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.

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.

The Value of In Vivo Reflectance Confocal Microscopy as an Assessment Tool in Chemotherapy-Induced Peripheral Neuropathy: A Pilot Study.

BACKGROUND: There is a lack of standardized objective and reliable assessment tools for chemotherapy-induced peripheral neuropathy (CIPN). In vivo reflectance confocal microscopy (RCM) imaging offers a non-invasive method to identify peripheral neuropathy markers, namely Meissner's corpuscles (MC). This study investigated the feasibility and value of RCM in CIPN. PATIENTS AND METHODS: Reflectance confocal microscopy was performed on the fingertip to evaluate MC density in 45 healthy controls and 9 patients with cancer (prior, during, and post-chemotherapy). Quantification was completed by 2 reviewers (one blinded), with maximum MC count/3 × 3 mm image reported. Quantitative Sensory Testing (QST; thermal and mechanical detection thresholds), Grooved pegboard test, and patient-reported outcomes measures (PROMS) were conducted for comparison. RESULTS: In controls (25 females, 20 males; 24-81 years), females exhibited greater mean MC density compared with males (49.9 ± 7.1 vs 30.9 ± 4.2 MC/3 × 3 mm; P = .03). Differences existed across age by decade (P < .0001). Meissner's corpuscle density was correlated with mechanical detection (ρ = -0.51), warm detection (ρ = -0.47), cold pain (ρ = 0.49) thresholds (P < .01); and completion time on the Grooved pegboard test in both hands (P ≤ .02). At baseline, patients had reduced MC density vs age and gender-matched controls (P = .03). Longitudinal assessment of MC density revealed significant relationships with QST and PROMS. Inter-rater reliability of MC count showed an intraclass correlation of 0.96 (P < .0001). CONCLUSIONS: The findings support the clinical utility of RCM in CIPN as it provides meaningful markers of sensory nerve dysfunction. Novel, prospective assessment demonstrated the ability to detect subclinical deficits in patients at risk of CIPN and potential to monitor neuropathy progression.

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.

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.

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.

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.

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.

Handheld optical coherence tomography-reflectance confocal microscopy probe for detection of basal cell carcinoma and delineation of margins.

We present a hand-held implementation and preliminary evaluation of a combined optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) probe for detecting and delineating the margins of basal cell carcinomas (BCCs) in human skin