Clinical performance of the Nevisense system in cutaneous melanoma detection: an international, multicentre, prospective and blinded clinical trial on efficacy and safety

Skin Cancer Diagnosis by Lesion, Physician, and Examination Type: A Systematic Review and Meta-Analysis

Importance

Skin cancer is the most common cancer in the US; accurate detection can minimize morbidity and mortality.

Objective

To assess the accuracy of skin cancer diagnosis by lesion type, physician specialty and experience, and physical examination method.

Data Sources

PubMed, Embase, and Web of Science.

Study Selection

Cross-sectional and case-control studies, randomized clinical trials, and nonrandomized controlled trials that used dermatologists or primary care physicians (PCPs) to examine keratinocytic and/or melanocytic skin lesions were included.

Data Extraction and Synthesis

Search terms, study objectives, and protocol methods were defined before study initiation. Data extraction was performed by a reviewer, with verification by a second reviewer. A mixed-effects model was used in the data analysis. Data analyses were performed from May 2022 to December 2023.

Main Outcomes and Measures

Meta-analysis of diagnostic accuracy comprised sensitivity and specificity by physician type (primary care physician or dermatologist; experienced or inexperienced) and examination method (in-person clinical examination and/or clinical images vs dermoscopy and/or dermoscopic images).

Results

In all, 100 studies were included in the analysis. With experienced dermatologists using clinical examination and clinical images, the sensitivity and specificity for diagnosing keratinocytic carcinomas were 79.0% and 89.1%, respectively; using dermoscopy and dermoscopic images, sensitivity and specificity were 83.7% and 87.4%, and for PCPs, 81.4% and 80.1%. Experienced dermatologists had 2.5-fold higher odds of accurate diagnosis of keratinocytic carcinomas using in-person dermoscopy and dermoscopic images compared with in-person clinical examination and images. When examining for melanoma using clinical examination and images, sensitivity and specificity were 76.9% and 89.1% for experienced dermatologists, 78.3% and 66.2% for inexperienced dermatologists, and 37.5% and 84.6% for PCPs, respectively; whereas when using dermoscopy and dermoscopic images, sensitivity and specificity were 85.7% and 81.3%, 78.0% and 69.5%, and 49.5% and 91.3%, respectively. Experienced dermatologists had 5.7-fold higher odds of accurate diagnosis of melanoma using dermoscopy compared with clinical examination. Compared with PCPs, experienced dermatologists had 13.3-fold higher odds of accurate diagnosis of melanoma using dermoscopic images.

Conclusions and Relevance

The findings of this systematic review and meta-analysis indicate that there are significant differences in diagnostic accuracy for skin cancer when comparing physician specialty and experience, and examination methods. These summary metrics of clinician diagnostic accuracy could be useful benchmarks for clinical trials, practitioner training, and the performance of emerging technologies.

Noninvasive Technologies for the Diagnosis of Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

Early cutaneous squamous cell carcinoma (cSCC) diagnosis is essential to initiate adequate targeted treatment. Noninvasive diagnostic technologies could overcome the need of multiple biopsies and reduce tumor recurrence. To assess performance of noninvasive technologies for cSCC diagnostics, 947 relevant records were identified through a systematic literature search. Among the 15 selected studies within this systematic review, 7 were included in the meta-analysis, comprising of 1144 patients, 224 cSCC lesions, and 1729 clinical diagnoses. Overall, the sensitivity values are 92% (95% confidence interval [CI] = 86.6-96.4%) for high-frequency ultrasound, 75% (95% CI = 65.7-86.2%) for optical coherence tomography, and 63% (95% CI = 51.3-69.1%) for reflectance confocal microscopy. The overall specificity values are 88% (95% CI = 82.7-92.5%), 95% (95% CI = 92.7-97.3%), and 96% (95% CI = 94.8-97.4%), respectively. Physician's expertise is key for high diagnostic performance of investigated devices. This can be justified by the provision of additional tissue information, which requires physician interpretation, despite insufficient standardized diagnostic criteria. Furthermore, few deep learning studies were identified. Thus, integration of deep learning into the investigated devices is a potential investigating field in cSCC diagnosis.

Clinical Utility of a Digital Dermoscopy Image-Based Artificial Intelligence Device in the Diagnosis and Management of Skin Cancer by Dermatologists

BACKGROUND

Patients with skin lesions suspicious for skin cancer or atypical melanocytic nevi of uncertain malignant potential often present to dermatologists, who may have variable dermoscopy triage clinical experience.

OBJECTIVE

To evaluate the clinical utility of a digital dermoscopy image-based artificial intelligence algorithm (DDI-AI device) on the diagnosis and management of skin cancers by dermatologists.

METHODS

Thirty-six United States board-certified dermatologists evaluated 50 clinical images and 50 digital dermoscopy images of the same skin lesions (25 malignant and 25 benign), first without and then with knowledge of the DDI-AI device output. Participants indicated whether they thought the lesion was likely benign (unremarkable) or malignant (suspicious).

RESULTS

The management sensitivity of dermatologists using the DDI-AI device was 91.1%, compared to 84.3% with DDI, and 70.0% with clinical images. The management specificity was 71.0%, compared to 68.4% and 64.9%, respectively. The diagnostic sensitivity of dermatologists using the DDI-AI device was 86.1%, compared to 78.8% with DDI, and 63.4% with clinical images. Diagnostic specificity using the DDI-AI device increased to 80.7%, compared to 75.9% and 73.6%, respectively.

CONCLUSION

The use of the DDI-AI device may quickly, safely, and effectively improve dermoscopy performance, skin cancer diagnosis, and management when used by dermatologists, independent of training and experience.

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis

BACKGROUND

Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics.

OBJECTIVES

To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL.

METHODS

Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824.

RESULTS

2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists.

CONCLUSIONS

Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.

Electrical Impedance Dermography: Background, Current State, and Emerging Clinical Opportunities

Electrical impedance dermography (EID), based on electrical impedance spectroscopy, is a specific technique for the evaluation of skin disorders that relies upon the application and measurement of painless, alternating electrical current. EID assesses pathological changes to the normal composition and architecture of the skin that influence the flow of electrical current, including changes associated with inflammation, keratinocyte and melanocyte carcinogenesis, and scarring. Assessing the electrical properties of the skin across a range of frequencies and in multiple directions of current flow can provide diagnostic information to aid in the identification of pathologic skin conditions. EID holds the promise of serving as a diagnostic biomarker and potential to be used in skin cancer detection and staging. EID may also be useful as a biomarker in monitoring effectiveness of treatment in individual patients and in therapeutic research. This review highlights ongoing efforts to improve mechanistic understanding of skin electrical changes, study of EID in a variety of clinical contexts, and further refine the technology to find greater clinical use in dermatology and dermatologic research.

