Single color virtual H&E staining with In-and-Out Net.

Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.

The utility of teledermatology in the evaluation of skin lesions.

INTRODUCTION: Past studies have shown mixed results about the accuracy of store-and-forward (SAF) teledermatology in the evaluation of skin lesions. The objective of this study is to determine the accuracy of SAF teledermatology in the diagnosis of skin lesions and biopsy decision compared to in-person clinical evaluation. METHODS: Histories and photographs of skin lesions gathered at clinic visits were sent as SAF consults to teledermatologists, whose diagnoses and biopsy decisions were recorded and compared statistically to the clinic data.Results and Discussion: We enrolled 206 patients with 308 lesions in the study. The study population was composed of 50% males (n = 104), and most patients were white (n = 179, 87%) and not Hispanic/Latino (n = 167, 81%). There was good concordance for biopsy decision between the clinic dermatologist (CD) and teledermatologist (TD) (Cohen's kappa (κ) = 0.51), which did not significantly differ when melanocytic lesions were excluded (κ = 0.54). The sensitivity and specificity of teledermatology based on biopsy decision was 0.71 and 0.85, respectively. Overall concordance in first diagnosis between the CD and TD was good (κ = 0.60). While there was no difference between CD and TD in proportion of correct diagnoses compared to histopathology, two skin cancers presentations were missed by TD. Study limitations included sample size, enrolment bias and differing amounts of teledermatologist case experience. Teledermatology has good concordance in diagnosis and biopsy decision when compared to clinic dermatology. Teledermatology may be utilized in the evaluation of skin lesions to expand access to dermatologic care.

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician. AIM: The aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis). APPROACH: Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative. RESULTS: Deep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images. CONCLUSIONS: Combining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.

Clinician Preferences for Current and Planned Future Use of Telemedicine.

Introduction: Many clinicians and patients tried telemedicine for the first time during the COVID-19 pandemic. In a prior survey, we found that clinicians who were adaptable and willing to incorporate technology into their clinical practice are more likely to utilize telemedicine. Seeking factors associated with current and future use of telemedicine, and identifying its relative advantages and drawbacks, may help determine the role of telemedicine after the pandemic. Questions/Purposes: We asked (1) which demographic factors and personal preferences are associated with current and planned future use of telemedicine, (2) what factors are associated with telemedicine utilization, and (3) what are clinician-reported advantages and disadvantages of telemedicine? Materials and Methods: Approximately 750 clinicians within a national multispecialty hospital group were invited to complete an online survey assessing telemedicine use and preferences, self-reported technology proficiency, and personal characteristics. A total of 284 clinicians started the survey, and 259 complete responses were analyzed using bivariate analysis and multivariable regression. Results: More frequent current telemedicine use was associated with being a nonsurgeon clinician, not primarily practicing in an inpatient setting, preferring either telemedicine or having no preference for discussing sensitive topics, and greater self-reported technological proficiency. Planned future telemedicine use was associated with greater self-reported troubleshoot ability and less desire for a hands-on physical examination. Clinicians reported that the top benefits of telemedicine are decreased barriers for patients and convenience for clinicians, and disadvantages are technical difficulties for both patients and clinicians. Conclusions: Telemedicine continues to be widely utilized by clinicians, particularly those who are confident in their ability to examine patients over video, and who can troubleshoot issues that arise on the platform. With continued reimbursement, telemedicine is likely to remain a convenient and effective method of caring for patients.

An innovative approach in the treatment of trigeminal trophic syndrome.

BACKGROUND: Trigeminal trophic syndrome (TTS) is a rare disorder that presents as ulceration of the nasal ala, and less often ipsilateral cheek, cutaneous lip, and forehead, following damage to the trigeminal nerve. TTS is often very difficult to treat due to anesthesia and paresthesia along the distribution of the trigeminal nerve, which often leads to a large behavioral component of this disease. METHODS: In this single case report, a thermoplastic mask was sutured in place overlying the chronic ulcerations of TTS in a 57-year-old man. RESULTS: Significant re-epithelization of the chronic ulcerations of TTS was seen after only 6 weeks of thermoplastic mask attachment. CONCLUSION: While TTS is a chronic and difficult to treat disease, the use of a thermoplastic mask in the treatment of this disease can significantly decrease the self-manipulation component and allow for re-epithelization and healing of the chronic ulcerations.

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging.

