High-frequency ultrasound accuracy in preoperative cutaneous melanoma assessment: A meta-analysis.

BACKGROUND: High-frequency ultrasound (HFUS) can safely and efficiently visualize cutaneous tumour characteristics including depth. OBJECTIVES: We aimed to evaluate its accuracy in measuring melanoma depth against the gold standard, histopathology, for treatment planning. METHODS: A review of publications was conducted in March 2023 through five electronic databases. Thirty-six included articles studied patients who received HFUS (≥10 MHz) measurements, melanoma biopsy or excision, and reported a tumour depth correlation coefficient between HFUS and histopathology. We analysed correlation coefficients between HFUS and histopathology, measured tumour depths and shed light on reasons for mismeasurements. Additionally, we identified the reporting of critical metrics including, lesion characteristics, melanoma subtype, type of correlation coefficient, 95% confidence intervals for Pearson coefficients and sample size. RESULTS: The most common tumour imaged was superficial spreading melanoma on the trunk and extremities, followed by head/face. Maximum ultrasound frequencies ranged from 13 MHz to 100 MHz with participants ranging from 5 to 264. Histopathology and HFUS correlation coefficients ranged from 0.417 to 0.997 (median: 0.94, mean: 0.89 and SD: 0.13). Lower frequency probes (10-20 MHz) were less accurate in assessing melanoma thickness, with a cumulative mean correlation coefficient of 0.87 compared to 0.94 (20-25 MHz) and 0.98 (≥70 MHz). Studies demonstrated higher sonographic accuracy in melanomas >0.75 mm. Additionally, ultrasound may report increased melanoma depth compared to histopathology for reasons including lymphocytic infiltration, presence of a nevus and shrinkage during specimen processing. Furthermore, we found a gap in the reporting of details such as fundamental characteristics of lesion populations. Specifically, 86% (31 out of 36) of the studies failed to report one or more critical metrics, such as mean, median or range of lesion depths. CONCLUSIONS: HFUS may serve as a supplementary tool for preoperative melanoma assessment, with increased accuracy in thicker tumours. Frequencies <20 MHz are less reliable in assessing depth. Frequencies ≥70 MHz demonstrate stronger correlations to histopathology. Higher ultrasound accuracy was seen for melanomas with Breslow depth >0.75 mm.

Crowdsourcing Skin Demarcations of Chronic Graft-Versus-Host Disease in Patient Photographs: Training Versus Performance Study.

BACKGROUND: Chronic graft-versus-host disease (cGVHD) is a significant cause of long-term morbidity and mortality in patients after allogeneic hematopoietic cell transplantation. Skin is the most commonly affected organ, and visual assessment of cGVHD can have low reliability. Crowdsourcing data from nonexpert participants has been used for numerous medical applications, including image labeling and segmentation tasks. OBJECTIVE: This study aimed to assess the ability of crowds of nonexpert raters-individuals without any prior training for identifying or marking cGHVD-to demarcate photos of cGVHD-affected skin. We also studied the effect of training and feedback on crowd performance. METHODS: Using a Canfield Vectra H1 3D camera, 360 photographs of the skin of 36 patients with cGVHD were taken. Ground truth demarcations were provided in 3D by a trained expert and reviewed by a board-certified dermatologist. In total, 3000 2D images (projections from various angles) were created for crowd demarcation through the DiagnosUs mobile app. Raters were split into high and low feedback groups. The performances of 4 different crowds of nonexperts were analyzed, including 17 raters per image for the low and high feedback groups, 32-35 raters per image for the low feedback group, and the top 5 performers for each image from the low feedback group. RESULTS: Across 8 demarcation competitions, 130 raters were recruited to the high feedback group and 161 to the low feedback group. This resulted in a total of 54,887 individual demarcations from the high feedback group and 78,967 from the low feedback group. The nonexpert crowds achieved good overall performance for segmenting cGVHD-affected skin with minimal training, achieving a median surface area error of less than 12% of skin pixels for all crowds in both the high and low feedback groups. The low feedback crowds performed slightly poorer than the high feedback crowd, even when a larger crowd was used. Tracking the 5 most reliable raters from the low feedback group for each image recovered a performance similar to that of the high feedback crowd. Higher variability between raters for a given image was not found to correlate with lower performance of the crowd consensus demarcation and cannot therefore be used as a measure of reliability. No significant learning was observed during the task as more photos and feedback were seen. CONCLUSIONS: Crowds of nonexpert raters can demarcate cGVHD images with good overall performance. Tracking the top 5 most reliable raters provided optimal results, obtaining the best performance with the lowest number of expert demarcations required for adequate training. However, the agreement amongst individual nonexperts does not help predict whether the crowd has provided an accurate result. Future work should explore the performance of crowdsourcing in standard clinical photos and further methods to estimate the reliability of consensus demarcations.

