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.