The potential of x-ray virtual histology in the diagnosis of skin tumors.

BACKGROUND: Histopathological analysis represents the gold standard in clinical practice for diagnosing skin neoplasms. While the current diagnostic workflow has specialized in producing robust and accurate results, interpreting tissue architecture and malignant cellular morphology correctly remains one of the greatest challenges for pathologists. This paper aims to explore the prospect of applying x-ray virtual histology to human skin tumor excisions and correlating it with the histological validation. MATERIALS AND METHODS: Seven skin biopsies containing intriguing melanoma types and pigmented skin lesions were scanned using x-ray Computed micro-Tomography (µCT) and then sectioned for conventional histology assessment. RESULTS: The tissue microarchitecture reconstructed by µCT offers detailed insights into diagnosing the malignancy or benignity of the skin lesions. Three-dimensional reconstruction via x-ray virtual histology reveals infiltrative patterns in basal cell carcinoma and evaluated invasiveness in melanoma. The technology enables the identification of pagetoid distributions of neoplastic cells and the assessment of melanoma depth in three dimensions. CONCLUSION: Although the proposed approach is not intended to replace conventional histology, the non-destructive nature of the sample and the clarity provided by virtual inspection demonstrate the promising impact of µCT as a valid support method prior to conventional histological sectioning. Indeed, µCT images can suggest the optimal sectioning position before using a microtome, as is commonly performed in histological practice. Moreover, the three-dimensional nature of the proposed approach paves the way for a more accurate assessment of significant prognostic factors in melanoma, such as Breslow thickness, by considering the whole micro-volume rather than a two-dimensional observation.

Lentigo Maligna and Lentigo Maligna Melanoma of the External Ear: Clinical and Dermoscopic Features of 19 Patients.

External ear lentigo maligna/lentigo melanoma (LM/LMM) represents approximately 1%-4% of all primary cutaneous melanomas. Over the past 20 years, dermoscopy has proven highly effective in early detection of LM/LMM, with recent studies identifying perifollicular linear projections (PLP) as a specific diagnostic criterion for early LM. However, in clinical practice, LM and LMM turn out to be very difficult to distinguish based on dermoscopic findings. Therefore, our retrospective monocentric study aimed to investigate dermoscopic characteristics, as well as the epidemiological and clinical data of 19 patients diagnosed with the external ear (EE) LM/LMM at the Oncologic Dermatology Unit in Bologna. Dermoscopic images were obtained using the FotoFinder Medicam 800HD, and specific criteria validated by the International Dermoscopy Society (IDS) for atypical pigmented facial lesions were assessed. Fisher's exact test was primarily used for statistical comparisons. As results, most of the patients were male (74%) with an average age (± SD) at diagnosis of 69.8 (± 15.1) years old. LMM appeared more commonly observed in elderly patients as compared to LM (mean 71.6 vs. 66.7, p = 0.514), presenting as pigmented macule (89.5%) of the ear lobule (23.9%). A statistically significant difference (p = 0.01) of tumour' diameter between LMM and LM was reported with the first resulting more than twice the size of the latter. Concerning dermoscopic findings, asymmetric pigmented follicles, obliteration of the follicular openings and grey circles were more frequently observed in LMM compared to LM (63.2% vs. 31.6%; 63.2% vs. 26.3%; 47.4% vs. 15.8%, respectively).

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images.

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Thermal Signature of Pigmented Lesions as Observed by Digital Infrared Thermal Imaging.

This study examined the thermal signature of pigmented lesions observed by digital infrared thermal imaging as a possible adjunct to physician diagnosis. Thermal images of pigmented lesions were compared to clinical examination by a plastic surgeon interested in skin diseases, dermatoscopy, and histopathology. A total of 35 patients with 55 pigmented skin lesions were considered. We found that all lesions emitting a dark signal on thermal imaging, compared to the nearby skin, were benign, while only one of all benign lesions (1.9%) had a bright "warm" signal. Benign lesions with papule/nodular morphology were dark in 87.5% of patients. All lesions diagnosed as malignant melanoma, both dermatoscopically and histologically, had plaque morphology; yet, only half demonstrated some signals on thermal imaging. Based on these results, we concluded that thermal imaging could be used as an adjunct to diagnosis when examining skin lesions. This study provided an introduction to using thermal imaging for spotting skin lesions.

Skin lesion segmentation using deep learning algorithm with ant colony optimization.

