Improved sensitivity for detection of pathogenic variants in familial NF2-related schwannomatosis.

PURPOSE: To determine the impact of additional genetic screening techniques on the rate of detection of pathogenic variants leading to familial NF2-related schwannomatosis. METHODS: We conducted genetic screening of a cohort of 168 second-generation individuals meeting the clinical criteria for NF2-related schwannomatosis. In addition to the current clinical screening techniques, targeted next-generation sequencing (NGS) and multiplex ligation-dependent probe amplification analysis, we applied additional genetic screening techniques, including karyotype and RNA analysis. For characterisation of a complex structural variant, we also performed long-read sequencing analysis. RESULTS: Additional genetic analysis resulted in increased sensitivity of detection of pathogenic variants from 87% to 95% in our second-generation NF2-related schwannomatosis cohort. A number of pathogenic variants identified through extended analysis had been previously observed after NGS analysis but had been overlooked or classified as variants of uncertain significance. CONCLUSION: Our study indicates there is added value in performing additional genetic analysis for detection of pathogenic variants that are difficult to identify with current clinical genetic screening methods. In particular, RNA analysis is valuable for accurate classification of non-canonical splicing variants. Karyotype analysis and whole genome sequencing analysis are of particular value for identification of large and/or complex structural variants, with additional advantages in the use of long-read sequencing techniques.

Metastasizing aneurysmal dermatofibroma initially diagnosed as angiosarcoma confirmed by CD63::PRKCD fusion gene detection with nanopore sequencing.

Dermatofibroma (DF) is a benign tumor that forms pedunculated lesions ranging in size from a few millimeters to 2 cm, usually affecting the extremities and trunks of young adults. Histopathologically, DF is characterized by the storiform proliferation of monomorphic fibroblast-like spindle cells. In addition to neoplastic cells, secondary elements such as foamy histiocytes, Touton-type giant cells, lymphoplasmacytes, and epidermal hyperplasia are characteristic histological features. Several histological variants, including atypical, cellular, aneurysmal, and lipidized variants, have been reported; cases with variant histologies are sometimes misdiagnosed as sarcomas. We present a case of metastasizing aneurysmal DF that was initially diagnosed as an angiosarcoma on biopsy. A 26-year-old woman was referred to our hospital with a gradually enlarging subcutaneous mass in her lower left leg. Positron emission tomography-computed tomography revealed high fluorodeoxyglucose uptake not only in the tumor but also in the left inguinal region. On biopsy, ERG and CD31-positive atypical spindle cells proliferated in slit-like spaces with extravasation, leading to the diagnosis of angiosarcoma. Histology of the wide-resection specimen was consistent with DF, and lymph node metastasis was also observed. Nanopore DNA sequencing detected CD63::PRKCD fusion and copy number gain, although CD63 was not included in the target region of adaptive sampling. This report highlights the importance of recognizing the unusual clinical, radiological, and pathological features of DF to avoid misdiagnosis, and the potential diagnostic utility of nanopore sequencer.

Novel MED15::ATF1 fusion in a pediatric melanoma with spitzoid features and aggressive presentation.

Childhood melanoma is a rare and biologically heterogeneous pediatric malignancy. The differential diagnosis of pediatric melanoma is usually broad, including a wide variety of spindle cell or epithelioid neoplasms. Different molecular alterations affecting the MAPK and PI3K/AKT/mTOR pathways, tumor suppressor genes, and telomerase reactivation have been implicated in melanoma tumorigenesis and progression. Here, we report a novel MED15::ATF1 fusion in a pediatric melanoma with spitzoid features and an aggressive clinical course.

The prognostic value of the Merkel cell polyomavirus serum antibody test: A dual institutional observational study.