Modulating microbiome-immune axis in the deployment-related chronic diseases of Veterans: report of an expert meeting.

The present report summarizes the United States Department of Veterans Affairs (VA) field-based meeting titled "Modulating microbiome-immune axis in the deployment-related chronic diseases of Veterans." Our Veteran patient population experiences a high incidence of service-related chronic physical and mental health problems, such as infection, irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), various forms of hematological and non-hematological malignancies, neurologic conditions, end-stage organ failure, requiring transplantation, and posttraumatic stress disorder (PTSD). We report the views of a group of scientists who focus on the current state of scientific knowledge elucidating the mechanisms underlying the aforementioned disorders, novel therapeutic targets, and development of new approaches for clinical intervention. In conclusion, we dovetailed on four research areas of interest: 1) microbiome interaction with immune cells after hematopoietic cell and/or solid organ transplantation, graft-versus-host disease (GVHD) and graft rejection, 2) intestinal inflammation and its modification in IBD and cancer, 3) microbiome-neuron-immunity interplay in mental and physical health, and 4) microbiome-micronutrient-immune interactions during homeostasis and infectious diseases. At this VA field-based meeting, we proposed to explore a multi-disciplinary, multi-institutional, collaborative strategy to initiate a roadmap, specifically focusing on host microbiome-immune interactions among those with service-related chronic diseases to potentially identify novel and translatable therapeutic targets.

Interrater reproducibility of the Myoton and durometer devices to quantify sclerotic chronic graft-versus-host disease.

Chronic graft-versus-host disease (cGVHD) is a severe complication in long-term survivors of allogeneic hematopoietic stem cell transplantation. This disease is challenging to manage clinically due to a lack of validated tools to quantitatively measure skin sclerosis. The current gold standard for measuring skin sclerosis is the NIH Skin Score which has only moderate agreement among clinicians and experts. To more accurately assess skin sclerosis in cGVHD, the Myoton and durometer devices can be used to directly measure biomechanical parameters of the skin. However, the reproducibility of these devices is not known in patients with cGVHD. To determine this reproducibility, three observers independently measured 10 anatomic sites in each of seven patients with sclerotic cGVHD using the Myoton and durometer. Clinical reproducibility was measured by mean pairwise differences (U-statistic) and intraclass correlation coefficients (ICCs) with 95% confidence intervals (CIs). Mean pairwise differences, expressed in true physical units, were used to report typical errors for each anatomic site and device. Mean pairwise differences were less than 11% of the average overall values for all five Myoton parameters and durometer hardness. These were lower for Myoton creep (4.1%), relaxation time (4.7%), and frequency (5.1%) than decrement (9.0%), stiffness (10.4%), and durometer hardness (9.0%). Myoton parameters creep, relaxation time, and frequency showed promise for capturing skin biomechanics more accurately than Myoton stiffness, decrement, or durometer hardness. Mean pairwise differences trended highest in the shin and volar forearm and lowest in the dorsal forearm. The interobserver ICC for overall (averaged across all measured body sites of a patient) creep (0.94; 95% CI 0.87-1.00), relaxation time (0.96; 95% CI 0.90-1.00), and frequency (0.95; 95% CI 0.88-1.00), trended higher than that for decrement (0.43; 95% CI 0.00-0.88), stiffness (0.92; 95% CI 0.81-1.00), and durometer hardness (0.82; 95% CI 0.61-1.00). Similar trends were observed in healthy participants. These findings can help clinicians design better studies to assess therapeutic response to new cGVHD treatments and support the interpretation of future measurements.

Prognostic Value of Cutaneous Disease Severity Estimates on Survival Outcomes in Patients With Chronic Graft-vs-Host Disease.

