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$5 device tests for breast cancer in under 5 seconds: study

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Re: $5 device tests for breast cancer in under 5 seconds: study

#81
post #24

As a reference. Currently an MRI scan can go from $400-$2000 (mostly covered by insurance) https://scan.com/body-parts/breast An MRI machine cost arounds $350.000 on average. https://www.blockimaging.com

A device such as this would never replace an MRI scan. The information provided is for screening purposes, at best. Also, diagnosis would typically be done using a mammography. The cost of such a scan is lower - around $100[0]. [0] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142190/

Diagnosis is done via biopsy.

Re: $5 device tests for breast cancer in under 5 seconds: study

#82

Press release: https://publishing.aip.org/publications/latest-content/would... Actual publication: https://pubs.aip.org/avs/jvb/article/42/2/023202/3262988/Hig... Experiment size is literally N=21, with 4 healthy participants, 3 in-situ breast cancers, and 14 invasive breast cancers. N=21 might as well be useless in my opinion. You can't draw any meaningful conclusions about statistical power of this test; if your pr…

I appreciate the links but I think this actually misses the point. The novelty isn't in diagnosis of cancer but in sensitivity/cost-efficacy in detection of known biomarkers for breast cancer (and associated risks of recurrence, etc). I'm not familiar with how common ELISA-based HER2 testing takes place, but it seems like it has some impact on drug decisions[^1]. In terms of applicability, it depends on whether or no…

The standard of care in the US is immunohistochemistry (IHC), with FISH testing in equivocal cases so not really ELISA based.

HER2 testing is done on all breast cancers as it affects treatment choices though the majority of breast cancers are not HER2 positive. (HER2 is also expressed in some normal tissue (notably cardiac) and is also seen in other cancers as well).

Re: $5 device tests for breast cancer in under 5 seconds: study

#83
post #37

Press release: https://publishing.aip.org/publications/latest-content/would... Actual publication: https://pubs.aip.org/avs/jvb/article/42/2/023202/3262988/Hig... Experiment size is literally N=21, with 4 healthy participants, 3 in-situ breast cancers, and 14 invasive breast cancers. N=21 might as well be useless in my opinion. You can't draw any meaningful conclusions about statistical power of this test; if your pr…

> We tried it with 21 people The new Theranos is here again

Not quite. Theranos was stating that they were capable of doing what was impossible prior. The real issue with Theranos was physics. A single drop of blood is simply not a large enough sample for them to do all the testing they claimed. As in, impossible with today's tech and in the near future.

These folks are just stating that, "so far, this looks promising".

At least, thats my read. YMMV.

Re: $5 device tests for breast cancer in under 5 seconds: study

#84

Press release: https://publishing.aip.org/publications/latest-content/would... Actual publication: https://pubs.aip.org/avs/jvb/article/42/2/023202/3262988/Hig... Experiment size is literally N=21, with 4 healthy participants, 3 in-situ breast cancers, and 14 invasive breast cancers. N=21 might as well be useless in my opinion. You can't draw any meaningful conclusions about statistical power of this test; if your pr…

> Table I shows the median and the range of digital readings by disease status and overall p-value using the Kruskal–Wallis test to examine if there exist statistically significant distinctions among two or more groups. The overall p-value is significant while the value for HER2 is 0.002, which show the probability of false-positive detection. This indicates that this sensor technology is an efficient way to detect H…

You'd need a much larger n to determine the false positive rate. Sensitivity by itself isn't very useful.

Re: $5 device tests for breast cancer in under 5 seconds: study

#85

Earlier quoted context omitted.

This statement belies a fundamental misunderstanding of statistics on your part.

If you're really good at statistics, you can just intuit what seems like a sufficient N. This is actually a powerful statistical method because you can vary your intuition depending on whether you want to believe the study was well-powered or not, enabling you to easily discard Bad Evidence and include Good Evidence. Few understand this.

No, that’s utter nonsense. Statistical power is well defined and the required N to reliably detect an effect is a function of its effect size, which is the thing that wannabe statisticians don’t understand.

Re: $5 device tests for breast cancer in under 5 seconds: study

#86
post #52
post #37

Earlier quoted context omitted.

> We tried it with 21 people The new Theranos is here again

No, not at all. Polar opposites. Theranos was actual lies. TFA is just being honest about a low sample size.

Without a larger n you can't really tell what the fp rate is and that means that the accuracy figure isn't very useful. If accuracy holds in light of a false positive rate that is much lower than other tests then this may well be very useful. But you can not conclude that at all based on the evidence presented.

Re: $5 device tests for breast cancer in under 5 seconds: study

#87
post #46

Earlier quoted context omitted.

