International expert recommendations on image acquisition for in vivo reflectance confocal microscopy of cutaneous tumors.

BACKGROUND: No international recommendations exist for a minimum imaging requirement per lesion using reflectance confocal microscopy (RCM). This may be beneficial given the increasing use of remote RCM interpretation internationally. OBJECTIVE: To develop international expert recommendations for image acquisition using tissue-coupled RCM for diagnosis of cutaneous tumors. METHODS: Using a modified Delphi approach, a core group developed the scope and drafted initial recommendations before circulation to a larger group, the Cutaneous Imaging Expert Resource Group of the American Academy of Dermatology. Each review round consisted of a period of open comment, followed by revisions. RESULTS: The recommendations were developed after 5 alternating rounds of review among the core group and the Cutaneous Imaging Expert Resource Group. These were divided into subsections of imaging personnel, recommended lesion criteria, clinical and lesion information to be provided, lesion preparation, image acquisition, mosaic cube settings, and additional captures based on lesion characteristics and suspected diagnosis. LIMITATIONS: The current recommendations are limited to tissue-coupled RCM for diagnosis of cutaneous tumors. It is one component of the larger picture of quality assurance and will require ongoing review. CONCLUSIONS: These recommendations serve as a resource to facilitate quality assurance, economical use of time, accurate diagnosis, and international collaboration.

Introduction to the Special Issue on "Keratinocyte Carcinomas: Biology and Evolving Non-Invasive Management Paradigms".

Keratinocyte carcinomas (KCs) are the most prevalent form of cancer worldwide, and their incidence is rising dramatically, with an increasing trend in recent years [...].

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.

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.

In Vivo Reflectance Confocal Microscopy as a Response Monitoring Tool for Actinic Keratoses Undergoing Cryotherapy and Photodynamic Therapy.

Reflectance confocal microscopy (RCM) presents a non-invasive method to image actinic keratosis (AK) at a cellular level. However, RCM criteria for AK response monitoring vary across studies and a universal, standardized approach is lacking. We aimed to identify reliable AK response criteria and to compare the clinical and RCM evaluation of responses across AK severity grades. Twenty patients were included and randomized to receive either cryotherapy (n = 10) or PDT (n = 10). Clinical assessment and RCM evaluation of 12 criteria were performed in AK lesions and photodamaged skin at baseline, 3 and 6 months. We identified the RCM criteria that reliably characterize AK at baseline and display significant reduction following treatment. Those with the highest baseline odds ratio (OR), good interobserver agreement, and most significant change over time were atypical honeycomb pattern (OR: 12.7, CI: 5.7-28.1), hyperkeratosis (OR: 13.6, CI: 5.3-34.9), stratum corneum disruption (OR: 7.8, CI: 3.5-17.3), and disarranged epidermal pattern (OR: 6.5, CI: 2.9-14.8). Clinical evaluation demonstrated a significant treatment response without relapse. However, in grade 2 AK, 10/12 RCM parameters increased from 3 to 6 months, which suggested early subclinical recurrence detection by RCM. Incorporating standardized RCM protocols for the assessment of AK may enable a more meaningful comparison across clinical trials, while allowing for the early detection of relapses and evaluation of biological responses to therapy over time.

Combining Reflective Confocal Microscopy and Dynamic Optical Coherence Tomography to Diagnose Melanoacanthoma: Case Report.

ABSTRACT: Few reported cases discuss distinguishing between melanoma and melanoacanthoma, a seborrheic keratosis (SK) variant, using noninvasive imaging devices. We present a case of a 38-year-old man with Fitzpatrick skin type IV with an asymmetric black papule showing clinical and dermoscopic features of both melanoma and SK. Reflectance confocal microscopy (RCM) and dynamic optical coherence tomography (d-OCT) were used for further evaluation. RCM revealed acanthotic epidermis with a mixed honeycomb and cobblestone pattern, polycyclic bulbous rete ridges, and bright plump cells within entrapped, edged, dermal papillae, compatible with pigmented SK. Also noted were a population of fairly uniform bright dendritic cells scattered quite evenly at all levels of the epidermis and the notable absence of concomitant features of a melanocytic neoplasm (roundish Pagetoid cells, sheets of roundish or dendritic cells at the dermal-epidermal junction, junctional thickenings, and melanocytic nests), suggesting melanoacanthoma. d-OCT showed well-circumscribed, regular, epidermal acanthosis, superficial rounded hypodense structures, normal vascular flow, and notable absence of wiry or contoured vessels, features typically seen in SKs and benign lesions, respectively. Similarly, histologic examination revealed characteristics of pigmented SK containing a population of evenly dispersed dendritic melanocytes (decorated using Melan-A stain) confirming a diagnosis of melanoacanthoma. This case highlights the advantages of incorporating both RCM and d-OCT into clinical practice to noninvasively differentiate melanoma from its clinical mimickers.

