Line-field confocal optical coherence tomography in dermato-oncology: A literature review towards harmonized histopathology-integrated terminology.

Non-invasive diagnostics like line-field confocal optical coherence tomography (LC-OCT) are being implemented in dermato-oncology. However, unification of terminology in LC-OCT is lacking. By reviewing the LC-OCT literature in the field of dermato-oncology, this study aimed to develop a unified terminological glossary integrated with traditional histopathology. A PRISMA-guided literature-search was conducted for English-language publications on LC-OCT of actinic keratosis (AK), keratinocyte carcinoma (KC), and malignant melanoma (MM). Study characteristics and terminology were compiled. To harmonize LC-OCT terminology and integrate with histopathology, synonymous terms for image features of AK, KC, and MM were merged by two authors, organized by skin layer and lesion-type. A subset of key LC-OCT image-markers with histopathological correlates that in combination were typical of AK, squamous cell carcinoma in situ (SCCis), invasive squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and MM in traditional histopathology, were selected from the glossary by an experienced dermatopathologist. Seventeen observational studies of AK (7 studies), KC (13 studies), MM (7 studies) utilizing LC-OCT were included, with 117 terms describing either AK, KC, or MM. These were merged to produce 45 merged-terms (61.5% reduction); 5 assigned to the stratum corneum (SC), 23 to the viable epidermis, 2 to dermo-epidermal junction (DEJ) and 15 to the dermis. For each lesion, mandatory key image-markers were a well-defined DEJ and presence of mild/moderate but not severe epidermal dysplasia for AK, severe epidermal dysplasia and well-defined DEJ for SCCis, interrupted DEJ and/or dermal broad infiltrative strands for invasive SCC, dermal lobules connected and/or unconnected to the epidermis for BCC, as well as single atypical melanocytes and/or nest of atypical melanocytes in the epidermis or dermis for MM. This review compiles evidence on LC-OCT in dermato-oncology, providing a harmonized histopathology-integrated terminology and key image-markers for each lesion. Further evaluation is required to determine the clinical value of these findings.

The utility of ultrasound for the diagnosis and treatment of primary and metastatic melanoma - a remaining oncological challenge.

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LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution.

The rising occurrence and notable public health consequences of skin cancer, especially of the most challenging form known as melanoma, have created an urgent demand for more advanced approaches to disease management. The integration of modern computer vision methods into clinical procedures offers the potential for enhancing the detection of skin cancer . The UNet model has gained prominence as a valuable tool for this objective, continuously evolving to tackle the difficulties associated with the inherent diversity of dermatological images. These challenges stem from diverse medical origins and are further complicated by variations in lighting, patient characteristics, and hair density. In this work, we present an innovative end-to-end trainable network crafted for the segmentation of skin cancer . This network comprises an encoder-decoder architecture, a novel feature extraction block, and a densely connected multi-rate Atrous convolution block. We evaluated the performance of the proposed lightweight skin cancer segmentation network (LSCS-Net) on three widely used benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, and ISIC 2018. The generalization capabilities of LSCS-Net are testified by the excellent performance on breast cancer and thyroid nodule segmentation datasets. The empirical findings confirm that LSCS-net attains state-of-the-art results, as demonstrated by a significantly elevated Jaccard index.

Comparison of 18 F-AlF-NOTA-PRGD2 and 18 F-FDG PET/CT Imaging in a Case of Primary Uveal Melanoma.

ABSTRACT: Uveal melanoma is the most common intraocular malignancy in adults with a high rate of metastasis and mortality. This study presented the PET/CT imaging of 18 F-AlF-NOTA-PRGD2 and 18 F-FDG in a patient with primary uveal melanoma. In addition to fundus photograph and ophthalmic ultrasonography, both 18 F-AlF-NOTA-PRGD2 and 18 F-FDG PET/CT imaging showed increased radioactive uptake in the lesions within the scan area. The tumoral lesions presented significantly higher uptake of 18 F-AlF-NOTA-PRGD2 compared with that of 18 F-FDG.

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models.

Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.

Multimodality imaging review of metastatic melanoma involving the breast.

