Detection Of Basal Cell Carcinoma Research Articles

Artificial intelligence algorithms and 3D volumetric rendering for basal cell carcinoma detection and tumor depth assessment in RCM-OCT images: a pilot study.

The Reflectance Confocal Microscopy - Optical Coherence Tomography (RCM-OCT) device has shown utility in detecting and assessing depth of basal cell carcinoma (BCC) in vivo but is challenging for novices to interpret. Artificial intelligence (AI) applied to RCM-OCT could aid readers. We trained artificial intelligence (AI) models, using OCT rasters of biopsy-confirmed BCC, to detect and create 3D BCC rendering and automatically measure tumor depth. Trained AI models were applied to a separate test set containing rasters of BCC, benign lesions, and normal skin. Blinded reader analysis and tumor depth correlation with histopathology were conducted. BCC detection improved from viewing OCT rasters only (sensitivity 73.3%, specificity 45.5%) to viewing rasters with AI-generated BCC rendering (sensitivity 86.7%, specificity 48.5%). A Pearson Correlation r2 = 0.59 (p=0.02) was achieved for the tumor depth measurement between AI and histologic measured depths. Thus, addition of AI to the RCM-OCT device may expand its utility widely.

Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs micrographic surgery workflow and effects on clinical staffing: A simulated, retrospective study

Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs micrographic surgery workflow and effects on clinical staffing: A simulated, retrospective study

Initial clinical manifestations and early diagnosis of basal cell skin carcinoma

Background/Aim: Basal cell carcinoma (BCC) is the most common cancer of the skin. It is believed that increased UV radiation from the sun accounts for almost 90 % of the risk of BCC. There is a growing trend in the incidence of BCC in a younger population. The aim of study was to analyse the initial clinical symptoms of BCC that may be important for the early detection of this skin tumour. Method: The study was a prospective, multicentre study performed in the period from March 2017 to February 2022. A total of 69 respondents with BCC were analysed. Respondents applied for a targeted examination to examine a suspicious skin lesion (due to certain symptoms) or were diagnosed with BCC by accident, when examining other skin changes. Respondents were divided into 2 groups. The first, Group I (35 respondents), consisted of respondents with nodular BCC. The second, Group II (34 respondents), consisted of respondents with superficial spreading BCC. Initially, a careful history and data on the characteristics, shape and character of the tumour were taken from all respondents. Data on all (even the smallest) initial symptoms and relevant signs of evolution, as well as subjective problems related to the tumour were noted. All respondents underwent dermoscopy of suspected skin changes. Results: A significant difference was found between the examined groups in the characteristics of bleeding, crust formation and tendency to injury in lesions, where they occur more often in patients with nodular BCC. Symptoms such as burning and flaking occurred significantly more often in patients with superficial spreading BCC (p < 0.01), as well as the diameter of lesions over 5 mm (p < 0.05). Conclusion: Early clinical diagnosis of BCC is possible with a tumour diameter of only a few mm. The predominant initial (highly susceptible) symptoms of nodular BCC were initial bleeding and / or scab formation on the lesions, as well as propensity to injury. The superficial spreading form of BCC was often larger than 5 mm in diameter, with more frequent scaling of the lesion, as well as burning and stinging sensations in the tumour area. Itching was observed to be a very common previous occurrence in the BCC initial focus zone in subjects of both study groups. Dermsocopy is a highly reliable diagnostic method for early detection of BCC.

Convolutional Neural Network Model for Basal Cell Carcinoma Detection

Basal cell carcinoma (BCC) is the most common type of skin cancer, accounting for approximately 80% of all cases. Early and accurate detection of BCC is essential for effective treatment and prevention of serious complications. In this work, a CNN (Convolutional Neural Networks) model is presented for the detection and classification of Basal Cell Carcinoma from dermoscopic cases. To create the proposed model, the HAM10000 dataset was used, which includes a set of images of dermatological lesions. Extensive experiments were performed to evaluate the accuracy of the model, as well as a comparison with the GRU and LSTM models. The results obtained indicate an accuracy of 93.5%, demonstrating that the proposed model has the ability to effectively identify and differentiate benign and malignant basal cell carcinoma lesions.

Advancements in Basal Cell Carcinoma Diagnosis: Non-Invasive Imaging and Multimodal Approach.

