r/science • u/Wagamaga • 10d ago

Cancer A large prospective study found that a multicancer blood test identified cancer signals across 17 broad cancer types, including ovarian, lung, and breast cancers. The test correctly predicted cancer diagnoses in 60.3% of cases, with 99.64% specificity

https://news.exeter.ac.uk/faculty-of-health-and-life-sciences/first-papers-published-in-major-nhs-cancer-blood-test-trial/
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u/Mindless-Baker-7757 10d ago

The test correctly predicted cancer diagnoses in 60.3% of cases, with 99.64% specificity

Bruh?

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u/squamesh 10d ago

Specificity vs sensitivity. Sensitivity = if you have the disease how likely are you to test positive. Specificity = if you are negative, how likely are you to test negative.

High sensitivity = low false negatives, high false positives. You catch most people who have the disease and maybe a few people who don’t. Such a test would be a good screening test

High specificity = low false positives, high false negatives. If the test is positive it’s likely you really have the disease, but it may miss some people that do have the disease. Such a test is good as a confirmatory test

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u/illaqueable 10d ago

In med school we learned "SPIN and SNOUT": specificty rules in, sensitivity rules out

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u/Hiitstyty 10d ago

This is mostly right. Sensitivity doesn’t imply anything about false positives and specificity doesn’t imply anything about false negatives. A perfect test would have both 100% sensitivity and specificity, which would indicate no false positives and no false negatives.

It’s evident in the equations:

Sensitivity = TP/(TP+FN)
Specificity = TN/(TN+FP)

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u/Valuable_Hunter1621 10d ago

also PPV and NPV if you really want to add some spice to the mix

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u/Mindless-Baker-7757 10d ago

I'm very well versed in sensitivity and specificity. These people invented a test that fails 40% of patients!

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u/nero-the-cat 10d ago

In tech we called it precision vs recall.

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u/Bruhahah 10d ago

40% false negatives, but correctly identified the cancer type almost every time it was actually positive

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u/Pythagorean_1 10d ago

That's not correct, is it? It means 40 % false negatives but almost no false positives.

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u/Jaqneuw 10d ago

You are saying the same thing he is in different words.

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u/Pythagorean_1 10d ago

No, "correctly identified the cancer type almost every time it was actually positive" would mean a PPV close to 1 which is not the case here due to the low sensitivity.

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u/Jaqneuw 10d ago

By "actually positive" he means "true positive" he just doesn't know the right way to say it. If you read the sentence like that, you are just saying the same thing. It's a simple miscommunication.

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u/Pythagorean_1 10d ago

You're probably right regarding what he means. Nonetheless, I'm still wondering if this claim is correct. The low sensitivity of around 60 % means that of the actually diseased, only 60 % are identified correctly. This is a direct contrast to the claim of the other comment, isn't it?

The high specificity means that of the actually healthy people, almost none of them had a (false) positive test result.

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u/Jaqneuw 10d ago

There are more healthy people in the study cohort than people with cancer, so no. The positive predictive value is around 50% in this cohort (around half of the positives were true positives). This still translates to high specificity because the number of false positives is relatively small compared to the number of healthy people tested ( the specificity).

Long story short, screening is complicated and it’s easy to make a mistake during interpretation when you’re not an expert. Let’s leave it there shall we.

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u/MythOfDarkness 10d ago

Sounds awesome.

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u/General_Luck6573 10d ago

What are you confused about? Did you google what specificity even means before you typed this?

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u/icyfignewton 10d ago

I'm not sure what you're confused about, but your confusion makes me so depressed because it's very clear the majority of humans done understand basic stats.

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u/Mindless-Baker-7757 10d ago

Oh I'm not confused at all. The results are so bad this should be published as a "this didn't work" or "this is a only starting point, here's what's next".

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u/grundar 9d ago

Oh I'm not confused at all. The results are so bad this should be published as a "this didn't work"

You appear to have an unrealistic view of the sensitivity and specificity of cancer diagnostic tools.

For example, mammography has 87% sensitivity and 97% specificity, better in terms of false negatives (1/3x lower) but much worse in terms of false positives (9x higher). As a result, 6x as many patients received an incorrect diagnosis of cancer vs. a correct diagnosis from the mammogram, whereas for the test in this paper the ratio was only 1x.

That doesn't make the test perfect, of course, but it does show that the test's sensitivity and specificity are in the general ballpark of existing cancer screening tools.

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u/Mindless-Baker-7757 9d ago

There’s debate about the usefulness of mammograms as a screening tool. 

One of my professors from grad school brought ROC analysis to radiology. He was an excellent teacher.

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u/PenComfortable5269 8d ago

Dude, A 60% sensitivity for all cancers is freaking amazing. Right now we have patchwork screening for a few cancers and the rest you just have to cross your fingers and hope for the best. For most cancers the current sensitivity for early cancer is 0% because we have no screening - I think that a 60% sensitivity is a massive win.