BACKGROUND: Merkel cell carcinoma (MCC) is an aggressive cancer with often poor outcomes. Limited biomarkers exist for predicting clinical outcomes. The Merkel cell polyomavirus (MCPyV) serum antibody test (AMERK) has shown potential for indicating better recurrence-free survival in a single-institution study. The study aimed to evaluate the link between initial AMERK serostatus and survival. Secondary objectives included examining the relationship between initial AMERK titer levels and tumor burden. METHODS: A retrospective cohort study across two institutions analyzed patients tested with AMERK within 90 days of MCC diagnosis. Regression models assessed the association of survival outcomes with serostatus, considering various factors. The relationship between AMERK titer and tumor burden indicators was evaluated using ANOVA. Significance testing was exploratory, without a fixed significance level. RESULTS: Of 261 MCC patients tested, 49.4% were initially seropositive (titer ≥75). Multivariable analysis showed that seropositivity improved recurrence, event-free, overall, and MCC-specific survival rates. Strong associations were found between initial AMERK titer and clinical, tumor, and nodal stages, tumor size, and disease extent. Notably, improved survival with seropositivity was observed only in patients with localized disease at initial presentation. CONCLUSION: Circulating antibodies to MCPyV oncoproteins, as indicated by the AMERK test, are linked with better survival in MCC patients with localized disease at presentation. This could enhance patient risk profiling and treatment personalization. The study's retrospective nature and exploratory analysis are key limitations. PLAIN LANGUAGE SUMMARY: Merkel cell carcinoma (MCC) is a potentially aggressive skin cancer, and tools to predict patient outcomes are limited. A blood test called anti-Merkel cell panel (AMERK), which checks for specific antibodies related to this cancer, might give us some clues. In this study, we looked at 261 MCC patients who took the AMERK test within 90 days of diagnosis. We found that patients with an initial positive AMERK result tended to have better outcomes, especially if their cancer was in the early stages. However, it is important to note that this study has limitations, including using retrospective data and exploratory analyses.

Outcomes in solid organ transplant recipients with a pretransplant diagnosis of melanoma.

Melanoma causes significant morbidity in solid organ transplant recipients (SOTRs). Melanomas diagnosed before transplantation can recur with intensive immunosuppression, but outcomes have not been well studied. We evaluated 901 non-Hispanic White SOTRs with a pretransplant melanoma identified using linked transplant and cancer registry data in the United States. Most pretransplant melanomas were invasive (60.7%), and the median time from diagnosis to transplantation was 5.1 years. After transplantation, 41 SOTRs developed a new invasive melanoma, corresponding to 9-fold increased risk compared with the general population (standardized incidence ratio, 9.2; 95% confidence interval [CI], 6.6-12). Twenty-two SOTRs died from melanoma after transplantation, corresponding to 52-fold increased risk (standardized mortality ratio, 52; 95% CI, 33-79). Risk factors for posttransplant melanoma included age at transplantation (adjusted hazard ratio [HR], 2.86; 95% CI, 1.24-6.60; for age 55+ vs <55 years) and maintenance immunosuppression with cyclosporine/azathioprine (adjusted HR, 2.53; 95% CI, 1.08-5.90). Melanoma mortality was strongly elevated after a posttransplant melanoma diagnosis (HR, 35.6; 95% CI, 14.0-90.4; adjusted for cyclosporine/azathioprine maintenance therapy and calendar year of transplantation). In conclusion, SOTRs with a pretransplant melanoma are at risk of adverse melanoma-related outcomes after transplantation. These findings support thorough dermatologic evaluation prior to transplantation and frequent posttransplant surveillance.

Cutaneous melanoma.

Cutaneous melanoma is a malignancy arising from melanocytes of the skin. Incidence rates are rising, particularly in White populations. Cutaneous melanoma is typically driven by exposure to ultraviolet radiation from natural sunlight and indoor tanning, although there are several subtypes that are not related to ultraviolet radiation exposure. Primary melanomas are often darkly pigmented, but can be amelanotic, with diagnosis based on a combination of clinical and histopathological findings. Primary melanoma is treated with wide excision, with margins determined by tumour thickness. Further treatment depends on the disease stage (following histopathological examination and, where appropriate, sentinel lymph node biopsy) and can include surgery, checkpoint immunotherapy, targeted therapy, or radiotherapy. Systemic drug therapies are recommended as an adjunct to surgery in patients with resectable locoregional metastases and are the mainstay of treatment in advanced melanoma. Management of advanced melanoma is complex, particularly in those with cerebral metastasis. Multidisciplinary care is essential. Systemic drug therapies, particularly immune checkpoint inhibitors, have substantially increased melanoma survival following a series of landmark approvals from 2011 onward.

