Detection Of Nonmelanoma Skin Cancer Research Articles

Using computational learning for non-melanoma skin cancer and actinic keratosis near-infrared hyperspectral signature classification

BackgroundThe early detection of Non-Melanoma Skin Cancer (NMSC) is essential to ensure patients receive the most effective treatment. Diagnostic screening tools for NMSC are crucial due to high confusion rates with other types of skin lesions, such as Actinic Keratosis. Nevertheless, current means of diagnosing and screening patients rely on either visual criteria, that are often conditioned by subjectivity and experience, or highly invasive, slow, and costly methods, such as histological diagnoses. From this, the objectives of the present study are to test if classification accuracies improve in the Near-Infrared region of the electromagnetic spectrum, as opposed to previous research in shorter wavelengths. MethodsThis study utilizes near-infrared hyperspectral imaging, within the range of 900.6 and 1454.8 nm. Images were captured for a total of 125 patients, including 66 patients with Basal Cell Carcinoma, 42 with cutaneous Squamous Cell Carcinoma, and 17 with Actinic Keratosis, to differentiate between healthy and unhealthy skin lesions. A combination of hybrid convolutional neural networks (for feature extraction) and support vector machine algorithms (as a final activation layer) was employed for analysis. In addition, we test whether transfer learning is feasible from networks trained on shorter wavelengths of the electromagnetic spectrum. ResultsThe implemented method achieved a general accuracy of over 80 %, with some tasks reaching over 90 %. F1 scores were also found to generally be over the optimal threshold of 0.8. The best results were obtained when detecting Actinic Keratosis, however differentiation between the two types of malignant lesions was often noted to be more difficult. These results demonstrate the potential of near-infrared hyperspectral imaging combined with advanced machine learning techniques in distinguishing NMSC from other skin lesions. Transfer learning was unsuccessful in improving the training of these algorithms. ConclusionsWe have shown that the Near-Infrared region of the electromagnetic spectrum is highly useful for the identification and study of non-melanoma type skin lesions. While the results are promising, further research is required to develop more robust algorithms that can minimize the impact of noise in these datasets before clinical application is feasible.

Skin Imaging Using Optical Coherence Tomography and Photoacoustic Imaging: A Mini-Review

This article provides an overview of the progress made in skin imaging using two emerging imaging modalities, optical coherence tomography (OCT) and photoacoustic imaging (PAI). Over recent years, these technologies have significantly advanced our understanding of skin structure and function, offering non-invasive and high-resolution insights previously unattainable. The review begins by briefly describing the fundamental principles of how OCT and PAI capture images. It then explores the evolving applications of OCT in dermatology, ranging from diagnosing skin disorders to monitoring treatment responses. This article continues by briefly describing the capabilities of PAI imaging, and how PAI has been used for melanoma and non-melanoma skin cancer detection and characterization, vascular imaging, and more. The third section describes the development of multimodal skin imaging systems that include OCT, PAI, or both modes. A comparative analysis between OCT and PAI is presented, elucidating their respective strengths, limitations, and synergies in the context of skin imaging.

Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review

Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.

Development and validation of a deep learning model for improving detection of nonmelanoma skin cancers treated with Mohs micrographic surgery

BackgroundReal-time review of frozen sections underpins the quality of Mohs surgery. There is an unmet need for low-cost techniques that can improve Mohs surgery by reliably corroborating cancerous regions of interest and surgical margin proximity. ObjectiveTo test that deep learning models can identify non-melanoma skin cancer (NMSC) regions in Mohs frozen section specimens. MethodsDeep learning models were developed on archival images of focused-microscopic views (FMV) containing regions of annotated, invasive NMSC between 2015 and 2018, then validated on prospectively collected images in a temporal cohort (2019-2021). ResultsThe tile-based classification models were derived using 1,423 FMV images from 154 patients and tested on 374 images from 66 patients. The best models detected basal cell carcinomas with a median average precision of 0.966 and median area under the receiver operating curve of 0.889 at 100x magnification (0.943 and 0.922 at 40x magnification). For invasive squamous cell carcinomas, high median average precision of 0.904 was achieved at 100x magnification. LimitationsSingle institution study with limited cases of SCC and rare NMSC. ConclusionDeep learning appears highly accurate for detecting skin cancers in Mohs frozen sections, supporting its potential for enhancing surgical margin control and increasing operational efficiency.

Retracted: Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network.

[This retracts the article DOI: 10.1155/2022/6952304.].

Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review.

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy.

A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images.

