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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.
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Carcinoma Basocelular , Carcinoma de Células Escamosas , Queratinocitos , Queratosis Actínica , Variaciones Dependientes del Observador , Neoplasias Cutáneas , Tomografía de Coherencia Óptica , Humanos , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico , Tomografía de Coherencia Óptica/métodos , Carcinoma Basocelular/diagnóstico por imagen , Carcinoma Basocelular/patología , Carcinoma Basocelular/diagnóstico , Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/patología , Queratinocitos/patología , Queratosis Actínica/diagnóstico por imagen , Queratosis Actínica/patología , Queratosis Actínica/diagnóstico , Microscopía Confocal/métodos , Lesiones Precancerosas/diagnóstico por imagen , Lesiones Precancerosas/patología , Femenino , Masculino , Anciano , Persona de Mediana EdadRESUMEN
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.
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Algoritmos , Aprendizaje Profundo , Dermoscopía , Neoplasias Cutáneas , Humanos , Dermoscopía/métodos , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/clasificación , Neoplasias Cutáneas/patología , Interpretación de Imagen Asistida por Computador/métodos , Bases de Datos Factuales , Piel/diagnóstico por imagen , Piel/patologíaRESUMEN
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.
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Rayos Infrarrojos , Melanoma , Neoplasias Cutáneas , Termografía , Humanos , Termografía/métodos , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico , Neoplasias Cutáneas/diagnóstico por imagen , Melanoma/patología , Melanoma/diagnóstico , Melanoma/diagnóstico por imagen , Femenino , Masculino , Adulto , Persona de Mediana Edad , Dermoscopía/métodos , Anciano , Adulto Joven , AdolescenteRESUMEN
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.
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Inteligencia Artificial , Melanoma , Humanos , Melanoma/diagnóstico , Melanoma/diagnóstico por imagen , Australia , Inteligencia Artificial/tendencias , Neoplasias Cutáneas/diagnóstico , Neoplasias Cutáneas/diagnóstico por imagen , Detección Precoz del Cáncer/métodos , Detección Precoz del Cáncer/tendenciasRESUMEN
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.
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Imagenología Tridimensional , Neurofibromatosis 1 , Neoplasias Cutáneas , Humanos , Neurofibromatosis 1/diagnóstico por imagen , Neurofibromatosis 1/patología , Neurofibromatosis 1/complicaciones , Reproducibilidad de los Resultados , Imagenología Tridimensional/métodos , Femenino , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Masculino , Adulto , Neurofibroma/diagnóstico por imagen , Neurofibroma/patología , Adulto Joven , Diseño de Equipo , Adolescente , Sensibilidad y Especificidad , Estudios de Factibilidad , Persona de Mediana Edad , Análisis de Falla de Equipo , Dermoscopía/métodos , Dermoscopía/instrumentaciónAsunto(s)
Dermoscopía , Melanoma Amelanótico , Enfermedades de la Uña , Neoplasias Cutáneas , Humanos , Dermoscopía/métodos , Melanoma Amelanótico/patología , Melanoma Amelanótico/diagnóstico por imagen , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico por imagen , Enfermedades de la Uña/patología , Enfermedades de la Uña/diagnóstico por imagen , Masculino , Femenino , Uñas/patología , Uñas/diagnóstico por imagen , Persona de Mediana Edad , AncianoAsunto(s)
Carcinoma Basocelular , Recurrencia Local de Neoplasia , Neoplasias Cutáneas , Tomografía de Coherencia Óptica , Humanos , Tomografía de Coherencia Óptica/métodos , Carcinoma Basocelular/cirugía , Carcinoma Basocelular/diagnóstico por imagen , Carcinoma Basocelular/patología , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/cirugía , Neoplasias Cutáneas/patología , Recurrencia Local de Neoplasia/diagnóstico por imagen , Cuidados Preoperatorios/métodos , Masculino , AncianoRESUMEN
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.
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Melanoma , Neoplasias Cutáneas , Melanoma/diagnóstico por imagen , Humanos , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Aprendizaje Profundo , AlgoritmosRESUMEN
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.
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Hipertermia Inducida , Nanopartículas , Imagen Óptica , Copolímero de Ácido Poliláctico-Ácido Poliglicólico , Animales , Nanopartículas/química , Ratones , Copolímero de Ácido Poliláctico-Ácido Poliglicólico/química , Línea Celular Tumoral , Hipertermia Inducida/métodos , Humanos , Terapia Fototérmica/métodos , Neoplasias Cutáneas/terapia , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Melanoma/terapia , Melanoma/diagnóstico por imagen , Fototerapia/métodosRESUMEN
Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.