Profiling of Copy Number Alterations Using Low-Coverage Whole-Genome Sequencing Informs Differential Diagnosis and Prognosis in Primary Cutaneous Follicle Center Lymphoma.

Primary cutaneous follicle center lymphoma (PCFCL) has an excellent prognosis using local treatment, whereas nodal follicular lymphoma (nFL), occasionally presenting with cutaneous spread, often requires systemic therapy. Distinction of the 2 diseases based on histopathology alone might be challenging. Copy number alterations (CNAs) have scarcely been explored on a genome-wide scale in PCFCL; however, they might serve as potential biomarkers during differential diagnosis and risk stratification. Low-coverage whole-genome sequencing is a robust, high-throughput method for genome-wide copy number profiling. In this study, we analyzed 28 PCFCL samples from 20 patients and compared the copy number profiles with a cohort of diagnostic samples of 64 nFL patients. Although the copy number profile of PCFCL was similar to that of nFL, PCFCL lacked amplifications of 18q, with the frequency peaking at 18q21.33 in nFL cases involving the BCL2 locus (PCFCL: 5.0% vs nFL: 31.3%, P = .018, Fisher exact test). Development of distant cutaneous spread was significantly associated with higher genomic instability including the proportion of genome altered (0.02 vs 0.13, P = .033) and number of CNAs (2 vs 9 P = .017), as well as the enrichment of 2p22.2-p15 amplification involving REL and XPO1 (6.3% vs 60.0%, P = .005), 3q23-q24 amplification (0.0% vs 50.0%, P = .004), 6q16.1-q23.3 deletion (6.3% vs 50.0%, P = .018), and 9p21.3 deletion covering CDKN2A and CDKN2B loci (0.0% vs 40.0%, P = .014, all Fisher exact test) in PCFCL. Analysis of sequential tumor samples in 2 cases harboring an unfavorable clinical course pointed to the acquisition of 2p amplification in the earliest common progenitor underlining its pivotal role in malignant transformation. By performing genome-wide copy number profiling on the largest patient cohort to date, we identified distinctive CNA alterations conceivably facilitating the differential diagnosis of PCFCL and secondary cutaneous involvement of nFL and potentially aiding the risk stratification of patients with PCFCL in the future.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis.

Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnosis of these tumors. Current pathologic diagnosis is primarily based on microscopic observation, which is laborious and time-consuming. With digitization, artificial intelligence can be used to improve the efficiency of pathologic diagnosis. This research aims to develop an end-to-end extendable framework for the diagnosis of skin tumor based on pathologic slide images. NF, BD, and SK were selected as target skin tumors. A two-stage skin cancer diagnosis framework is proposed in this article, which consists of two parts: patches-wise diagnosis, and slide-wise diagnosis. Patches-wise diagnosis compares different convolutional neural networks to extract features and distinguish categories from patches generated in whole slide images. Slide-wise diagnosis combines attention graph gated network model prediction with post-processing algorithm. This approach can fuse information from feature-embedding learning and domain knowledge to draw conclusions. Training, validation, and testing were performed on NF, BD, SK, and negative samples. Accuracy and receiver operating characteristic curves were used to evaluate the classification performance. This study investigated the feasibility of skin tumor diagnosis from pathologic images and may be the first instance of applying deep learning to address these three types of tumor diagnoses in skin pathology.

Association between Medicaid Expansion and Cutaneous Melanoma Diagnosis and Outcomes: Does Where You Live Make a Difference?

