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Computer Detection Effective In Spotting Cancer
Anti-Globalism notes a large study out of the UK indicating that computer-aided detection can be as effective at spotting breast cancer as two experts reading the x-rays. Mammograms in Britain are routinely checked by two radiologists or technicians, which is thought to be better than a single review (in the US only a single radiologist reads each mammogram). In a randomized study of 31,000 women, researchers found that a single expert aided by a computer does as well as two pairs of eyes. CAD spotted nearly the same number of cancers, 198 out of 227, compared to 199 for the two readers. "In places like the United States, 'Where single reading is standard practice, computer-aided detection has the potential to improve cancer-detection rates to the level achieved by double reading,' the researchers said."
...you're #199. If the computer provides that much advantage when combined with a single person, it stands to reason that it would also provide a huge advantage when two people read the charts. Unfortunately, knowing our medical system in the U.S., they'll probably just use this as an excuse to pay only one doctor to read the chart....
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It doesn't say if the 198 that CAD found were a subset of the 199 that the two readers found.. So would two readers + CAD have found more than 199? Or did both groups miss the same 28?
http://content.nejm.org/cgi/content/full/NEJMoa0803545
That's the original research. If you read the Yahoo article you'll see the researchers got money from the manufacturer of a computer-aided reading system.
Worry not, this is standard practice. Although there is general support that CAD (computer-assisted diagnosis) is effective vs. a second reader, there is still a bit of controversy in the field from time to time, since the results have not been overwhelmingly in favor of CAD yet. There's always at least one talk on the general usefulness of CAD at conferences. Sometimes whole sections get devoted to the topic.
What is a bit more puzzling is why it isn't as prevalent in diagnosis of other types of cancer. Most of the computer-aided detection algorithms draw on general machine learning and image processing techniques rather than specific domain-knowledge of the breast, and thus many of them can be applied, sometimes without any changes, to other organs. There is nothing particularly special about the breast.
My group developed a CAD system for MRI images of the brain, and in the course of performing experiments to put in the paper, I decided to run a few images from a breast CAD project through the classifier. Sure enough, the classifier we had developed for MRIs correctly classified 96% of the mammograms we fed it as well.
Because the computer systems are expensive and it hasn't been clear that they work as good or better than humans. It's a very complex issue and has been studied for quite some time. In particular, the issue is "false positives" which cause anxiety and often prompt additional, invasive, expensive testing. From a rather quick Google Review of Available Information and Literature (GRAIL):
TFA doesn't even mention the false positive rate, just the fact that it found as many cancers as the double Radiologist method. So keep your pantyhose on. It's something that should get better with time and experience, but it's hard to say that the system is ready for universal application.
Faster! Faster! Faster would be better!
Parent poster is not offtopic, this troll is expressing its experience with this technology. It's saying that it got a false positive (fp) and underwent chemotherapy as a result, which caused the hair to fall off its genitalia (shaved pussy). Quite a tragic tale.
Trolls are really an amazing species once you learn to understand their language.
Most results are presented via ROC curve (for the uninitiated, this is a curve that plots true positive rate against false positive rate based on some threshold for classifying a lesion), so the FPR can theoretically be reduced if you're willing to lose sensitivity as well.
The thing is, the outcomes are not balanced. The risk of missing a cancer is considered far greater than the risk of returning a false positive, so the algorithms are usually created with sensitivity rather than specificity in mind. In my opinion (and since I work on some of these algorithms, my opinion is important :)), this is as it should be, and we should worry about specificity only if we can keep a comparable level of sensitivity.
In any case, the article Yahoo is sourcing from does mention the specificity (which is 1-false positive rate), and it is encouraging: with CAD, the specificity was 96.9%, vs. 97.4% for double reading. Given that sensitivity was also similar (87.2% vs. 87.7%), this article paints CAD in a very favorable light.
Because you have to *PROVE* with clinical certainty (ie. research studies) that the computer system is as good as an expert under all conditions. A mammogram is a two dimensional monochrome picture of a three-dimensional object. As you are attempting to detect a life-threatening defect using a piece of software, false alarms can be as devastating to the patient as missed detections, and thus have the same lawsuit risks.
Also, this requires the entire hospital to have a digital patient record management system, in order to handle digital X-ray images. Many hospitals and dentists are still using photographic plates and paper records. With the digital system, everything from doctors notes to X-rays, CAT and MRI scans are automatically placed into the patients record when they are generated. The resulting data is then accessible to any consultant or doctor involved with the patient. The new system has the advantage that there is no need to wait for X-ray plates to be developed.