Line-Field Confocal Optical Coherence Tomography for the Diagnosis of Skin Tumors: A Systematic Review and Meta-Analysis

A line-field confocal optical coherence tomography (LC-OCT) combines confocal microscopy and optical coherence tomography into a single, rapid, easy-to-use device. This meta-analysis was performed to determine the reliability of LC-OCT for diagnosing malignant skin tumors. PubMed, EMBASE, Web of Science databases, and the Cochrane Library were searched for research studies in the English language from inception till December 2023. To assess quality and the risk of bias, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. The sensitivity and specificity of each study were calculated. The bivariate summary sensitivity and specificity were calculated using the linear mixed model. Five studies with 904 reported per lesion analyses in our study; the specificity and sensitivity ranged from 67% to 97% and 72% to 92%, respectively. The pooled specificity and sensitivity were 91% (95% CI: 76-97%) and 86.9% (95% CI: 81.8-90.8%), respectively. The summary sensitivity and specificity from the bivariate approach are 86.9% (95% CI: 81.8-90.8%) and 91.1% (95% CI: 76.7-97.0%), respectively. The area under the curve is 0.914. LC-OCT shows great sensitivity and specificity in diagnosing malignant skin tumors. However, due to the limited number of studies included in our meta-analysis, it is premature to elucidate the true potential of LC-OCT.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation

Background

Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy.

Patients and methods

Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN.

Results

The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test).

Conclusions

Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Multicenter prospective blinded melanoma detection study with a handheld elastic scattering spectroscopy device

Background

The elastic scattering spectroscopy (ESS) device (DermaSensor Inc., Miami, FL) is a noninvasive, painless, adjunctive tool for skin cancer detection.

Objectives

To investigate the performance of the ESS device in the detection of melanoma.

Methods

A prospective, investigator-blinded, multicenter study was conducted at 8 United States (US) and 2 Australian sites. All eligible skin lesions were clinically concerning for melanoma, examined with the ESS device, subsequently biopsied according to dermatologists' standard of care, and evaluated with histopathology. A total of 311 participants with 440 lesions were enrolled, including 44 melanomas (63.6% in situ and 36.4% invasive) and 44 severely dysplastic nevi.

Results

The observed sensitivity of the ESS device for melanoma detection was 95.5% (95% CI, 84.5% to 98.8%, 42 of 44 melanomas), and the observed specificity was 32.5% (95% CI, 27.2% to 38.3%). The positive and negative predictive values were 16.0% and 98.1%, respectively.

Limitations

The device was tested in a high-risk population with lesions selected for biopsy based on clinical and dermoscopic assessments of board-certified dermatologists. Most enrolled lesions were pigmented.

Conclusion

The ESS device's high sensitivity and NPV for the detection of melanoma suggest the device may be a useful adjunctive, point-of-care tool for melanoma detection.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography

Objective.Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising.Approach.In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs).Main results.We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape.Significance.In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Skin Barrier Function Assessment: Electrical Impedance Spectroscopy Is Less Influenced by Daily Routine Activities Than Transepidermal Water Loss

BACKGROUND

Skin barrier function assessment is commonly done by measuring transepidermal water loss (TEWL). An important limitation of this method is the influence of intrinsic and extrinsic factors. Electrical impedance spectroscopy (EIS) is a lesser-established method for skin barrier function assessment. Some influential factors have been described, but no guidelines exist regarding the standardization of these measurements.

OBJECTIVE

To evaluate the effect size of daily routine activities on TEWL and EIS, as well as their correlation with age and anatomical differences.

METHODS

Healthy participants (n=31) were stratified into three age groups (18-29, 30-49, and ≥50 years). In a climate-controlled room, EIS and TEWL measurements were performed on the left and right volar forearm and abdomen.

RESULTS

Body cream application decreased TEWL and EIS values after 15 and 90 minutes. Skin washing decreased TEWL for 15 minutes and EIS values for at least 90 minutes. TEWL was increased 5 minutes after moderate to intense exercise. Coffee intake increased TEWL on the abdomen after 60 minutes. TEWL and EIS values did not correlate with participants' age and no anatomical differences were observed. No correlation was observed between TEWL and EIS.

CONCLUSION

Body cream application and skin washing should be avoided at least 90 minutes prior to measurements of TEWL and EIS. Exercise and coffee intake should also be avoided prior to TEWL measurements. EIS may be a promising tool for skin barrier function assessment as it is less affected by daily routine activities than TEWL.

Mind the Gap: A Questionnaire on the Distance between Diagnostic Advances and Clinical Practice in Skin Cancer Treatment

Background and Objectives: Significant progress has been made in skin cancer diagnosis, with a surge in available technologies in recent years. Despite this, the practical application and integration of these technologies in dermatology and plastic surgery remain uneven. Materials and Methods: A comprehensive 20-question survey was designed and distributed using online survey administration software (Google Forms, 2018, Google, Mountain View, CA, USA) from June 2023 to September 2023. The survey aimed to assess the knowledge and utilization of dermatologic diagnostic advancements among plastic surgeons in various European countries. Results: Data were obtained from 29 plastic surgeons across nine European countries, revealing a notable gap between diagnostic technologies and their routine use in surgical practice. The gap for some technologies was both cognitive and applicative; for electrical impedance spectroscopy (EIS) and multispectral imaging, only 6.9% of the sample knew of the technologies and no surgeons in the sample used them. In the case of other technologies, such as high-frequency ultrasound (HFUS), 72.4% of the sample knew about them but only 34.5% used them, highlighting a more significant application problem. Conclusions: Spotlighting this discrepancy provides a valuable foundation for initiating collaborative efforts between units and facilitating knowledge exchange among diverse specialists. This, in turn, contributes to advancing clinical practice by integrating the innovative opportunities presented by ongoing research.

Household laundry detergents disrupt barrier integrity and induce inflammation in mouse and human skin

BACKGROUND

Epithelial barrier impairment is associated with many skin and mucosal inflammatory disorders. Laundry detergents have been demonstrated to affect epithelial barrier function in vitro using air-liquid interface cultures of human epithelial cells.

METHODS

Back skin of C57BL/6 mice was treated with two household laundry detergents at several dilutions. Barrier function was assessed by electric impedance spectroscopy (EIS) and transepidermal water loss (TEWL) measurements after the 4 h of treatments with detergents. RNA sequencing (RNA-seq) and targeted multiplex proteomics analyses in skin biopsy samples were performed. The 6-h treatment effect of laundry detergent and sodium dodecyl sulfate (SDS) was investigated on ex vivo human skin.

RESULTS

Detergent-treated skin showed a significant EIS reduction and TEWL increase compared to untreated skin, with a relatively higher sensitivity and dose-response in EIS. The RNA-seq showed the reduction of the expression of several genes essential for skin barrier integrity, such as tight junctions and adherens junction proteins. In contrast, keratinization, lipid metabolic processes, and epidermal cell differentiation were upregulated. Proteomics analysis showed that the detergents treatment generally downregulated cell adhesion-related proteins, such as epithelial cell adhesion molecule and contactin-1, and upregulated proinflammatory proteins, such as interleukin 6 and interleukin 1 beta. Both detergent and SDS led to a significant decrease in EIS values in the ex vivo human skin model.

CONCLUSION

The present study demonstrated that laundry detergents and its main component, SDS impaired the epidermal barrier in vivo and ex vivo human skin. Daily detergent exposure may cause skin barrier disruption and may contribute to the development of atopic diseases.

