DCIS is ductal carcinoma in situ -- it's non invasive cancer cells that are fully contained within the milk ducts. It is not entirely clear what the connection between DCIS and invasive cancer is. Our detection methods have enabled us in increase our rates of early detection but we have not seen a commensurate decline in breast cancer deaths. So it stands to reason that some cancers may not ever become fatal. The problem is that we don't know why, so we are treating every detected cancer as if it will eventually kill us. We have heard the mantra over and over that early detection saves lives, but to quote the Brothers Gershwin, it ain't necessarily so.
Further complicating this is something that defies logic to me. It seems like "Do I have cancer" should be a yes or no question, but apparently the accuracy of that answer depends on who is looking at the cells. Consider these startling statistics from the article:
One study in 2002, by doctors at Northwestern University Medical Center, reviewed the pathology in 340 breast cancer cases and found that 7.8 percent of them had errors serious enough to change plans for surgery.And this one concerning a more recent Komen study:
It seems that where a person lives can affect the accuracy of diagnosis.In 2006, Susan G. Komen for the Cure, an influential breast cancer survivors’ organization, released a startlingstudy. It estimated that in 90,000 cases, women who receive a diagnosis of D.C.I.S. or invasive breast cancer either did not have the disease or their pathologist made another error that resulted in incorrect treatment.
To diagnose a breast cancer, pathologists look at slides mounted with thin slices of breast tissue. The slides are stained with a purplish dye that highlights patterns of circles and dots, each representing a cell, its nucleus and membrane. The diagnosis turns on the appearance of these cells under a microscope.
At larger hospitals, the findings are often presented to a tumor board, in which a team of doctors from various disciplines reviews the pathology report and develops a treatment plan.If a person doesn't have access to a large hospital or cancer center, such procedures may not be in place. There are a couple of profiles of human error in smaller areas in the article.
But the problem is just limited to error; standardization is lacking in the diagnostic process.
Beyond diagnostic errors, there are different schools of thought about what constitutes D.C.I.S. Variations in diagnoses may depend partly on where a woman is treated.
In San Francisco, Dr. Lagios uses a criterion that says some breast lesions under two millimeters are not D.C.I.S., even if they have the other markers of the condition.
At Beth Israel Deaconess Medical Center in Boston, also renowned for its breast pathology services, those lesions are considered D.C.I.S., according to Dr. Connolly.Receiving a cancer diagnosis might be the scariest moment any of us could ever face. Imagine your doctor telling you that it may or may not be breast cancer depending on who is examining the slides. I think that the natural human tendency would be to react to fear, to have the most extreme slash and burn treatments available.
Those were the choices I made because I wanted to be sure that I'd done everything possible. Now, in my case it was warranted. I had no gray areas. But it might not be necessary for some people and once fear starts driving the bus, the bus is in trouble.
We need more research in one very specific area -- determining why some cancers become life-threatening and why some don't. That has to be our next frontier.
2 comments:
Excellent post! You make some great points here. I too was diagnosed with DCIS and it is only now five years later that I understand more fully what exactly that is. At the time all I heard was the word cancer. Thanks for highlighting this and the need to be examined and diagnosed at centers of excellence.
What helps me to deal with diagnosis, assessment, or measurement of any kind is knowing that there is uncertainty associated with any result. Said another way, there is a possibility that the result is wrong.
One type of 'wrong' is when the result confirms a false hypothesis (labwork indicates cancer but the you really doesn't have cancer). This is often referred to as a 'false positive' in labwork. In stats its known as Type I error or alpha risk.
The other type of wrong is when the result disconfirms a true hypothesis (your mammogram says you don't have breast cancer when you really do). This is often referred to as a 'false negative', of Type II error or beta risk.
Neither types of error can be totally eliminated from any measurement process. Thru intelligent improvement, however, probabilities associated with Type I,II error can be reduced.
Key to making good decisions under any measurement regime is to grasp how big the alpha and beta risks are, and treating any result with some skepticism.
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