Abstract
Histopathology for tumor margin assessment is time-consuming and
expensive. High-resolution full-field optical coherence tomography
(FF-OCT) images fresh tissues rapidly at cellular resolution and
potentially facilitates evaluation. Here, we define FF-OCT features of
normal and neoplastic skin lesions in fresh ex vivo tissues and assess
its diagnostic accuracy for malignancies. For this, normal and
neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT,
and their features were described. Two expert OCT readers conducted a
blinded analysis to evaluate their diagnostic accuracies, using
histopathology as the ground truth. A convolutional neural network was
built to distinguish and outline normal structures and tumors. Of the
113 tissues imaged, 95 (84%) had a tumor (75 BCCs and 17 SCCs). The
average reader diagnostic accuracy was 88.1%, with, a sensitivity of
93.7%, and a specificity of 58.3%. The AI model achieved a diagnostic
accuracy of 87.6%±5.9%, sensitivity of 93.2%±2.1%, and specificity
of 81.2%±9.2%. A mean intersection-over-union of 60.3%±10.1% was
achieved when delineating the nodular BCC from normal structures.
Limitation of the study was the small sample size for all tumors,
especially SCCs. However, based on our preliminary results, we envision
FF-OCT to rapidly image fresh tissues, facilitating surgical margin
assessment. AI algorithms can aid in automated tumor detection, enabling
widespread adoption of this technique.
Introduction
Non-melanocytic skin cancer (NMSC) is the most prevalent cancer
worldwide, accounting for ~5.4 million cases diagnosed
and treated annually in the US alone1. Among all
NMSCs, basal cell carcinoma (BCC) is the most common type
(~4.3 million cases), followed by squamous cell
carcinoma (SCC; ~1 million cases)1.
NMSCs are rarely fatal and seldom metastatic, but they can be highly
infiltrative and aggressive and have a high recurrence
rate2.
Surgical excision and Mohs micrographic surgery are widely accepted
procedures for the margin assessment and complete removal of the NMSC
with a high cure rate of 95% to 99% respectively3.
To achieve a high cure rate and preserve healthy skin, histopathological
examination of the excised tissue is the gold standard. However,
histopathology evaluations require time-consuming tissue preparation,
extensive laboratory facilities, and well-trained
technicians4,5.
Ex vivo optical imaging devices including confocal microscopes and
optical coherence tomography (OCT) have been developed for the rapid
evaluation of fresh tissues to obviate tissue
processing5-11. In this article, we describe the
utility of a novel full-field OCT (FF-OCT) microscope (ApolloVue® B100
image system, Apollo Medical Optics, Ltd.) device. OCT relies on a low
coherence interferometer and the light scattering properties of skin
structures to construct cross-sectional images of
tissue12. However, the existing OCT devices have a low
resolution (3-10 µm axial and 3-7.5 µm lateral resolution), which
hinders the differentiation of normal skin structures from tumors and
tumor subtyping12,13. In contrast, the novel FF-OCT
microscope has an axial resolution of 1.5 µm and a lateral resolution of
1.1 µm, which is far superior to the existing OCT devices. Wang et al
demonstrated that even a novice (without OCT experience) can read these
images with 93% to 100% sensitivity and 21% to 54% specificity.
However, this study was performed on paraffin-embedded thick tissue
sections, which doesn’t equate to the evaluation of freshly excised
tissues14.
Although the emergent cellular-resolution optical coherence tomography
(OCT) could significantly accelerate the clinical adoption of OCT to
assist physicians in interpreting images15-18,
interpretation of OCT images often requires an expert with extensive
training in reading these images, posing a major barrier to integrating
OCT in clinics6,9. Thus, deep learning algorithms, in
particular convolutional networks (CNN), have become a powerful tool for
analyzing medical images to assist physicians in detecting, classifying,
segmenting, and even diagnosing tissue images19. CNN
has the advantage of automatically extracting features and is not
limited to features defined by the human eye. At present, many studies
have used CNN to identify basal cell carcinoma in stained
images20 and segment nuclei from stained
images21,22 and dermal fillers in OCT images of mouse
skin23.
In our study, for the first time, we imaged fresh, non-labeled (without
using any exogenous dye/contrast agent) tissues obtained during Mohs
surgery using a high-resolution FF-OCT microscope. First, we defined
FF-OCT features of all normal skin structures and various NMSC tumors,
particularly BCC, and some benign lesions. Next, to demonstrate the
feasibility of this device in a surgical setting, we performed a blinded
analysis by two OCT experts (1 pathologist and 1 Mohs surgeon) to access
the diagnostic accuracy (sensitivity and specificity) of detecting
tumors. Lastly, to overcome the limitations of reading gray-scale images
and mitigate the mosaic artifacts due to image stitching, we generated a
deep-learning algorithm that can differentiate BCC tumor nodules from
sebaceous glands and other non-tumor tissues.