Cutaneous Oncology: Strategies for Melanoma Prevention, Diagnosis, and Therapy

Skin cancer comprises one-third of all diagnosed cancer cases and remains a major health concern. Genetic and environmental parameters serve as the two main risk factors associated with the development of skin cancer, with ultraviolet radiation being the most common environmental risk factor. Studies have also found fair complexion, arsenic toxicity, indoor tanning, and family history among the prevailing causes of skin cancer. Prevention and early diagnosis play a crucial role in reducing the frequency and ensuring effective management of skin cancer. Recent studies have focused on exploring minimally invasive or non-invasive diagnostic technologies along with artificial intelligence to facilitate rapid and accurate diagnosis. The treatment of skin cancer ranges from traditional surgical excision to various advanced methods such as phototherapy, radiotherapy, immunotherapy, targeted therapy, and combination therapy. Recent studies have focused on immunotherapy, with the introduction of new checkpoint inhibitors and personalized immunotherapy enhancing treatment efficacy. Advancements in multi-omics, nanotechnology, and artificial intelligence have further deepened the understanding of the mechanisms underlying tumoral growth and their interaction with therapeutic effects, which has paved the way for precision oncology. This review aims to highlight the recent advancements in the understanding and management of skin cancer, and provide an overview of existing and emerging diagnostic, prognostic, and therapeutic modalities, while highlighting areas that require further research to bridge the existing knowledge gaps.

Electrical impedance spectroscopy detects skin barrier dysfunction in childhood atopic dermatitis

BACKGROUND

Skin barrier dysfunction is associated with the development of atopic dermatitis (AD), however methods to assess skin barrier function are limited. We investigated the use of electrical impedance spectroscopy (EIS) to detect skin barrier dysfunction in children with AD of the CARE (Childhood AlleRgy, nutrition, and Environment) cohort.

METHODS

EIS measurements taken at multiple time points from 4 months to 3-year-old children, who developed AD (n = 66) and those who did not (n = 49) were investigated. Using only the EIS measurement and the AD status, we developed a machine learning algorithm that produces a score (EIS/AD score) which reflects the probability that a given measurement is from a child with active AD. We investigated the diagnostic ability of this score and its association with clinical characteristics and age.

RESULTS

Based on the EIS/AD score, the EIS algorithm was able to clearly discriminate between healthy skin and clinically unaffected skin of children with active AD (area under the curve 0.92, 95% CI 0.85-0.99). It was also able to detect a difference between healthy skin and AD skin when the child did not have active AD. There was no clear association between the EIS/AD score and the severity of AD or sensitisation to the tested allergens. The performance of the algorithm was not affected by age.

CONCLUSIONS

This study shows that EIS can detect skin barrier dysfunction and differentiate skin of children with AD from healthy skin and suggests that EIS may have the ability to predict future AD development.

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial

BACKGROUND

Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions.

METHODS

In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995).

FINDINGS

The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B.

INTERPRETATION

The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice.

FUNDING

MetaOptima Technology.

Fundamentals of Skin Bioimpedances

The bioimpedances of tissues beyond the stratum corneum, which is the outermost layer of skin, contain crucial clinical information. Nevertheless, bioimpedance measurements of both the viable skin and the adipose tissue are not widely used, mainly because of the complex multilayered skin structure and the electrically insulating nature of the stratum corneum. Here, a theoretical framework is established for analyzing the impedances of multilayered tissues and, in particular, of skin. Then, strategies are determined for the system-level design of electrodes and electronics, which minimize 4-wire (or tetrapolar) measurement errors even in the presence of a top insulating tissue, thus enabling non-invasive characterizations of tissues beyond the stratum corneum. As an example, non-invasive measurements of bioimpedances of living tissues are demonstrated in the presence of parasitic impedances which are much (e.g., up to 350 times) higher than the bioimpedances of the living tissues beyond the stratum corneum, independently on extreme variations of the barrier (tape stripping) or of the skin-electrode contact impedances (sweat). The results can advance the development of bioimpedance systems for the characterization of viable skin and adipose tissues in several applications, including transdermal drug delivery and the assessment of skin cancer, obesity, dehydration, type 2 diabetes mellitus, cardiovascular risk, and multipotent adult stem cells.

Electrical Impedance Dermography Differentiates Squamous Cell Carcinoma In Situ from Inflamed Seborrheic Keratoses

There are no currently available low-cost, noninvasive methods for discerning the depth of squamous cell carcinoma (SCC) invasion or distinguishing SCC from its benign mimics, such as inflamed seborrheic keratosis (SK). We studied 35 subjects with subsequently confirmed SCC or SK. Subjects underwent electrical impedance dermography measurements at six frequencies to assess the electrical properties of the lesion. Averaged greatest intrasession reproducibility values were 0.630 for invasive SCC at 128 kHz, 0.444 for SCC in situ at 16 kHz, and 0.460 for SK at 128 kHz. Electrical impedance dermography modeling revealed significant differences between SCC and inflamed SK in normal skin (P < 0.001) and also between invasive SCC and SCC in situ (P < 0.001), invasive SCC and inflamed SK (P < 0.001), and SCC in situ and inflamed SK (P < 0.001). A diagnostic algorithm classified SCC in situ from inflamed SK with an accuracy of 0.958, a sensitivity of 94.6%, and a specificity of 96.9%; it also classified SCC in situ from normal skin with an accuracy of 0.796, a sensitivity of 90.2%, and a specificity of 51.2%. This study provides preliminary data and a methodology that can be used in future studies to further advance the value of electrical impedance dermography and inform biopsy decision making in patients with lesions suspicious of SCC.

Detection of cutaneous malignant melanoma by tape stripping of pigmented skin lesions - A systematic review

BACKGROUND

Cutaneous malignant melanoma (MM) is potentially aggressive, and numerous clinically suspicious pigmented skin lesions are excised, causing unnecessary mutilation for patients at high healthcare costs, but without histopathological evidence of MM. The high number of excisions may be lowered by using more accurate diagnostics. Tape stripping (TS) of clinically suspicious lesions is a non-invasive diagnostic test of MM that can potentially lower the number needed to biopsy/excise.

MATERIALS AND METHODS

The aim is to determine the diagnostic accuracy of TS in detecting MM in clinically suspicious pigmented skin lesions. This systematic review following PRISMA guidelines searched PubMed, Web of Science, and Embase (September 2022) using melanoma combined with tape stripping, adhesive patch(es), pigmented lesion assay, or epidermal genetic information retrieval.

RESULTS

Ten studies were included. Sensitivity ranged from 68.8% (95% confidence interval [CI] 51.5, 82.1) to 100% (95% CI 91.0, 100). Specificity ranged from 69.1% (95% CI 63.8, 74.0) to 100% (95% CI 78.5, 100). A pooled analysis of five studies testing the RNA markers LINC00518 and PRAME found a sensitivity of 86.9% (95% CI 81.7, 90.8) and a specificity of 82.4% (95% CI 80.8, 83.9).

CONCLUSION

Overall quality of studies was low, and the reliability of sensitivity and specificity is questionable. However, TS may supplement well-established diagnostic methods as pooled analysis of five studies indicates a moderate sensitivity. Future studies are needed to obtain more reliable data as independent studies with no conflict of interest.

