A Prospective Double-Blinded Comparison of Reflectance Confocal Microscopy with Conventional Histopathology for In Vivo Assessment in Oral Cancer.

PURPOSE: We investigated reflectance confocal microscopy (RCM) as a possible noninvasive approach for the diagnosis of cancer and real-time assessment of surgical margins. EXPERIMENTAL DESIGN: In a phase I study on 20 patients, we established the RCM imaging morphologic features that distinguish oral squamous cell carcinoma (OSCC) from normal tissue with a newly developed intraoral RCM probe. Our subsequent phase II prospective double-blinded study in 60 patients tested the diagnostic accuracy of RCM against histopathology. Five RCM videos from the tumor and five from normal surrounding mucosa were collected on each patient, followed by a 3-mm punch biopsy of the imaged area. An experienced RCM reader, who was blinded to biopsy location and histologic diagnosis, examined the videos from both regions and classified each as "tumor" or "not tumor" based on RCM features established in phase I. Hematoxylin and eosin slides from the biopsies were read by a pathologist who was blinded to RCM results. Using histology as the gold standard, we calculated the sensitivity and specificity of RCM. RESULTS: We report a high agreement between the blinded readers (95% for normal tissue and 81.7% for tumors), high specificity (98.3%) and negative predictive values (96.6%) for normal tissue identification, and high sensitivity (90%) and positive predictive values (88.2%) for tumor detection. CONCLUSIONS: RCM imaging is a promising technology for noninvasive in vivo diagnosis of OSCC and for real-time intraoperative evaluation of mucosal surgical margins. Its inherent constraint, however, stems from the diminished capability to evaluate structures located at more substantial depths within the tissue.

Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.

Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.

Role of reflectance confocal microscopy for in vivo investigation of oral disorders: White, red and pigmented lesions.

In vivo reflectance confocal microscopy (RCM) is poorly investigated in oral pathology due to the peculiar anatomical and topographical oral mucosa features. A dedicated handheld confocal microscope with an intra-oral probe was developed for oral mucosa imaging. The main objective was to describe the healthy oral mucosa and the cytoarchitectural findings detectable in different oral disorders by means of the newly designed handheld confocal microscope. Secondary aim was to identify the main RCM criteria that differentiate oral lesions in order to provide algorithm for a rapid non-invasive evaluation. This observational retrospective study included all consecutive patients with oral disorders and volunteers with healthy oral mucosa who underwent RCM examination in our outpatient clinic from September 2018 to December 2021. Three different investigators examined together the RCM images to detect the key features and secondary criteria for each type of oral lesion collected. The study population included 110 patients affected by oral lesions and seven volunteers with healthy oral mucosae. A total of 15 oral disorders were imaged and divided in three main groups: white, red and pigmented lesions. Key features and secondary criteria were identified for every single type of oral disease. RCM permits a cytoarchitectural evaluation of the oral mucosae affected by inflammatory, dysplastic and neoplastic diseases, thus orienting the clinicians towards non-invasive diagnosis and enhancing the diagnostic management. The "tree diagrams" proposed allow a schematic and simplified view of confocal features for each type of oral disease, thus drastically reducing the diagnostic timing.

Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy.

Basal cell carcinoma (BCC) is the most common skin cancer, and its incidence is rising. Millions of benign biopsies are performed annually for BCC diagnosis, increasing morbidity, and healthcare costs. Non-invasive in vivo technologies such as multiphoton microscopy (MPM) can aid in diagnosing BCC, reducing the need for biopsies. Furthermore, the second harmonic generation (SHG) signal generated from MPM can classify and prognosticate cancers based on extracellular matrix changes, especially collagen type I. We explored the potential of MPM to differentiate collagen changes associated with different BCC subtypes compared to normal skin structures and benign lesions. Quantitative analysis such as frequency band energy analysis in Fourier domain, CurveAlign and CT-FIRE fibre analysis was performed on SHG images from 52 BCC and 12 benign lesions samples. Our results showed that collagen distribution is more aligned surrounding BCCs nests compared to the skin's normal structures (p < 0.001) and benign lesions (p < 0.001). Also, collagen was orientated more parallelly surrounding indolent BCC subtypes (superficial and nodular) versus those with more aggressive behaviour (infiltrative BCC) (p = 0.021). In conclusion, SHG signal from type I collagen can aid not only in the diagnosis of BCC but could be useful for prognosticating these tumors. Our initial results are limited to a small number of samples, requiring large-scale studies to validate them. These findings represent the groundwork for future in vivo MPM for diagnosis and prognosis of BCC.

