensional datasets, applying a convolutional neural network to the imaging aspect whilst a deep neural network to the non-imaging aspect. A dimensionality reduction technique such as autoencoder will subsequently have to be applied to generate a clinically workable model. Being a clinician primarily, I am able to bring clinical rationale to the model and intuit the origin of certain biases from my prototype pipelines. On the flip side, I have struggled to become fluent with the computational code necessary to tackle these problems, and often still feel like I am at the equivalent level of asking for directions to the bathroom back in my GCSE German days.
In becoming a hybrid scientist, I’ve long since acknowledged that I will not be the best coder in the room. I am still climbing the steep learning curve of computer languages and code writing, grateful for this opportunity to realise my potential in this field. Machine learning is ever encroaching on not just our daily lives but in our clinical practice, usually for the better. I imagine that as early adopters turn into the early majority, those of us who have chosen to embrace this technology will be in a position to better develop and understand the tools that will benefit our future patients. After all, soon we will not just be collaborating with each other but also with Dr Siri and Dr Alexa.