Incidentalomas

Incidentalomas are things a physician may find during the normal course of an exam, such as an imaging study, which may or may not be of any significance, but most likely may be followed up on. For example if a woman over 60 complains to her physician about a bloated feeling in the abdomen and then is sent for a CAT scan and the image of the ovaries is uncertain, then significant follow up is ordered even though it looks like an old cyst. She may just be eating the wrong thing but now we have a mass set of tests and specialists involved.

Now consider the same thing but apply it to a genetic testing. A patient is worried about breast cancer and BRCA gene is tested and appears to be abnormal. What next. Well we know the stats and the patient is so informed. But what if it is some other gene? Some gene where we do not really know that well, say a 5% or even 20% increase in risk. Then what. Make it even more complex. Assume we have all normal genes but that key genes are methylated in their promoter regions. Have we tests for that as well? The gene may be there but can never be expressed. Furthermore perhaps we must look at genes which are organ specific. The list goes on.

In the recent NEJM there is an excellent piece on this issue. They state:  

The problem with the genomic–radiologic analogy is more than a matter of semantics. The comparison may give nonexperts a false impression of our ability to efficiently interpret genetic or genomic findings and to understand how they might affect a person's health. It perpetuates a myth about the level of our current understanding of the genome and of individual genetic variants — the notion that we can interpret all the information from genomic sequencing as quickly and accurately as we can interpret an x-ray. This myth can affect the public, patients, research participants, and clinicians who lack training or experience in genetics or genomics. And the myth will become more problematic as genomic sequencing becomes faster, cheaper, and more widespread. Despite impressive ongoing efforts that will continue to yield great progress, we are not at the point where interpreting a genomic data set is similar to interpreting a radiologic study.

Indeed, genetic test results are useful in a small body of applications. Furthermore we often do not know what to do if we discover a more complex issue. Thus the true concern as to their current use.

One need look no farther than the multiplicity of tests for Ca. Which one really works. And WHY? Yet to be answered.