BACKGROUND: Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis. OBJECTIVE: This paper seeks to evaluate the effectiveness of the Hybrid ResUNet model for skin lesion classification and assess its impact on optimizing ACO performance to bridge the gap between computational efficiency and clinical utility. METHODS: The study used a deep learning design on a complex dataset that included a variety of skin lesions. The method includes training a Hybrid ResUNet model with standard parameters and fine-tuning using ACO for hyperparameter optimization. Performance was evaluated using traditional metrics such as accuracy, dice coefficient, and Jaccard index compared with existing models such as residual network (ResNet) and U-Net. RESULTS: The proposed hybrid ResUNet model exhibited excellent classification accuracy, reflected in the noticeable improvement in all evaluated metrics. His ability to describe complex lesions was particularly outstanding, improving diagnostic accuracy. Our experimental results demonstrate that the proposed Hybrid ResUNet model outperforms existing state-of-the-art methods, achieving an accuracy of 95.8%, a Dice coefficient of 93.1%, and a Jaccard index of 87.5. CONCLUSION: The addition of ResUNet to ACO in the proposed Hybrid ResUNet model significantly improves the classification of skin lesions. This integration goes beyond traditional paradigms and demonstrates a viable strategy for deploying AI-powered tools in clinical settings. FUTURE WORK: Future investigations will focus on increasing the version's abilities by using multi-modal imaging information, experimenting with alternative optimization algorithms, and comparing real-world medical applicability. There is also a promising scope for enhancing computational performance and exploring the model's interpretability for more clinical adoption.

Clinical and Dermoscopic Patterns of Basal Cell Carcinoma and Its Mimickers in Skin of Color: A Practical Summary.

The diagnosis of basal cell carcinoma (BCC) in dark phototypes can be a challenging task due to the lack of relevant clues and its variable presentation. In this regard, there is growing evidence that dermoscopy may benefit the recognition of BCC even for skin of color (SoC). The objective of this review is to provide an up-to-date overview on clinical and dermoscopic patterns of BCC in SoC, also comparing such findings with those of the main clinical mimickers reported in the literature. A comprehensive search of the literature through the PubMed electronic database was carried out in order to identify papers describing the clinical and dermoscopic features of BCC in dark phototypes (IV-VI). By finding macroscopic clinical presentations of BCCs in SoC patients and any possible clinical mimickers considered in the retrieved papers, we built a differential diagnosis list and analyzed the dermoscopic findings of such conditions to facilitate the diagnosis of BCC. BCC in darker skin may present as pigmented nodular lesions, pigmented patches or plaques, ulcers, erythematous nodular lesions, erythematous plaques or patches, or scar-like lesions, depending on its subtype and body site. The differential diagnosis for BCC in patients with SoC includes squamous cell carcinoma, melanoma, nevi, adnexal tumors and sebaceous keratosis. Additionally, it differs from that of Caucasians, as it also includes lesions less common in fair skin, such as dermatosis papulosa nigra, melanotrichoblastoma, and pigmented dermatofibrosarcoma protuberans, and excludes conditions like actinic keratosis and keratoacanthoma, which rarely appear in darker skin. The resulting differences also include infectious diseases such as deep cutaneous mycosis and inflammatory dermatoses. The most prevalent differentiating dermoscopic feature for BCC includes blue, black and gray dots, though arborizing vessels still remain the predominant BCC feature, even in dark phototypes. Diagnostic approach to BCC in dark-skinned patients varies due to the prevalence of dermoscopy findings associated with hyperpigmented structures. Clinicians should be aware of such points of differentiation for a proper management of this tumor in SoC.

Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study.

BACKGROUND: To evaluate the role of the novel quantitative imaging biomarker (QIB) SUVX% of 18F-FDG uptake extracted from early 18F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patients with metastatic melanoma (mM) patients receiving immune-checkpoint inhibitors (ICI). PATIENTS AND METHODS: In this prospective non-interventional, one-centre clinical study, patients with mM, receiving ICI treatment, were regularly followed by 18F-FDG PET/CT. Patients were scanned at baseline, early point at week four (W4), week sixteen (W16) and week thirty-two (W32) after ICI initiation. A convolutional neural network (CNN) was used to segment three organs: lung, bowel, thyroid. QIB of irAE - SUVX% - was analyzed within the target organs and correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. RESULTS: A total of 242 18F-FDG PET/CT images of 71 mM patients were prospectively collected and analysed. The early W4 scan showed improved detection only for the thyroid gland compared to W32 scan (p=0.047). The AUROC for detection of irAE in the three target organs was highest when SUVX% was extracted from W16 scan and was 0.76 for lung, 0.53 for bowel and 0.81 for thyroid. SUVX% extracted from W4 scan did not improve detection of irAE compared to W16 scan (lung: p = 0.54, bowel: p = 0.75, thyroid: p = 0.3, DeLong test), as well as compared to W32 scan in lungs (p = 0.32) and bowel (p = 0.3). CONCLUSIONS: Early time point 18F-FDG PET/CT at W4 did not lead to statistically significant earlier detection of irAE. However, organ 18F-FDG uptake as quantified by SUVX% proved to be a consistent QIB of irAE. To better assess the role of 18F-FDG PET/CT in irAE detection, the time evolution of 18F-FDG PET/CT quantifiable inflammation would be of essence, only achievable in multi centric studies.

