Computational Model Based on Optical Coherence Tomography (OCT) Skin Scanning to Identify and Quantify Acute Radiation Dermatitis (ARD): A Prospective Diagnostic Study

BACKGROUND

Acute radiation dermatitis (ARD) is the most widely reported radiotherapy-induced adverse event. Currently, there is no objective or reliable method to measure ARD.

OBJECTIVE

Our main objective was to identify and quantify the effects of radiotherapy with a computational model using optical coherence tomography (OCT) skin scanning. Secondary objectives included determining the ARD impact of different radiotherapeutic schemes and adjuvant topical therapies.

METHODS

We conducted a prospective, single-center case series study in a tertiary referral center of patients with breast cancer who were eligible for whole breast radiotherapy (WBRT).

RESULTS

A total of 39 women were included and distributed according to the radiotherapeutic schemes (15, 20, and 25 fractions). A computational model was designed to quantitatively analyze OCT findings. After radiotherapy, OCT scanning was more sensitive revealing vascularization changes in 84.6% of the patients (vs 69.2% of the patients with ARD by clinical examination). OCT quantified an increased vascularization at the end of WBRT (P<.05) and a decrease after 3 months (P=.032). Erythematous skin changes by OCT were more pronounced in the 25-fraction regime.

CONCLUSION

An OCT computational model allowed for the identification and quantification of vascularization changes on irradiated skin, even in the absence of clinical ARD. This may allow the design of standardized protocols for ARD beyond the skin color of the patients involved.

Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease

BACKGROUND

As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.

OBJECTIVE

This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection.

METHODS

An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance.

RESULTS

Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users.

CONCLUSIONS

The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

Monitoring multidimensional aspects of quality of life after cancer immunotherapy: protocol for the international multicentre, observational QUALITOP cohort study

INTRODUCTION

Immunotherapies, such as immune checkpoint inhibitors and chimeric antigen receptor T-cell therapy, have significantly improved the clinical outcomes of various malignancies. However, they also cause immune-related adverse events (irAEs) that can be challenging to predict, prevent and treat. Although they likely interact with health-related quality of life (HRQoL), most existing evidence on this topic has come from clinical trials with eligibility criteria that may not accurately reflect real-world settings. The QUALITOP project will study HRQoL in relation to irAEs and its determinants in a real-world study of patients treated with immunotherapy.

METHODS AND ANALYSIS

This international, observational, multicentre study takes place in France, the Netherlands, Portugal and Spain. We aim to include about 1800 adult patients with cancer treated with immunotherapy in a specifically recruited prospective cohort, and to additionally obtain data from historical real-world databases (ie, databiobanks) and medical administrative registries (ie, national cancer registries) in which relevant data regarding other adult patients with cancer treated with immunotherapy has already been stored. In the prospective cohort, clinical health status, HRQoL and psychosocial well-being will be monitored until 18 months after treatment initiation through questionnaires (at baseline and 3, 6, 12 and 18 months thereafter), and by data extraction from electronic patient files. Using advanced statistical methods, including causal inference methods, artificial intelligence algorithms and simulation modelling, we will use data from the QUALITOP cohort to improve the understanding of the complex relationships among treatment regimens, patient characteristics, irAEs and HRQoL.

ETHICS AND DISSEMINATION

All aspects of the QUALITOP project will be conducted in accordance with the Declaration of Helsinki and with ethical approval from a suitable local ethics committee, and all patients will provide signed informed consent. In addition to standard dissemination efforts in the scientific literature, the data and outcomes will contribute to a smart digital platform and medical data lake. These will (1) help increase knowledge about the impact of immunotherapy, (2) facilitate improved interactions between patients, clinicians and the general population and (3) contribute to personalised medicine.

TRIAL REGISTRATION NUMBER

NCT05626764.

Analysis of dermoscopic changes of blue nevi on digital follow-up: a 21-year retrospective cohort study

BACKGROUND

Blue nevi are benign dermal melanocytic proliferations that are often easy to recognize clinically. Rarely, these lesions can display atypical features, suggesting the presence of a malignant blue nevus or mimicking cutaneous metastases of melanoma.

OBJECTIVE

To describe the clinical evolution of blue nevi over time and to assess the need for monitoring these lesions.

METHODS

We conducted a retrospective cohort study of 103 patients who were followed between December 1998 and November 2019. An artificial intelligence algorithm was used to identify blue nevi from the databases of two digital epiluminescence devices. Changes in the area of each lesion were calculated with a segmentation neural network.

RESULTS

We included 123 blue nevi from 103 patients. Most of the lesions segmented, 99 (91.7%), were considered stable. Of the 9 (8.3%) growing blue nevi identified, 2 (1.85%) showed significant growth. The studied growing blue nevi turned out to be cellular blue nevi, presented with a low tumor mutation burden and GNAQ c.626A>T alteration was identified in both lesions.

LIMITATIONS

Some clinical variants of blue nevi might not be included.