3D printable phantom for mimicking electrical properties of dermal tissue

Skin cancer is one of the most ubiquitous forms of cancer that is often overdiagnosed or missed by traditional diagnostic techniques. Bioimpedance spectroscopy (BIS) is a technology that aims to take advantage of the variations in electrical properties of tissue to identify ectopic formations. It is difficult to develop BIS technologies without obtaining tumor tissue samples. One solution is to use a "tissue phantom," a synthetic structure that mimics the properties of tissue. Current solutions using natural biomaterials, such as gelatin, have not been able to create complex tissue geometries that are vital to honing BIS diagnostics. However, semi-synthetic polymers, such has gelatin methacrylate (GelMA), offer the benefits of possessing similar electrical properties to their respective source biomaterial while being 3D printable. In this work, we first measured the impedance of porcine dermal tissue. We then applied these impedance measurements to create an electrically accurate tissue phantom using a photocurable hydrogel, GelMA, and varying concentrations of NaCl, aluminum powder, and titanium dioxide powder.

New Technologies in Diagnosis and Prognosis of Melanocytic Lesions

Analysis of morphological characteristics for the diagnosis of melanoma remains a challenge. New technologies for the diagnosis and prognosis of melanocytic lesions have been emerging to ensure earlier and more accurate detection. In this article, we review multiple technologies that improve melanoma diagnostic accuracy such as electrical impedance spectroscopy, pigmented lesion assay, reflectance confocal microscopy, and gene expression profile tests.

Electrical impedance spectroscopy significantly enhances correct biopsy choice for pigmented skin lesions beyond clinical evaluation and dermoscopy

The purpose of this study was to investigate whether EIS technology can further improve correct biopsy choices beyond clinical and dermoscopic evaluation for melanoma (MM), severe dysplastic nevi (SDN) and benign PSLs. Images of 49 MMs, SDNs and benign PSLs were randomly selected from a prior study and were provided in a reader-type survey study to dermatologists to evaluate for biopsy. A total of 33,957 biopsy decisions were analyzed. Respondents significantly improved on the correct biopsy choice with the addition of dermoscopy versus clinical image alone for melanoma and severely dysplastic nevi. Respondents also showed a statistically significant improvement in correct biopsy choice beyond their dermoscopic evaluation when integrating the EIS score versus dermoscopy with clinical images for MM, SDN and benign lesions. Respondents also made fewer incorrect biopsy choices with the addition of the EIS score versus dermoscopy and clinical image for MM and benign lesions. Sub-analyses of biopsy choices were also conducted based on experience and practice type. The findings from this study demonstrate that the integration of EIS technology into PSL biopsy decisions has the potential to significantly improve the accuracy of lesion selection for biopsy beyond clinical and dermoscopic evaluation alone.

Clinical Utility of an AI-powered, Handheld Elastic Scattering Spectroscopy Device on the Diagnosis and Management of Skin Cancer by Primary Care Physicians

BACKGROUND

Patients with lesions suspicious for skin cancer often present to primary care physicians (PCPs), who may have limited training in skin cancer diagnosis.

OBJECTIVE

To measure the impact of an adjunctive handheld device for PCPs that employs elastic scattering spectroscopy (ESS) on the diagnosis and management of skin cancer.

METHODS

Fifty-seven PCPs evaluated 50 clinical images of skin lesions (25 malignant and 25 benign), first without and then with knowledge of the handheld ESS device output, and in each case indicated if a lesion was likely to be benign or malignant.

RESULTS

The diagnostic sensitivity of the PCPs with and without the use of the ESS device was 88% (95% CI, 84%-92%) and 67% (95% CI, 62%-72%), respectively (P < .0001). In contrast, no significant difference was observed in the diagnostic specificity. The management sensitivity of the physicians with and without the use of the ESS device was 94% (95% CI, 91%-96%) and 81% (95% CI, 77%-85%), respectively (P = .0009). Similarly, no significant difference was observed in the management specificity.

CONCLUSION

The use of the ESS device may have the potential to help improve skin cancer diagnosis and confidence in management decision-making in a primary care setting.

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review

Bioelectrical impedance analysis and bioelectrical impedance spectroscopy (BIA/BIS) of tissues reveal important information on molecular composition and physical structure that is useful in diagnostics and prognostics. The heterogeneity in structural elements of cells, tissues, organs, and the whole human body, the variability in molecular composition arising from the dynamics of biochemical reactions, and the contributions of inherently electroresponsive components, such as ions, proteins, and polarized membranes, have rendered bioimpedance challenging to interpret but also a powerful evaluation and monitoring technique in biomedicine. BIA/BIS has thus become the basis for a wide range of diagnostic and monitoring systems such as plethysmography and tomography. The use of BIA/BIS arises from (i) being a noninvasive and safe measurement modality, (ii) its ease of miniaturization, and (iii) multiple technological formats for its biomedical implementation. Considering the dependency of the absolute and relative values of impedance on frequency, and the uniqueness of the origins of the α-, β-, δ-, and γ-dispersions, this targeted review discusses biological events and underlying principles that are employed to analyze the impedance data based on the frequency range. The emergence of BIA/BIS in wearable devices and its relevance to the Internet of Medical Things (IoMT) are introduced and discussed.

Diagnostics Using Non-Invasive Technologies in Dermatological Oncology

The growing incidence of skin cancer, with its associated mortality and morbidity, has in recent years led to the developing of new non-invasive technologies, which allow an earlier and more accurate diagnosis. Some of these, such as digital photography, 2D and 3D total-body photography and dermoscopy are now widely used and others, such as reflectance confocal microscopy and optical coherence tomography, are limited to a few academic and referral skin cancer centers because of their cost or the long training period required. Health care professionals involved in the treatment of patients with skin cancer need to know the implications and benefits of new non-invasive technologies for dermatological oncology. In this article we review the characteristics and usability of the main diagnostic imaging methods available today.

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasive melanoma, 88 melanoma in situ, 115 dysplastic naevi, and 48 non-dysplastic naevi. The study included a training set of 358,800 pixels and a validation set of 7,313 pixels, which was then tested with a training set of 24,375 pixels. The majority vote classification achieved high overall sensitivity of 95% and a specificity of 92% (95% confidence interval (95% CI) 0.024-0.029) in differentiating malignant from benign lesions. In the pixel-wise classification, the overall sensitivity and specificity were both 82% (95% CI 0.005-0.005). When divided into 4 subgroups, the diagnostic accuracy was lower. Hyperspectral imaging provides high sensitivity and specificity in distinguishing between naevi and melanoma. This novel method still needs further validation.

Reflectance Confocal Microscopy and Electrical Impedance Spectroscopy in the Early Detection of Melanoma in Changing Lesions during Long-term Follow-up of Very High-risk Patients

Electrical impedance spectroscopy has clinical relevance in diagnosing malignancy in melanocytic lesions. Sixty-eight lesions with changes during digital follow-up of patients at very high risk of developing melanoma were prospectively included in this study from February to December 2016. Electrical impedance spectroscopy and reflectance confocal microscopy were performed to evaluate their performance in this subset of difficult lesions. Forty-six lesions were considered suspicious on reflectance confocal microscopy and were excised, of these, 19 were diagnosed as melanoma. Fifteen melanomas were detected by electrical impedance spectroscopy, while 4 received a score lower than 4, which suggested no malignancy. The addition of reflectance confocal microscopy improves accuracy while maintaining the same sensitivity. In the case of electrical impedance spectroscopy scores <4, lesions exhibiting changes in follow-up may need short-term monitoring or excision if dermoscopy shows criteria for melanoma. Results of electrical impedance spectroscopy in this subset of very early lesions should be carefully considered due to the risk of false negatives.

