Slashdot Log In
Computer Detection Effective In Spotting Cancer
Posted by
kdawson
on Sat Oct 04, 2008 08:33 PM
from the mechanical-helper dept.
from the mechanical-helper dept.
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."
Related Stories
This discussion has been archived.
No new comments can be posted.
The Fine Print: The following comments are owned by whoever posted them. We are not responsible for them in any way.
Full
Abbreviated
Hidden
Loading... please wait.
This is good unless... (Score:3, Insightful)
...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....
Having worked on a different computer diagnosis... (Score:5, Interesting)
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 :-)
Parent
Lupus? (Score:4, Funny)
Depends... what's its false positive and false negative rate for Lupus?
Parent
As many? Or more? (Score:3, Interesting)
Link (Score:2, Interesting)
http://content.nejm.org/cgi/content/full/NEJMoa0803545 [nejm.org]
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.
Re: (Score:2)
as long as we rely on private sector funded commercial research to advance medical science/technology, we will run into the issue of potential researcher bias as there's an inherent conflict of interest. this is also true of the pharmaceutical industry.
unless we as a society decide that public research is an important area of government funding, problems of slanted studies and deceptive/false research findings will continue to plague the medical field and other areas of research which depend on corporate fu
Ah yes (Score:2, Informative)
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.
Thank God! (Score:2)
As a med student, I couldn't be more pleased about this. Hopefully by the time I get out there, they'll have these standard in hospitals. And, more importantly, part of the standard of care, so when they screw up, I wont be sued.
Have you ever tried to see a small diffuse tumor on an X-ray before? It take a Jedi mind trick on just to convince yourself they're there.
X-rays are cheap, fast, and awesome for bones/opaque liquids, but my eyeballs can't see loose tissue worth a crap.
Computer detected, thus... (Score:2)
I detect computers in my room, thus that means there is cancer in my room? Ick.
Nothing New (Score:2)
Computer Aided Diagnosis has been around for years - it's just now it's becoming more popular.
Don't get me wrong - this is great stuff, just not new.
Re: (Score:2)
Real proof that it works better than a trained radiologist in a particular application is new.
8 eyes in India just as cheap as 2 in the US (Score:2)
As well as, or nearly as well as (Score:2)
>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.
"As well as"?
> CAD spotted nearly
or "nearly as well as"?
> the same number of cancers, 198 out of 227, compared to 199 for the two readers.
Ah, 198 vs 199 - it seems their first statement is not accurate. I wonder why people keep doing this - they use numbers accurately enough, but use language inaccurately.
Re: (Score:2)
Re: (Score:2)
What has this to do with statistics, apart from 'lies, damned lies, and statistics'.
I did pass statistics, btw, though it was some time ago. I don't recall learning that 'as well as' being the same as '198 equals 199' (iirc). If it were as good as, it would be 199.
* I can't easily look back at the original numbers, so they might not have been as above.
Not good enough. (Score:2)
It's all well and good to say that it's almost as good as two humans together, but I'm sure the couple of dozens of people who slip through the cracks would have something to say to the contrary.
I mean, imagine if you had two bullet-proof vests -- one with multiple layers that let bullets through 23 out
Re:WTF? just WTF? (Score:5, Interesting)
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.
Parent
Re: (Score:2)
nothing particularly special about the breast
Says you.
What, too obvious? Meh. Anywho, I think much of the attention to breast cancer is unwarranted. There are far more common and more dangerous cancers in each sex. I hate to put it this way, but it's fairly easy to isolate breast cancer vs. a brain tumor or liver cancer (mastectomy might not be the favorite choice, but it's pretty easy)
Am I sexist? I don't think so, I just wish that, say, similar attention was being paid to prostate cancer. As far as I know, they're roughly equally prevalent and equal
Re: (Score:2)
Not true (Score:3, Informative)
Re:WTF? just WTF? (Score:4, Interesting)
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.
Parent
Re: (Score:2)
Re: (Score:3, Funny)
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.
Re: (Score:2, Informative)
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
Re: (Score:2)
3 billion men beg to differ.
Re: (Score:2)
Well, hopefully imaging can be augmented with this:
http://www.boston.com/news/globe/health_science/articles/2004/09/23/hope_seen_for_early_test_to_detect_breast_cancer/ [boston.com]
By Robert Cooke, Globe Correspondent | September 23, 2004
Harvard researchers at Children's Hospital Boston have developed a simple urine test that appears to detect breast cancer early and accurately track tumor growth.
The findings are still preliminary, but if further research supports them, the test could be a major advance in the effort to catch breast cancer before it turns deadly. The Boston scientists are searching for similar markers in urine for other cancers.
Re: (Score:2)
You must have test your algorithm on some pretty easy breast cancer cases and/or tested on a pretty small sample. They are reporting 87.6% accuracy using a human and their CAD system, while you are getting 96% using just a computer designed for a different type of cancer. Something doesn't add up.
