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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