Sure we can be technically more surgical in our terminology, but GP is addressing the usage of 'accuracy' in the news headline. In that context, 'accuracy' is kinda a catchall term for both accuracy and precision.

It's a bit difficult to say, isn't it? The headline is using the term accuracy correctly, the reader might be ascribing the meaning you are to it, especially if they are non technical. As was the parent comment. My goal in pointing out the difference was not to be snarky. It was to point out the very real statistical consequences. Any model can be accurate on a sufficiently biased dataset, but what matters once a scr…

Exactly. This is the real test and so far we simply do not know the answer. It's a nice first step but the headline is simply not justified. But whether solar power, wind power or cancer it's 99.99% of the time (possibly more nines) far less impressive by the time all of the data is in. And that's fine, but headline writers seem to be stuck in a hype cycle.

Re: $5 device tests for breast cancer in under 5 seconds: study

#88
post #57

Earlier quoted context omitted.

You can absolutely draw conclusions about the statistical power of the test. That’s what statistics is for. If this sample data is randomly sampled it looks like it will be a fairly high precision test for invasive cancers, with room for false negatives. You would have had to have gotten very unlucky to see such a difference in distributions even on a small sample like this. But sure, let’s get more samples.

Thank you for this! I get frustrated with the "N of only 21? Might as well flip a coin!" responses. Like you say, the whole purpose of statistical testing is to give an accurate, numeric value that says how likely the results are due to chance. One thing I'd note, though, is that the paper's title is "High sensitivity saliva-based biosensor in detection of breast cancer biomarkers: HER2 and CA15-3". My understanding…

No matter what the value of N is, someone always comes out of the woodwork to complain about it. It's a pretty common criticism of any study that relies on statistics, and one anyone can make in a few sentences.

Re: $5 device tests for breast cancer in under 5 seconds: study

#89

Earlier quoted context omitted.

This statement belies a fundamental misunderstanding of statistics on your part.

If you're really good at statistics, you can just intuit what seems like a sufficient N. This is actually a powerful statistical method because you can vary your intuition depending on whether you want to believe the study was well-powered or not, enabling you to easily discard Bad Evidence and include Good Evidence. Few understand this.

You don't intuit it, you calculate it. If you claim to be able to predict coin flips with a specific accuracy I can give you the exact number of trails N you need to be x% confident.

If you claim 90% accuracy and I want to be 95% confident with the standard alpha of 0.05 you need 38 trials.

Re: $5 device tests for breast cancer in under 5 seconds: study

#90
post #57

Press release: https://publishing.aip.org/publications/latest-content/would... Actual publication: https://pubs.aip.org/avs/jvb/article/42/2/023202/3262988/Hig... Experiment size is literally N=21, with 4 healthy participants, 3 in-situ breast cancers, and 14 invasive breast cancers. N=21 might as well be useless in my opinion. You can't draw any meaningful conclusions about statistical power of this test; if your pr…

You can absolutely draw conclusions about the statistical power of the test. That’s what statistics is for. If this sample data is randomly sampled it looks like it will be a fairly high precision test for invasive cancers, with room for false negatives. You would have had to have gotten very unlucky to see such a difference in distributions even on a small sample like this. But sure, let’s get more samples.

With so few controls, it’s still not a well designed comparison. Most scientists deal with low sample sizes, especially in first trials. However, what is often overlooked is the need for sufficient numbers of negative controls. If they only had 4 control patients, that’s completely inadequate to draw any real conclusions. With access to a biobank at U of Florida, they should have been able to test more samples — especially with a $5 test.

There are two other issues with this paper [1] that I quickly see. Their main figure lacks errors bars. It’s pretty clear to me that the groups would overlap quite a bit. More numbers would make this problem clearer (either to narrow error bars or clearly show an overlap). The lack of error bars across the paper make me think they didn’t do any technical replicates, which is also a problem.

I’m also not sure a one way test is correct here, but I’m also not entirely sure how they are measuring the data. In this one way analysis, all you can tell is if one group is higher than the other. When the data are so unbalanced, what you don’t see is what the predictive value of the test is — false positive/negative. That’s the real issue here. It looks like you’d have a really high false negative rate, as the cancer samples have a much wider range than the controls. This is the worst thing you can have in a test like this.

Finally — this paper was published in the “journal of vacuum sciences and technology B.” There is no way this paper got a valid peer review to make these claims. I don’t know anything about this journal, but I doubt it has much experience with cancer testing.

[1] https://pubs.aip.org/avs/jvb/article/42/2/023202/3262988/Hig...

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