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.

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.

The potential utility of integrated reflectance confocal microscopy-optical coherence tomography for guiding triage and therapy of basal cell carcinomas.

The increasing rate of incidence and prevalence of basal cell carcinomas (BCCs) worldwide, combined with the morbidity associated with conventional surgical treatment has led to the development and use of alternative minimally invasive non-surgical treatments. Biopsy and pathology are used to guide BCC diagnosis and assess margins and subtypes, which then guide the decision and choice of surgical or non-surgical treatment. However, alternatively, a noninvasive optical approach based on combined reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) imaging may be used. Optical imaging may be used to guide diagnosis and margin assessment at the bedside, and potentially facilitate non-surgical management, along with long-term monitoring of treatment response. Noninvasive imaging may also complement minimally invasive treatments and help further reduce morbidity. In this paper, we highlight the current state of an integrated RCM/OCT imaging approach for diagnosis and triage of BCCs, as well as for assessing margins, which therefore may be ultimately used for guiding therapy.

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is expanding from real-time diagnosis at the bedside to include a capture, store, and forward model with image interpretation and diagnosis occurring offsite, similar to radiology. As the patient may no longer be present at the time of image interpretation, quality assurance is key during image acquisition. Herein, we introduce a quality assurance process by means of automatically quantifying diagnostically uninformative areas within the lesional area by using RCM and coregistered dermoscopy images together. We trained and validated a pixel-level segmentation model on 117 RCM mosaics collected by international collaborators. The model delineates diagnostically uninformative areas with 82% sensitivity and 93% specificity. We further tested the model on a separate set of 372 coregistered RCM-dermoscopic image pairs and illustrate how the results of the RCM-only model can be improved via a multimodal (RCM + dermoscopy) approach, which can help quantify the uninformative regions within the lesional area. Our data suggest that machine learning-based automatic quantification offers a feasible objective quality control measure for RCM imaging.

Artifacts and landmarks: pearls and pitfalls for in vivo reflectance confocal microscopy of the skin using the tissue-coupled device.

Reflectance confocal microscopy (RCM) is a non-invasive imaging tool for cellular-level examination of skin lesions, typically from the epidermis to the superficial dermis. Clinical studies show RCM imaging is highly sensitive and specific in the diagnosis of skin diseases. RCM is disseminating from academic tertiary care centers with early adopter "experts" into diverse clinical settings, with image acquisition performed by technicians and image interpretation by physicians. In the hands of trained users, RCM serves an aid to accurately diagnose and monitor skin tumors and inflammatory processes. However, exogenous and endogenous artifacts introduced during imaging can obscure RCM images, limiting or prohibiting interpretation. Herein we review the types of artifacts that may occur and techniques for mitigating them during image acquisition, to assist technicians with qualitative image assessment and provide physicians guidance on identifying artifacts that may confound interpretation. Finally, we discuss normal skin "landmarks" and how they can (i) obscure images, (ii) be exploited for additional diagnostic information, and (iii) simulate pathological structures. A deeper understanding of the principles and methods behind RCM imaging and the varying appearance of normal skin structures in the acquired images aids technicians in capturing higher quality image sets and enables physicians to increase interpretation accuracy.

Evaluation of a Combined Reflectance Confocal Microscopy-Optical Coherence Tomography Device for Detection and Depth Assessment of Basal Cell Carcinoma.