(1) Background: The aim of this study was to correlate the diagnostic criteria described in dermatoscopy, ultrasonography (US), ex vivo confocal microscopy, and histology to the most common subtypes of basal cell carcinoma (BCC). (2) Methods: We conducted a prospective study including 46 BCC cases, which were analyzed with dermatoscopy using the Delta 30 dermatoscope and Vidix 4.0 videodermoscope, with US using a high-resolution 20 MHz linear probe, with confocal microscopy, along with histopathological analysis. (3) Results: This study categorized BCC by histological subtype, with nodular being the most common (84.8%) and various other subtypes represented. US measurements of tumor thickness correlated strongly with the histopathological depth of invasion index (DI). Dermatoscopy analysis revealed significant associations between specific features and BCC subtypes. The DI was directly related to arborized vessels but inversely related to short, fine telangiectasias, maple-leaf-like areas, and spoke-wheel areas. The presence of ulceration was directly related to the DI. Confocal microscopy images exhibited several characteristics, including fluorescence, nuclear crowding, peripheral palisading, clefting, increased nuclear-cytoplasmic (N/C) ratio, and a "cauliflower-like" appearance. (4) Conclusion: The advanced detection of BCC through imagistic techniques like dermatoscopy, confocal microscopy, and ultrasound improves the diagnosis and may offer valuable insights for treatment in the future by evaluating lesion characteristics.

Detection and subtyping of basal cell carcinoma in whole-slide histopathology using weakly-supervised learning

The frequency of basal cell carcinoma (BCC) cases is putting an increasing strain on dermatopathologists. BCC is the most common type of skin cancer, and its incidence is increasing rapidly worldwide. AI can play a significant role in reducing the time and effort required for BCC diagnostics and thus improve the overall efficiency of the process. To train such an AI system in a fully-supervised fashion however, would require a large amount of pixel-level annotation by already strained dermatopathologists. Therefore, in this study, our primary objective was to develop a weakly-supervised for the identification of basal cell carcinoma (BCC) and the stratification of BCC into low-risk and high-risk categories within histopathology whole-slide images (WSI). We compared Clustering-constrained Attention Multiple instance learning (CLAM) with StreamingCLAM and hypothesized that the latter would be the superior approach. A total of 5147 images were used to train and validate the models, which were subsequently tested on an internal set of 949 images and an external set of 183 images. The labels for training were automatically extracted from free-text pathology reports using a rule-based approach. All data has been made available through the COBRA dataset. The results showed that both the CLAM and StreamingCLAM models achieved high performance for the detection of BCC, with an area under the ROC curve (AUC) of 0.994 and 0.997, respectively, on the internal test set and 0.983 and 0.993 on the external dataset. Furthermore, the models performed well on risk stratification, with AUC values of 0.912 and 0.931, respectively, on the internal set, and 0.851 and 0.883 on the external set. In every single metric the StreamingCLAM model outperformed the CLAM model or is on par. The performance of both models was comparable to that of two pathologists who scored 240 BCC positive slides. Additionally, in the public test set, StreamingCLAM demonstrated a comparable AUC of 0.958, markedly superior to CLAM’s 0.803. This difference was statistically significant and emphasized the strength and better adaptability of the StreamingCLAM approach.

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of sophisticated clinical decision support systems to provide real-time feedback to clinicians that could have a role in optimizing the diagnostic workup of BCC. There were 287 annotated whole-slide images of frozen sections from tangential biopsies, of which 121 contained BCC, that were used to train and test an AI pipeline to recognize BCC. Regions of interest were annotated by a senior dermatology resident, an experienced dermatopathologist and an experienced Mohs surgeon, with concordance of annotations noted on final review. Final performance metrics included a sensitivity and specificity of 0.73 and 0.88, respectively. Our results on a relatively small dataset suggest the feasibility of developing an AI system to aid in the workup and management of BCC.

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.

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.

Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data.

Background: Extended skin malignancies of the head and neck region are among the most common cancer types and are associated with numerous diagnostic and therapeutical problems. The radical resection of skin cancer in the facial area often leads to severe functional and aesthetic impairment, and precise margin assessments can avoid the extensive safety margins. On the other hand, the complete removal of the cancer is essential to minimize the risk of recurrence. Reliable intraoperative assessments of the wound margins could overcome this discrepancy between minimal invasiveness and safety distance in the head and neck region. With the help of reflectance confocal laser microscopy (RCM), cells can be visualized in high resolution intraoperatively. The combination with deep learning and automated algorithms allows an investigator independent and objective interpretation of specific confocal imaging data. Therefore, we aimed to apply a deep learning algorithm to detect malignant areas in images obtained via in vivo confocal microscopy. We investigated basal cell carcinoma (BCC), as one of the most common entities with well-described in vivo RCM diagnostic criteria, within a preliminary feasibility study. Patients and Methods: We included 62 patients with histologically confirmed BCC in the head and neck region. All patients underwent in vivo confocal laser microscope scanning. Approximately 382 images with BCC structures could be obtained, annotated, and proceeded for further deep learning model training. Results: A sensitivity of 46% and a specificity of 85% in detecting BCC regions could be achieved using a convolutional neural network model (“MobileNet”). Conclusion: The preliminary results reveal the potential and limitations of the automated detection of BCC with in vivo RCM. Further studies with a larger number of cases are required to obtain better predictability.