European consensus-based interdisciplinary guideline for melanoma. Part 1: Diagnostics: Update 2022

Cutaneous melanoma (CM) is potentially the most dangerous form of skin tumor and causes 90% of skin cancer mortality. A unique collaboration of multi-disciplinary experts from the European Dermatology Forum (EDF), the European Association of Dermato-Oncology (EADO) and the European Organization for Research and Treatment of Cancer (EORTC) was formed to make recommendations on CM diagnosis and treatment, based on systematic literature reviews and the experts' experience. The diagnosis of melanoma can be made clinically and shall always be confirmed with dermatoscopy. If a melanoma is suspected, a histopathological examination is always required. Sequential digital dermatoscopy and full body photography can be used in high-risk patients to improve the detection of early melanoma. Where available, confocal reflectance microscopy can also improve clinical diagnosis in special cases. Melanoma shall be classified according to the 8th version of the American Joint Committee on Cancer classification. Thin melanomas up to 0.8 mm tumor thickness do not require further imaging diagnostics. From stage IB onwards, examinations with lymph node sonography are recommended, but no further imaging examinations. From stage IIC onwards whole-body examinations with computed tomography (CT) or positron emission tomography CT (PET-CT) in combination with brain magnetic resonance imaging are recommended. From stage III and higher, mutation testing is recommended, particularly for BRAF V600 mutation. It is important to provide a structured follow-up to detect relapses and secondary primary melanomas as early as possible. There is no evidence to define the frequency and extent of examinations. A stage-based follow-up scheme is proposed which, according to the experience of the guideline group, covers the optimal requirements, but further studies may be considered. This guideline is valid until the end of 2024.

Adjunctive Diagnostic Methods for Skin Cancer Detection: A Review of Electrical Impedance-Based Techniques

Skin cancer is among the fastest-growing cancers with an excellent prognosis, if detected early. However, the current method of diagnosis by visual inspection has several disadvantages such as overlapping tumor characteristics, subjectivity, low sensitivity, and specificity. Hence, several adjunctive diagnostic techniques such as thermal imaging, optical imaging, ultrasonography, tape stripping methods, and electrical impedance imaging are employed along with visual inspection to improve the diagnosis. Electrical impedance-based skin cancer detection depends upon the variations in electrical impedance characteristics of the transformed cells. The information provided by this technique is fundamentally different from other adjunctive techniques and thus has good prospects. Depending on the stage, type, and location of skin cancer, various impedance-based devices have been developed. These devices when used as an adjunct to visual methods have increased the sensitivity and specificity of skin cancer detection up to 100% and 87%, respectively, thus demonstrating their potential to minimize unnecessary biopsies. In this review, the authors track the advancements and progress made in this technique for the detection of skin cancer, focusing mainly on the advantages and limitations in the clinical setting. © 2022 Bioelectromagnetics Society.

Reverse Iontophoretic Extraction of Skin Cancer-Related Biomarkers

Non-invasive methods for early diagnosis of skin cancer are highly valued. One possible approach is to monitor relevant biomarkers such as tryptophan (Trp) and kynurenine (Kyn), on the skin surface. The primary aim of this in vitro investigation was, therefore, to examine whether reverse iontophoresis (RI) can enhance the extraction of Trp and Kyn, and to demonstrate how the Trp/Kyn ratio acquired from the skin surface reflects that in the epidermal tissue. The study also explored whether the pH of the receiver medium impacted on extraction efficiency, and assessed the suitability of a bicontinuous cubic liquid crystal as an alternative to a simple buffer solution for this purpose. RI substantially enhanced the extraction of Trp and Kyn, in particular towards the cathode. The Trp/Kyn ratio obtained on the surface matched that in the viable skin. Increasing the receiver solution pH from 4 to 9 improved extraction of both analytes, but did not significantly change the Trp/Kyn ratio. RI extraction of Trp and Kyn into the cubic liquid crystal was comparable to that achieved with simple aqueous receiver solutions. We conclude that RI offers a potential for non-invasive sampling of low-molecular weight biomarkers and further investigations in vivo are therefore warranted.

Intraoperative pathologically-calibrated diagnosis of lymph nodes involved by breast cancer cells based on electrical impedance spectroscopy; a prospective diagnostic human model study

BACKGROUND

Nodal status evaluation is a crucial step in determining prognostic factors and managing treatment strategies for breast cancer patients. Preoperative (CNB), intraoperative (SLNB), and even postoperative techniques (Formalin-Fixed Paraffin-Embedded sectioning, FFPE) have definite limitations of precision or sometimes are time-consuming for the result declaration. The primary purpose of this prospective study is to provide a precise complementary system for distinguishing lymph nodes (LNs) involved by cancerous cells in breast cancer patients intraoperatively.

METHODS

The proposed system, Electrical Lymph Scoring(ELS), is designed based on the dielectric properties of the under-test LNs. The system has a needle-shaped 2-electrode probe entered into SLNs or ALNs dissected from patients through standard surgical guidelines. Impedance magnitude in f = 1 kH (Z_1kHz) and Impedance Phase Slope in frequency ranges of 100 kHz-500 kHz (IPS) were then extracted from the impedance spectroscopy data in a cohort study of 77 breast cancer patients(totally 282 dissected LNs) who had been undergone surgery before (n = 55) or after (n = 22) chemical therapies (non-neoadjuvant or neoadjuvant chemotherapy). A new admittance parameter(Y_n^') also proposed for LN detection in neoadjuvant chemotherapy patients.

RESULTS

Considering the permanent pathology result as the gold standard checked by two independent expert pathologists, a significant correlation was observed between the presence of cancerous cells in LNs and individual ranges of the ELS electrical responses. Compared with normal LNs containing fatty ambient and immune cells, LNs involved by cancerous clusters would reduce the Z_1kHz and increase the IPS. These changes correlate with fat metabolism by cancer cells due to their Fatty Acid Oxidation (FAO) in LN, which results in different dielectric properties between high and low-fat content of normal and cancerous LNs, respectively.

CONCLUSIONS

By finding the best correlation between our defined impedimetric parameters and pathological states of tested LNs, a real-time intraoperative detection approach was developed for highly-sensitive (92%, P<0.001) diagnosis of involved sentinel or axillary LNs. The impact of real-time intraoperative scoring of SLNs would make a pre-estimation about the necessity of excising further LNs to help the surgeon for less invasive surgery, especially in the absence of frozen-section equipment.