BACKGROUND: Early detection and standardized treatment are crucial for enhancing outcomes for patients with cutaneous melanoma, the commonly diagnosed skin cancer. However, access to quality health care services remains a critical barrier for many patients, particularly the uninsured. Whereas Medicaid expansion (ME) has had a positive impact on some cancers, its specific influence on cutaneous melanoma remains understudied. METHODS: The National Cancer Database identified 87,512 patients 40-64 years of age with a diagnosis of non-metastatic cutaneous melanoma between 2004 and 2017. In this study, patient demographics, disease characteristics, and treatment variables were analyzed, and ME status was determined based on state policies. Standard univariate statistics were used to compare patients with a diagnosis of non-metastatic cutaneous melanoma between ME and non-ME states. The Kaplan-Meier method and log-rank tests were used to evaluate overall survival (OS) between ME and non-ME states. Multivariable Cox regression models were used to examine associations with OS. RESULTS: Overall, 28.6 % (n = 25,031) of the overall cohort was in ME states. The patients in ME states were more likely to be insured, live in neighborhoods with higher median income quartiles, receive treatment at academic/research cancer centers, have lower stages of disease, and receive surgery than the patients in non-ME states. Kaplan-Meier analysis found enhanced 5-year OS for the patients in ME states across all stages. Cox regression showed improved survival in ME states for stage II (hazard ratio [HR], 0.84) and stage III (HR, 0.75) melanoma. CONCLUSIONS: This study underscores the positive association between ME and improved diagnosis, treatment, and outcomes for patients with non-metastatic cutaneous melanoma. These findings advocate for continued efforts to enhance health care accessibility for vulnerable populations.

Primary cutaneous lymphoma: recommendations for clinical trial design and staging update from the ISCL, USCLC, and EORTC.

The number of patients with primary cutaneous lymphoma (PCL) relative to other non-Hodgkin lymphomas (NHLs) is small and the number of subtypes large. Although clinical trial guidelines have been published for mycosis fungoides/Sézary syndrome, the most common type of PCL, none exist for the other PCLs. In addition, staging of the PCLs has been evolving based on new data on potential prognostic factors, diagnosis, and assessment methods of both skin and extracutaneous disease and a desire to align the latter with the Lugano guidelines for all NHLs. The International Society for Cutaneous Lymphomas (ISCL), the United States Cutaneous LymphomaConsortium (USCLC), and the Cutaneous Lymphoma Task Force of the European Organization for the Research and Treatment of Cancer (EORTC) now propose updated staging and guidelines for the study design, assessment, endpoints, and response criteria in clinical trials for all the PCLs in alignment with that of the Lugano guidelines. These recommendations provide standardized methodology that should facilitate planning and regulatory approval of new treatments for these lymphomas worldwide, encourage cooperative investigator-initiated trials, and help to assess the comparative efficacy of therapeutic agents tested across sites and studies.

Service implications of the revised 2022 National Institute for Health and Care Excellence (NICE) follow-up guidelines for stage IA-IIC melanoma.

BACKGROUND: The 2022 National Institute for Health and Care Excellence melanoma guideline update made significant changes to follow-up. The aim of this study was to assess the impact these changes will have on a national melanoma cohort over a 5-year follow-up interval. METHODS: Anonymized, individual-level, population-scale, linkable primary and secondary care National Health Service data for an 18-year interval (2000-2018) in Wales, UK were analysed. These data were used to predict the number of patients over a 10-year interval (2020-2030) that would be diagnosed with melanoma. Follow-up schedules for the 2015 and 2022 National Institute for Health and Care Excellence melanoma guidelines were then used to calculate the number of clinician-led appointments, the number of radiological investigations, and the total healthcare cost between 2025 and 2030, corresponding to a 5-year patient follow-up interval, for those with stage IA-IIC melanoma. RESULTS: Between 2025 and 2030 it is predicted that implementation of the 2022 guidelines would lead to 21 122 (range 19 194-23 083) fewer clinician-led appointments for patients with stage IA-IIC melanoma. However, there would be a significant increase in the number of radiological investigations (7812; range 7444-8189). These changes would lead to a €2.74 million (€1.87 million-€3.61 million) reduction in the total cost of follow-up over the interval 2025-2030. CONCLUSION: Melanoma follow-up guideline changes will result in a substantial reduction in the number of clinical follow-up appointments, but a significant additional burden to radiological services. The overall cost of follow-up at a national level will be reduced.