Importance: Prior studies have demonstrated an association between cutaneous chronic graft-vs-host disease (cGVHD) and mortality. Assessment of the prognostic value of different measures of disease severity would assist in risk stratification. Objective: To compare the prognostic value of body surface area (BSA) and National Institutes of Health (NIH) Skin Score on survival outcomes stratified by erythema and sclerosis subtypes of cGVHD. Design, Setting, and Participants: Multicenter prospective cohort study from the Chronic Graft-vs-Host Disease Consortium including 9 medical centers in the US, enrolled from 2007 through 2012 and followed until 2018. Participants were adults and children with a diagnosis of cGVHD requiring systemic immunosuppression and with skin involvement during the study period, who had longitudinal follow-up. Data analysis was performed from April 2019 to April 2022. Exposures: Patients underwent continuous BSA estimation and categorical NIH Skin Score grading of cutaneous cGVHD at enrollment and every 3 to 6 months thereafter. Main Outcomes and Measures: Nonrelapse mortality (NRM) and overall survival (OS), compared between BSA and NIH Skin Score longitudinal prognostic models, adjusted for age, race, conditioning intensity, patient sex, and donor sex. Results: Of 469 patients with cGVHD, 267 (57%) (105 female [39%]; mean [SD] age, 51 [12] years) had cutaneous cGVHD at enrollment, and 89 (19%) developed skin involvement subsequently. Erythema-type disease had earlier onset and was more responsive to treatment compared with sclerosis-type disease. Most cases (77 of 112 [69%]) of sclerotic disease occurred without prior erythema. Erythema-type cGVHD at first follow-up visit was associated with NRM (hazard ratio, 1.33 per 10% BSA increase; 95% CI, 1.19-1.48; P < .001) and OS (hazard ratio, 1.28 per 10% BSA increase; 95% CI, 1.14-1.44; P < .001), while sclerosis-type cGVHD had no significant association with mortality. The model with erythema BSA collected at baseline and first follow-up visits retained 75% of the total prognostic information (from all covariates including BSA and NIH Skin Score) for NRM and 73% for OS, with no statistical difference between prognostic models (likelihood ratio test χ2, 5.9; P = .05). Conversely, NIH Skin Score collected at the same intervals lost significant prognostic information (likelihood ratio test χ2, 14.7; P < .001). The model incorporating NIH Skin Score instead of erythema BSA accounted for only 38% of the total information for NRM and 58% for OS. Conclusions and Relevance: In this prospective cohort study, erythema-type cutaneous cGVHD was associated with increased risk of mortality. Erythema BSA collected at baseline and follow-up predicted survival more accurately than the NIH Skin Score in patients requiring immunosuppression. Accurate assessment of erythema BSA may assist in identifying patients with cutaneous cGVHD at high risk for mortality.

Hyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft-versus-host disease.

In 51 lesions from 15 patients with the inflammatory skin condition chronic graft-versus-host-disease, hyperspectral imaging accurately delineated active erythema and post-inflammatory hyperpigmentation. The method was validated by dermatologist-approved confident delineations of only definitely affected and definitely unaffected areas in photographs. A prototype hyperspectral imaging system acquired a 2.5 × 3.5 cm2 area of skin at 120 wavelengths in the 450-850 nm range. Unsupervised extraction of unknown absorbers by endmember analysis achieved a comparable accuracy to that of supervised extraction of known absorbers (melanin, hemoglobin) by chromophore mapping: 0.78 (IQR: 0.39-0.85) vs. 0.83 (0.53-0.91) to delineate erythema and 0.74 (0.57-0.87) vs. 0.73 (0.52-0.84) to delineate hyperpigmentation. Both algorithms achieved higher specificity than sensitivity. Whereas a trained human confidently marked a median of 7% of image pixels, unsupervised and supervised algorithms delineated a median of 14% and 27% pixels. Hyperspectral imaging could overcome a fundamental practice gap of distinguishing active from inactive manifestations of inflammatory skin disease.

Mpox-A Rapidly Evolving Disease.

The 2022 mpox outbreak has rapidly emerged onto the global medical scene while the world continues to grapple with the COVID-19 pandemic. Unlike COVID-19, however, most patients with mpox present with skin findings, the evolving clinical presentation of which may be mistaken for other common skin diseases, particularly sexually transmitted infections. This Special Communication provides an overview of the evolution of mpox skin findings from its initial description in humans in 1970 to the present-day multinational outbreak.

Effect of camera distance and angle on color of diverse skin tone-based standards in smartphone photos.

Accurate and reproducible color capture is vital in medical photography. Camera distance and angle are particularly important as they are highly variable in a clinical setting. To account for variability in illumination, camera technology, and geometric effects, color standards are often used for color correction. To explore how geometry affects color, we quantified the change in CIELAB color value of a color standard for diverse skin tones at varying smartphone camera distances and angles. Whereas both chromaticity (a* and b*) and lightness (L*) were affected by angle, distance only affected L* (standard error of measurement, SEM > 1 CIELAB unit). Flash usage did not generally reduce distance and angle associated variability. Compared to compressed (JPG) format, raw (DNG) images had decreased median variability across different distances and angles. These findings suggest that in medical photography, inconsistent camera distance and angle can increase variability in photographed skin appearance over time.