Comparing 3D imaging devices for the measurement of cutaneous neurofibromas in patients with Neurofibromatosis Type 1.

BACKGROUND: Cutaneous neurofibromas (cNFs) are a major cause of disfigurement in patients with Neurofibromatosis Type 1 (NF1). However, clinical trials investigating cNF treatments lack standardised outcome measures to objectively evaluate changes in cNF size and appearance. 3D imaging has been proposed as an objective standardised outcome measure however various systems exist with different features that affect useability in clinical settings. The aim of this study was to compare the accuracy, precision, feasibility, reliability and accessibility of three imaging systems. MATERIALS AND METHODS: We compared the Vectra-H1, LifeViz-Micro and Cherry-Imaging systems. A total of 58 cNFs from 13 participants with NF1 were selected for imaging and analysis. The primary endpoint was accuracy as measured by comparison of measurements between imaging systems. Secondary endpoints included reliability between two operators, precision as measured with the average coefficient of variation, feasibility as determined by time to capture and analyse an image and accessibility as determined by cost. RESULTS: There was no significant difference in accuracy between the three devices for length or surface area measurements (p > 0.05), and reliability and precision were similar. Volume measurements demonstrated the most variability compared to other measurements; LifeViz-Micro demonstrated the least measurement variability for surface area and image capture and analysis were fastest with LifeViz-Micro. LifeViz-Micro was better for imaging smaller number of cNFs (1-3), Vectra-H1 better for larger areas and Cherry for uneven surfaces. CONCLUSIONS: All systems demonstrated excellent reliability but possess distinct advantages and limitations. Surface area is the most consistent and reliable parameter for measuring cNF size in clinical trials.

Dual-Modal Near-Infrared Organic Nanoparticles: Integrating Mild Hyperthermia Phototherapy with Fluorescence Imaging.

Purpose: Our study seeks to develop dual-modal organic-nanoagents for cancer therapy and real-time fluorescence imaging, followed by their pre-clinical evaluation on a murine model. Integrating NIR molecular imaging with nanotechnology, our aim is to improve outcomes for early-stage cutaneous melanoma by offering more effective and less invasive methods. This approach has the potential to enhance both photothermal therapy (PTT) and Sentinel Lymph Node Biopsy (SLNB) procedures for melanoma patients. Methods: NIR-797-isothiocyanate was encapsulated in poly(D,L-lactide-co-glycolide) acid (PLGA) nanoparticles (NPs) using a two-step protocol, followed by thorough characterization, including assessing loading efficiency, fluorescence stability, and photothermal conversion. Biocompatibility and cellular uptake were tested in vitro on melanoma cells, while PTT assay, with real-time thermal monitoring, was performed in vivo on tumor-bearing mice under irradiation with an 808 nm laser. Finally, ex vivo fluorescence microscopy, histopathological assay, and TEM imaging were performed. Results: Our PLGA NPs, with a diameter of 270 nm, negative charge, and 60% NIR-797 loading efficiency, demonstrated excellent stability and fluorescence properties, as well as efficient light-to-heat conversion. In vitro studies confirmed their biocompatibility and cellular internalization. In vivo experiments demonstrated their efficacy as photothermal agents, inducing mild hyperthermia with temperatures reaching up to 43.8 °C. Ex vivo microscopy of tumor tissue confirmed persistent NIR fluorescence and uniform distribution of the NPs. Histopathological and TEM assays revealed early apoptosis, immune cell response, ultrastructural damage, and intracellular material debris resulting from combined NP treatment and irradiation. Additionally, TEM analyses of irradiated zone margins showed attenuated cellular damage, highlighting the precision and effectiveness of our targeted treatment approach. Conclusion: Specifically tailored for dual-modal NIR functionality, our NPs offer a novel approach in cancer PTT and real-time fluorescence monitoring, signaling a promising avenue toward clinical translation.

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Interobserver agreement on line-field confocal optical coherence tomography image markers in keratinocyte carcinomas and precursor lesions.