Re: (Score:2)
Yes, we're still investigating why the accuracy we obtained was so high. We've ruled out overfitting. I suspect it may be because our images are galactograms (i.e., mammograms with contrast injected into the ducts prior to imaging), which can more easily visualize certain types of abnormalities than unenhanced mammograms. I believe they also come from a single scanner, which isn't usually the case in clinical studies (although I wouldn't expect this to have as much impact in mammography as it does in modali
Re: (Score:3, Interesting)
Re:WTF? just WTF? (Score:4, Informative)
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 [breast-can...search.com] 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.
Parent
Re:WTF? just WTF? (Score:5, Informative)
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.
Parent
Re: (Score:2)
"Saying that computers can be as good as a human at some things is like saying different brands of cow milk taste the same. Why is this not standard now! Computers are more capable at many tasks, especially things that are repetitive and tedious."
And what computers can't do. Cheap labor can.
Re: (Score:2)
Medical device companies and universities have been working on this problem for years. It just isn't ready for prime-time usage. People go to school for about a decade to become a pathologist, and replicating that kind of domain knowledge isn't an easy task.
If you are a non-programmer, I understand how it seems like a trivial task to identify abnormal cells in tissue. We can naturally recognize similar/dissimilar cells with our vision, but to do this with a computer requires some serious mathematics, nam
Re: (Score:2)
Clustering algorithms are generally unsupervised. The domain knowledge isn't usually necessary in clustering so much as verifying that the cluster results make sense, since they're much more difficult to quantify than supervised tasks, such as classification, where your data is already labeled.
Of course, you need to do that too before you can present meaningful results and convince people that a system works.
(*As an aside: although you can certainly use a clustering algorithm to segment, the problem you've
Re: (Score:2)
In all seriousness, how else do you assign a weight between two vertices without domain knowledge?
Re: (Score:2)
Are you referring to the distance metric used in clustering algorithms? Most simply use Euclidean or cosine distance unless they have specific reason to believe a different metric would work better. It isn't really domain dependent.
Or did you mean how "else could you segment a lesion other than by clustering it in the absence of domain knowledge?" There are numerous ways, but edge detection filtering, fuzzy-connectedness segmentation, and watershed segmentation are the first ones that come to mind. They're
Re: (Score:2)
From my limited understanding, the algorithm is a dedicated clustering algorithm (it actually incorporates k-means), but it also utilizes a graph-cut algorithm for segmentation.
Re: (Score:2)
That's the presumption, but it's often false. An algorithm can often outperform an expert's intuition. See the chapter on "Evidence Based Medicine" in the book "Super Crunchers," or the chapter on diagnosing heart attack in Malcolm Gladwell's Blink. Often in these classification tasks, an expert using a statistical tool performs worse than the statistical tool alone, because
Re: (Score:2)
you still need a weight function to determine how similar two vertices are. This is where domain knowledge comes into play. A clustering algorithm without a measure of similarity between points is useless.
Re:WTF? just WTF? (Score:4, Insightful)
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.
Parent
Re: (Score:2)
Re: (Score:3, Insightful)
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.
It is news (Score:2)
Why is this news and NOT standard practice already?
Actually it is reasonably widely used as a diagnostic aid and becoming more so all the time, at least in the US. I've personally done consulting work in radiology clinics where they use computers to assist diagnosis. That said, it is still a developing technology and every scan is read by a radiologist too (which is just common sense) but these system do occasionally catch something the radiologist missed and vice-versa. It provides another set of eyes which don't get tired and that is a useful thing.
Why
Re: (Score:2)
Because it's not nearly that straightforward. Reliable image recognition in a clinical setting is tricky. Mammograms are a good place to start because they're fairly well controlled. There are rarely any serious artefacts or patient motion. Breast tumors are fairly obvious, and the cost of false positives isn't very high (a biopsy, which is a bit painful but is a simple outpatient procedure done under local anesthetic).
Much of the stuff radiologists do is a LOT harder. Even with the easy stuff, compute
Re: (Score:3, Insightful)
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 radiologi
Re: (Score:2, Funny)
Trolls are really an amazing species once you learn to understand their language.
Re: (Score:2)
In other news, mass firings across the nation as radiologists caught e-mailing photos of topless women around, about 31,000 photos in all.
Re: (Score:3, Informative)
Because mammography is an extremely non-sensitive test.
http://mammography.ucsf.edu/inform/html/graphics/graph2a5.gif [ucsf.edu]
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 bre
Re: (Score:2)
Aim higher. You can easily make a machine that flags 100% of the women who have breast cancer. Unfortunately it also gives the nod to 100% of the ones who don't.
Re: (Score:2)
Seems to be that the graph shows that 50 too late to start getting mammograms. From what I understand, it is recommended that women start getting mammograms at 35.
Also, isn't the point of the mammogram to detect anomalies before they turn into cancer? The numbers for whatever reason, seem a bit skewed in an attempt to get the most disproportionate ratio possible.