34785 Epidermal reconstruction during ex vivo confocal microscopy for detection of superficial basal cell carcinoma with 3D-mosaicking and intensity projection

Background: Ex vivo confocal microscopy (EVCM) rapidly images fresh tissues to create digitally colorized H&E-images. It has a high sensitivity (79.8%-100%) and specificity (78%-100%) for the detection of residual basal cell carcinoma (BCC) during Mohs surgery. However, with current tissue mounting techniques, tissue flattening affects the visualization of the entire epidermis, impacting detection of superficial BCC (sBCC).

Design and Validation of a Handheld Optical Polarization Imager for Preoperative Delineation of Basal Cell Carcinoma.

Simple SummarySkin cancer is the most common malignancy in humans. The goal of this study was to design, implement, and clinically test a novel handheld optical polarization imaging (OPI) system for rapid and noninvasive detection of basal cell carcinoma (BCC) margins. The device is compact, lightweight, and can be operated with minimal training. To validate the handheld imager, 10 subjects with biopsy-confirmed BCC were imaged prior to Mohs surgery. The optical images were processed using a spectral encoding method to increase the accuracy of the tumor boundary delineation. Preoperative margin assessment results from the OPI were compared to the surgeon’s clinical evaluation and to the gold standard of histopathology. Our findings indicate that OPI may be a valuable tool for optimizing surgical treatment of skin cancer.Background: Accurate removal of basal cell carcinoma (BCC) is challenging due to the subtle contrast between cancerous and normal skin. A method aiding with preoperative delineation of BCC margins would be valuable. The aim of this study was to implement and clinically validate a novel handheld optical polarization imaging (OPI) device for rapid, noninvasive, in vivo assessment of skin cancer margins. Methods: The handheld imager was designed, built, and tested. For clinical validation, 10 subjects with biopsy-confirmed BCC were imaged. Presumable cancer margins were marked by the study surgeon. The optical images were spectrally encoded to mitigate the impact of endogenous skin chromophores. The results of OPI and of the surgeon’s preoperative visual assessment were compared to clinical intraoperative histopathology. Results: As compared to the previous prototype, the handheld imager incorporates automated image processing and has 10-times shorter acquisition times. It is twice as light and provides twice as large a field of view. Clinical validation demonstrated that margin assessments using OPI were more accurate than visual assessment by the surgeon. The images were in good correlation with histology in 9 out of 10 cases. Conclusions: Handheld OPI could improve the outcomes of skin cancer treatments without impairing clinical workflows.

Non-Melanoma Skin Cancer Clearance after Medical Treatment Detected with Noninvasive Skin Imaging: A Systematic Review and Meta-Analysis.

Simple SummaryNon-melanoma skin cancers (NMSC) represent about one-third of all malignancies. While surgery is the current gold standard treatment, many nonsurgical approaches are available for selected cases. Currently, there are no studies concerning the overall impact of dermoscopy, optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) for NMSC treatment monitoring. Therefore, we aim to review the current literature and provide an updated summary of noninvasive skin imaging in NMSC medical treatment management and the diagnostic accuracy of the most advanced technologies.Background/Objectives: Non-melanoma skin cancer (NMSC) treated with nonsurgical therapies can be monitored with noninvasive skin imaging. The precision of dermoscopy, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) in detecting clearance is unclear. We aim to report the proportion of persisting tumors identified with noninvasive technologies available in the literature. Methods: A systematic literature search was conducted on the PubMed and Cochrane Public Library Databases for articles published prior to November 2021. Statistical analyses were conducted with MedCalc 14.8.1 software. Results: A total of eight studies (352 lesions) reporting noninvasive imaging for NMSC clearance following nonsurgical treatment were included. Most (n = 7) reported basal cell carcinoma (BCC), and one study reported squamous cell carcinoma (SCC) clearance. A meta-analysis of the BCC clearance revealed that the summary effect for RCM was higher, as compared to the other techniques. Interestingly, the sensitivity and specificity for OCT were 86.4% (95% CI: 65.1–97.1) and 100% (95% CI: 94.8–100.0), respectively, whilst, for RCM, they reached 100% (95%CI: 86.8–100) and 72.5% (95% CI: 64.4–79.7), respectively. Conclusions: Routine clinical examination and dermoscopy underperform when employed for NMSC clearance monitoring, although they represent the first approach to the patient. OCT and RCM seem to improve the detection of persistent BCC after medical treatment.

Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?

Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised ones.

A Non-Healing Lesion on the Anterior Chest of a Patient Receiving Hemodialysis: A Case Study

Basal cell carcinoma is the most common type of skin cancer, and patients receiving hemodialysis are at increased risk of developing them. This article describes a case of a patient with a basal cell carcinoma in which the diagnosis was delayed due to the lesion masquerading as granulation tissue at the site of a previous tunnelled hemodialysis catheter. Early detection of basal cell carcinomas is important because delayed detection increases the risk of local invasion and the requirement for more complex surgical management.

Diagnostic accuracy of basal cell carcinoma in dermatology setting in Serbia: A single-center study

Background/Aim. The growing incidence of skin tumors requires an accurate diagnosis. Dermoscopy, especially in vivo, enhances the diagnosis of basal cell carcinoma (BCC). Total body skin examination (TBSE), a visual inspection of the patient?s total body surface, is considered a basic step in the dermatological exam, especially in skin cancer screening. However, TBSE is still a matter of debate regarding its expediency in a real clinical setting. The aim of this study was to analyze the diagnostic accuracy of BCC detected and treated by referred dermatologists. Methods. The retrospective analysis included a five-year period of BCC detection during TBSE by visual inspection and dermoscopy. We calculated sensitivity, specificity and positive predictive value for BCC using histopathological results as the correct diagnosis. Results. Out of 3,346 biopsied skin tumors, 49.58% were malignant and 50.42% benign. The most common malignant tumor was BCC, accounting for 84.09%. Localization of BCCs was mainly on the trunk (38.92%) and the H-zone of the face (37.63%). Other localizations were face (non-H-zone) (6.67%), neck (3.01%), scalp (3.37%), arms (6.88%) and limbs (3.51%). Of all BCCs, 0.83% were recurrent BCC. The sensitivity for the diagnosis of BCC was 97.71%, and the positive predictive value was 95.08%. Conclusion. In the dermatology setting, TBSE and visual inspection with in vivo dermoscopy result in a very good diagnostic performance of BCC.

Еfficiency of Basal Cell Diagnosis Skin Carcinomas Using Dermatoscopy in Combination with Ultrasound (Literature Review; Description of Clinical Cases)

Introduction. Basal cell carcinoma of the skin is the most common skin cancer in people with fair skin color. Early detection of basal cell carcinoma and proper surgical approach are crucial to reduce the incidence of this tumor. Visual examination is the first screening tool used by clinicians, but sometimes it is not enough to differentiate and determine treatment tactics. Therefore, non-invasive methods such as dermatoscopy and ultrasonography of the skin have recently become widely used, which provide a clear diagnosis, fully correlate with the pathomorphological diagnosis and allow determining the invasiveness of the process and the scope of surgery. The aim of the study. To establish the effectiveness of the diagnosis of basal cell carcinoma of the skin using dermatoscopy in combination with ultrasound, to clarify and justify the feasibility of using a double diagnostic test "Dermatoscopy + Ultrasound" in the diagnosis of skin tumors for future surgery. Materials and methods. Two cases of basal cell basal cell carcinoma in young patients with atypical localization, dermatoscopic and ultrasonographic parameters, correlation with pathomorphological diagnosis are described. Dermatoscopic parameters were evaluated according to G. Kittler's algorithm, skin ultrasound was performed on an expert class device "ACUSON Juniper Ultrasound System" with color and energy Doppler modes by a linear multifrequency transducer (5-17MHz) according to the original protocol developed by G.Alakhverdian. Results. A feature of the described clinical cases is the young age of patients (21 and 29 years), which is an atypical phenomenon in relation to the manifestations of BCC and indicates that recently basal cell carcinoma begins to occur at a young age and its typical location changes. The obtained data of dermatoscopic and ultrasonographic examination confirm specific clinical symptoms and are completely correlated with pathomorphological diagnosis. Conclusions. Dermatoscopy in combination with skin ultrasonography are reliable non-invasive techniques that allow real-time detection of clinical and subclinical lesions and should be used to diagnose and monitor for all skin cancers. Keywords: basal cell carcinoma of the skin, dermatoscopy, ultrasonography of the skin.