Tertiary lymphoid structures accompanied by fibrillary matrix morphology impact anti-tumor immunity in basal cell carcinomas.

Tertiary lymphoid structures (TLS) are specialized lymphoid formations that serve as local repertoire of T- and B-cells at sites of chronic inflammation, autoimmunity, and cancer. While presence of TLS has been associated with improved response to immune checkpoint blockade therapies and overall outcomes in several cancers, its prognostic value in basal cell carcinoma (BCC) has not been investigated. Herein, we determined the prognostic impact of TLS by relating its prevalence and maturation with outcome measures of anti-tumor immunity, namely tumor infiltrating lymphocytes (TILs) and tumor killing. In 30 distinct BCCs, we show the presence of TLS was significantly enriched in tumors harboring a nodular component and more mature primary TLS was associated with TIL counts. Moreover, assessment of the fibrillary matrix surrounding tumors showed discrete morphologies significantly associated with higher TIL counts, critically accounting for heterogeneity in TIL count distribution within TLS maturation stages. Specifically, increased length of fibers and lacunarity of the matrix with concomitant reduction in density and alignment of fibers were present surrounding tumors displaying high TIL counts. Given the interest in inducing TLS formation as a therapeutic intervention as well as its documented prognostic value, elucidating potential impediments to the ability of TLS in driving anti-tumor immunity within the tumor microenvironment warrants further investigation. These results begin to address and highlight the need to integrate stromal features which may present a hindrance to TLS formation and/or effective function as a mediator of immunotherapy response.

In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response.

Response to immunotherapies can be variable and unpredictable. Pathology-based phenotyping of tumors into 'hot' and 'cold' is static, relying solely on T-cell infiltration in single-time single-site biopsies, resulting in suboptimal treatment response prediction. Dynamic vascular events (tumor angiogenesis, leukocyte trafficking) within tumor immune microenvironment (TiME) also influence anti-tumor immunity and treatment response. Here, we report dynamic cellular-level TiME phenotyping in vivo that combines inflammation profiles with vascular features through non-invasive reflectance confocal microscopic imaging. In skin cancer patients, we demonstrate three main TiME phenotypes that correlate with gene and protein expression, and response to toll-like receptor agonist immune-therapy. Notably, phenotypes with high inflammation associate with immunostimulatory signatures and those with high vasculature with angiogenic and endothelial anergy signatures. Moreover, phenotypes with high inflammation and low vasculature demonstrate the best treatment response. This non-invasive in vivo phenotyping approach integrating dynamic vasculature with inflammation serves as a reliable predictor of response to topical immune-therapy in patients.

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition.

