Combining scans to refine biopsies

Could cleverly combining medical imaging technologies pave the way for virtual biopsies?
26 January 2021

Interview with 

Mireia Crispin Ortuzar, Cancer Research UK Cambridge Institute


An operating theatre team performing surgery


One area medical imaging is routinely used in is cancer diagnosis. And while scans like x-ray, ct and ultrasound  are relatively non invasive, sometimes it is necessary to do an invasive procedure to better understand what’s going on, for instance a biopsy. In a proof of concept paper published recently looking at ovarian cancer, Cambridge University scientists say that by combining ultrasound and CT scanning in real time, they may be able to reduce, refine and maybe even one day replace the need for doing physical biopsies at all... Adam Murphy spoke with Mireia Crispin Ortuzar, a Research Fellow at the Cancer Research UK Cambridge Institute...

Mireia - Well, the first thing to keep in mind is that cancer is a very broad term. We know over 200 different types of cancer. So figuring out the type of cancer and how advanced it is is one of the very first things that an oncologist does to decide how to treat a patient. Now, imaging is used as part of that process. It's very important. It helps us see if there is cancer, how big it is and whether it has spread to other parts of the body. But, the potential of imaging to tell us the exact type of cancer that we're dealing with is limited. There has been some progress recently using computational analysis techniques, but in general, we still need to gather additional information for a complete diagnosis. So the reason biopsies are so important is that they help doctors diagnose the exact type of cancer. They're essentially very small samples of tissue that are extracted directly from the tumour with a fine needle. And then the samples are examined in a microscope and doctors can literally see the tumour cells directly, if there are any, and produce an accurate diagnosis. And having an actual tissue sample also allows you to, for example, look at the genome of the tumour cells and potentially identify important mutations for which special treatments are available. So they play a really crucial role in the cancer journey.

Adam - So then what is it that this paper has done that might ease that burden on biopsies?

Mireia - The challenge with some types of cancer, for example, ovarian cancer, is that they are extremely diverse. We know that even within a single tumour, there may be regions with different underlying biology and therefore potentially different response to treatment. And the problem is that right now that diversity is not taken into account when you take your biopsies, simply because there are no methods to do it. So the main aim when you take a biopsy is simply to extract some tumour cells - independently of where exactly they came from from inside the tumour. So what our team has done in the past is that we found that applying computational pattern recognition techniques on CT scans can help you to identify some of these distinct regions inside the tumour, which we call habitats because they have this kind of spatial distribution. And this is really exciting because CT scans are part of the standard of care for cancer patients. The great majority of patients will get them. So it's data that is very easy to get.

So our hypothesis was if we could show those habitats in real time to the radiologist who is taking the biopsy, they may be able to take those biopsies strategically, targeting specific habitats to obtain a better understanding of these really complex tumours. Now, the main challenge is that biopsies are usually taken using ultrasound imaging, which helps guide the needle. So what we have done is to design a technique that allows us to overlay a map of these habitats in real time on the ultrasound that is being used to take the biopsy, so that we can make sure that that biopsy is being taken from exactly those regions of the tumour that we are interested in.

So in this specific paper, we focused on high grade serous ovarian cancer, which is the most common type of ovarian cancer. It usually presents itself when it's already metastatic. And so we were able to look at six patients and we looked at two different sites of disease, the pelvis and the omentum. So for two patients, we looked at the pelvis, for four patients we looked at the omentum. And we were able to understand the heterogeneity of the disease in both cases. And the reason it's so important to have techniques that allow us to capture that variation is that the better we are at understanding the underlying heterogeneous biology, the better we will be at choosing the right treatment for individual patients.

Adam - We mentioned this was a proof of concept paper. So, what does this mean for actually being used on patients?

Mireia - A short scale aim is to be more strategic about the biopsies that we do take. In other words, you know, if we can only take one biopsy, can we make sure that it will be the most informative one? So for patients, the experience would be pretty much identical to now. They would still come in and have a biopsy. But the important difference is that we would be getting a lot more useful information from it. And this could be really key for cancers that exhibit this type of heterogeneity of spatial diversity that makes them so hard to treat.

If we can characterise habitats using imaging data well enough, it might be possible to eliminate the need to take actual tissue samples and therefore replace biopsies. And this is a long-term vision, which will still require a lot of work, because we need to make sure that our understanding of the biology behind the tumour habitats is highly accurate. It's guiding our way, it's our long-term vision. But I think that being strategic about the biopsies that you take is the intermediate aim that we're going towards.


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