The Safety and Effectiveness of Elastic Scattering Spectroscopy and Machine Learning in the Evaluation of Skin Lesions for Cancer

Background Elastic scattering spectroscopy (ESS) is an optical biopsy technique that can distinguish between a normal and abnormal tissue in vivo without the need to remove it. The handheld device measures ESS spectra of skin lesions and classifies lesions as either malignant or benign with an output of “Investigate Further” or “Monitor,” respectively, with positive results accompanied by a spectral score output from 1 to 10, indicating how similar the lesion is to the malignant lesions the device was trained on. The algorithm was trained and validated with over 11,000 spectral scans from over 3500 skin lesions. Objective The purpose of this study was to evaluate the safety and effectiveness of the handheld ESS device in detecting the most common types of skin cancer. Methods A prospective, single-arm, investigator-blinded, multicenter study conducted at 4 investigational sites in the United States was performed. Patients who presented with skin lesions suggestive of melanoma, basal cell carcinoma, squamous cell carcinoma, and other highly atypical lesions were evaluated with the handheld ESS device. A validation performance analysis was performed with 553 lesions from 350 subjects with algorithm version 2.0. An independent test set of 281 lesions was selected and used to evaluate device performance in the detection of melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Statistical analyses included overall effectiveness analyses for sensitivity and specificity as well as subgroup analyses for lesion diagnoses. Results The overall sensitivity of the device was 92.3% (95% CI: 87.1 to 95.5%). The sensitivity for subgroups of lesions was 95% (95% CI 75.1% to 99.9%) for melanomas, 94.4% (95% CI 86.3% to 98.4%) for BCCs, and 92.5% (95% CI 83.4% to 97.5%) for SCCs. The overall device specificity was 36.6% (95% CI 29.3% to 44.6%). There was no statistically significant difference between the dermatologist performance and the ESS device (P=.2520). The specificity of the device was highest for benign melanocytic nevi (62.5%) and seborrheic keratoses (78.2%). The overall positive predictive value (PPV) was 59.8%, and the negative predictive value (NPV) was 81.9% with the study’s malignancy prevalence rate of 51%. For a prevalence rate of 5%, the PPV was estimated to be 7.1%, and the NPV was estimated to be 98.9%. For a prevalence rate of 7%, the PPV was estimated to be 9.8%, and the NPV was estimated to be 98.4%. For a prevalence rate of 15%, the PPV was estimated to be 20.3%, and the NPV was 96.4%. Conclusions The handheld ESS device has a high sensitivity for the detection of melanoma, BCC, and SCC. Coupled with clinical exam findings, this device can aid physicians in detecting a variety of skin malignancies. The device output can aid teledermatology evaluations by helping frontline providers determine which lesions to share for teledermatologist evaluation as well as potentially benefitting teledermatologists’ virtual evaluation, especially in instances of suboptimal photo quality. Acknowledgments This study was sponsored by Dermasensor Inc. Conflicts of Interest None declared.

In vivo reflectance confocal microscopy of wounds: feasibility of intraoperative basal cell carcinoma margin assessment.

BackgroundIn vivo reflectance confocal microscopy (RCM) is well established in non-melanoma skin cancer detection and screening. However, there is no sufficient validation regarding intraoperatively obtained images of wound margins. A reliable and fast resection margin detection is of high clinical relevance. Hence, we aimed to investigate feasibility and validity of in vivo RCM imaging for wound margins assessment compared with standard skin surface imaging and the gold standard histopathology.MethodsA surgical incision through the center of a large basal cell carcinoma (BCC) affected area in the head and face region was performed. After removing half of the tumor, the wound margins of the remaining half as well as the corresponding skin surface were scanned with an in vivo RCM. A total of 50 wound margin images with BCC, 50 images of BCC-free margins and the corresponding skin surface images from 50 patients were compared with each other and with histopathological findings. Presence of confocal diagnostic criteria for BCC in images was analyzed.ResultsAn overall sensitivity and specificity in detection of BCC in wound margins was 88.5%, and 91.7% compared to skin surface imaging and 97.8% and 90.7%, respectively, compared to histopathology. We identified all known confocal patterns of healthy skin and BCC in wound margin scans: damage of the epidermal layer above the lesion and cellular pleomorphism, elongated and monomorphic basaloid nuclei, nuclear polarization, an increased number of dilated blood vessels with high leukocyte traffic, inflammatory cells.ConclusionsThe accuracy of in vivo RCM imaging of wound margins is comparable with a standard skin surface imaging. The intraoperative detection of BCC areas in wound margins is as precise as the standard skin imaging and may be supportive for surgical interventions.