Line-field confocal optical coherence tomography (LC-OCT) is a new technology for skin cancer diagnostics. However, the interobserver agreement (IOA) of known image markers of keratinocyte carcinomas (KC), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), as well as precursors, SCC in situ (CIS) and actinic keratosis (AK), remains unexplored. This study determined IOA on the presence or absence of 10 key LC-OCT image markers of KC and precursors, among evaluators new to LC-OCT with different levels of dermatologic imaging experience. Secondly, the frequency and association between reported image markers and lesion types, was determined. Six evaluators blinded to histopathologic diagnoses, assessed 75 LC-OCT images of KC (21 SCC; 21 BCC), CIS (12), and AK (21). For each image, evaluators independently reported the presence or absence of 10 predefined key image markers of KCs and precursors described in an LC-OCT literature review. Evaluators were stratified by experience-level as experienced (3) or novices (3) based on previous OCT and reflectance confocal microscopy usage. IOA was tested for all groups, using Conger's kappa coefficient (κ). The frequency of reported image marker and their association with lesion-types, were calculated as proportions and odds ratios (OR), respectively. Overall IOA was highest for the image markers lobules (κ = 0.68, 95% confidence interval (CI) 0.57;0.78) and clefting (κ = 0.63, CI 0.52;0.74), typically seen in BCC (94%;OR 143.2 and 158.7, respectively, p < 0.001), followed by severe dysplasia (κ = 0.42, CI 0.31;0.53), observed primarily in CIS (79%;OR 7.1, p < 0.001). The remaining seven image-markers had lower IOA (κ = 0.06-0.32) and were more evenly observed across lesion types. The lowest IOA was noted for a well-defined (κ = 0.07, CI 0;0.15) and interrupted dermal-epidermal junction (DEJ) (κ = 0.06, CI -0.002;0.13). IOA was higher for all image markers among experienced evaluators versus novices. This study shows varying IOA for 10 key image markers of KC and precursors in LC-OCT images among evaluators new to the technology. IOA was highest for the assessments of lobules, clefting, and severe dysplasia while lowest for the assessment of the DEJ integrity.

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic categories might be helpful. METHODS: A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Dermoscopic images were classified by a binary CNN trained to differentiate melanocytic from non-melanocytic lesions (FotoFinder Systems, Bad Birnbach, Germany). Primary endpoint was the accuracy of the CNN's classification in comparison with dermatologists reviewing level-II information. Secondary endpoints included dermatologists' accuracies according to their level of experience and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed an accuracy and ROC AUC with corresponding 95 % confidence intervals (CI) of 91.0 % (83.8 % to 95.2 %) and 0.981 (0.962 to 1). In level I, dermatologists showed a mean accuracy of 83.7 % (82.5 % to 84.8 %). With level II information, the accuracy improved to 87.8 % (86.7 % to 88.9 %; p < 0.001). When comparing accuracies of CNN and dermatologists in level II, the CNN's accuracy was higher (91.0 % versus 87.8 %, p < 0.001). For experts with level II information results were on par with the CNN (91.0 % versus 90.4 %, p = 0.368). CONCLUSIONS: The tested CNN accurately differentiated melanocytic from non-melanocytic skin lesions and outperformed dermatologists. The CNN may support clinicians and could be used in an ensemble approach combined with other CNN models.

Melanoma imaging and diagnosis: What does the future hold?

BACKGROUND: In Australia, artificial intelligence (AI) is increasingly being used in the field of melanoma diagnosis. Early diagnosis is arguably the most important prognostic factor for melanoma survival. The use of digital monitoring of naevi, especially dysplastic naevi, might reduce the number of biopsies needed in managing patients at risk of melanoma, especially in patients with high naevi counts. OBJECTIVE: This article discusses advances in imaging and early diagnosis including the use of AI in this process. DISCUSSION: The benefits of performing biopsies must be balanced with the potential to cause harm. Whole-body imaging can assist with more accurate detection of changing lesions and enable clinicians to focus on lesions where change is detected.

Fractional differentiation based image enhancement for automatic detection of malignant melanoma.

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

[Translated article] Dermoscopy of Squamous Cell Carcinoma: From Actinic Keratosis to Invasive Forms.

When the dermoscopy of squamous cell carcinoma and its precursors we differentiate among keratin-related, vascular, and pigment-related criteria. Non-pigmented actinic keratoses are characterized by the "strawberry pattern". Pigmented actinic keratosis shows a significant dermatoscopic overlap with lentigo maligna, but the presence of pigmented scales, erythema, and prominent follicles favors its diagnosis. Bowen's disease is characterized by clustered glomerular vessels, white-yellowish scales, and brown or grey dots arranged in lines in its pigmented variant. Finally, dermoscopy allows us to detect invasive squamous cell carcinoma in its early stages and differentiate it from its precursors. Furthermore, its presentation may vary depending on the degree of differentiation, with keratin-associated criteria predominating in well-differentiated tumors, while an atypical vascular pattern will predominate in poorly differentiated tumors.