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Algoritmos , Estudios de Factibilidad , Rayos Infrarrojos , Neoplasias Cutáneas , Termografía , Humanos , Neoplasias Cutáneas/diagnóstico , Neoplasias Cutáneas/diagnóstico por imagen , Termografía/métodos , Termografía/instrumentaciónRESUMEN
BACKGROUND: Primary cutaneous mucinous carcinoma is a rare neoplasia of the sweat gland. The age-adjusted incidence was 0.024 tumors per 100,000 person-years. It is possible that the actual number of tumors may be slightly higher than previously estimated as some cases of primary cutaneous mucinous carcinoma may have been mistaken for benign tumors and removed by laser therapy without histologic examination. CASE PRESENTATION: We report a 58-year-old Chinese man with primary cutaneous mucinous carcinoma. The patient presented to our care with an indolent nodule on the left cheek, which was proven to be a mucinous adenocarcinoma by excisional biopsy and immunohistochemical staining. Following a comprehensive evaluation, including whole-body computed tomography and positron emission tomography, metastases from other sites were ruled out and the patient was diagnosed with primary cutaneous mucinous carcinoma. The patient underwent an additional wide resection surgery to ensure a safe margin and was then recommended to undergo regular follow-up. CONCLUSION: This case is one of the few published Chinese cases in literature of primary cutaneous mucinous carcinoma. Diagnosis of primary cutaneous mucinous carcinoma is challenging, and treatment options are limited. Collaboration between clinicians and pathologists is crucial for optimal outcomes. Further studies with longer follow-up periods are necessary to provide evidence for the management of this disease.
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Adenocarcinoma Mucinoso , Mejilla , Neoplasias Cutáneas , Humanos , Masculino , Persona de Mediana Edad , Adenocarcinoma Mucinoso/patología , Adenocarcinoma Mucinoso/diagnóstico por imagen , Adenocarcinoma Mucinoso/cirugía , Mejilla/patología , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/cirugía , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/diagnóstico , Tomografía Computarizada por Rayos X , Neoplasias Faciales/patología , Neoplasias Faciales/cirugía , Neoplasias Faciales/diagnóstico por imagen , Neoplasias Faciales/diagnósticoRESUMEN
Gorlin-Goltz syndrome, also known as basal cell nevus syndrome, is a rare condition characterised by skeletal abnormalities, odontogenic keratocysts and basal cell nevi. Diagnosis of this condition is based on major and minor clinical and radiological criteria. Oral medicine and radiology specialists are crucial in diagnosing this condition due to the oral and maxillofacial symptoms. However, conventional radiographs may not provide enough information for an accurate diagnosis, including the two-dimensional imaging modalities. Therefore, the importance of advanced digital imaging for diagnosis is highlighted in this case report of a male patient in his late 20s. The patient had missing teeth and asymptomatic multiple swelling in the orofacial region for 2 months. Routine clinical examination, radiographic investigations and histopathological evaluation led to incidental finding of multiple cystic lesions in the maxillary and mandibular region which on further evaluation led to the final diagnosis of Gorlin-Goltz syndrome.
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Síndrome del Nevo Basocelular , Hallazgos Incidentales , Humanos , Síndrome del Nevo Basocelular/diagnóstico , Síndrome del Nevo Basocelular/diagnóstico por imagen , Masculino , Adulto , Neoplasias Cutáneas/diagnóstico , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico por imagen , Diagnóstico DiferencialRESUMEN
Correlative imaging of cutaneous tumors provides additional information to the standard histopathologic examination. However, the joint progress in the establishment of analytical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) in clinical practice is still limited. Their combination provides complementary information as it is also shown in our study in terms of major biotic (Ca, Mg, and P) and trace (Cu and Zn) elements. To elucidate changes in the elemental composition in tumors, we have compiled a set of malignant tumors (Squamous Cell Carcinoma, Basal Cell Carcinoma, Malignant Melanoma, and Epithelioid Angiosarcoma), one benign tumor (Pigmented Nevus) and one healthy-skin sample. The data processing was based on a methodological pipeline involving binary image registration and affine transformation. Thus, our paper brings a feasibility study of a practical methodological concept that enables us to compare LIBS and LA-ICP-MS results despite the mutual spatial distortion of original elemental images. Moreover, we also show that LIBS could be a sufficient pre-screening method even for a larger number of samples according to the speed and reproducibility of the analyses. Whereas LA-ICP-MS could serve as a ground truth and reference technique for preselected samples.