SIGNIFICANCE: Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications. AIM: Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. APPROACH: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue. RESULTS: The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes. CONCLUSION: These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Toward automated assessment of mole similarity on dermoscopic images.

Purpose: Current skin cancer detection relies on dermatologists' visual assessments of moles directly or dermoscopically. Our goal is to show that our similarity assessment algorithm on dermoscopic images can perform as well as a dermatologist's assessment. Approach: Given one target mole and two other moles from the same patient, our model determines which mole is more similar to the target mole. Similarity was quantified as the Euclidean distance in a feature space designed to capture mole properties such as size, shape, and color. We tested our model on 18 patients, each of whom had at least five moles, and compared the model assessments of mole similarity with that of three dermatologists. Fleiss' Kappa agreement coefficients and iteration tests were used to evaluate the agreement in similarity assessment among dermatologists and our model. Results: With the selected features of size, entropy (color variation), and cluster prominence (asymmetry), our algorithm's similarity assessments agreed moderately with the similarity assessments of dermatologists. The mean Kappa of 1000 iteration tests was 0.49 ( confidence interval ( CI ) = [ 0.23 , 0.74 ] ) when comparing three dermatologists and our model, which is comparable to the agreement in similarity assessment among the dermatologists themselves (the mean Kappa of 1000 iteration tests for three dermatologists was 0.48, CI = [ 0.19 , 0.77 ] .) By contrast, the mean Kappa was 0.22 ( CI = [ - 0.00 , 0.43 ] ) when comparing the similarity assessments of three dermatologists and random guesses. Conclusions: Our study showed that our image feature-engineering-based algorithm can effectively assess the similarity of moles as dermatologists do. Such a similarity assessment could serve as the foundation for computer-assisted intra-patient evaluation of moles.

Clinician Telemedicine Perceptions During the COVID-19 Pandemic.

Background: Telemedicine utilization increased exponentially due to the coronavirus disease 2019 (COVID-19) pandemic. As a result, most clinicians now have experience with telemedicine. Questions/Purposes: We studied factors independently associated with a clinician desiring to continue telemedicine services after the COVID-19 pandemic. Secondarily, we sought factors independently associated with clinician satisfaction with the quality of care provided through telemedicine and factors associated with telemedicine platform preference by clinicians. Methods: A large multispecialty medical group of physicians were invited to complete a survey, including demographics, telemedicine experience, satisfaction with various elements of telemedicine encounters, desired features in a telemedicine platform, personality traits, and preferences. A total of 220 complete responses were analyzed. Results: A desire to continue offering telemedicine visits after the COVID-19 pandemic was independently associated with a higher satisfaction with the quality of telemedicine care, endorsement of the ease of performing a physical examination with telemedicine, belief that adaptability is an important element of being a clinician, and less preference for in-person work meetings over virtual meetings. Higher satisfaction with the quality of telemedicine care was associated with belief that adaptability is an important element of being a clinician, clinicians who identify as being more perceiving (value flexibility) than judging (value organization), providers from relatively urban counties, and those with less preference for in-person work meetings over virtual meetings. Clinicians ranked ease of use for patients and physicians as the most important features of telemedicine platforms. Conclusions: The observed association of clinician personality and interpersonal preferences with the appeal, satisfaction, and perceived effectiveness of telemedicine merit additional study. Early implementation of telemedicine might be easiest with clinicians that take pride in their adaptability and value a technology-based workstyle.

Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening.

A key challenge in melanoma diagnosis is the large number of unnecessary biopsies on benign nevi, which requires significant amounts of time and money. To reduce unnecessary biopsies while still accurately detecting melanoma lesions, we propose using Raman spectroscopy as a non-invasive, fast, and inexpensive method for generating a "second opinion" for lesions being considered for biopsy. We collected in vivo Raman spectral data in the clinical skin screening setting from 52 patients, including 53 pigmented lesions and 7 melanomas. All lesions underwent biopsies based on clinical evaluation. Principal component analysis and logistic regression models with leave one lesion out cross validation were applied to classify melanoma and pigmented lesions for biopsy recommendations. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.903 and a specificity of 58.5% at perfect sensitivity. The number needed to treat for melanoma could have been decreased from 8.6 (60/7) to 4.1 (29/7). This study in a clinical skin screening setting shows the potential of Raman spectroscopy for reducing unnecessary skin biopsies with in vivo Raman data and is a significant step toward the application of Raman spectroscopy for melanoma screening in the clinic.

Superpixel Raman spectroscopy for rapid skin cancer margin assessment.