New regulation of medical devices in the EU: impact in dermatology

Medical device (MD) is a broad term that encompasses products ranging from, for example, examination gloves to digital dermoscopy systems; all of which are regulated by a new regulatory framework in the EU from May 2021. The new Medical Device Regulation (MDR) (Regulation EU 2017/745) will have a significant effect on suppliers of MD and will have subsequent effects also for dermatologists and other clinicians. Medical device software and apps are reclassified leading to more stringent requirements on documentation within, e.g. clinical evidence, as well as regulatory authority control. The changes will likely have positive effects on quality, to the benefit of patients. There will, however, be implications affecting the availability and support of existing devices and the introduction of new devices, as well as a likely price increase due to the higher costs for suppliers. Dermatologists, other clinicians and administrators need to be aware of the effects of MDR to ensure that existing devices and new purchases can be used as planned. Specifically, clinicians need to be aware of the following: (i) improved quality of MD and follow‐up of incidents can be expected. (ii) Only ‘non‐significant’ updates will be permitted after May 2021 to many existing systems and devices unless approved under the new MDR. (iii) Existing devices that do not achieve approval under the new regulation will no longer be manufactured after May 2024. (iv) New products and methods will take longer time to be approved and available. (v) Prices will likely increase. (vi) Suppliers of products that do not fulfil the new regulation will disappear, and the availability of consumables, spare parts or upgrades might be discontinued. (vii) A trend to oligopoly may appear in the market. It is therefore important to check with your suppliers as to how and when they will adhere to the new MDR regulation.

Bedside Differentiation Between Benign and Malignant Pigmented Skin Tumours by Four Diagnostic Imaging Technologies - A Pilot Study

Fast diagnosis of suspicious pigmented skin lesions is imperative, but current bedside skin imaging technologies are either limited in penetration depth or resolution. Combining imaging methods is therefore highly relevant for skin cancer diagnostics. This pilot study evaluates the ability of optical coherence tomography, reflectance confocal microscopy, photoacoustic imaging and high-frequency ultrasound to differentiate malignant from benign pigmented skin lesions. A total of 41 pigmented skin tumours were scanned prior to excision. Morphologic features and blood vessel characteristics were analysed in reflectance confocal microscopy, optical coherence tomography, high-frequency ultrasound and photoacoustic imaging images and diagnostic accuracy assessed. Three novel photoacoustic imaging features, 7 reflectance confocal microscopy features and two optical coherence tomography features were detected with a high correlation to malignancy, diagnostic accuracy > 71%. No significant features were found in high-frequency ultrasound. Conclusively, optical coherence tomography, reflectance confocal microscopy and photoacoustic imaging in combination enables image-guided evaluation of suspicious pigmented skin tumours at the bedside. Combining these advanced techniques may help to diagnose skin cancer more efficiently.

Non-invasive diagnostic techniques in pigmentary skin disorders and skin cancer

BACKGROUND

Diagnosis of pigmentary skin disorders, pre-cancerous and cancerous skin diseases is traditionally relied on visual assessment. The most widely applied invasive diagnostic technique is the skin biopsy. There have been significant technological advances in non-invasive diagnostic methods for skin disorders.

OBJECTIVE

The objective of this article is to discuss different non-invasive diagnostic modalities, used in the diagnosis of pigmentary skin disorders and cutaneous cancers.

METHODS

Comprehensive literature search was performed to screen articles related to non-invasive diagnostic techniques in pigmentary skin disorders and cutaneous cancers. Articles published in journals indexed in PubMed were searched along with those in Google Scholar. Clinical trials, review articles, case series, case reports and other relevant articles were considered for review. References of relevant articles were also considered for review.

RESULTS

Dermoscopy and ultrasonography were the only non-invasive diagnostic and imaging techniques available to dermatologists for many years. The advent of computed tomography (CT) and magnetic resonance imaging (MRI) augmented the visualization of deeper structures. Confocal laser microscopy (CLM) and reflectance spectrophotometers have showed promising results in the non-invasive detection of pigmented lesions. Optical coherence tomography (OCT), electrical impedance spectroscopy (EIS), multispectral imaging, high frequency ultrasonography (HFUS) and adhesive patch biopsy aid in the accurate diagnosis of benign, as well as neoplastic skin diseases.

CONCLUSION

There have been significant advancements in non-invasive methods for diagnosis of dermatological diseases. These techniques can be repeatedly used in a comfort manner for the patient, and may offer an objective way to follow the course of a disease.

Electrical impedance spectroscopy for the characterization of skin barrier in atopic dermatitis

BACKGROUND

Allergic disorders such as atopic dermatitis (AD) are strongly associated with an impairment of the epithelial barrier, in which tight junctions and/or filaggrin expression can be defective. Skin barrier assessment shows potential to be clinically useful for prediction of disease development, improved and earlier diagnosis, lesion follow-up, and therapy evaluation. This study aimed to establish a method to directly assess the in vivo status of epithelial barrier using electrical impedance spectroscopy (EIS).

METHODS

Thirty-six patients with AD were followed during their 3-week hospitalization and compared with 28 controls. EIS and transepidermal water loss (TEWL) were measured in lesional and non-lesional skin. Targeted proteomics by proximity extension assay in serum and whole-genome sequence were performed.

RESULTS

Electrical impedance spectroscopy was able to assess epithelial barrier integrity, differentiate between patients and controls without AD, and characterize lesional and non-lesional skin of patients. It showed a significant negative correlation with TEWL, but a higher sensitivity to discriminate non-lesional atopic skin from controls. During hospitalization, lesions reported a significant increase in EIS that correlated with healing, decreased SCORAD and itch scores. Additionally, EIS showed a significant inverse correlation with serum biomarkers associated with inflammatory pathways that may affect the epithelial barrier, particularly chemokines such as CCL13, CCL3, CCL7, and CXCL8 and other cytokines, such as IRAK1, IRAK4, and FG2, which were significantly high at admission. Furthermore, filaggrin copy numbers significantly correlated with EIS on non-lesional skin of patients.

CONCLUSIONS

Electrical impedance spectroscopy can be a useful tool to detect skin barrier dysfunction in vivo, valuable for the assessment of AD severity, progression, and therapy efficacy.

Increased Uniformity in Diagnostic Accuracy of Pigmented Lesions Using Electrical Impedance Spectroscopy Information

BACKGROUND

Novel non-invasive technologies augment information available to a clinician to enhance diagnosis. Electrical impedance spectroscopy (EIS) is a highly sensitive technology used before biopsy to differentiate equivocal lesions through differences in electrical resistance of benign versus malignant cells. A recent study of an EIS device approved by the United States Food and Drug Administration supported this device's impact on clinical management among dermatology residents. The device provides an EIS score, which increases with greater likelihood of malignancy.

OBJECTIVE

We investigated whether the addition of an EIS score improved uniformity and diagnostic accuracy of pigmented lesions.

METHODS

A post-hoc analysis of previously collected data from a survey of 164 dermatology residents was performed. Residents were asked to determine whether they would biopsy a lesion based on clinical morphology alone versus clinical morphology with an EIS score. A total of 45 lesions were assessed (including 17 malignant and 28 benign lesions). Subjects were grouped by percent correct pre-EIS score biopsy decisions and divided into quartiles.

RESULTS

With clinical assessment alone, the mean correct decisions to biopsy was 59.9%. With the addition of EIS score, the mean increased to 71.0%. All quartiles significantly increased their correct biopsy decisions with EIS (P<0.001), but the lowest scoring quartiles improved more than the highest scoring quartiles.

CONCLUSION

The data from the EIS device were designed to be integrated into the biopsy decision as an additional piece of information in the diagnostic pathway. The study findings are consistent with this objective. In addition to clinical judgment, the use of the EIS score most increased the lowest-scoring residents, but all were improved after integrating the EIS score. EIS information improved homogeneity of ability and diagnostic accuracy.

