Today, we’re speaking to Dr Garth Funston, a GP and Clinical Senior Lecturer in Primary Care Cancer Research at Queen Mary University of London.
Title of paper: Using large language models to identify pre-diagnostic clinical features of ovarian cancer from healthcare records: a population-based case-control study
Available at: https://doi.org/10.3399/BJGP.2025.0366
Most women with ovarian cancer present with symptoms, but many symptoms are recorded only in free text healthcare records and missed by studies and clinical decision support tools that rely on coded data. We found that using large language models (LLMs) to extract symptoms from free text records substantially increased symptom detection and strengthened associations with ovarian cancer. Incorporating LLM-extracted symptom information into research and clinical decision tools may support identification of women at higher risk of cancer and aid appropriate investigation.
Transcript
This transcript was generated using AI and has not been reviewed for accuracy. Please be aware it may contain errors or omissions.
Speaker A
00:00:00.800 - 00:00:50.940
Hi and welcome to BJGP Interviews. I'm Nada Khan and I'm one of the Associate editors of the Journal. Thanks for listening to this podcast today.
In today's episode, we're talking to Dr. Garth Funston, who is an academic GP and clinical senior Lecturer in Primary Care Research at Queen Mary University of London.
We're here to talk about his recent paper in the BJDP which is titled Using Large Language Models to Identify Pre Diagnostic Clinical Features of Ovarian and Cancer from Healthcare Records.
So, Garth, thanks so much for talking to us again today, but I wonder, just before we get into the AI side of this paper, can you briefly explain the clinical problem you're trying to address here with ovarian cancer diagnosis in general practice?
Speaker B
00:00:51.500 - 00:01:55.010
So most women with ovarian cancer are diagnosed after they develop symptoms and see their doctor. The challenge is that most symptoms are really non specific. There's no real red flag symptoms for ovarian cancer.
That makes it a real clinical challenge for the GP to kind of recognize it and perform tests.
So the symptoms are things like abdominal and pelvic pain, persistent bloating, urinary urgency and frequency, things that we see really frequently in gp. So knowing when to consider ovarian cancer is the big challenge.
And we know that certainly a proportion of women see their GP multiple times before the diagnosis. Now we're lucky for ovarian cancer in that we have reasonably good triage tests and CA125 and transvaginal ultrasound.
So the challenge really is to identify women with these non specific symptoms early so as we can work out who to test and hopefully improve early diagnosis and on outcomes in that way.
Speaker A
00:01:55.250 - 00:02:14.530
Yeah, and I'm sure you're well aware of sort of the body work around this area and people like Willie Hamilton, who's done work around early diagnosis of ovarian cancer, along with Claire Bankhead, and they did some really interesting work around things like bloating, didn't they? But that was slightly different, I think, and a little bit that's some time ago now, isn't it?
Speaker B
00:02:14.930 - 00:02:39.230
Yeah, it was some time ago. I think all of that is, you know, fundamental and still holds true.
And they did a lot of work around things like IBS and in women over, over 50 and things like that that are kind of these subtle signs that we need to be aware of with ovarian cancer.
So, yeah, we know there's lots of features that are associated with ovarian cancer, but it's recognizing when to invest to get those features because they're so common.
Speaker A
00:02:39.630 - 00:02:49.310
Yeah. And do you think that's why it's described as difficult to diagnose early in general practice? Is it because the symptoms are so common?
What are your thoughts on that?
Speaker B
00:02:49.390 - 00:03:48.750
I think there's a few reasons.
I think ovarian cancer used to be called, certainly in the media, the kind of the silent killer and terminology, which I really, really frustrates me, because we know it's not. We know that most women of symptoms for diagnosis. We actually know that from this paper and other papers that are symptoms in early stage cancer.
But that kind of thought around ovarian cancer still holds. Secondly, the symptoms are nonspecific, they're reasonably common. I mean, you know, I probably see a.
A patient with abdominal pain most days and it's kind of working out which ones to investigate for ovarian cancer. Yeah. And so I think those are the main things. And thirdly, it's, you know, it's not the most common common cancer.
GP will see people probably only encounter a case of ovarian cancer every three to five years, a new case. And that's the extra challenge. It's kind of suspecting it when it's a rare thing in primary care.
Speaker A
00:03:49.100 - 00:04:03.500
Yeah. And one thing I found really interesting about this work is that you're using free text clinical records rather than just coded data.
So can you tell us a little bit about the data you accessed here and why it was so important to use this free text data?
Speaker B
00:04:04.220 - 00:05:09.600
So a lot of the work that we do with primary care data focuses on coded data and certainly within the uk, because that's really the data we can actually access within UK for research purposes. But up to 80% of clinical information is not in that coded format, it's in the free text.
And work from people like Sarah Price in the past have shown that often subtle things that we need to pick up are in the free text and GPS don't code that.
So it's something I've been really keen to use in research for many years now to try and look at what extra information is there in the free text that could help us in both research and clinical practice and kind of picking up these cancers. And the data we accessed was from the United States, it was from healthcare clinics associated with the University of Washington.
And that included kind of coded data, but also the free text medical records of patients which had been anonymized and were accessed in a kind of a safe and appropriate way.
Speaker A
00:05:10.000 - 00:05:40.140
Yeah.
And I think a lot of clinical staff listening to this will certainly, certainly appreciate that a Lot goes into the notes that we just type in that doesn't really get coded. So it's phenomenal that you're able to access that data.
And this paper uses large language models or LLMs, which some people might associate, associate with tools like ChatGPT, but just at a very basic level. Can you just talk us through what actually is a large language model and what sort of it was used for in this, in this study?
Speaker B
00:05:40.950 - 00:06:49.130
Large language models, lots of people use them on a daily basis. Absolutely right.
Things like ChatGPT, they're essentially a tool for our purposes which we use to extract information from the free text medical records. Now natural language processing approaches have been used actually for many years, kind of rule based approaches.
Other models, these require lots of training. You need to lots of highly annotated records and notes to train the models.
Advantage of large language models, things like GPT, is they need less annotated notes and we did still do some of that, but they require less and that makes them much easier to apply and use in practice. We use them in this setting to effectively pull out key information on symptoms.
We predefined a list of 17 symptoms from the literature which were associated with ovarian cancer and we used the large language models to go through the notes, pull out information on those symptoms that we could use in the study alongside the coded data.
Speaker A
00:06:50.090 - 00:07:03.350
And I think that as we've been discussing, these large language models are probably really useful for this kind of data. I think especially because a lot of general practice is narrative and contextual as we've been discussing as well.
Speaker B
00:07:03.350 - 00:07:38.940
Yeah, I think, I mean there's two challenges with using free text data. One is access requirements because there's lots of concerns around confidentiality. The other is just the volume of it.
You've got these massive records that you know, contain lots of information, lots of writing, go back years. How do you actually process that to find the key information that you need?
I think large language models are a really useful tool here because with a bit of training you can use them to actually extract the information that's pertinent to your kind of question.
Speaker A
00:07:39.340 - 00:07:48.620
So let's go into what you found and I'm really interested to know about what kind of patterns or features was this model able to identify before an ovarian cancer diagnosis.
Speaker B
00:07:49.180 - 00:09:06.690
So we looked at 17, 17 features. We find actually that 14 of the features were more frequently recorded within the free text and coded...