Introduction to the Biophotonics Congress 2022 feature issue.

A feature issue is being presented by a team of guest editors containing papers based on studies presented at the Optica Biophotonics Congress: Biomedical Optics held on April 24-27, 2022 in Fort Lauderdale, Florida, USA.

Non-Expert Markings of Active Chronic Graft-Versus-Host Disease Photographs: Optimal Metrics of Training Effects.

Lack of reliable measures of cutaneous chronic graft-versus-host disease (cGVHD) remains a significant challenge. Non-expert assistance in marking photographs of active disease could aid the development of automated segmentation algorithms, but validated metrics to evaluate training effects are lacking. We studied absolute and relative error of marked body surface area (BSA), redness, and the Dice index as potential metrics of non-expert improvement. Three non-experts underwent an extensive training program led by a board-certified dermatologist to mark cGVHD in photographs. At the end of the 4-month training, the dermatologist confirmed that each trainee had learned to accurately mark cGVHD. The trainees' inter- and intra-rater intraclass correlation coefficient estimates were "substantial" to "almost perfect" for both BSA and total redness. For fifteen 3D photos of patients with cGVHD, the trainees' median absolute (relative) BSA error compared to expert marking dropped from 20 cm2 (29%) pre-training to 14 cm2 (24%) post-training. Total redness error decreased from 122 a*·cm2 (26%) to 95 a*·cm2 (21%). By contrast, median Dice index did not reflect improvement (0.76 to 0.75). Both absolute and relative BSA and redness errors similarly and stably reflected improvements from this training program, which the Dice index failed to capture.

Measuring Biomechanical Properties Using a Noninvasive Myoton Device for Lymphedema Detection and Tracking: A Pilot Study.

Background: Improved techniques for lymphedema detection and monitoring of disease progression are needed. This study aims to use the noninvasive MyotonPRO Device to detect differences in biomechanical skin characteristics in patients with breast cancer-related lymphedema (BCRL). Methods: The handheld Myoton device was used to measure skin parameters including dynamic skin stiffness, oscillation frequency (tone), mechanical stress relaxation time, and creep in 11 women diagnosed with BCRL. Seven anatomical sites were measured bilaterally for each participant. The average values in the affected arms were compared with those in the contralateral unaffected arms. Results: Among the 11 female participants with unilateral BCRL Stages 0 to II, the combined averages for dynamic skin stiffness and frequency measurements were decreased in the affected arms when compared with those for the contralateral control arms (ratio < 1). The median ratio of stiffness (affected to unaffected control arm) was 0.91 (interquartile range [IQR] 0.78-1.03) while frequency was 0.94 (IQR 0.89-1.0). Skin relaxation time and creep averages were increased in the affected arms. The relaxation time median ratio (affected to unaffected control arm) was 1.07 (IQR 1.02-1.14) and the median ratio of creep was 1.06 (IQR 1.03-1.16). Conclusions: This study suggests the Myoton can detect differences in skin biomechanical parameters of the affected and unaffected arms in patients with BCRL. Larger studies are needed to draw strong conclusions.

Expanding Personalized, Data-Driven Dermatology: Leveraging Digital Health Technology and Machine Learning to Improve Patient Outcomes.

The current revolution of digital health technology and machine learning offers enormous potential to improve patient care. Nevertheless, it is essential to recognize that dermatology requires an approach different from those of other specialties. For many dermatological conditions, there is a lack of standardized methodology for quantitatively tracking disease progression and treatment response (clinimetrics). Furthermore, dermatological diseases impact patients in complex ways, some of which can be measured only through patient reports (psychometrics). New tools using digital health technology (e.g., smartphone applications, wearable devices) can aid in capturing both clinimetric and psychometric variables over time. With these data, machine learning can inform efforts to improve health care by, for example, the identification of high-risk patient groups, optimization of treatment strategies, and prediction of disease outcomes. We use the term personalized, data-driven dermatology to refer to the use of comprehensive data to inform individual patient care and improve patient outcomes. In this paper, we provide a framework that includes data from multiple sources, leverages digital health technology, and uses machine learning. Although this framework is applicable broadly to dermatological conditions, we use the example of a serious inflammatory skin condition, chronic cutaneous graft-versus-host disease, to illustrate personalized, data-driven dermatology.

Association of Leukocyte Adhesion and Rolling in Skin With Patient Outcomes After Hematopoietic Cell Transplantation Using Noninvasive Reflectance Confocal Videomicroscopy.