Virtual Biopsy by Electrical Impedance Spectroscopy in Barrett's Carcinoma

Purpose

Early detection of adenocarcinomas in the esophagus is crucial for achieving curative endoscopic therapy. Targeted biopsies of suspicious lesions, as well as four-quadrant biopsies, represent the current diagnostic standard. However, this procedure is time-consuming, cost-intensive, and examiner-dependent. The aim of this study was to test whether impedance spectroscopy is capable of distinguishing between healthy, premalignant, and malignant lesions. An ex vivo measurement method was developed to examine esophageal lesions using impedance spectroscopy immediately after endoscopic resection.

Methods

After endoscopic resection of suspicious lesions in the esophagus, impedance measurements were performed on resected cork-covered tissue using a measuring head that was developed, with eight gold electrodes, over 10 different measurement settings and with frequencies from 100 Hz to 1 MHz.

Results

A total of 105 measurements were performed in 60 patients. A dataset of 400 per investigation and a total of more than 42,000 impedance measurements were therefore collected. Electrical impedance spectroscopy (EIS) was able to detect dysplastic esophageal mucosa with a sensitivity of 81% in Barrett’s esophagus.

Conclusion

In summary, EIS was able to distinguish different tissue characteristics in the different esophageal tissues. EIS thus holds potential for further development of targeted biopsies during surveillance endoscopy.

Trial Registration

NCT04046601

Emerging Minimally Invasive Technologies for the Detection of Skin Cancer

With the increasing incidence of skin cancer, many noninvasive technologies to detect its presence have been developed. This review focuses on reflectance confocal microscopy (RCM), optical coherence tomography (OCT), high-frequency ultrasound (HFUS), electrical impedance spectroscopy (EIS), pigmented lesion assay (PLA), and Raman spectroscopy (RS) and discusses the basic principle, clinical applications, advantages, and disadvantages of each technology. RCM provides high cellular resolution and has high sensitivity and specificity for the diagnosis of skin cancer. OCT provides lower resolution than RCM, although its evaluable depth is deeper than that of RCM. RCM and OCT may be useful in reducing the number of unnecessary biopsies, evaluating the tumor margin, and monitoring treatment response. HFUS can be mainly used to delineate tumor depths or margins and monitor the treatment response. EIS provides high sensitivity but low specificity for the diagnosis of skin malignancies. PLA, which is based on the genetic information of lesions, is applicable for the detection of melanoma with high sensitivity and moderate-to-high specificity. RS showed high accuracy for the diagnosis of skin cancer, although more clinical studies are required. Advances in these technologies for the diagnosis of skin cancer can lead to the realization of optimized and individualized treatments.

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

In vivo two-photon-excited cellular fluorescence of melanin, NAD(P)H, and keratin enables an accurate differential diagnosis of seborrheic keratosis and pigmented cutaneous melanoma

SIGNIFICANCE

Seborrheic keratoses (SKs) are harmless pigmented skin lesions (PSLs) that may be confused clinically not only with other benign conditions but also with cutaneous melanoma (CM). As SKs are one of the most common neoplasms in adults, the importance of their correct diagnosis is high. Misclassifying SK as malignant is not rare and leads to a high number of unnecessary biopsies. On the other hand, misdiagnosing CM as SK may have a large impact on prognosis or therapy.

AIM

In the non-invasive technique of dermatofluoroscopy, the fluorophores in melanocytes and keratinocytes are excited in vivo with nanosecond laser pulses and the resulting spectrally resolved, melanin-dominated fluorescence signals are used to differentiate between pigmented benign lesions and CM.

APPROACH

In this single-center, non-interventional study, 33 PSLs of 20 patients were scanned with dermatofluoroscopy in vivo. For all included cases, dermatofluoroscopic signals were compared to pathology classification.

RESULTS

The characteristic spectral features of SK were identified, where the signals are dominated by keratin, NAD(P)H, and melanin. The fluorescence spectra of SKs differed substantially from those of CM: a characteristic spectrum of SK has been identified in 27 of 28 SKs.

CONCLUSIONS

The high-accuracy differential diagnosis between CM and SK is possible with dermatofluoroscopy.

Using convolutional neural networks for tick image recognition - a preliminary exploration

Smartphone cameras and digital devices are increasingly used in the capture of tick images by the public as citizen scientists, and rapid advances in deep learning and computer vision has enabled brand new image recognition models to be trained. However, there is currently no web-based or mobile application that supports automated classification of tick images. The purpose of this study was to compare the accuracy of a deep learning model pre-trained with millions of annotated images in Imagenet, against a shallow custom-build convolutional neural network (CNN) model for the classification of common hard ticks present in anthropic areas from northeastern USA. We created a dataset of approximately 2000 images of four tick species (Ixodes scapularis, Dermacentor variabilis, Amblyomma americanum and Haemaphysalis sp.), two sexes (male, female) and two life stages (adult, nymph). We used these tick images to train two separate CNN models - ResNet-50 and a simple shallow custom-built. We evaluated our models' performance on an independent subset of tick images not seen during training. Compared to the ResNet-50 model, the small shallow custom-built model had higher training (99.7%) and validation (99.1%) accuracies. When tested with new tick image data, the shallow custom-built model yielded higher mean prediction accuracy (80%), greater confidence of true detection (88.7%) and lower mean response time (3.64 s). These results demonstrate that, with limited data size for model training, a simple shallow custom-built CNN model has great prospects for use in the classification of common hard ticks present in anthropic areas from northeastern USA.

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8-93.2%) and specificity (85.2%, 95%CI 84.9-85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3-93.1%; specificity 65.2%, 55-74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.

Diagnostic tools used for melanoma: A survey of Australian general practitioners and dermatologists

BACKGROUND/OBJECTIVE

Diagnostic tools such as dermoscopy, sequential digital dermoscopy imaging (SDDI), total body photography (TBP) and automated diagnostic tools are available to assist in early melanoma diagnosis. The use, accessibility and barriers of dermoscopy have been well studied; however, there are few similar studies regarding SDDI, TBP and automated diagnostic tools. We aim to understand the use of these diagnostic aids amongst Australian general practitioners (GPs) and dermatologists.

METHODS

Between June 2019 and January 2020, GPs and dermatologists across Australia were invited to participate in an online survey. Surveys were distributed through GP and dermatology organisations.

RESULTS

A total of 227 survey responses were received, 175 from GPs and 52 from dermatologists. Amongst GPs, 44.6% worked in a skin cancer clinic. Dermoscopy was used at least occasionally by 98.9% of all GPs. SDDI was used by 93.6% of skin cancer GPs, 80.8% of dermatologists and 45.3% of generalist GPs. TBP was used or recommended by 77.1% of generalist GPs, 82.3% of skin cancer GPs and 86.5% of dermatologists. The most common barriers to the use of TBP were cost, limited accessibility, poor patient compliance, and time required for both patients and doctors. Very few clinicians reported using automated diagnostic tools. There was an interest in future diagnostic aids for melanoma in 88% of GPs and dermatologists.