Skin cancer is one of the most common cancers worldwide. Diagnosis relies on visual inspection and dermoscopy followed by biopsy for histopathological confirmation. While the sensitivity of dermoscopy is high, the lower specificity results in 70%-80% of the biopsies being diagnosed as benign lesions on histopathology (false positives on dermoscopy). Reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) imaging can noninvasively guide the diagnosis of skin cancers. RCM visualizes cellular morphology in en-face layers. It has doubled the diagnostic specificity for melanoma and pigmented keratinocytic skin cancers over dermoscopy, halving the number of biopsies of benign lesions. RCM acquired billing codes in the USA and is now being integrated into clinics. However, limitations such as the shallow depth (~200 µm) of imaging, poor contrast for nonpigmented skin lesions, and imaging in en-face layers result in relatively lower specificity for the detection of nonpigmented basal cell carcinoma (BCCs) - superficial BCCs contiguous with the basal cell layer and deeper infiltrative BCCs. In contrast, OCT lacks cellular resolution but images tissue in vertical planes down to a depth of ~1 mm, which allows the detection of both superficial and deeper subtypes of BCCs. Thus, both techniques are essentially complementary. A "multi-modal," combined RCM-OCT device simultaneously images skin lesions in both en-face and vertical modes. It is useful for the diagnosis and management of BCCs (nonsurgical treatment for superficial BCCs vs. surgical treatment for deeper lesions). A marked improvement in specificity is obtained for detecting small, nonpigmented BCCs over RCM alone. RCM and RCM-OCT devices are bringing a major paradigm shift in the diagnosis and management of skin cancers; however, their use is currently limited to academic tertiary care centers and some private clinics. This paper familiarizes clinicians with these devices and their applications, addressing translational barriers into routine clinical workflow.

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.

SIGNIFICANCE: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology). AIM: Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption. APPROACH: We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations. RESULTS: While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow. CONCLUSIONS: The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.

Combined PARP1-Targeted Nuclear Contrast and Reflectance Contrast Enhance Confocal Microscopic Detection of Basal Cell Carcinoma.

Reflectance confocal microscopy (RCM) with endogenous backscattered contrast can noninvasively image basal cell carcinomas (BCCs) in skin. However, BCCs present with high nuclear density, and the relatively weak backscattering from nuclei imposes a fundamental limit on contrast, detectability, and diagnostic accuracy. We investigated PARPi-FL, an exogenous nuclear poly(adenosine diphosphate ribose) polymerase (PARP1)-targeted fluorescent contrast agent, and fluorescence confocal microscopy toward improving BCC diagnosis. Methods: We tested PARP1 expression in 95 BCC tissues using immunohistochemistry, followed by PARPi-FL staining in 32 fresh surgical BCC specimens. The diagnostic accuracy of PARPi-FL contrast was evaluated in 83 surgical specimens. The optimal parameters for permeability of PARPi-FL through intact skin was tested ex vivo on 5 human skin specimens and in vivo in 3 adult Yorkshire pigs. Results: We found significantly higher PARP1 expression and PARPi-FL binding in BCCs than in normal skin structures. Blinded reading of RCM-and-fluorescence confocal microscopy images by 2 experts demonstrated a higher diagnostic accuracy for BCCs with combined fluorescence and reflectance contrast than for RCM alone. Optimal parameters (time and concentration) for PARPi-FL transepidermal permeation through intact skin were successfully determined. Conclusion: Combined fluorescence and reflectance contrast may improve noninvasive BCC diagnosis with confocal microscopy.

In vivo optical imaging-guided targeted sampling for precise diagnosis and molecular pathology.

Conventional tissue sampling can lead to misdiagnoses and repeated biopsies. Additionally, tissue processed for histopathology suffers from poor nucleic acid quality and/or quantity for downstream molecular profiling. Targeted micro-sampling of tissue can ensure accurate diagnosis and molecular profiling in the presence of spatial heterogeneity, especially in tumors, and facilitate acquisition of fresh tissue for molecular analysis. In this study, we explored the feasibility of performing 1-2 mm precision biopsies guided by high-resolution reflectance confocal microscopy (RCM) and optical coherence tomography (OCT), and reflective metallic grids for accurate spatial targeting. Accurate sampling was confirmed with either histopathology or molecular profiling through next generation sequencing (NGS) in 9 skin cancers in 7 patients. Imaging-guided 1-2 mm biopsies enabled spatial targeting for in vivo diagnosis, feature correlation and depth assessment, which were confirmed with histopathology. In vivo 1-mm targeted biopsies achieved adequate quantity and high quality of DNA for next-generation sequencing. Subsequent mutational profiling was confirmed on 1 melanoma in situ and 2 invasive melanomas, using a 505-gene mutational panel called Memorial Sloan Kettering-Integrated mutational profiling of actionable cancer targets (MSK-IMPACT). Differential mutational landscapes, in terms of number and types of mutations, were found between invasive and in situ melanomas in a single patient. Our findings demonstrate feasibility of accurate sampling of regions of interest for downstream histopathological diagnoses and molecular pathology in both in vivo and ex vivo settings with broad diagnostic, therapeutic and research potential in cutaneous diseases accessible by RCM-OCT imaging.

Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention.

Reflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4-5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved [Formula: see text] classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.

Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels.

Reflectance confocal microscopy (RCM) is a non-invasive imaging tool that reduces the need for invasive histopathology for skin cancer diagnoses by providing high-resolution mosaics showing the architectural patterns of skin, which are used to identify malignancies in-vivo. RCM mosaics are similar to dermatopathology sections, both requiring extensive training to interpret. However, these modalities differ in orientation, as RCM mosaics are horizontal (parallel to the skin surface) while histopathology sections are vertical, and contrast mechanism, RCM with a single (reflectance) mechanism resulting in grayscale images and histopathology with multi-factor color-stained contrast. Image analysis and machine learning methods can potentially provide a diagnostic aid to clinicians to interpret RCM mosaics, eventually helping to ease the adoption and more efficiently utilizing RCM in routine clinical practice. However standard supervised machine learning may require a prohibitive volume of hand-labeled training data. In this paper, we present a weakly supervised machine learning model to perform semantic segmentation of architectural patterns encountered in RCM mosaics. Unlike more widely used fully supervised segmentation models that require pixel-level annotations, which are very labor-demanding and error-prone to obtain, here we focus on training models using only patch-level labels (e.g. a single field of view within an entire mosaic). We segment RCM mosaics into "benign" and "aspecific (nonspecific)" regions, where aspecific regions represent the loss of regular architecture due to injury and/or inflammation, pre-malignancy, or malignancy. We adopt Efficientnet, a deep neural network (DNN) proven to accurately accomplish classification tasks, to generate class activation maps, and use a Gaussian weighting kernel to stitch smaller images back into larger fields of view. The trained DNN achieved an average area under the curve of 0.969, and Dice coefficient of 0.778 showing the feasibility of spatial localization of aspecific regions in RCM images, and making the diagnostics decision model more interpretable to the clinicians.

An international 3-center training and reading study to assess basal cell carcinoma surgical margins with ex vivo fluorescence confocal microscopy.

BACKGROUND: Novel solutions are needed for expediting margin assessment to guide basal cell carcinoma (BCC) surgeries. Ex vivo fluorescence confocal microscopy (FCM) is starting to be used in freshly excised surgical specimens to examine BCC margins in real time. Training and educational process are needed for this novel technology to be implemented into clinic. OBJECTIVE: To test a training and reading process, and measure diagnostic accuracy of clinicians with varying expertise level in reading ex vivo FCM images. METHODS: An international three-center study was designed for training and reading to assess BCC surgical margins and residual subtypes. Each center included a lead dermatologic/Mohs surgeon (clinical developer of FCM) and three additional readers (dermatologist, dermatopathologist, dermatologic/Mohs surgeon), who use confocal in clinical practice. Testing was conducted on 30 samples. RESULTS: Overall, the readers achieved 90% average sensitivity, 78% average specificity in detecting residual BCC margins, showing high and consistent diagnostic reading accuracy. Those with expertise in dermatologic surgery and dermatopathology showed the strongest potential for learning to assess FCM images. LIMITATIONS: Small dataset, variability in mosaic quality between centers. CONCLUSION: Suggested process is feasible and effective. This process is proposed for wider implementation to facilitate wider adoption of FCM to potentially expedite BCC margin assessment to guide surgery in real time.