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Neoplasias Cutáneas , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Humanos , Terapia por Láser , Melanoma/diagnóstico por imagen , Melanoma/patología , Espectrometría de Masas/métodos , Carcinoma Basocelular/diagnóstico por imagen , Oligoelementos/análisis , Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/patología , Análisis Espectral/métodos , Nevo Pigmentado/diagnóstico por imagen , Rayos LáserRESUMEN
This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the International Skin Imaging Collaboration (ISIC) dataset, spanning 2016-2020, encompassing a variety of skin lesions. Our approach integrates deep learning with a comprehensive radiomics analysis, utilizing a vast array of quantitative image features to precisely quantify skin lesion patterns. The dataset includes cases of three, four, and eight different skin lesion types. Our methodology was benchmarked against seven classification methods from the ISIC 2020 challenge and prior research using a binary decision framework. The proposed hybrid model demonstrated superior performance in distinguishing benign from malignant lesions, achieving area under the receiver operating characteristic curve (AUROC) scores of 99%, 95%, and 96%, and multiclass decoding AUROCs of 98.5%, 94.9%, and 96.4%, with sensitivities of 97.6%, 93.9%, and 96.0% and specificities of 98.4%, 96.7%, and 96.9% in the internal ISIC 2018 challenge, as well as in the external Jinan and Longhua datasets, respectively. Our findings suggest that the integration of radiomics and deep learning, utilizing dermoscopic images, effectively captures the heterogeneity and pattern expression of skin lesions.
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Aprendizaje Profundo , Dermoscopía , Humanos , Dermoscopía/métodos , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Curva ROC , Piel/diagnóstico por imagen , Piel/patología , Procesamiento de Imagen Asistido por Computador/métodos , Enfermedades de la Piel/diagnóstico por imagen , Enfermedades de la Piel/patología , Interpretación de Imagen Asistida por Computador/métodos , RadiómicaRESUMEN
Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.
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Aprendizaje Automático , Neoplasias Cutáneas , Humanos , Neoplasias Cutáneas/diagnóstico , Neoplasias Cutáneas/diagnóstico por imagen , Detección Precoz del Cáncer/métodos , Algoritmos , Inteligencia Artificial , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.
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Melanoma , Nevo Pigmentado , Neoplasias Cutáneas , Humanos , Melanoma/diagnóstico por imagen , Melanoma/clasificación , Melanoma/patología , Melanoma/diagnóstico , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/clasificación , Neoplasias Cutáneas/diagnóstico , Femenino , Nevo Pigmentado/diagnóstico por imagen , Nevo Pigmentado/diagnóstico , Nevo Pigmentado/clasificación , Nevo Pigmentado/patología , Masculino , Persona de Mediana Edad , Adulto , Proyectos Piloto , Anciano , Melanocitos/patología , Iluminación/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Sensibilidad y EspecificidadRESUMEN
BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a superficial sarcoma characterized by infiltrative growth with tentacle-like borders. Mohs micrographic surgery (MMS) is the preferred treatment option for DFSP. However, the imprecise boundary localization in MMS leads to an increased number of Mohs layers required and a longer surgery time. High-frequency ultrasound has excellent tissue recognition capability for DFSP, allowing for precise boundary marking. MATERIALS AND METHODS: In this study, we retrospectively analyzed 14 cases of DFSP treated with MMS using preoperative ultrasound localization and three-dimensional reconstruction at Xiangya Hospital over the past 5 years. We also reviewed previous studies on MMS for DFSP treatment. RESULTS: It was found that the average number of Mohs layers for patients after preoperative ultrasound localization was 1.57, ranging from 1 to 3, which was less than the previously reported 1.86 layers, ranging from 1 to 12. This effectively reduced the number of Mohs layers required. CONCLUSIONS: By utilizing preoperative high-frequency ultrasound to determine the boundaries and depth of DFSP, the number of Mohs layers can be effectively reduced, leading to less workload for pathological examination, shorter operation time, and reduced surgical risks for patients. Ultrasound imaging data can be used for three-dimensional reconstruction, enabling less experienced Mohs surgeons to have a visual understanding of the morphology and extent of infiltration of the lesions. This aids in developing optimal surgical plans, smoothing the learning curve, and promoting the wider adoption of MMS.