Importance: The limited tissue sampling of a biopsy can lead to an incomplete assessment of basal cell carcinoma (BCC) subtypes and depth. Reflectance confocal microscopy (RCM) combined with optical coherence tomography (OCT) imaging may enable real-time, noninvasive, comprehensive three-dimensional sampling in vivo, which may improve the diagnostic accuracy and margin assessment of BCCs. Objective: To determine the accuracy of a combined RCM-OCT device for BCC detection and deep margin assessment. Design, Setting, and Participants: This pilot study was carried out on 85 lesions from 55 patients referred for physician consultation or Mohs surgery at Memorial Sloan Kettering Skin Cancer Center in Hauppauge, New York. These patients were prospectively and consecutively enrolled in the study between January 1, 2017, and December 31, 2017. Patients underwent imaging, with the combined RCM-OCT probe, for previously biopsied, histopathologically confirmed BCCs and lesions clinically or dermoscopically suggestive of BCC. Only patients with available histopathologic examination after imaging were included. Main Outcomes and Measures: Improvements in sensitivity, specificity, and diagnostic accuracy for BCC using the combined RCM-OCT probe as well as the correlation between OCT-estimated depth and histopathologically measured depth were investigated. Results: In total, 85 lesions from 55 patients (27 [49%] were female and 28 [51%] were male with a median [range] age of 59 [21-90] years) were imaged. Imaging was performed on 25 previously biopsied and histopathologically confirmed BCCs and 60 previously nonbiopsied but clinically or dermoscopically suspicious lesions. Normal skin and BCC features were correlated and validated with histopathologic examination. In previously biopsied lesions, residual tumors were detected in 12 of 25 (48%) lesions with 100% sensitivity (95% CI, 73.5%-100%) and 23.1% specificity (95% CI, 5.0%-53.8%) for combined RCM-OCT probe. In previously nonbiopsied and suspicious lesions, BCCs were diagnosed in 48 of 60 (80%) lesions with 100% sensitivity (95% CI, 92.6%-100%) and 75% specificity (95% CI, 42.8%-94.5%). Correlation was observed between depth estimated with OCT and depth measured with histopathologic examination: the coefficient of determination (R2) was 0.75 (R = 0.86; P < .001) for all lesions, 0.73 (R = 0.85; P < .001) for lesions less than 500 µm deep, and 0.65 (R = 0.43; P < .001) for lesions greater than 500 µm deep. Conclusions and Relevance: Combined RCM-OCT imaging may be prospectively used to comprehensively diagnose lesions suggestive of BCC and triage for treatment. Further validation of this device must be performed on a larger cohort.

Risk-adjusted hospital mortality rates for stroke: evidence from the Australian Stroke Clinical Registry (AuSCR).

OBJECTIVES: Hospital data used to assess regional variability in disease management and outcomes, including mortality, lack information on disease severity. We describe variance between hospitals in 30-day risk-adjusted mortality rates (RAMRs) for stroke, comparing models that include or exclude stroke severity as a covariate. DESIGN: Cohort design linking Australian Stroke Clinical Registry data with national death registrations. Multivariable models using recommended statistical methods for calculating 30-day RAMRs for hospitals, adjusted for demographic factors, ability to walk on admission, stroke type, and stroke recurrence. SETTING: Australian hospitals providing at least 200 episodes of acute stroke care, 2009-2014. MAIN OUTCOME MEASURES: Hospital RAMRs estimated by different models. Changes in hospital rank order and funnel plots were used to explore variation in hospital-specific 30-day RAMRs; that is, RAMRs more than three standard deviations from the mean. RESULTS: In the 28 hospitals reporting at least 200 episodes of care, there were 16 218 episodes (15 951 patients; median age, 77 years; women, 46%; ischaemic strokes, 79%). RAMRs from models not including stroke severity as a variable ranged between 8% and 20%; RAMRs from models with the best fit, which included ability to walk and stroke recurrence as variables, ranged between 9% and 21%. The rank order of hospitals changed according to the covariates included in the models, particularly for those hospitals with the highest RAMRs. Funnel plots identified significant deviation from the mean overall RAMR for two hospitals, including one with borderline excess mortality. CONCLUSIONS: Hospital stroke mortality rates and hospital performance ranking may vary widely according to the covariates included in the statistical analysis.

Enlightening the Pink: Use of Confocal Microscopy in Pink Lesions.

Solitary pink lesions can pose a particular challenge to dermatologists because they may be almost or completely featureless clinically and dermoscopically, previously requiring biopsy to exclude malignancy. However, these lesions usually are not particularly challenging histopathologically. Thus, the incorporation of in vivo reflectance confocal microscopy into the clinical practice, which allows for noninvasive examination of the skin at the cellular level revealing features previously seen only on histopathology, is particularly useful for this subset of clinically difficult lesions.

Reflectance confocal microscopy: an effective diagnostic tool for dermatophytic infections.

Current methods for diagnosing dermatophytic infections have various drawbacks. Analysis via skin scrapings and biopsies can be invasive and/or take too long to yield results. Reflectance confocal microscopy (RCM) is an emerging in vivo imaging technology that can potentially be used to diagnose cutaneous dermatophytic infections. This modality provides high-resolution images of the skin extending to the level of the superficial reticular dermis that could reveal the presence of fungal hyphae. In this retrospective chart review, we investigated the application of RCM as a diagnostic tool in the setting of a private practice. Images were used to diagnose dermatophyte infections and the results were compared to those of other established diagnostic methods. We found RCM to be a potentially effective and highly sensitive tool in the diagnosis of cutaneous dermatophytic infections.