(The article states 0.5% false positive rate and about 50% true positive rate, but I would need to know the the prevalence of cancer in the population to compute what I am asking for).
A quick Google search suggests the prevalence of pancreatic cancer in the population is 13 per 100,000.
So if you gave this test with a 0.005 false positive rate and 0.5 true positive rate to 100,000 people it would miss diagnose 500 people and only correctly detect 7 cancers.
So given you had a positive test result there would be a 1-(7/500)=98.6% chance you did _not_ have pancreatic cancer.
Doesn't seem very useful in that light...
The article says:
"The test, which is also being piloted by NHS England in the autumn, is aimed at people at higher risk of the disease including patients aged 50 or older"
I believe the name for what you describe is "positive predictive value" or PPV - defined in the paper as the "proportion of true positives among those with a positive test result". According to the paper, their PPV for cancer detection is 44.4% (28.6%-79.9%, presumably the 95% CI).
As a point of comparison, one source I found reports a much lower PPV for the mammogram - single digits on initial screening, rising to 28% post biopsy: https://www.bcsc-research.org/statistics/screening-performan...
The paper notes that PPV can be a more useful metric than sensitivity. Their multi-cancer approach includes some hard to detect cancers that decrease the overall sensitivity, but increase PPV.
Edit: the paper also states: "The extrapolated PPV reported here based on SEER cancer incidence and clinical stage distribution was 44.4% in the screening-eligible 50-79-year age group, which is higher than that of currently recommended screening tests, as PPV is driven by specificity and population incidence."
They also add the caveat that "studies in intended-use populations that will provide more accurate PPV estimates are ongoing".
So if we test a random person, there is a 1% * 50% = 0.5% chance that person is tested positive for cancer because they have cancer.
And a 0.99% * 0.5% = 0.5% chance that a person is tested false positive.
This means if the test shows positive for somebody, it’s about 50-50 that they actually have cancer - correct?
Thats why it may be a good idea to test higher risk populations or people with health issues.
It could make the ratio of absolute true positives to false positives 10x better