CONCLUSIONS

Most blue nevi remain stable during their evolution. Rarely, they can show progressive growth, although histopathological or molecular signs of malignancy have not been identified.

Common genetic variants associated with melanoma risk or naevus count in patients with wildtype MC1R melanoma

BACKGROUND

Hypomorphic MC1R variants are the most prevalent genetic determinants of melanoma risk in the white population. However, the genetic background of patients with wildtype (WT) MC1R melanoma is poorly studied.

OBJECTIVES

To analyse the role of candidate common genetic variants on the melanoma risk and naevus count in Spanish patients with WT MC1R melanoma.

METHODS

We examined 753 individuals with WT MC1R from Spain (497 patients and 256 controls). We used OpenArray reverse-transcriptase polymerase chain reaction to genotype a panel of 221 common genetic variants involved in melanoma, naevogenesis, hormonal pathways and proinflammatory pathways. Genetic models were tested using multivariate logistic regression models. Nonparametric multifactor dimensionality reduction (MDR) was used to detect gene-gene interactions within each biological subgroup of variants.

RESULTS

We found that variant rs12913832 in the HERC2 gene, which is associated with blue eye colour, increased melanoma risk in individuals with WT MC1R [odds ratio (OR) 1·97, 95% confidence interval (CI) 1·48-2·63; adjusted P < 0·001; corrected P < 0·001]. We also observed a trend between the rs3798577 variant in the oestrogen receptor alpha gene (ESR1) and a lower naevus count, which was restricted to female patients with WT MC1R (OR 0·51, 95% CI 0·33-0·79; adjusted P = 0·002; corrected P = 0·11). This sex-dependent association was statistically significant in a larger cohort of patients with melanoma regardless of their MC1R status (n = 1497; OR 0·71, 95% CI 0·57-0·88; adjusted P = 0·002), reinforcing the hypothesis of an association between hormonal pathways and susceptibility to melanocytic proliferation. Last, the MDR analysis revealed four genetic combinations associated with melanoma risk or naevus count in patients with WT MC1R.

CONCLUSIONS

Our data suggest that epistatic interaction among common variants related to melanocyte biology or proinflammatory pathways might influence melanocytic proliferation in individuals with WT MC1R. What is already known about this topic? Genetic variants in the MC1R gene are the most prevalent melanoma genetic risk factor in the white population. Still, 20-40% of cases of melanoma occur in individuals with wildtype MC1R. Multiple genetic variants have a pleiotropic effect in melanoma and naevogenesis. Additional variants in unexplored pathways might also have a role in melanocytic proliferation in these patients. Epidemiological evidence suggests an association of melanocytic proliferation with hormonal pathways and proinflammatory pathways. What does this study add? Variant rs12913832 in the HERC2 gene, which is associated with blue eye colour, increases the melanoma risk in individuals with wildtype MC1R. Variant rs3798577 in the oestrogen receptor gene is associated with naevus count regardless of the MC1R status in female patients with melanoma. We report epistatic interactions among common genetic variants with a role in modulating the risk of melanoma or the number of naevi in individuals with wildtype MC1R. What is the translational message? We report a potential role of hormonal signalling pathways in melanocytic proliferation, providing a basis for better understanding of sex-based differences observed at the epidemiological level. We show that gene-gene interactions among common genetic variants might be responsible for an increased risk for melanoma development in individuals with a low-risk phenotype, such as darkly pigmented hair and skin.

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.

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.

Deep learning automated pathology in ex vivo microscopy

Standard histopathology is currently the gold standard for assessment of margin status in Mohs surgical removal of skin cancer. Ex vivo confocal microscopy (XVM) is potentially faster, less costly and inherently 3D/digital compared to standard histopathology. Despite these advantages, XVM use is not widespread due, in part, to the need for pathologists to retrain to interpret XVM images. We developed artificial intelligence (AI)-driven XVM pathology by implementing algorithms that render intuitive XVM pathology images identical to standard histopathology and produce automated tumor positivity maps. XVM images have fluorescence labeling of cellular and nuclear biology on the background of endogenous (unstained) reflectance contrast as a grounding counter-contrast. XVM images of 26 surgical excision specimens discarded after Mohs micrographic surgery were used to develop an XVM data pipeline with 4 stages: flattening, colorizing, enhancement and automated diagnosis. The first two stages were novel, deterministic image processing algorithms, and the second two were AI algorithms. Diagnostic sensitivity and specificity were calculated for basal cell carcinoma detection as proof of principal for the XVM image processing pipeline. The resulting diagnostic readouts mimicked the appearance of histopathology and found tumor positivity that required first collapsing the confocal stack to a 2D image optimized for cellular fluorescence contrast, then a dark field-to-bright field colorizing transformation, then either an AI image transformation for visual inspection or an AI diagnostic binary image segmentation of tumor obtaining a diagnostic sensitivity and specificity of 88% and 91% respectively. These results show that video-assisted micrographic XVM pathology could feasibly aid margin status determination in micrographic surgery of skin cancer.

