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.

Assessing Treatment Delays for Vitiligo Patients: A Retrospective Chart Review.

Many barriers to care exist for vitiligo patients that can impact patients' quality of life. Because early treatment of vitiligo is more efficacious, we investigated the factors associated with delay to treatment via a retrospective chart review of 102 consecutive patients attending an academic outpatient clinic over a 36-month time frame. Demographic information, clinical characteristics of vitiligo, and treatment details were obtained via a standardized questionnaire given to all patients with vitiligo. Our findings emphasize the need to investigate barriers to care to reduce health disparities among individuals with vitiligo.

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.

Validation of remote atopic dermatitis severity assessment with the Eczema Area and Severity Ondex in children using caregiver-provided photos and videos.

BACKGROUND/OBJECTIVES: We sought to quantify the reliability and validity of remote atopic dermatitis (AD) severity assessment using the Eczema Area and Severity Index (EASI) applied to caregiver-provided photos (p-EASI) and videos (v-EASI). METHODS: Children (0-17 years) with a physician diagnosis of AD were recruited. Caregivers took photos and a video of their child's skin. A clinician scored in-person EASI on the same day, then p-EASI and v-EASI for each participant 10 days or more between ratings. Two additional clinicians scored p-EASI and v-EASI. Lin's concordance correlation coefficient (CCC) was employed to assess criterion validity using in-person EASI as the gold standard. Intraclass correlation coefficients (ICCs) were calculated to assess interrater reliability of p-EASI and v-EASI. RESULTS: Fifty racially and ethnically diverse children (age [mean ± SD]: 4.3 ± 4.4 years; 42% female) with a range of AD severity (EASI: 6.3 ± 6.4) and Fitzpatrick skin types (1-2: 9%; 3-4: 60%; 5-6: 31%) were enrolled and received in-person EASI assessment. Fifty had p-EASI and 49 had v-EASI by the same in-person rater, and by two additional raters. The CCC and ICC for p-EASI were 0.89, 95% CI [0.83, 0.95] and 0.81, 95% CI [0.71, 0.89], respectively. The CCC and ICC for v-EASI were 0.75, 95% CI [0.63, 0.88] and 0.69, 95% CI [0.51, 0.81], respectively. CONCLUSIONS: In this diverse population with a range of skin tones, p-EASI showed good criterion validity and good interrater reliability. v-EASI showed moderate to good criterion validity and moderate interrater reliability. Both may be reliable and valid options for remote AD severity assessment.

UV Exposure and the Risk of Keratinocyte Carcinoma in Skin of Color: A Systematic Review.

Importance: Ultraviolet radiation exposure is an important modifiable risk factor for keratinocyte carcinoma (KC) in fair-skinned non-Hispanic White populations; however, the evidence for this relationship in darker-skinned populations is less certain. Objective: To assess and synthesize the published data concerning the association between UV exposure and the risk of KC in individuals with skin of color. Evidence Review: PubMed, Cochrane, and Web of Science databases were searched from database origin through January 2022. Studies deemed eligible included UV exposure as a risk factor for KC in individuals with skin of color, defined as any race other than non-Hispanic White, Fitzpatrick skin types IV to VI, or tanning ability of rarely or never burns. The UV index, irradiance, latitude, history of phototherapy, history of sunburn, or occupational exposure were used as measures of exposure. The Oxford Centre for Evidence-Based Medicine guidelines were used to assess evidence quality. Findings: A total of 72 716 articles appeared in the search. After duplicate removal, 29 393 database records were screened, 454 full-text articles were assessed, a forward and reverse citation search was performed, and 12 articles, with clinical data spanning the years 1990 to 2019, met inclusion criteria. More than 32 970 KCs in individuals with skin of color were included. Eight studies found no association between UV exposure and KC, while 4 studies showed a positive association. Study types included 1 ecological study, 9 cohort studies, and 2 case-control studies. The quality of the studies was rated from moderate to low (2b to 4). Conclusions and Relevance: Results of this systematic review show that the evidence assessing the association of UV exposure with KC is of moderate to low quality. The studies that found no association were among patients receiving phototherapy. Studies assessing nonphototherapy-related UV exposure, such as geographic location or occupation, found small positive associations in primarily East Asian individuals. There were no studies performed in the US, no studies among Black individuals, and only 1 study among a Hispanic population. Further research is required to better assess whether these associations exist across populations of patients with darker skin types.

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.

Interventions to improve primary care provider management of atopic dermatitis: A systematic review.

Most atopic dermatitis (AD) patients are managed by primary care providers (PCPs). PCP discomfort diagnosing and managing AD leads to suboptimal patient outcomes. In order to determine the efficacy of interventions aimed at improving PCP management of AD, a systematic literature review was performed for interventions to improve primary care management of AD. PubMed, MEDLINE, Embase, Scopus, LILACS, Cochrane, GREAT, and CINAHL were searched from database origin to February 24, 2020. Two reviewers independently performed the title/abstract and full-text review, and data extraction. Overall, 3009 non-duplicate articles were screened; 145 full-text articles were assessed. Thirteen studies met inclusion criteria, including 8 randomized controlled trials, 2 cohorts, 2 qualitative studies, and 1 unspecified design. Seven interventions (53.8%) significantly improved PCP knowledge/ability and/or a patient outcome, including patients consulting with a dermatology-trained nurse, pairing clinical education with expert consultation, pairing trainees with clinical mentors, giving clinicians a treatment guide, pairing clinical education with a treatment guide, and providing an eczema action plan. Studies had moderate-high risk-of-bias, moderate quality, and heterogeneous designs. There are few studies published and little evidence supporting the efficacy of interventions aimed at improving primary care management of AD. Further research is required to develop and implement effective interventions to improve primary care management of AD.

UV Exposure and the Risk of Cutaneous Melanoma in Skin of Color: A Systematic Review.

Importance: While current evidence supports UV exposure as an important risk factor for cutaneous melanoma in fair-skinned populations, the evidence for this association in skin of color is less certain. Objective: To critically assess and synthesize the published data regarding the association between UV exposure and the risk of cutaneous melanoma in skin of color. Evidence Review: A search was conducted including PubMed, Cochrane, and Web of Science databases from database origin to June 3, 2020. Only peer-reviewed original studies were screened in full text. Eligible studies analyzed UV exposure as a risk factor for cutaneous melanoma in people with skin of color, which was defined broadly as any race/ethnicity other than non-Hispanic White, Fitzpatrick skin types IV through VI, or tanning ability of rarely or never burns. Measures of UV exposure included UV index, irradiance, latitude, history of phototherapy, and history of sunburn. Evidence quality was assessed using criteria from the Oxford Centre for Evidence-Based Medicine. Findings: After duplicate removal, 11 059 database records were screened, 548 full-text articles were assessed, and 13 met inclusion criteria. Study types included 7 ecological studies, 5 cohort studies, and 1 case-control study. All studies used race and/or ethnicity to categorize the participants, and more than 7700 melanomas in skin of color were included. Of the 13 studies that met inclusion criteria, 11 found no association between UV exposure and melanoma in skin of color, 1 study showed a small positive relationship in Black males, and 1 showed a weak association in Hispanic males. All studies were of moderate to low quality (Oxford Centre ratings 2b to 4). Conclusions and Relevance: In this systematic review, the evidence suggests that UV exposure may not be an important risk factor for melanoma development in people with skin of color. Current recommendations promoting UV protection for melanoma prevention in skin of color are not supported by most current studies. However, evidence is of moderate to low quality, and further research is required to fully elucidate this association.

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.

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.