The early detection of Non-Melanoma Skin Cancer (NMSC) is crucial to achieve the best treatment outcomes. Shape is considered one of the main parameters taken for the detection of some types of skin cancer such as melanoma. For NMSC, the importance of shape as a visual detection parameter is not well-studied. A dataset of 993 standard camera images containing different types of NMSC and benign skin lesions was analysed. For each image, the lesion boundaries were extracted. After an alignment and scaling, Elliptic Fourier Analysis (EFA) coefficients were calculated for the boundary of each lesion. The asymmetry of lesions was also calculated. Then, multivariate statistics were employed for dimensionality reduction and finally computational learning classification was employed to evaluate the separability of the classes. The separation between malignant and benign samples was successful in most cases. The best-performing approach was the combination of EFA coefficients and asymmetry. The combination of EFA and asymmetry resulted in a balanced accuracy of 0.786 and an Area Under Curve of 0.735. The combination of EFA and asymmetry for lesion classification resulted in notable success rates when distinguishing between benign and malignant lesions. In light of these results, skin lesions’ shape should be integrated as a fundamental part of future detection techniques in clinical screening.

Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network.

One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.

In vivo reflectance confocal microscopy of wounds: feasibility of intraoperative basal cell carcinoma margin assessment.

BackgroundIn vivo reflectance confocal microscopy (RCM) is well established in non-melanoma skin cancer detection and screening. However, there is no sufficient validation regarding intraoperatively obtained images of wound margins. A reliable and fast resection margin detection is of high clinical relevance. Hence, we aimed to investigate feasibility and validity of in vivo RCM imaging for wound margins assessment compared with standard skin surface imaging and the gold standard histopathology.MethodsA surgical incision through the center of a large basal cell carcinoma (BCC) affected area in the head and face region was performed. After removing half of the tumor, the wound margins of the remaining half as well as the corresponding skin surface were scanned with an in vivo RCM. A total of 50 wound margin images with BCC, 50 images of BCC-free margins and the corresponding skin surface images from 50 patients were compared with each other and with histopathological findings. Presence of confocal diagnostic criteria for BCC in images was analyzed.ResultsAn overall sensitivity and specificity in detection of BCC in wound margins was 88.5%, and 91.7% compared to skin surface imaging and 97.8% and 90.7%, respectively, compared to histopathology. We identified all known confocal patterns of healthy skin and BCC in wound margin scans: damage of the epidermal layer above the lesion and cellular pleomorphism, elongated and monomorphic basaloid nuclei, nuclear polarization, an increased number of dilated blood vessels with high leukocyte traffic, inflammatory cells.ConclusionsThe accuracy of in vivo RCM imaging of wound margins is comparable with a standard skin surface imaging. The intraoperative detection of BCC areas in wound margins is as precise as the standard skin imaging and may be supportive for surgical interventions.

Autoantibodies against p53, MMP-7, and Hsp70 as Potential Biomarkers for Detection of Nonmelanoma Skin Cancers.

Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are two predominant histological types of nonmelanoma skin cancer (NMSC), lacking effective early diagnostic markers. In this study, we assessed the diagnostic value of autoantibodies against p53, MMP-7, and Hsp70 in skin SCC and BCC. ELISA was performed to detect levels of autoantibodies in sera from 101 NMSC patients and 102 normal controls, who were recruited from the Cancer Hospital of Shantou University Medical College. A receiver operator characteristic curve was used to evaluate the diagnostic value. The serum levels of autoantibodies against p53, MMP-7, and Hsp70 were higher in NMSCs than those in the normal controls (all P < 0.01). The AUC of the three-autoantibody panel was 0.841 (95% CI: 0.788-0.894) with the sensitivity and specificity of 60.40% and 91.20% when differentiating NMSCs from normal controls. Furthermore, measurement of this panel could differentiate early-stage skin cancer patients from normal controls (AUC: 0.851; 95% CI: 0.793-0.908). Data from Oncomine showed that the level of p53 mRNA was elevated in BCC (P < 0.05), and the Hsp70 mRNA was upregulated in SCC (P < 0.001). This serum three-autoantibody panel might function in assisting the early diagnosis of NMSC.