Influence of germline genetic variants on dermoscopic features of melanoma

Nevus count is highly determined by inherited variants and has been associated with the origin of melanoma. De novo melanomas (DNMMs) are more prevalent in patients with a low nevus count and have distinctive dermoscopic features than nevus-associated melanomas. We evaluated the impact of nine single nucleotide polymorphisms (SNPs) of MTAP (rs10811629, rs2218220, rs7023329 and rs751173), PLA2G6 (rs132985 and rs2284063), IRF4 (rs12203592), and PAX3 (rs10180903 and rs7600206) genes associated with nevus count and melanoma susceptibility, and the MC1R variants on dermoscopic features of 371 melanomas from 310 patients. All MTAP variants associated with a low nevus count were associated with regression structures (peppering and mixed regression), blue-whitish veil, shiny white structures, and pigment network. SNPs of PLA2G6 (rs132985), PAX3 (rs7600206), and IRF4 (rs12203592) genes were also associated with either shiny white structures or mixed regression (all corrected p-values ≤ .06). Melanomas from red hair color MC1R variants carriers showed lower total dermoscopy score (p-value = .015) and less blotches than melanomas from non-carriers (p-value = .048). Our results provide evidence that germline variants protective for melanoma risk and/or associated with a low nevus count are associated with certain dermoscopic features, more characteristic of de novo and worse prognosis melanomas.

A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

Machine Learning in Melanoma Diagnosis. Limitations About to be Overcome

BACKGROUND

Automated image classification is a promising branch of machine learning (ML) useful for skin cancer diagnosis, but little has been determined about its limitations for general usability in current clinical practice.

OBJECTIVE

To determine limitations in the selection of skin cancer images for ML analysis, particularly in melanoma.

METHODS

Retrospective cohort study design, including 2,849 consecutive high-quality dermoscopy images of skin tumors from 2010 to 2014, for evaluation by a ML system. Each dermoscopy image was assorted according to its eligibility for ML analysis.

RESULTS

Of the 2,849 images chosen from our database, 968 (34%) met the inclusion criteria for analysis by the ML system. Only 64.7% of nevi and 36.6% of melanoma met the inclusion criteria. Of the 528 melanomas, 335 (63.4%) were excluded. An absence of normal surrounding skin (40.5% of all melanomas from our database) and absence of pigmentation (14.2%) were the most common reasons for exclusion from ML analysis.

DISCUSSION

Only 36.6% of our melanomas were admissible for analysis by state-of-the-art ML systems. We conclude that future ML systems should be trained on larger datasets which include relevant non-ideal images from lesions evaluated in real clinical practice. Fortunately, many of these limitations are being overcome by the scientific community as recent works show.

Basal cell carcinoma characterization using fusion ex vivo confocal microscopy: a promising change in conventional skin histopathology

BACKGROUND

Ex vivo confocal microscopy (CM) works under two modes, fluorescence and reflectance, allowing the visualization of different structures. Fluorescence CM (FCM) requires a contrast agent and has been used for the analysis of basal cell carcinomas (BCCs) during Mohs surgery. Conversely, reflectance CM (RCM) is mostly used for in vivo diagnosis of equivocal skin tumours. Recently, a new, faster ex vivo confocal microscope has been developed which simultaneously uses both lasers (fusion mode).

OBJECTIVES

To describe the BCC features identified on reflectance, fluorescence and fusion modes using this novel device. To determine the best mode to identify characteristic BCC features. To develop a new staining protocol to improve the visualization of BCC under the different modes.

METHODS

From September 2016 to June 2017, we prospectively included consecutive BCCs which were excised using Mohs surgery in our department. The lesions were evaluated using ex vivo CM after routine Mohs surgery. The specimens were first stained with acridine orange and then stained using both acetic acid and acridine orange.

RESULTS

We included 78 BCCs (35 infiltrative, 25 nodular, 12 micronodular, 6 superficial). Most features were better visualized with the fusion mode using the double staining. We also identified new CM ex vivo features, dendritic and plump cells, which have not been reported previously.

CONCLUSIONS

Our results suggest that nuclei characteristics are better visualized in FCM but cytoplasm and surrounding stroma are better visualized in RCM. Thus, the simultaneous evaluation of reflectance and fluorescence seems to be beneficial due to its complementary effect. What's already known about this topic? Ex vivo fluorescent confocal microscopy (FCM) is an imaging technique that allows histopathological analysis of fresh tissue. FCM is faster - at least one-third of the time - than conventional methods. FCM has a sensitivity of 88% and a specificity of 99% in detecting basal cell carcinomas (BCCs). What does this study add? Reflectance and fluorescence modes can be used simultaneously in a new ex vivo CM device. Each mode complements the other, resulting in an increase in the detection of BCC features in fusion mode. A combined staining using acetic acid and acridine orange enhances the visualization of tumour and stroma without damaging the tissue for further histopathological analysis.