Melanoma is among the most commonly reported non-mammary primary tumors to metastasize to the breast. Unfortunately, evidence of melanoma metastasis to any site portends a poor prognosis. Imaging studies can be useful in the early detection of metastatic melanoma which is essential for appropriate management of this disease. There have been very few previous studies on the imaging findings of metastatic melanoma especially across multiple imaging modalities. This review aims to describe these imaging features seen on mammography, ultrasound, magnetic resonance imaging (MRI) and fluorodeoxyglucose-positron emission tomography computed tomography (FDG PET/CT) using three case examples. Our findings, consistent with previous studies, describe melanoma metastases to the breast as largely non-specific, round or oval masses with circumscribed margins and homogeneous internal enhancement.

Immune subtyping of melanoma whole slide images using multiple instance learning.

Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P< 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.

Caliper, contrast enhanced-ultrasound or laser speckle contrast imaging: Techniques to follow mice melanoma growth.

Due to morphological characteristics, metastatic melanoma is a cancer for which vascularization is not a diagnostic criterion. Laser speckle contrast imaging (LSCI) and contrast enhanced ultrasound (CEUS) are two imaging techniques that will be explored in this study, which aims to confirm these two techniques for monitoring tumor vascularization. B16F10 cells were xenografted to C57BL/6 mice treated with anti-PD1 or 0.9% NaCl. Tumor volume was measured daily while CEUS and LSCI were performed weekly. LSCI and CEUS analyses showed a decrease in tumor perfusion in both groups of mice. Although both CEUS and LSCI are useful for measuring tumor volume, LSCI appears to be more robust and effective for monitoring tumor microcirculation. Non-invasive investigations are needed to better predict tumor vascularization: CEUS and LSCI have a good applicability in a mice model.

Integrated lithium niobate microwave photonic processing engine.

Integrated microwave photonics (MWP) is an intriguing technology for the generation, transmission and manipulation of microwave signals in chip-scale optical systems1,2. In particular, ultrafast processing of analogue signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters3-5, microwave signal processing6-9 and image recognition10,11. An ideal integrated MWP processing platform should have both an efficient and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power and also a low-loss functional photonic network to implement various signal-processing tasks. Moreover, large-scale, low-cost manufacturability is required to monolithically integrate the two building blocks on the same chip. Here we demonstrate such an integrated MWP processing engine based on a 4 inch wafer-scale thin-film lithium niobate platform. It can perform multipurpose tasks with processing bandwidths of up to 67 GHz at complementary metal-oxide-semiconductor (CMOS)-compatible voltages. We achieve ultrafast analogue computation, namely temporal integration and differentiation, at sampling rates of up to 256 giga samples per second, and deploy these functions to showcase three proof-of-concept applications: solving ordinary differential equations, generating ultra-wideband signals and detecting edges in images. We further leverage the image edge detector to realize a photonic-assisted image segmentation model that can effectively outline the boundaries of melanoma lesion in medical diagnostic images. Our ultrafast lithium niobate MWP engine could provide compact, low-latency and cost-effective solutions for future wireless communications, high-resolution radar and photonic artificial intelligence.

A novel NIR fluorescent probe inhibits melanoma progression through apoptosis and ERK/DRP1-mediated mitochondrial fission.

Melanoma, a highly metastatic malignant tumour, necessitated early detection and intervention. This study focuses on a hemicyanine fluorescent probe activated by near-infrared (NIR) light for bioimaging and targeted mitochondrial action in melanoma cells. IR-418, our newly designed hemicyanine-based NIR fluorescent probe, demonstrated effective targeting of melanoma cell mitochondria for NIR imaging. In vitro and in vivo experiments revealed IR-418's inhibition of melanoma growth through the promotion of mitochondrial apoptosis (Bax/Bcl-2/Cleaved Caspase pathway). Moreover, IR-418 inhibited melanoma metastasis by inhibiting mitochondrial fission through the ERK/DRP1 pathway. Notably, IR-418 mitigated abnormal ATL and ASL elevations caused by tumours without inflicting significant organ damage, indicating its high biocompatibility. In conclusion, IR-418, a novel hemicyanine-based NIR fluorescent probe targeting the mitochondria, exhibits significant fluorescence imaging capability, anti-melanoma proliferation, anti-melanoma lung metastasis activities and high biosafety. Therefore, it has significant potential in the early diagnosis and treatment of melanoma.

Multiple metastasis of malignant melanoma. The diagnostic role of ultrasonography starting from a parotid tumor.

