Raman spectroscopy detects bladder cancer
David L. Shenkenberg
To localize a tumor in the bladder, doctors routinely perform a cystoscopy, a procedure
that involves inserting a lensed, lighted tube into a patient to enable their search
for potentially cancerous sites. Cancer can easily be missed because doctors rely
only on a narrow optical tube and their eyesight for visual analysis. Fluorescent
labeling and other markers have been suggested, but these methods only stain for
Raman spectroscopy can produce a complete biochemical
profile of the tissue and can be integrated into a cystoscope through fiber optics.
For the initial stage of bladder cancer, which is particularly difficult to detect
with cystoscopy, Raman spectroscopy might be more sensitive.
Doctors use cystoscopes to search
for bladder cancer, but the view is limited, so researchers want to integrate them
with Raman spectroscopy equipment to provide biochemical information during the
After performing cystoscopy, doctors
biopsy suspicious tissue and view it under the microscope, in contrast to Raman
spectroscopy, which can examine tissue nondestructively. However, analysis of Raman
spectra is a painstaking process — time consuming for both doctors and patients.
So, Bas W.D. de Jong and colleagues at Erasmus Medical Center in Rotterdam, the
Netherlands, have programmed software to use algorithms that enable automatic analysis
of Raman spectra.
The investigators examined bladder
tissue from 15 patients with a Leica microscope equipped with an 80x Olympus objective
with a 0.75 NA because, as de Jong explained, it enabled near-IR measurements with
low noise. For an 845-nm excitation source, the researchers employed a tunable Spectra-Physics
Ti:sapphire laser that was pumped with a Coherent argon-ion laser. Raman scatter
passed through a chevron filter, which de Jong said was more stable than a holographic
filter, and into a Renishaw spectrometer. The scientists focused the beam 2 μm
below the tissue surface with a spot size of <1 μm2 and collected the signal
in 20 s.
They programmed the software to group
biochemical data into broad clusters and to look for subtle differences between
them. To test the accuracy of those algorithms, they instructed the software to
exclude one patient and to use the remaining patient data to predict whether that
patient had cancer; the process was repeated for each patient.
From a pathologist’s independent
histological investigation, the researchers knew which tissues represented cancer
and which did not. They used that information to determine whether the Raman spectroscopy
and their algorithms yielded the same results.
They found that bladder tumor tissue
expressed more lipids, glycogen and nucleic acids than nontumor tissue and had a
higher protein content. However, nontumor tissue contained higher amounts of certain
lipids such as arachidonic acid and stearic acid.
The researchers also detected β-carotene
in one tumor tissue sample. Anecdotally, carotene is believed to combat bladder
cancer, so the researchers assumed that the patient purposefully ingested it. This
finding suggested that Raman spectroscopy could be used to examine whether drugs
are absorbed at their target sites.
Overall, the algorithms correctly identified
94 percent of the tissue. Considering only false-negatives and false-positives,
respectively, the algorithms were 94 and 92 percent accurate. The scientists noted
that the cutoffs for false-positive and false-negative prediction could be adjusted
to meet a doctor’s preference.
Inflamed tissue caused false-positive
results because it exhibits high RNA and DNA expression, as does tumor tissue. However,
de Jong said that the differences between tumor tissue and inflamed tissue will
reveal themselves as they test more samples and input their spectra into a database.
Analytical Chemistry, Nov. 15, 2006, pp. 7761-7769.
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