Discussion:
In this manuscript, we described, for the first time, cellular features
of normal skin and NMSCs including BCC, and a benign lesion with a novel
high-resolution FF-OCT device in fresh ex vivo tissues. Through a
blinded analysis, we demonstrated the potential utility of this device
for identifying and classifying neoplastic keratinocytic lesions. The
FF-OCT had high sensitivity in detecting all the tumors but had a
low-moderate specificity.
Amongst all tumor types, nBCC had the highest sensitivity for tumor
detection, however, had a moderate specificity. False-positive results
were due to the inability to distinguish tumor nodules from sebaceous
glands. However, sebaceous glands exhibit bright punctate dots and have
sparse surrounding collagen that can aid in differentiating their
identification. The deep learning algorithms employed could largely
segment nBCC regions. However, there were still some false positives on
nBCC segmentation, which might be due to the similar OCT appearance
between nBCC and sebaceous glands. On the contrary, SCC and sBCC had low
sensitivity but very high specificity. The low sensitivity could be a
result of incomplete visualization of the epidermis, which hindered the
detection of these tumors originating from the epidermis. Incomplete
visualization of the epidermis was caused by the use of tangentially
excised Mohs specimens and resultant tissue flattening issues
encountered during imaging. We believe such an issue can be improved
with a vertically excised specimen. In the future, it may also be
possible to resolve the flattening issue by using the newly described
digital tissue flattening26, which can use then expand
the application of this device to evaluate Mohs surgical margins.
Similarly, iBCCs and mnBCCs had a low sensitivity. This could possibly
be due to the difficulty of identifying small strands or foci of these
tumors among the bright and dense collagenous background.
The inter-rater reliability (Kohen’s kappa, Supplemental Table
2 ) values of the two OCT experts were below 0.4. This could be related
to the inability to differentiate BCC tumor nests from normal sebaceous
glands or follicular epithelium by the grayscale imaging, especially for
small BCC strands or nests. Ex vivo confocal microscope can create
digitally colored purple and pink images that simulates H&E-stained
tissue sections.
The major limitation of this study was a small sample of tumors. Another
limitation is the gray-scale nature of images that requires
interpretation by experts in this field. Thus, future studies are
warranted using a large sample size (including benign lesions) and
performing a multi-reader diagnostic accuracy study. Furthermore, deep
learning algorithms can be integrated to convert grayscale images into
digitally colored purple and pink images27, similar to
the images created by an ex vivo confocal microscope. This would improve
visualization of the OCT images and reduce the learning curve .
Moreover, AI can aid in the automated detection of tumors, leading to
its wider adoption20,28.
Based on our pilot study, we envision FF-OCT as an alternative for
time-consuming and tedious histopathology to enable a rapid assessment
of the tumor margins in the surgical excision samples, potentially
reducing their recurrence rate. At least, FF-OCT may offer a role in
initial specimen screening for the margin status in the operation room
to facilitate completeness of tumor removal before conventional
histological confirmation. It can also be combined with the in-vivo
imaging techniques such as optical coherence tomography and confocal
microscopy that has limited penetration depth and often cannot used to
evaluate deeper surgical margins. Additionally, FF-OCT can analyze small
biopsies at the bedside before they are submerged in formalin for
further processing. If indicated, all or part of the specimen can be
preserved for molecular analysis as the tissue is neither processed nor
sectioned. Lastly, since the FF-OCT images are digitally stored they can
be read and analyzed remotely by a specialist, as a telehealth
tool29, for evaluation of ex vivo tissue, especially
beneficial for rural or underserved areas. Although different ex vivo
imaging technologies exists, knowledge of this novel device is essential
to the consumers so they can tailor their needs based on the device cost
and capability. Ultimately, ex-vivo OCT may not necessary replace the
current rapid pathology process but may help to fill the gap in the
under-served community or rural area where an extensive lab set-up and
trained technicians may not be readily available.
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