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1.
Sci Rep ; 11(1): 3679, 2021 Feb 11.
Artigo em Inglês | MEDLINE | ID: mdl-33574486

RESUMO

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.

2.
J Cutan Pathol ; 2021 Feb 11.
Artigo em Inglês | MEDLINE | ID: mdl-33576022

RESUMO

BACKGROUND: Novel solutions are needed for expediting margin assessment to guide 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 3-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 3 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. This article is protected by copyright. All rights reserved.

3.
Med Image Anal ; 67: 101841, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33142135

RESUMO

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.

4.
J Biophotonics ; : e202000207, 2020 Dec 14.
Artigo em Inglês | MEDLINE | ID: mdl-33314673

RESUMO

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.

5.
J Cutan Pathol ; 2020 Sep 28.
Artigo em Inglês | MEDLINE | ID: mdl-32989842

RESUMO

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.

6.
J Cancer ; 11(20): 6019-6024, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32922542

RESUMO

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.

8.
J Invest Dermatol ; 140(6): 1214-1222, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-31838127

RESUMO

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.

9.
Dermatol Online J ; 25(8)2019 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-31553856

RESUMO

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.


Assuntos
Pontos de Referência Anatômicos , Artefatos , Microscopia Confocal , Dermatopatias/patologia , Pele/patologia , Humanos
10.
JAMA Dermatol ; 154(10): 1175-1183, 2018 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-30140851

RESUMO

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.


Assuntos
Carcinoma Basocelular/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Tomografia de Coerência Óptica , Adulto , Idoso , Idoso de 80 Anos ou mais , Biópsia , Carcinoma Basocelular/patologia , Feminino , Humanos , Masculino , Microscopia Confocal , Pessoa de Meia-Idade , Imagem Multimodal , Projetos Piloto , Estudos Prospectivos , Sensibilidade e Especificidade , Pele/patologia , Neoplasias Cutâneas/patologia , Adulto Jovem
11.
J Biomed Opt ; 22(7): 76006, 2017 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-28697233

RESUMO

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

Assuntos
Carcinoma Basocelular/diagnóstico por imagem , Microscopia Confocal , Tomografia de Coerência Óptica , Estudos Transversais , Humanos , Projetos Piloto
13.
Neurology ; 69(23): 2121-7, 2007 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-17898322

RESUMO

OBJECTIVE: To assess feasibility of noninvasive in vivo reflectance confocal microscopy (RCM) of Meissner corpuscles (MCs) as a measure of sensory neuropathy (SN). BACKGROUND: MCs are touch-pressure sensation receptors in glabrous skin. Skin biopsy studies suggest that fingertip MC density (MCs/mm(2)) is a sensitive measure of diabetic and idiopathic SN. In vivo RCM of skin is an emerging field, with applications including evaluation of cancer. It is painless and noninvasive. Feasibility of in vivo RCM of MCs has not been explored. METHODS: Fifteen adults (10 controls, 5 SN subjects) underwent in vivo RCM at the fingertip (Digit V) and thenar eminence. In vivo RCM was conducted to determine whether MCs were visible within dermal papillae and, if visible, to characterize their imaging appearance and assess MCs/mm(2) at each site. RESULTS: MCs were identified in dermal papillae at all sites in controls. MCs appeared as heterogeneous bright structures within dermal papillae, which appeared as dark "pits." Mean MC density in controls was 12 +/- 5.3/mm(2) (Digit V) and 5.1 +/- 2.2/mm(2) at the thenar eminence. MC density in SN was lower than controls at Digit V (2.8 +/- 5.7/mm(2), p = 0.01) and the thenar eminence (1.4 +/- 1.1/mm(2), p = 0.004). MCs were absent in a sensory neuronopathy; milder reductions in MC density were seen among diabetic and HIV-positive subjects. CONCLUSIONS: Meissner corpuscles (MCs) can be visualized and quantitated in controls and sensory neuropathy (SN) using in vivo reflectance confocal microscopy (RCM). In vivo RCM of MCs has potential for noninvasive detection and monitoring of SN, if subsequent studies show that with denervation or reinnervation, reliable and recognizable changes or loss can be detected using our described approaches.


Assuntos
Mecanorreceptores/patologia , Polirradiculoneuropatia/patologia , Adulto , Biópsia por Agulha , Estudos de Viabilidade , Feminino , Humanos , Masculino , Microscopia Confocal , Projetos Piloto , Pele/patologia
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