To use a software analogy, if your downtime detection system kept producing false negatives, would your solution to be just turn it off? You'd get some better night's sleep, but you'd pay for it when the system really went down and you had no idea.
To use a software analogy, if your downtime detection system kept producing false negatives, would your solution to be just turn it off? You'd get some better night's sleep, but you'd pay for it when the system really went down and you had no idea.
It's probably more accurate to use a software analogy about performance metrics. We measure random request spikes now and again that strain the system. It's probably fine, but later on down the line, something could change that results in an outage during one of these spikes. Do we proactively fix the problem even if no change is expected? Or do we wait till there is definitely a problem before taking action?
I'm not an expert though.
“Don’t worry about it, I don’t think it’s a real issue so we’re just going to ignore it”
Do you think a doctor is more likely to call something "possible cancer" and recommend that you either have a specialist do a biopsy (keeping in mind that many of these will be... hard to reach) or at least have a follow up MRI in 3, 6, 9 months?
Or tell you it's "pretty unlikely to be cancer, I don't think we need to worry about it" and then get sued for 20M when they are wrong about 1 in 100 cases (not to mention missing out on all the potential income from above).
At least in the US, the incentives here are grossly misaligned.
The reasons are complex, but the short answer is that cancer treatment is extremely hard on your body, and even if you don’t treat, stress can literally make you sick. I recommend reading The Emperor of all Maladies if you want to really get a sense for how delicate the problem of early screening is.
The thing is, when researchers talk about “worse outcomes” they’re often comparing survival (or rather lack of) against terrible side-effects.
What this fails entirely to capture is that doing something to increase your odds of survival, damn the consequences, is an individual choice. It shouldn’t be up to a health economist to make that judgement.
What you're failing to capture is that this is a hard problem because it's both an individual choice and a collective one as well. Those "terrible side effects" might actually end up killing someone. You're choosing between a high-chance lottery on a small population or a low chance lottery on a far larger one. It's not that simple.
But right now it is likely to cause a huge waste of time, resources, and yes, human lives to know about every little lump in your body.
Unfortunately too many radiologists and specialists are more focused on upping cash flow than medical care.
Why is it likely? We already have a lot of MRI data. There are already a lot of incidental findings. It might also be an issue of the MRI not being able to produce enough information to discriminate.
> To use a software analogy, if your downtime detection system kept producing false negatives, would your solution to be just turn it off? You'd get some better night's sleep, but you'd pay for it when the system really went down and you had no idea.
The analogy is rather something like this: your downtime detector is not just a "ping" but a full web browser that tests everything and it sometimes flags things that are not actually issues. So you don't turn it off, but you only use it when you have another signal that indicates that something might be going wrong.
This is the main reason. Well technically the opposite of the main reason but more or less it's the same. MRIs are extremely high fidelity nowadays and as a result it's really really hard to read an MRI. Every person is different and there's a lot of variations and weird quirks. You get all the data rather than clearly identified problem areas like you get with say a CT w/ contrast, etc.
That's actually exactly why it's important to have MRIs more frequently to be able to establish baselines and identify trends as they develop.
How? How do you establish baselines? How do you build a classification of incidental findings? It's very possible that you'll find a lot of types and not a lot of representatives of each type. And then you have to correlate that to actual clinical results, but the population will be so heterogeneous that it'll be really hard to find an actual result.
It's not just "let's throw more data at the problem".
If you have records of the locations and sizes of various atypical structures and forms throughout the body going back for years and all of a sudden one of them starts changing in size at a rate disproportionate to its history, that's probably cause to dig a little deeper.
It's certainly not "throw more data at the problem". Instead it's about giving the data a time axis with some decent fidelity.
That sentence is doing a lot of heavy lifting.
- What's "disproportionate to its history"? Obviously something going from 1mm to 10cm is worth checking out, but what about something going from 1mm to 2mm? Might be a tumor, might be that the position is just slightly different.
- What about other less measurable factors? Example, border features. That's harder to measure and things like movement or different machines can change how the borders of a feature look. How do you know what's a baseline and what's not.
- How frequently do you run these scans? It's likely that if something "starts changing in size" suddenly it will start giving symptoms before you have your next scheduled scan.
> It's certainly not "throw more data at the problem". Instead it's about giving the data a time axis with some decent fidelity.
It's definitely throwing more data at the problem, and you're assuming that it's viable to give "a time axis with decent fidelity". MRIs are much more complicated to interpret than people think, and screening is a much harder problem too. There are a lot of studies testing MRI imaging as a screening technique (among other techniques) and they don't always show an increase in survival rates.
Not always. There are bunch of studies for MRI screening in high-risk populations for specific cancers. There are scoring systems for a lot of them based on imaging features and they do find asymptomatic cancers.
In fact, if you add low-risk populations to the studies used to design imaging scores, you might end up adding more noise and making the study more difficult and the scoring less accurate.
That's true but not in a useful way for improving MRI screening.
What we have is lots of days from people who were sent to get an MRI because they had a complaint.
That's a very different group than people doing screening.
If false positives are ok, why not build a down time detector that rolls a die every 5 mins and alert on hitting a 6.
It's perceived as much less (medico-legally) risky to "do something" (or more often "refer the patient to someone else to do something") than not do something.
I would argue that getting "predatory capitalism" out of the way has sharply curtailed MRI availability where that's been tried. Maybe we should loosen the leash on capitalism a bit to get better care...
Like what?