Exploring the utility of Deep Red Anthraquinone 5 for digital staining of ex vivo confocal micrographs of optically sectioned skin.

We investigated the utility of the fluorescent dye Deep Red Anthraquinone 5 (DRAQ5) for digital staining of optically sectioned skin in comparison to acridine orange (AO). Eight fresh-frozen thawed Mohs discard tissue specimens were stained with AO and DRAQ5, and imaged using an ex vivo confocal microscope at three wavelengths (488 nm and 638 nm for fluorescence, 785 nm for reflectance). Images were overlaid (AO + Reflectance, DRAQ5 + Reflectance), digitally stained, and evaluated by three investigators for perceived image quality (PIQ) and histopathological feature identification. In addition to nuclear staining, AO seemed to stain dermal fibers in a subset of cases in digitally stained images, while DRAQ5 staining was more specific to nuclei. Blinded evaluation showed substantial agreement, favoring DRAQ5 for PIQ (82%, Cl 75%-90%, Gwet's AC 0.74) and for visualization of histopathological features in (81%, Cl 73%-89%, Gwet's AC 0.67), supporting its use in digital staining of multimodal confocal micrographs of skin.

Optical imaging guided- 'precision' biopsy of skin tumors: a novel approach for targeted sampling and histopathologic correlation.

Dermoscopy and reflectance confocal microscopy (RCM) are two noninvasive, optical imaging tools used to facilitate clinical diagnosis. A biopsy technique that produces exact correlation with optical imaging features is not previously reported. To evaluate the applications of a novel feature-focused 'precision biopsy' technique that correlates clinical-dermoscopy-RCM findings with histopathology. This was a prospective case-series performed during August 2017 and June 2019 at a tertiary care cancer. We included consecutive patients requiring a precise dermoscopy-RCM-histopathologic correlation. We performed prebiopsy dermoscopy and both wide probe and handheld RCM of suspicious lesions. Features of interest were isolated with the aid of paper rings and a 2 mm punch biopsy was performed in the dermoscopy- or RCM-highlighted area. Tissue was processed either en face or with vertical sections. One-to-one correlation with histopathology was obtained. Twenty-three patients with 24 lesions were included in the study. The mean age was 64.6 years (range 22-91 years); there were 16 (69.6%) males, 14 (58.3%) lesions biopsied were on head and neck region. We achieved tissue-conservation diagnosis in 100% (24/24), 13 (54.2%) were clinically equivocal lesions, six (25%) were selected for 'feature correlation' of structures on dermoscopy or RCM, and five (20.8%) for 'correlation of new/unknown' RCM features seen on follow-up. The precision biopsy technique described herein is a novel method that facilitates direct histopathological correlation of dermoscopy and RCM features. With the aids of optical imaging devices, accurate diagnosis may be achieved by minimally invasive tissue extraction.

In vivo imaging characterization of basal cell carcinoma and cutaneous response to high-dose ionizing radiation therapy: A prospective study of reflectance confocal microscopy, dermoscopy, and ultrasonography.

BACKGROUND: Radiation therapy (RT) is a treatment option for select skin cancers. The histologic effects of RT on normal skin or skin cancers are not well characterized. Dermoscopy, high-frequency ultrasonography (HFUS), and reflectance confocal microscopy (RCM) are noninvasive imaging modalities that may help characterize RT response. OBJECTIVES: To describe changes in the tumor and surrounding skin of patients with basal cell carcinoma (BCC) treated with RT. METHODS: The study was conducted between 2014 and 2018. Patients with biopsy-proven BCCs were treated with 42 Gy in 6 fractions using a commercially available brachytherapy device. Dermoscopy, HFUS, and RCM were performed before treatment and at 6 weeks, 3 months, and 12 months after RT. RESULTS: A total of 137 imaging assessments (RCM + dermoscopy + HFUS) were performed in 12 patients. BCC-specific features were present in 81.8%, 91%, and 17% of patients imaged with dermoscopy, RCM, and HFUS at baseline, respectively, before treatment. After treatment, the resolution of these features was noted in 33.4%, 91.7%, and 100% of patients imaged with the respective modalities. No recurrences were seen after a mean of 31.7 months of follow-up. LIMITATIONS: Small sample size and no histopathologic correlation. CONCLUSION: Dermoscopy and HFUS were not as reliable as RCM at characterizing BCC RT response.