Automated detection of nonmelanoma skin cancer using digital images: a systematic review

BackgroundComputer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies.MethodsEight databases (PubMed, Google Scholar, Embase, IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and the ACM Digital Library) were searched to identify diagnostic studies of NMSC using image-based machine learning models. Two reviewers independently screened eligible articles. The level of evidence of each study was evaluated using a five tier rating system, and the applicability and risk of bias of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.ResultsThirty-nine studies were reviewed. Twenty-four models were designed to detect basal cell carcinoma, two were designed to detect squamous cell carcinoma, and thirteen were designed to detect both. All studies were conducted in silico. The overall diagnostic accuracy of the classifiers, defined as concordance with histopathologic diagnosis, was high, with reported accuracies ranging from 72 to 100% and areas under the receiver operating characteristic curve ranging from 0.832 to 1. Most studies had substantial methodological limitations, but several were robustly designed and presented a high level of evidence.ConclusionMost studies of image-based NMSC classifiers report performance greater than or equal to the reported diagnostic accuracy of the average dermatologist, but relatively few studies have presented a high level of evidence. Clinical studies are needed to assess whether these technologies can feasibly be implemented as a real-time aid for clinical diagnosis of NMSC.

Quick Detection of Nonmelanoma Skin Cancer by Histopathology: Feasibility and Diagnostic Accuracy of Immediate Cutaneous Diagnosis

Background: Dermoscopy, diagnostic cytology, confocal microscopy are used to improve diagnostic accuracy. Frozen section techniques have been used to control margins but not for diagnosis. Objectives: We used a new frozen section technique called immediate cutaneous diagnosis for nonmelanoma skin cancer, assessing its feasibility and accuracy in obtaining a result within 15 minutes and recording limitations. Methods and Materials: An observational, prospective clinical study was designed. Biological samples were divided into two: one half was included in paraffin, following the standard procedure, and immediate cutaneous diagnosis was applied in the other. Fresh tissue was examined by frozen sections with perpendicular slides and staining with toluidine blue. Two hundred and sixty patients were enrolled. Results: Immediate cutaneous diagnosis-toluidine blue had a sensitivity of 98.7% (95%CI: 93.0-100%) and a specificity of 92.6% (95%CI: 87.4-96.1%) in detecting nonmelanoma skin cancer. Inter-rater agreement between two dermatopathologists using Cohen’s kappa was 0.796 (P<0.0001) in a subsample of 101 cases. Immediate cutaneous diagnosis-toluidine blue had a sensitivity for squamous epithelial lesions of 78.3% (95%CI: 63.689.1%) and a specificity of 98.4% (95%CI: 95.5-99.7%).

MicroRNA profiling for early detection of nonmelanoma skin cancer.

microRNAs (miRNAs) are single-stranded, noncoding RNA molecules. Given the vast regulatory potential of miRNAs and their often tissue-specific and disease-specific expression patterns, miRNAs are being assessed as possible biomarkers to aid diagnosis and prediction of different types and stages of cancers, including skin cancer. Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common forms of nonmelanoma skin cancer (NMSC). BCC originates from the basal layer of the epidermis, while SCC arises from epidermal keratinocytes or from the dermal appendages. Although NMSCs are currently the most common types of malignancies, both BCC and SCC have a better than 95% cure rate if detected early. To identify plasma miRNAs suitable for early detection of NMSC. Expression profiles of 741 miRNAs were evaluated using high-throughput real-time quantitative PCR from plasma samples in 42 patients with NMSC and 282 healthy controls (HCs). Our results demonstrated that in patients with NMSC, compared with HCs,expression levels of miR-30e-3p, miR-145-5p, miR-186-5p and miR-875-5p were significantly (P<0.05) upregulated, while those of miR-19a-3p, miR-25-3p, miR-30a-5p, miR-451 and miR-576-3p were significantly downregulated. Our study suggests that the miRNAs with significant changes in expression (miR-19a-3p, miR-25-3p, miR-30a-5p, miR-145-5p and miR-186-5p) could serve as novel noninvasive biomarkers for detection ofNMSC.

Detection of Non-Melanoma Skin Cancer by in vivo Fluorescence Imaging with Fluorocoxib A

Non-melanoma skin cancer (NMSC) is the most common form of cancer in the US and its incidence is increasing. The current standard of care is visual inspection by physicians and/or dermatologists, followed by skin biopsy and pathologic confirmation. We have investigated the use of in vivo fluorescence imaging using fluorocoxib A as a molecular probe for early detection and assessment of skin tumors in mouse models of NMSC. Fluorocoxib A targets the cyclooxygenase-2 (COX-2) enzyme that is preferentially expressed by inflamed and tumor tissue, and therefore has potential to be an effective broadly active molecular biomarker for cancer detection. We tested the sensitivity of fluorocoxib A in a BCC allograft SCID hairless mouse model using a wide-field fluorescence imaging system. Subcutaneous allografts comprised of 1000 BCC cells were detectable above background. These BCC allograft mice were imaged over time and a linear correlation (R2 = 0.8) between tumor volume and fluorocoxib A signal levels was observed. We also tested fluorocoxib A in a genetically engineered spontaneous BCC mouse model (Ptch1+/− K14-Cre-ER2 p53fl/fl), where sequential imaging of the same animals over time demonstrated that early, microscopic lesions (100 μm size) developed into visible macroscopic tumor masses over 11 to 17 days. Overall, for macroscopic tumors, the sensitivity was 88% and the specificity was 100%. For microscopic tumors, the sensitivity was 85% and specificity was 56%. These results demonstrate the potential of fluorocoxib A as an in vivo imaging agent for early detection, margin delineation and guided biopsies of NMSCs.