Spontaneous Raman micro-spectroscopy has been demonstrated great potential in delineating tumor margins; however, it is limited by slow acquisition speed. We describe a superpixel acquisition approach that can expedite acquisition between ~×100 and ×10 000, as compared to point-by-point scanning by trading off spatial resolution. We present the first demonstration of superpixel acquisition on rapid discrimination of basal cell carcinoma tumor from eight patients undergoing Mohs micrographic surgery. Results have been demonstrated high discriminant power for tumor vs normal skin based on the biochemical differences between nucleus, collagen, keratin and ceramide. We further perform raster-scanned superpixel Raman imaging on positive and negative margin samples. Our results indicate superpixel acquisition can facilitate the use of Raman microspectroscopy as a rapid and specific tool for tumor margin assessment.

Successful Use of the 308-nm Excimer Laser in Early Patch Stage Mycosis Fungoides.

Introduction: Mycosis fungoides (MF), a type of cutaneous T-cell lymphoma, is a rare condition with a variety of treatment options. A frequently utilized method in the treatment of early-stage MF is phototherapy. Full body phototherapy can be associated with photoaging of the skin and an increased risk of developing skin cancer. Targeted phototherapy, with a 308-nm excimer laser, may be just as effective and of a lower risk. This makes it especially useful in the treatment of patients with dysplastic nevi (DN) or other conditions which can put them at a higher risk of developing skin cancer. There are currently limited data on the treatment of early-stage MF with an excimer laser. Case Report: This study reports on a 43-year-old female patient presented to the clinic with early-stage (Ia) MF. Given her history of DN, she wished to pursue targeted phototherapy with an excimer laser. Localized light treatment was performed via a 308-nm excimer laser, 3 times weekly, for a total of 17 treatments to affected lesions. Following excimer laser treatment, she had a clinical resolution of her patches. On the follow-up clinic visits, she maintained her excellent response 12 months out. Conclusion: Targeted phototherapy with a 308-nm excimer laser may be a safer and equally effective alternative to generalized phototherapy in the treatment of early-stage MF. This case report demonstrates its efficacy and advantages over traditional generalized phototherapy.

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis.

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low-cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.

Biophysical basis of skin cancer margin assessment using Raman spectroscopy.

Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.

Association Between Delusions of Infestation and Prescribed Narcotic and Stimulant Use.

OBJECTIVE: Patients diagnosed with delusions of infestation (DOI) at a psychodermatology clinic appeared to have a higher incidence of being prescribed narcotic or stimulant medications compared with the general dermatologic clinic population with chronic pruritic conditions. A retrospective study was conducted examining the correlation between patients with DOI and prescribed psychoactive medications. METHODS: Ninety-two patients with a diagnosis of DOI, seen at our University Psychodermatology Clinic, served as the study population. The comparison group (N=354) included dermatology patients seen at a dermatology clinic by the same dermatologist for itching, including adults seen for chronic pruritic conditions and contact dermatitis. For both groups, the reported use of any psychoactive prescription medications was noted. RESULTS: Patients with DOI were significantly more likely than other dermatology patients to receive prescriptions for narcotics [adjusted odds ratio (OR)=2.19; confidence interval (CI)=1.21-3.99) and stimulants (OR=5.44; CI=2.37-12.52). Patients with DOI were also more likely to be female (OR=2.49; CI=1.47-4.22) than patients who did not have such delusions. DISCUSSION: Few data are available concerning the etiology and management of DOI. Findings from this study indicated an association between the diagnosis of DOI and the prescribing of narcotics and stimulants, even when sex and age were taken into account. This information may be used to assist with the diagnosis of patients presenting with DOI and possible treatment options. It will be important to determine if these medications are a cause of the condition, or are merely correlated with other medical conditions.

Acral cutaneous metastasis from a primary breast carcinoma following chemotherapy with bevacizumab and paclitaxel.

Acral cutaneous metastasis from internal malignancy is rare, and the pathogenesis is not completely understood. We present the case of a 54-year-old black woman with a history of breast cancer diagnosed 2 years prior who developed multiple cutaneous metastases to acral sites involving the palmar and plantar surfaces of the hands and feet. This case represents an unusual metastatic pattern of breast carcinoma that arose after treatment with bevacizumab, an angiogenesis inhibitor that binds vascular endothelial growth factor A. We propose that bevacizumab-induced vascular changes localized to acral skin may have contributed to the metastatic pattern seen in our patient.