Angulated small nests and cords: Key diagnostic histopathologic features of infiltrative basal cell carcinoma can be identified using integrated reflectance confocal microscopy-optical coherence tomography.

BACKGROUND: Accurate basal cell carcinoma (BCC) subtyping is requisite for appropriate management, but non-representative sampling occurs in 18% to 25% of biopsies. By enabling non-invasive diagnosis and more comprehensive sampling, integrated reflectance confocal microscopy-optical coherence tomography (RCM-OCT) may improve the accuracy of BCC subtyping and subsequent management. We evaluated RCM-OCT images and histopathology slides for the presence of two key features, angulation and small nests and cords, and calculated (a) sensitivity and specificity of these features, combined and individually, for identifying an infiltrative BCC subtype and (b) agreement across modalities. METHODS: Thirty-three RCM-OCT-imaged, histopathologically-proven BCCs (17 superficial and/or nodular; 16 containing an infiltrative component) were evaluated. RESULTS: The presence of angulation or small nests and cords was sufficient to identify infiltrative BCC on RCM-OCT with 100% sensitivity and 82% specificity, similar to histopathology (100% sensitivity, 88% specificity, kappa = 0.82). When both features were present, the sensitivity for identifying infiltrative BCC was 100% using either modality and specificity was 88% on RCM-OCT vs 94% on histopathology, indicating near-perfect agreement between non-invasive and invasive diagnostic modalities (kappa = 0.94). CONCLUSIONS: RCM-OCT can non-invasively identify key histopathologic features of infiltrative BCC offering a possible alternative to traditional invasive biopsy.

Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net).

In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce the need for traditional biopsy. However, reading and analysis of the optical microscopic images are generally still qualitative, relying mainly on visual examination. Here we present an automated semantic segmentation method called "Multiscale Encoder-Decoder Network (MED-Net)" that provides pixel-wise labeling into classes of patterns in a quantitative manner. The novelty in our approach is the modeling of textural patterns at multiple scales (magnifications, resolutions). This mimics the traditional procedure for examining pathology images, which routinely starts with low magnification (low resolution, large field of view) followed by closer inspection of suspicious areas with higher magnification (higher resolution, smaller fields of view). We trained and tested our model on non-overlapping partitions of 117 reflectance confocal microscopy (RCM) mosaics of melanocytic lesions, an extensive dataset for this application, collected at four clinics in the US, and two in Italy. With patient-wise cross-validation, we achieved pixel-wise mean sensitivity and specificity of 74% and 92%, respectively, with 0.74 Dice coefficient over six classes. In the scenario, we partitioned the data clinic-wise and tested the generalizability of the model over multiple clinics. In this setting, we achieved pixel-wise mean sensitivity and specificity of 77% and 94%, respectively, with 0.77 Dice coefficient. We compared MED-Net against the state-of-the-art semantic segmentation models and achieved better quantitative segmentation performance. Our results also suggest that, due to its nested multiscale architecture, the MED-Net model annotated RCM mosaics more coherently, avoiding unrealistic-fragmented annotations.

Perioperative Noninvasive Optical Imaging: A Changing Paradigm for Management of Keratinocyte Carcinomas.

One of the major challenges faced when treating high-risk keratinocyte carcinoma (KC) is the unpredictable subclinical extension. Yaroslavsky et al. (2020) evaluated dual-wavelength optical polarization imaging (OPI) for the detection for KC margins before Mohs surgery with promising results. OPI might be useful as a screening tool to limit unnecessary surgery.