Vintage computer adverts: http://www.vintageadbrowser.com/computers-and-software-ads
I fell right into that one, didn't I? :)
I agree. I actually much prefer working with brains; the organs themselves are more interesting and analyzing the images tends to involve more challenges than 2D mammograms. Volumes vs. static images, spatiotemporal analysis, the option of acquiring functional data to map the lesion to cognitive deficits... I find it a very interesting area. Unfortunately, early diagnosis doesn't always make a difference in certain forms of brain cancer. This needs more research in treatment rather than in diagnosis.
Now we're going into the sociological dynamics of research, which turn out to be really messy, but I'm pretty sure the disproportionate amount of interest in breast cancer is in no small part fueled by the ample funding that gets provided to it vs. other types of cancer. However, as I mentioned in the other post, a lot of the CAD methods tend to be general, and breast cancer is really only a specific application, so this is perhaps not as bad as it sounds (if others apply existing methods elsewhere). Given that other forms of cancer strike more often or have greater mortality rates, and that this one tends to strike only half of the population with any frequency (although it is possible for it to develop in men as well), I think something like pancreatic or colon cancer would be more useful to direct some of the study towards, particularly because the current methods for diagnosis are wholly inadequate in the case of pancreatic cancer and rather invasive in the case of colon cancer.
Prostate cancer may also be a useful cancer to study more due to its high prevalence, but it's also gender-specific and the survival rates are rather high already, so I don't think it would be the first cancer to research on my list.
system, there is a synergy between man and machine. Our system was for a general practitioner (general diagnosis with symptoms, physical findings, history, tests, etc as input). The computer is somewhat "dumb", but it always checks all the possibilities. The doctor would be looking for the usual stuff, and sometimes miss the more exotic diseases that would turn up from time to time. The machine would flag some exotic condition with a high probability, and the doctor would go "Interesting! I hadn't thought of that, let's check it out." Dr. House probably doesn't need one :-)
I worked on the first clinically useful mammo CAD system (also the first to have FDA approval in the US). Unlike some of the smaller scale (often academic) programs I saw at the time, there was a large degree of domain knowledge (i.e. breast specific) in our codes.
This is pretty typical in other pattern recognition domains as well. You can get a certain distance with fairly generic approach algorithmics, but to really push the performance boundaries, you need local info and approaches as well.
This paper doesn't actually say anything particularly new relative to our results 10 years ago, but it's a broader study than was available then.
Be careful: A slight improvement in the classifier (or acceptance of another false positive or two) and you may have to make that argument in the other direction. The difference is accuracy is not statistically significant for a binary classification problem of that size.
What this article demonstrates is that current state-of-the-art CAD is nearly as good as a second reader. The performance of the radiologist is pretty much fixed; the algorithm's performance is not.
Some people like to call it Computer Aided Risk Estimation (CARE), although some also use this term as a subfield of CAD, but unfortunately, the terminology has become entrenched by this time.
Because mammography is an extremely non-sensitive test.
http://mammography.ucsf.edu/inform/html/graphics/graph2a5.gif
This shows how few women the test can actually benefit - 37 out of 10,000 over all lifetimes. Even worse is that the women who are diagnosed falsely positive far outstrips those that actually have cancer by orders of magnitude. This creates a harmful burden on the falsely diagnosed women - creating morbidity and even mortality.
You can make a machine that gets 99.9% of all women who do have breast cancer. Unfortunately, out of the 9740 that never will/don't have breast cancer 9700 will be falsely diagnosed as having it.
that's a statistically insignificant difference in accuracy. i think the conclusion to be drawn from this is that computer-aided detection is much more effective than an unaided human expert. this has significant implications when doing cost-benefit analysis.
the cost of an extra computer is likely a lot less than another technician or radiologist. so this data will help medical institutions make better use of funds while improving quality of patient care. it doesn't mean they have to lay off their radiologists/technicians and replace them with computers, but perhaps they could add a computer to their radiology lab and allocate new personnel for other tasks that demand human judgment.
Where do you get your stats from? I've seen otherwise (ACS site, for starters. School, too, in my community health class. I'm in nursing). Furthermore, of all the inequities in research and healthcare, this is just one that is female-positive. Take, for example, cardiovascular health and women. Women are treated differently when it comes to suspected heart attacks and other issues of cardiovascular health, and it usually winds up killing them.
Because government-funded research is inherently free of any and all bias. It is never politically motivated, and areas to research and not to research are chosen purely on scientific merit by a government bureaucrat, whose #1 goal is not to increase and extend his own power. </sarcasm>
Seriously though, there are a lot of people who believe exactly that, and even if the commercial research may be biased, at least that's known and out in the open.
Depends... what's its false positive and false negative rate for Lupus?
I've got a preventative treatment for prostrate cancer.
It's called trans-fatty acids.
Take enough of it and your odds of getting prostrate cancer go way down.
There's plenty of scientific evidence to back my claims.