Morphoea presenting histopathologically as mycosis fungoides: an illustrative series of four cases.

AIMS: There have been exceptional reports of morphoea presenting with epidermal changes overlapping histopathologically with cutaneous T cell lymphoma of the mycosis fungoides type (MF). This phenomenon gives rise to an ambiguous clinicopathological scenario in which distinguishing these conditions may be challenging. The aim of this study is to characterise the clinical, histopathological and molecular findings of this phenomenon through a case series. METHODS AND RESULTS: Four patients with classical clinical presentation of morphoea but unusual histopathology displaying typical findings of morphoea, together with intra-epidermal CD8 positive lymphocytes indistinguishable from MF, were identified. The clinical phenotypes of morphoea were varied, and they all presented early in the active phase of the disease. They all exhibited intra-epidermal lymphocytes with tagging and cytological atypia. Pautrier-like microabscesses were also seen. Using molecular analysis, two cases showed clonal TCR gene rearrangement. Follow-up of all cases has been consistent with classical morphoea. CONCLUSION: Early morphoea can seldom present with atypical clonal intra-epidermal lymphocytes indistinguishable from MF. The fact that these changes can occur in several different clinical subtypes of morphoea raises the possibility that this could be a pattern of inflammation in early disease more common than currently appreciated.

The clinico-pathological spectrum of plaque-type blue naevi and their potential for malignant transformation.

AIMS: Plaque-type blue naevi are rare melanocytic tumours presenting as large, pigmented plaques at birth or during childhood. There is a risk for malignant transformation, but no larger comprehensive studies exist and the diagnosis is challenging, especially on limited biopsy material. The aim is to describe the clinicopathological features and behaviour of the disease more comprehensively. METHODS AND RESULTS: We retrieved eight plaque-type blue naevi, presenting as large, pigmented plaques (median = 7 cm; range = 3-26) most frequently affecting the scalp (four) followed by the cheek, arm, abdominal wall and gluteal cleft (one each), with a slight female predilection. Median age at time of biopsy was 39.5 years (range = 15-90), but three tumours had been present at birth and one since childhood. Histopathologically, the tumours were poorly circumscribed and composed of cellular fascicles of uniform spindle cells in a background of variably prominent pigmented dendritic cells affecting dermis and subcutaneous tissues. The majority had mutations in GNAQ. One tumour showed malignant transformation, characterised by an expansile nodule of pleomorphic epithelioid melanocytes with rhabdoid morphology, high mitotic activity and areas of necrosis. This patient developed metastatic melanoma to lymph nodes. All patients are alive with a median follow-up of 60 months. CONCLUSION: Plaque-type blue naevi are diagnostically challenging tumours with risk for malignant transformation. Awareness and familiarity with the salient clinicopathological features are necessary for reliable diagnosis, and long-term clinical follow-up is required to monitor for malignant transformation.

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms that objectively categorise the most relevant features in order of importance. The data set comprises 122 tumours (39 benign, 44 atypical and 39 malignant) from four different countries. BRAF and NRAS mutation status was evaluated in 51. Analysis of variance score was performed to rank 22 clinicopathological variables. The Gaussian naive Bayes algorithm achieved in distinguishing Spitz naevus from malignant spitzoid tumours with an accuracy of 0.95 and kappa score of 0.87, utilising the 12 most important variables. For benign versus non-benign Spitz tumours, the test reached a kappa score of 0.88 using the 13 highest-scored features. Furthermore, for the atypical Spitz tumours (AST) versus Spitz melanoma comparison, the logistic regression algorithm achieved a kappa value of 0.66 and an accuracy rate of 0.85. When the three categories were compared most AST were classified as melanoma, because of the similarities on histological features between the two groups. Our results show promise in supporting the histological classification of these tumours in clinical practice, and provide valuable insight into the use of ML to improve the accuracy and objectivity of this process while minimising interobserver variability. These proposed algorithms represent a potential solution to the lack of a clear threshold for the Spitz/spitzoid tumour classification, and its high accuracy supports its usefulness as a helpful tool to improve diagnostic decision-making.