We presented the case of a patient with a slowly developing right parotid tumor over the course of several years. Multimodal ultrasonography proved relevant for malignancy. Subsequent imaging identified tumors in numerous organs, considered metastases. Ultrasonography consolidated all identified aspects. Biopsy from an adrenal mass and histological examination evidenced the presence of a malignant, metastatic melanoma with cutaneous origin. Considerations are made regarding the role of ultrasonography in such cases.

Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis.

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using the same evaluation technique on patient cells. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.

Positron Emission Tomography/Computed Tomography Transformation of Oncology: Melanoma and Skin Malignancies.

Skin cancers are the most common cancers, with melanoma resulting in the highest cause of death in this category. Accurate clinical, histologic, and imaging staging with fludeoxyglucose positron emission tomography (FDG PET) is most important to guide patient management. Whilst surgical excision with clear margins is the gold-standard treatment for primary cutaneous melanoma, targeted therapies have generated remarkable and rapid clinical responses in melanoma, for which FDG PET also plays an important role in assessment of treatment response and post-therapy surveillance. Non-FDG PET tracers, advanced PET technology, and PET radiomics may potentially change the landscape of the utilization of PET in the imaging of patients with cutaneous malignancies.

Single-Photon Emission Computed Tomography/Computed Tomography Utilization for Extremity Melanomas at a High-Volume Center.

INTRODUCTION: Planar lymphoscintigraphy (PL) is commonly used in mapping before sentinel lymph node biopsy (SLNB) for invasive cutaneous melanoma. Recently, single-photon emission computed tomography (SPECT)/ computed tomography (CT) has been utilized, in addition to PL, for detailed anatomic information and detection of sentinel lymph nodes (SLNs) outside of the primary nodal basin in truncal and head and neck melanoma. Following a protocol change due to COVID-19, our institution began routinely obtaining both PL and SPECT-CT imaging for all melanoma SLN mapping. We hypothesized that SPECT-CT is associated with higher instances of SLNBs from "nontraditional" nodal basins (NTNB) for extremity melanomas. METHODS: Patients with extremity melanoma (2017-2022) who underwent SLNB were grouped into SPECT-CT with PL versus PL alone. Outcomes were total SLNs removed, + or-SLN status, total NTNB sampled, and postoperative complication rate. Poisson regression and logistic regression models were used to assess association of SPECT-CT with patient outcomes. RESULTS: Of 380 patients with extremity melanoma, 42.11% had SPECT-CT. There were no differences between the groups with regards to age at diagnosis or sex. From 2020 to 2022, all patients underwent SPECT-CT. SPECT-CT was associated with increased odds of SLNB from an NTNB, (odds ratio = 2.39 [95% confidence interval: 1.25-4.67]). There was no difference in odds of number of SLNs sampled, SLN positivity rate, or postoperative complication rate with SPECT-CT. CONCLUSIONS: Routine SPECT-CT was associated with higher incidence of SLNB in NTNB but did not increase number of SLNs removed or SLN positivity rate. The added value of routine SPECT-CT in cutaneous melanoma of the extremities remains to be defined.

Pre-treatment 18F-FDG-PET/CT parameters as biomarkers for progression free survival, best overall response and overall survival in metastatic melanoma patients undergoing first-line immunotherapy.

BACKGROUND: Checkpoint inhibitors have drastically improved the therapy of patients with advanced melanoma. 18F-FDG-PET/CT parameters might act as biomarkers for response and survival and thus can identify patients that do not benefit from immunotherapy. However, little literature exists on the association of baseline 18F-FDG-PET/CT parameters with progression free survival (PFS), best overall response (BOR), and overall survival (OS). MATERIALS AND METHODS: Using a whole tumor volume segmentation approach, we investigated in a retrospective registry study (n = 50) whether pre-treatment 18F-FDG-PET/CT parameters of three subgroups (tumor burden, tumor glucose uptake and non-tumoral hematopoietic tissue metabolism), can act as biomarkers for the primary endpoints PFS and BOR as well as for the secondary endpoint OS. RESULTS: Compared to the sole use of clinical parameters, baseline 18F-FDG-PET/CT parameters did not significantly improve a Cox proportional-hazard model for PFS (C-index/AIC: 0.70/225.17 and 0.68/223.54, respectively; p = 0.14). A binomial logistic regression analysis for BOR was not statistically significant (χ2(15) = 16.44, p = 0.35), with a low amount of explained variance (Nagelkerke's R2 = 0.38). Mean FDG uptake of the spleen contributed significantly to a Cox proportional-hazard model for OS (HR 3.55, p = 0.04). CONCLUSIONS: The present study could not confirm the capability of the pre-treatment 18F-FDG-PET/CT parameters tumor burden, tumor glucose uptake and non-tumoral hematopoietic tissue metabolism to act as biomarkers for PFS and BOR in metastatic melanoma patients receiving first-line immunotherapy. The documented potential of 18F-FDG uptake by immune-mediating tissues such as the spleen to act as a biomarker for OS has been reproduced.

