From symptoms to signals: Using AI for early diagnosis of ovarian cancer

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