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Dermatofibrosarcoma , Cirugía de Mohs , Neoplasias Cutáneas , Humanos , Dermatofibrosarcoma/cirugía , Dermatofibrosarcoma/patología , Dermatofibrosarcoma/diagnóstico por imagen , Cirugía de Mohs/métodos , Neoplasias Cutáneas/cirugía , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico por imagen , Masculino , Femenino , Estudios Retrospectivos , Persona de Mediana Edad , Adulto , Ultrasonografía/métodos , Imagenología Tridimensional , Anciano , Cirugía Asistida por Computador/métodos , Resultado del TratamientoRESUMEN
BACKGROUND: Skin neoplasms, particularly basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are prevalent forms of skin malignancies. To enhance accurate diagnosis, non-invasive techniques including high-frequency ultrasound (HFUS) are crucial. HFUS offers deeper penetration compared to reflectance confocal microscopy (RCM), and optical coherence tomography (OCT), making it valuable for examining skin structures. The aim of this study was to investigate and diagnose localized manifestation of BCC and SCC with HFUS and compare it with pathology results in patients referred to Razi Hospital, Tehran, Iran. METHOD AND MATERIALS: This study included patients diagnosed with BCC and SCC, with clinical and pathological confirmation, attending the oncology clinic of Razi Hospital, Tehran, Iran, from 2022 to 2023. Exclusion criteria comprised metastatic and recurrent cases, patients who underwent treatment or surgery, and tumors located in anatomically challenging areas. HFUS with a 20 MHz probe and Doppler ultrasound were employed to examine the skin. Tumors were subsequently excised, fixed in formalin, and sent for pathological assessment. Ultrasound findings were compared with pathology results. RESULTS: The study assessed 40 patients, with half diagnosed with SCC and the other half with BCC. The majority of SCC patients were male (80%), while BCC patients were relatively evenly divided between males (65%) and females (35%). The mean age was 59.15 ± 11.9 years for SCC and 63.4 ± 8.9 years for BCC. Cheeks (20%) and lips (35%) were the most common sampling sites for BCC and SCC, respectively. The correlation coefficients for tumor size and depth between ultrasound and pathology were 0.981 and 0.912, respectively, indicating a high level of agreement between the two methods. CONCLUSION: In BCC patients, there was complete agreement between sonographic loco-regional extension and pathology findings. However, some discordance (30%) was observed in SCC cases. The study demonstrated a strong correlation between ultrasound and pathology in accurately detecting the depth and extent of the tumor. However, due to the inclusion of only patients with positive pathology, it is not appropriate to evaluate the diagnostic test values and compare them with pathology results. Therefore, it is highly recommended to carry out additional studies with larger sample sizes to further validate these findings.
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Carcinoma Basocelular , Carcinoma de Células Escamosas , Neoplasias Cutáneas , Humanos , Carcinoma Basocelular/diagnóstico por imagen , Carcinoma Basocelular/patología , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/patología , Femenino , Masculino , Persona de Mediana Edad , Anciano , Ultrasonografía/métodos , Adulto , Anciano de 80 o más Años , IránRESUMEN
BACKGROUND: In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment. METHODS: A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction. RESULTS: A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%. CONCLUSION: Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.
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Melanoma , Neoplasias Cutáneas , Humanos , Neoplasias Cutáneas/diagnóstico por imagen , Neoplasias Cutáneas/patología , Melanoma/diagnóstico por imagen , Melanoma/patología , Algoritmos , Redes Neurales de la Computación , Procesamiento de Imagen Asistido por Computador/métodos , Interpretación de Imagen Asistida por Computador/métodos , Dermoscopía/métodos , Aprendizaje ProfundoRESUMEN
Background and Objectives: Amelanotic/hypomelanotic melanomas (AHMs) account for 2-8% of all cutaneous melanomas. Due to their clinical appearance and the lack of specific dermoscopic indicators, AHMs are challenging to diagnose, particularly in thinner cutaneous lesions. The aim of our study was to evaluate the clinicopathological and dermoscopic features of thin AHMs. Identifying the baseline clinical-pathological features and dermoscopic aspects of thin AHMs is crucial to better understand this entity. Materials and Methods: We divided the AHM cohort into two groups based on Breslow thickness: thin (≤1.00 mm) and thick (>1.00 mm). This stratification helped identify any significant clinicopathological differences between the groups. For dermoscopic analysis, we employed the "pattern analysis" approach, which involves a simultaneous and subjective assessment of different criteria. Results: Out of the 2.800 melanomas analyzed for Breslow thickness, 153 were identified as AHMs. Among these, 65 patients presented with thin AHMs and 88 with thick AHMs. Red hair color and phototype II were more prevalent in patients with thin AHMs. The trunk was the most common anatomic site for thin AHMs. Patients with thin AHMs showed a higher number of multiple melanomas. Dermoscopic analysis revealed no significant difference between thin AHMs and thick AHMs, except for a more frequent occurrence of residual reticulum in thin AHMs. Conclusions: Thin AHMs typically affect individuals with lower phototypes and red hair color. These aspects can be related to the higher presence of pheomelanin, which provides limited protection against sun damage. This also correlates with the fact that the trunk, a site commonly exposed to intermittent sun exposure, is the primary anatomical location for thin AHMs. Multiple primary melanomas are more common in patients with thin AHMs, likely due to an intrinsic predisposition as well as greater periodic dermatologic follow-ups in this class of patients. Apart from the presence of residual reticulum, no other significant dermoscopic differences were observed, complicating the differential diagnosis between thin and thick AHMs based on dermoscopy alone.