Importance: Hematopoietic cell transplantation (HCT) is a potential cure for hematologic cancer but is associated with a risk of relapse and death. Dynamic biomarkers to predict relapse and inform treatment decisions after HCT are a major unmet clinical need. Objective: To identify a quantitative characteristic of leukocyte-endothelial interactions after HCT and test its associations with patient outcomes. Design, Setting, and Participants: In this prospective single-center cohort study from June 2017 to January 2020, patients of any age, sex, race, and ethnicity who had HCT for hematologic cancer were referred by health care professionals as either suspected of having symptoms or not having symptoms of acute graft-vs-host disease between 25 and 161 days after HCT. Patients underwent noninvasive skin videomicroscopy. Videos of dermal microvascular flow were recorded with a reflectance confocal microscope. Two blinded observers (J.R.P. and Z.Z.) counted leukocytes adherent to and rolling along the vessel wall per hour (A&R). Of 57 enrolled patients, 1 relapsed before imaging and was excluded, resulting in 56 patients included in analyses. Main Outcomes and Measures: Relapse of cancer, relapse-free survival, and overall survival. Results: Among the 56 patients (median age, 59 years; 38 [68%] male) who underwent imaging a median of 40 days after HCT, 21 had high A&R and 35 had low A&R. After correcting for the revised Disease Risk Index, patients with high A&R had higher rates of relapse (hazard ratio [HR], 4.24; 95% CI, 1.32-13.58; P = .02), reduced relapse-free survival (HR, 3.29; 95% CI, 1.26-8.55; P = .02), and reduced overall survival (HR, 3.06, 95% CI, 1.02-9.19; P = .05). These associations were preserved after correcting for possible confounders, steroid treatment, and acute graft-vs-host disease status. In the prognostic adequacy calculation by using Cox models, the new imaging biomarker (A&R) accounted for 82% to 95% of the prognostic information to predict each outcome. By contrast, the best existing clinical predictor routinely available, the revised Disease Risk Index, accounted for 10% to 28% of the prognostic information in the same model. Conclusions and Relevance: In this cohort study, leukocyte-endothelial interactions, visualized directly in skin after HCT, were associated with the patient outcomes of relapse, relapse-free survival, and overall survival. Assessing this dynamic marker could help patients at high risk for relapse who may benefit from interventions, such as early withdrawal of immunosuppression.

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety. OBJECTIVE: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI. EVIDENCE REVIEW: In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for "artificial intelligence" and "reporting guidelines," as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus. FINDINGS: A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology. CONCLUSIONS AND RELEVANCE: Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.

Novel therapies and innovation for systemic sclerosis skin ulceration.

Skin ulceration is an important cause of morbidity in systemic sclerosis and can occur at anytime during disease progression. Incident disease cohorts are important for understanding whether skin ulceration represents active vasculopathy versus resultant damage. Biomarkers for skin ulcer pathogenesis, both serum and imaging, are under investigation to elucidate the functional consequences of the structural abnormalities. Novel therapeutics for the treatment of vasculopathy benefit from reliable biomarkers able to predict the disease evolution remains an important unmet need. Nonetheless, a diagnostic approach that captures early skin ulceration and treatments that restore vascular and immune homeostasis is critical for effective systemic sclerosis (SSc) vasculopathy management.

Optimal biomechanical parameters for measuring sclerotic chronic graft-versus-host disease.

Skin biomechanical parameters (dynamic stiffness, frequency, relaxation time, creep, and decrement) measured using a myotonometer (MyotonPRO) could inform management of sclerotic disease. To determine which biomechanical parameter(s) can accurately differentiate sclerotic chronic graft-versus-host disease (cGVHD) patients from post-hematopoietic cell transplant (post-HCT) controls, 15 sclerotic cGVHD patients and 11 post-HCT controls were measured with the myotonometer on 18 anatomic sites. Logistic regression and two machine learning algorithms, LASSO regression and random forest, were developed to classify subjects. In univariable analysis, frequency had the highest overfit-corrected area under the receiver operating characteristic curve (AUC 0.91). Backward stepwise selection and random forest machine learning identified frequency and relaxation time as the optimal parameters for differentiating sclerotic cGVHD patients from post-HCT controls. LASSO regression selected the combination of frequency and relaxation time (overfit-corrected AUC 0.87). Discriminatory ability was maintained when only the sites accessible while the patient is supine (12 sites) were used. We report the distribution of values for these highly discriminative biomechanical parameters, which could inform assessment of disease severity in future quantitative biomechanical studies of sclerotic cGVHD.