OCT or optical coherence tomography is really the optical analog to ultrasound imaging.
So instead of putting in sound waves to produce an image, we put in light waves and measure
those reflections that come back.
Because we use light, we have a much better, higher resolution than ultrasound. There’s
always a tradeoff between depth of imaging and resolution. So we can only image a few
millimeters into tissue, but we can do so with micron scale resolution.
So there’s advantages to that, certainly we are able to see features, cells, tissues
that are very close to what you would see in histology or what a pathologist would use
to make a diagnosis.
With that ability, just as a pathologist will look at a biopsy of a tissue specimen to make
diagnosis, we can produce that same type of image and conceivably be able to provide that
information in real time.
So one of the major projects we have going is to take this type of instrument into the
hospital clinical settings and, in particular, into surgery, where surgeons can be able to
analyze the tissue that’s being removed and be able to determine if
the tumor margins are clear of disease.
So a major question that they all have is after dissecting a tumor, surgeons really
have the macroscopic view of the tissue; they don’t have the microscopic perspective.
Being able to look at this microscopically and look at tumor margins, make sure all the
tumor was resected, that gives us significant advantage over that particular procedure.
So our clinical translational work has been evolving over the past several years. We essentially
had to develop some of these protocols and make the collaborations with the clinical
collaborators and it has taken some time. But in the last one to two year we’ve had
ongoing clinical trials where we’ve been imaging patients in the operating room, and
using that information to assess how well OCT works.
So, in addition to having our optical imaging system, our OCT system, in the operating suite,
we’ve been looking at tumor margins there prior to sending the tissue onto pathology.
So that allows us to be able to compare our image data with the actual pathology that
is used for the diagnosis.
So far, statistics are looking very good. In fact, we’ve got almost a perfect record
of identifying in the operating room what pathologists will later diagnose.
So looking at tumor margins, also being able to look at lymph nodes. Lymph nodes act as
the body’s filter for disease. And when cancers spread,
they frequently go to the lymph nodes. And so, once again, we’ve been able to use our
OCT imaging to look at lymph node structures and determine,‘Has the cancer really spread?’
and find that information out during the procedure, rather than having to wait days or weeks for
the pathology report.
We’ve gotten a lot of attention just because I think it touches everyone in some way. Our
health, our personal health, and the health of people that we know, that’s all very
close to us.
And so being able to have a technology that impacts that and where people can see the
advantage that we’ll have on health care, it’s very rewarding. We see that with the
students and the scientists involved with this research. I think they develop that interest
because they can see the immediate impact that it has on patients.
I think this technology has a quite wide range of applications. So, most of our work is focused
on breast cancer detection. But obviously any type of solid tumor detection where there’s
always a question of,
‘Did the surgeon get all of the tumor?’
‘How far has the tumor spread?’
These are the sorts of questions that I think we want to answer for many other types of
procedures.
So we want to go in the direction of making our system faster, making it able to become
more user friendly, so a technician or a surgeon in a very busy operating room can quickly
acquire this data to allow the computer to make some of the diagnostic assessment. This
is done in many other techniques; in mamography, in assessing pap smears, where a computer
makes the first pass and says, ‘Is this area normal or abnormal?’
And why that’s important is because we have such a large volume of data that we get that
this operator, surgeon, can’t physically look through all of it.
So we really need these types of computer algorithms to come in and classify data to
point out what might be abnormal. So that’s another area that we’re headed to.
And also I think that this technology, being able to make it more compact, being able to
make it more efficient for use in the hospital setting is going to be important as well.