Restrospective reappraisal of the prognostic classification of spitzoid melanocytic neoplasms after BRAF and NRAS mutation characterisation: a single institution experience.

AIMS: The current WHO classification of melanocytic tumours excludes neoplasms showing BRAF or NRAS mutations from the Spitz category. This study aimed to review and reclassify atypical melanocytic tumours with spitzoid morphological features diagnosed between 2009 and 2021 in our hospital after expanding the molecular profile, including BRAF and NRAS mutations in all cases. METHODS AND RESULTS: A total of 71 neoplasms showing spitzoid features (Spitz-like) and atypia were included. The risk of progression of tumours was first studied by integrating the morphology, immunohistochemistry (p16, Ki67, HMB45 and PRAME) and fluorescence in-situ hybridisation (FISH) results (melanoma multiprobe and 9p21). In a second step, after expanding the molecular study, including BRAF and NRAS mutational status, the neoplasms were finally classified into four subgroups: atypical Spitz tumour (AST, n = 45); BRAF-mutated naevus/low-grade melanocytoma with spitzoid morphology (BAMS, n = 2); Spitz melanoma (SM, n = 14); and BRAF or NRAS mutated melanoma with spitzoid features (MSF, n = 10). Follow-up of patients revealed uneventful results for AST and BAMS. Only one SM presented lymph node metastasis after 134 months. Conversely, patients with MSF showed an unfavourable outcome: three developed lymph node metastases after a mean time of 22 months, with one patient presenting distant metastasis and dying of the disease 64 months from diagnosis. The progression-free survival showed significant differences between the four groups of spitzoid tumours (P < 0.001) and between both melanoma subtypes (P = 0.012). CONCLUSIONS: The classification and prognostication of atypical neoplasms with spitzoid features requires the integration of histomorphology with the molecular investigation of tumours, which should include BRAF and NRAS mutational status.

Evaluation of High-Dimensional Data Classification for Skin Malignancy Detection Using DL-Based Techniques.

Skin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data's underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data's complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer.

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning.

Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

Identifying and addressing unmet clinical needs in primary cutaneous B-cell lymphoma: A consensus-based paper from an ad-hoc international panel.

Primary cutaneous B-cell lymphomas (PCBCLs) are lymphoproliferative disorders that appear on the skin without evidence of extracutaneous manifestations at the time of diagnosis. There is a lack of evidence-based guidelines for their clinical management due to the availability of very few large scale studies and controlled clinical trials. Here we present and discuss a series of major unmet clinical needs (UCNs) in the management of PCBCLs by a panel of 16 experts involved in research and clinical practice of PCBCL. The Panel produced recommendations on the appropriateness of the clinical decisions concerning the identified clinical needs and proposed research for improving the knowledge needed to solve them. Recommendations and proposals were achieved by multiple-step formalized procedures to reach a consensus after a comprehensive analysis of the scientific literature. Recommendations and proposals lay in the domain of classification uncertainties of PCBCL, optimization of diagnosis, optimization of prognosis, optimization of staging and critical issues on therapeutic strategies with particular focus on new treatments. These recommendations are intended for use not only by experts but above all by dermatologists and hematologists with limited experience in the field of PCBCLs as well as general practitioners.

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial.

BACKGROUND: Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics. OBJECTIVES: To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. METHODS: This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data. RESULTS: In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. CONCLUSIONS: The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.