CONCLUSION

Dermoscopy, SDDI and TBP were commonly used by responding Australian skin cancer GPs and dermatologists in this survey. Automated diagnostic tools were not reported to be used routinely. Several barriers were identified for use of TBP.

Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin

BACKGROUND

The quest for diagnostic tools for the detection of cutaneous malignant melanoma (cMM) is ongoing. A challenge in cMM care is not overlooking cMM at an early stage, while simultaneously avoiding unnecessary biopsies or excisions of benign pigmented skin lesions (PSLs). A novel hyperspectral imaging (HSI) device is shown to have potential for differentiating equivocal PSLs in Asian skin types. Our objective was to assess the accuracy of the HSI device in distinguishing between cMM and benign PSLs in patients with Caucasian skin types.

METHODS

Patients with Caucasian skin types (Fitzpatrick I-II), enrolled for excisional biopsies of PSLs were included and examined using the HSI device. The discrimination index (DI) was used to demonstrate the sensitivity (SE) and specificity (SP) in comparison with the re-evaluated histopathology diagnoses.

RESULTS

In 186 patients, 202 pigmented skin lesions were included. The sensitivity to detect cMM was 96.7% (87/90), and the specificity for benign lesions was 42.1% (45/107). The AUC was 0.800 (95% confidence interval (CI): 0.740-0.861).

CONCLUSIONS

Our novel HSI device showed a high sensitivity in detecting malignant lesions in patients with Caucasian skin types. Compared with analogous technologies, as multispectral imaging or electrical impedance spectroscopy, our device showed similar or better accuracy in differentiating cMM from benign PSLs. Therefore, it might be a useful clinical tool in skin types I-IV and where further triage of pigmented skin lesions is important.

Retrospective evaluation of the performance of the electrical impedance spectroscopy system Nevisense in detecting keratinocyte cancers

BACKGROUND

Keratinocyte cancers, also referred to as non-melanoma skin cancers (NMSCs), are one of the most common malignant skin tumors. We performed a retrospective analysis of lesions from patients of a private dermatology practice to evaluate the use of electrical impedance spectroscopy (EIS) in detecting keratinocyte malignancies. The aim of the study is to assess the accuracy of the technique and to rate its use as supportive tool in NMSC diagnosis.

MATERIAL AND METHODS

The period evaluated ranges from September 2015 to November 2019. In total, 1712 lesions from 951 patients were included. All lesions suspicious for malignancy were gauged with the Nevisense device. Excised lesions were sent in for histopathological classification, and the results were compared to the Nevisense score.

RESULTS

A total of 767 lesions (44.8%) received a negative score (0-3) from the Nevisense system and 945 lesions (55.2%) a positive score (4-10). The combination of the dermatologist's visual assessment plus the technical determined Neviscore resulted in the excision of 52.5% of all 1712 suspicious lesions whereof 15% were found to be malignant. The sensitivity of Nevisense was 98.4% for NMSC detection.

CONCLUSION

Electrical impedance spectroscopy was found to be a valuable adjunct support tool in clinical decisions for cases with suspicion for NMSC.

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions

BACKGROUND

Convolutional neural networks (CNNs) efficiently differentiate skin lesions by image analysis. Studies comparing a market-approved CNN in a broad range of diagnoses to dermatologists working under less artificial conditions are lacking.

MATERIALS AND METHODS

One hundred cases of pigmented/non-pigmented skin cancers and benign lesions were used for a two-level reader study in 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Additionally, dermoscopic images were classified by a CNN approved for the European market as a medical device (Moleanalyzer Pro, FotoFinder Systems, Bad Birnbach, Germany). Primary endpoints were the sensitivity and specificity of the CNN's dichotomous classification in comparison with the dermatologists' management decisions. Secondary endpoints included the dermatologists' diagnostic decisions, their performance according to their level of experience, and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC).

RESULTS

The CNN revealed a sensitivity, specificity, and ROC AUC with corresponding 95% confidence intervals (CI) of 95.0% (95% CI 83.5% to 98.6%), 76.7% (95% CI 64.6% to 85.6%), and 0.918 (95% CI 0.866-0.970), respectively. In level I, the dermatologists' management decisions showed a mean sensitivity and specificity of 89.0% (95% CI 87.4% to 90.6%) and 80.7% (95% CI 78.8% to 82.6%). With level II information, the sensitivity significantly improved to 94.1% (95% CI 93.1% to 95.1%; P < 0.001), while the specificity remained unchanged at 80.4% (95% CI 78.4% to 82.4%; P = 0.97). When fixing the CNN's specificity at the mean specificity of the dermatologists' management decision in level II (80.4%), the CNN's sensitivity was almost equal to that of human raters, at 95% (95% CI 83.5% to 98.6%) versus 94.1% (95% CI 93.1% to 95.1%); P = 0.1. In contrast, dermatologists were outperformed by the CNN in their level I management decisions and level I and II diagnostic decisions. More experienced dermatologists frequently surpassed the CNN's performance.

CONCLUSIONS

Under less artificial conditions and in a broader spectrum of diagnoses, the CNN and most dermatologists performed on the same level. Dermatologists are trained to integrate information from a range of sources rendering comparative studies that are solely based on one single case image inadequate.

Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia

We undertook this study in light of an uncontrolled rise of melanoma incidence and mortality rates in the United Kingdom (UK). We aim to assess the effectiveness of prevention and early melanoma diagnosis in the UK's National Health Service (NHS) in comparison to the Australian system that has limited the melanoma rise. We compare the prevention campaigns against skin cancer and the stage at which melanoma is diagnosed. We analyse key drivers of early diagnosis. Overall, Australia has performed better than the UK and provides an example for the UK's NHS for better preventing melanoma and diagnosing it. Technologies under development, such as tele-dermatology and artificial intelligence applications, could aid in making melanoma early diagnosis easier, more cost-efficient, and lessen the burden on health systems. Diagnoses also provide the data to help public health officials target prevention programs.

Novel 3D printing-based probe for impedance spectroscopic examination of oral mucosa: design and preliminary testing with phantom models

The diagnosis of oral potentially malignant disorders currently relies on histopathological examination of surgically removed biopsies causing pain and discomfort for the patient. We hypothesise that non-invasive bioimpedance spectroscopy (BIS) method would overcome these problems and could make possible regular screening of at-risk patients. Previously several hand-made probes have been introduced in such BIS studies. However, for the first time, we aimed to design a 3D printed probe and test it with model samples (saline solutions, cucumber and porcine tongue). We found that it is extremely crucial to select proper printable materials and optimise electrode geometries to avoid electrochemical corrosion problems, short-circuiting and other signal instabilities related to miniaturised probe. However, our final prototype constructed with four high purity silver made electrodes showed a good linearity (R ² = 0.999) in diluted saline solution measurements over a wide conductivity range (0.25‒8 mS/cm), which covers well the range of values for the different biological tissues. Moreover, our data show that high reproducibility of the manufacturing and measurement is one important merit in the present 3D printed probe. However, further studies are needed to clarify the importance of fixed pressure especially when the tetrapolar 3D printed probe is used as a hand-held apparatus.

Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues

SIGNIFICANCE

Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics.

AIM

We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration.

APPROACH

Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves.

RESULTS

Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs.

CONCLUSIONS

From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.