Increased expression of CD24 in nonmelanoma skin cancer

Skin cancer detection is based on the macroscopic and microscopic appearance of the lesions and the experience of the surgeon. The final diagnosis is done by pathological analysis, based on established criteria. Currently, there is no serum marker that can be used for the diagnosis of skin cancer. CD24, a mucin-like glycoprotein, is overexpressed in a variety of cancers including skin malignancies. Evaluate the potential utility of CD24 expression in peripheral blood leukocytes (PBLs) for the detection of nonmelanoma skin cancers (NMSC). Twenty-nine consented individuals attending Tel Aviv Sourasky Medical Center for excision of suspected skin lesions, and 21 age- and gender-matched subjects were prospectively recruited. The resected lesions were examined by an expert dermatopathologist. PBLs were isolated from blood samples and protein extracts were subjected to sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting. The study was double blinded. CD24 expression in PBLs distinguishes between NMSC and healthy subjects, with high sensitivity (81%) and specificity (67%) for basal cell carcinoma, and 100% and 71%, respectively, for squamous cell carcinoma. The CD24 test can successfully distinguish NMSC from healthy subjects. CD24 may serve as a new potential and promising diagnostic biomarker for the detection and surveillance of NMSC.

Multimodal confocal microscopy for diagnosing nonmelanoma skin cancers

The standard diagnostic procedure for skin cancers is invasive biopsy followed by histopathological evaluation. The biopsy may result in scarring and infection. A reliable way to noninvasively image suspicious lesions with high resolution and contrast would be valuable. In this study, the suitability of dye-enhanced multimodal confocal microscopy for the detection of nonmelanoma skin cancers was evaluated. For the experiments we used fresh tumor material stained using 0.2 mg/ml or 0.05 mg/ml aqueous solutions of methylene blue (MB) or toluidine blue (TB), respectively. Reflectance, fluorescence, and fluorescence polarization images of skin specimens stained with MB and TB were excited by 656 nm and 633 nm light, respectively. Fluorescence emission and anisotropy were registered between 690 nm and 710 nm. In addition, reference reflectance images at 830 nm were acquired. In total we imaged, analyzed, and compared to histology at least 10 samples of each tumor-type including nodular basal cell carcinoma (BCC), infiltrative basal cell carcinoma, and squamous cell carcinoma (SCC). The morphological features and appearance of skin structures in the fluorescence images correlate well with corresponding histology for all investigated tumor-types. Multi-spectral reflectance images provide information on the tissue spectral responses and are complimentary to the fluorescence images. The differences detected by fluorescence polarization in cancerous and normal structures may be used for cancerous tissue discrimination. Our results indicate the feasibility of using multimodal confocal microscopy as real-time tool for detecting skin pathology.

Epidemiologic investigation of nonmelanoma skin cancer mortality: the Rhode Island Follow-Back Study.

The Rhode Island Follow-Back Study was initiated to elucidate the magnitude and characteristics of nonmelanoma skin cancer mortality. All deaths reported among Rhode Island residents during 1979 through 1987 and attributed to nonmelanoma skin cancer were investigated and medical records sought. Most were misclassified, primarily due to squamous cell carcinoma of mucosal surfaces in the head and neck. In Rhode Island and nationally, this source of misclassification is increasingly frequent. Appropriate adjustment of national statistics for misclassification reveals a consistent decline in nonmelanoma skin cancer mortality rates from 1969 through 1988, although the actual numbers of deaths are increasing due to growth and aging of the population. Examination of the records of those Rhode Island residents who did die from nonmelanoma skin cancer demonstrated that most had squamous cell carcinoma, and many of those arose from the ear. These analyses suggest that particular emphasis should be placed on the ear in public health campaigns geared toward the prevention and early detection of nonmelanoma skin cancer.