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel 'F' Flag feature for early detection. This novel 'F' indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN 'F' Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the 'F' indicator, resulting in the highest specificity of + 7.34%.

[Translated article] Reflectance Confocal Microscopy Terminology in Spanish: A Delphi Consensus Study.

The terminology used to describe reflectance confocal microscopy (RCM) findings in both melanocytic and nonmelanocytic lesions has been standardized in English. We convened a panel of Spanish-speaking RCM experts and used the Delphi method to seek consensus on which Spanish terms best describe RCM findings in this setting. The experts agreed on 52 terms: 28 for melanocytic lesions and 24 for nonmelanocytic lesions. The resulting terminology will facilitate homogenization, leading to a better understanding of structures, more standardized descriptions in clinical registries, and easier interpretation of clinical reports exchanged between dermatologists.

Concordance between reflectance confocal microscopy and histopathology for the diagnosis of acral lentiginous melanoma.

BACKGROUND: Acral lentiginous melanoma (ALM) is a highly malignant and invasive type of melanoma with unique locations of onset. Its incidence is increasing and early diagnosis is challenging. Reflectance confocal microscopy (RCM) is a non-invasive technique that provides an accurate image of tissue pathology. There are few reports on the use of RCM for the assessment of ALM. MATERIALS AND METHODS: In this retrospective study, data from 31 patients with a clinical diagnosis of ALM were collected. RCM image features were compared with histopathological findings to determine the concordance between the two methods. The sensitivity, specificity, positive predictive value, and negative predictive value of RCM for the diagnosis of ALM were evaluated. RESULTS: RCM and histopathology findings were concordant in 29 of 31 patients (93.5%). There were no false-negative results, although there were two false positives in RCM diagnosis. The sensitivity of RCM for diagnosing ALM was 100%, specificity was 50%, positive predictive value was 93.1%, and negative predictive value was 100%. CONCLUSIONS: RCM showed substantial concordance with histopathology in the diagnosis of ALM. It is a reliable and valuable non-invasive diagnostic tool that holds promise for the early diagnosis of ALM.

Ultrasound-enhanced nano catalyst with ferroptosis-apoptosis combined anticancer strategy for metastatic uveal melanoma.

Uveal melanoma is the most common primary ocular tumor owing to its highly invasive and metastatic characteristics. Currently, standard clinical treatment has an unsatisfied curative effect due to the lack of an effective approach to inhibit the tumor metastasis. Therefore, it is necessary to develop a new strategy that can both restraint local tumors and suppress the ocular tumor metastasis. Herein, we developed ultrasound-responsive nanoparticles (FeP NPs) that can both hinder the growth of in situ ocular tumor and prevent the tumor metastasis through the ferroptosis-apoptosis combined-anticancer strategy. The FeP NPs were assembling by stimulating gallic acid-Fe (III) and paclitaxel, then could be internalized into tumor cells under the cooperative effect of ultrasound, which further activates the intracellular Fenton reaction and generates high reactive oxygen species levels, ultimately leading to mitochondrial damage, lipid per-oxidation, and apoptosis. The FeP NPs can efficiently inhibit the tumor growth in an orthotopic uveal melanoma model. More importantly, the level of the promoting-metastatic factor nerve growth factor receptor (NGFR) secreted by cancer cells is significantly reduced, further limits cancer metastasis to the cervical lymph node and finally inhibits lung metastasis of uveal melanoma. We believe that these designed ultrasound-enhanced nanoparticles possess potential clinical application for preventing the regeneration and metastasis of uveal melanoma.