I've seen instances where this is used as an excuse for what is, ostensibly, a trick to dimiss people using something that sounds vaguely professional. Like when doctors say they don't want to do additional x-rays because of the risk of radiation exposure, nvm that if it comes out slightly blurry they'll ask to redo it, or if you're cautious about it initially they'll tell you how it's not big deal and there's more radiation in a cluster of bananas.
A lot of potential harms are at a societal level as well—from a public health perspective, if everyone starts having regular MRIs that produce incidental findings which require followup, you’re suddenly tying up lots of resources that would otherwise be available to actually-sick people. A person with symptomatic problems whose treatment is delayed because they can’t get an appointment because the specialists are booked full with incidental findings, that person is indirectly harmed by this.
The radiation from CT scans is not especially concerning at an individual level when there is a compelling reason for it, but, if we’re suddenly doing tons more to investigate incidental MRI findings, there may well be a point where those scans are causing a significant amount of cancer overall—a recent study suggested, I believe, that CT scans may be responsible for 5% of cancers already.
Well, from the original article: "And if someone is over-diagnosed, gets a biopsy and develops an infection, that's a direct harm."
Contrast-induced nephropathy?
Gadolinium accumulation in the brain doesn't sound good for you...
Although I think this argument is usually talking about the risks of the resulting procedures (eg an injury or complication related to a biopsy done for a finding on imaging).
It takes a lot to clear the dye.
Personally I’d rather have cancer checked out rather than have a “wait and die” approach
And the person that's making the recommendation on whether or not to check it out may get sued for $10M if they tell you it's probably nothing and they're wrong, but have no harm come to them if they tell you it's worth having some other doctor do a biopsy.
And they might make an extra couple hundred bucks every time you have to come back and see them to follow up on this spot.
And the radiologist interpreting the MRIs... the same perverse incentives regarding how they interpret a "spot."
It's more like if your CI build fails in 95% of runs, and in only 2% of runs it indicates a real bug, do you have a bad CI which is next to useless for detecting bugs? I'd say yes, that's exactly what you have. No developer is going to pay any attention to this CI, and if you tell the developers they must ensure tests pass on such CI, they would rebel.
And if there's one thing where AI models really do already excel at it's classifying and noticing patterns.
Many dermatologists (not all of them yet, at least not in the EU), for example already have software classifiers using pictures of one's skin and helping guide diagnosis. I've lots of moles/nevi and freckles on my skin: I'm one of those Gen X kids raised by parents that had no idea that sun exposure and sunburns was a bad thing so I regularly get warning shots and my body, especially my back, if full of scars for for my entire life dermatologists have regularly removed concerning little buggers and sent them to the lab for further analysis.
Nowadays my dermatologist is helped in her classification by hardware/software.
I don't see why that wouldn't be the way forward for full scan MRI: they'll begin to be more and more hooked up to AI classifiers.
It always takes time: it's not as if the tech comes out and in 48 hours every hospital/physician is equipped with it.
It's literally the future is here (classifiers helping dermatologists find concerning nevi), just not evenly distributed (many dermatologists still don't have access to these latest machines).
I don't see any way that the hospital systems running healthcare in the US would embrace a technology that reduces false positives (income) without decreasing false negatives (risk and lost income) at least as much.
More and more European countries are currently adopting Lung Cancer screening programs. It's usually limited to people with a certain amount of cigarette-pack-years, but still gives the opportunity for driving more of the innovation you're talking about. I think the main challenge at the moment is that nothing in healthcare is prepared of looking at those scans effectively, a radiologist has full medical education + additional specialization - without effective procedures you'll never be able to provide full-body scans with any meaningful impact.
Ofcourse in America poor people don't have access to healthcare so it's a lot easier. But in a universal healthcare system everything has to be rationed.
Most people do have things “wrong” with their body, but they are asymptomatic. The human body can and does cope with a certain amount of failure and/or anomaly as a part of normal operation that we otherwise consider healthy.
The problem is that this information is often not actionable. An MRI is great at identifying which ways your body doesn’t look like a textbook reference body, but it doesn’t necessarily tell you what those things are or whether they will ever cause you problems. The way the body naturally responds to problems doesn’t always look perfect on a scan but if it results in no symptoms it is the best result. And for most asymptomatic findings, taking an invasive next step has more risk than the finding itself. And these findings will always be in the back of the patients head, whether relevant or not, and might complicate how they seek care for other real issues later on.
Its the doctors doing this, not the MRI.
There's this weird definition switch that always happens with the "overdiagnosis" defense where the information gets blamed for the overdiagnosis. An MRI doesn't provide any diagnosis in any sense of the definition. A doctor does.
Claiming an overdiagnosis defense is essentually implying the medical industry is worse for most than doing nothing.
But in the scenarios this article is talking about (Prenuvo, et al), these aren’t scans ordered by a doctor, and there is no other evidence. It is just a patient getting some MRI findings of unknown clinical significance dumped in their lap.
The problem isn’t an overdiagnosis by doctors. The problem is that there’s no doctor diagnosing anything in these instances.
This article sees methodological failure, I see training data. Training data that could ultimately be used to refine low resolution scans into targeted high resolution scans as needed driving down costs and driving up accuracy. We've already demonstrated AI upscaling, what's the blocker to doing the same for MRI?
And finally, who are any of us to tell people what they can do with their money? China has these things down to $70. And they're leaning in hard on improving them. Cue obligatory China cuts corners blah blah blah. Sigh.