Malignant Skin Lesions Research Articles

Impact of skin biopsy practices: A comprehensive nationwide study on skin cancer and melanoma biopsies.

Due to a multitude of factors, skin cancer incidence is increasing and challenges medical professionals in biopsy decision-making. While skin cancer may have a profound impact on the patient and be costly for society, there is little knowledge about the number and cost of benign skin lesions biopsied as collateral damage. This study evaluates the number and costs of skin biopsies in Denmark over 15 years, focusing on benign and malignant skin lesions and melanomas across medical settings. It aims to determine the benign to malignant ratio (BMR) and number needed to biopsy (NNB) and estimate the direct cost of benign skin lesion biopsies in the Cancer Pathway from the perspective of a public healthcare system. The study included 4,481,207 biopsy specimens from January 2007 to June 2022 from the Danish Pathology Data Bank, of which 151,988 from the Cancer Pathway were included in the primary analysis of BMR. The national reimbursement rates for biopsies were used, alongside histopathological examination costs extracted from several pathology departments, for a Monte-Carlo simulation of a simple cost and sensitivity analysis. The number of biopsies increased by 39.1% from 2007 to 2021. Overall BMR for malignancy was 4.1:1, and NNB for melanoma was 31.8, but biopsies performed on clinical suspicion of malignancy or melanoma had a BMR and NNB of 1.5:1 and 2.8, respectively. The cost of benign skin biopsies performed on suspicion of cancer or melanoma in 2021 was €6.6M, predominantly in hospitals. A healthcare system that employs filtering functions before biopsy of skin lesions can achieve some of the lowest BMR reported in the world, but with most benign skin lesion excisions due to suspicion of malignancy performed in the expensive hospital setting. Including clinical reason for biopsy in diagnostic accuracy studies using NNB is crucial.

An Efficient Algorithm for Automated Skin Lesion Detection: A Non-Machine Learning Approach

Skin lesion detection is a very important task in medical applications such as disease diagnostic and cancer detection. This paper suggests an efficient algorithm for skin lesion detection relying on advanced image processing techniques without using machine learning or deep learning algorithms. A set of algorithms is proposed to enhance traditional image processing methods to scrutinize dermatological images, concentrating on melanoma detection. The proposed method combines color space transformation, contrast enhancement, active contour segmentation. ABCD criteria (Asymmetry, Border irregularity, Color variegation, and Diameter) is used in feature extraction. The approach is evaluated on the ISIC (International Skin Image Collaboration) 2020 dataset, accomplishing an accuracy of 84% in differentiating between malignant and benign skin lesions. The results indicate the potential of non-machine learning techniques in dermatological image analysis, providing computationally efficient and interpretable alternative to data-intensive deep learning algorithms.

Reflectance Confocal Microscopy and Dermoscopy for the Diagnosis of Solitary Hypopigmented Pink Lesions: A Narrative Review.

Diagnosing solitary pink skin lesions poses a significant challenge due to the scarcity of specific clinical and dermoscopic criteria. Several benign lesions, such as cherry angioma, clear cell acanthoma, dermal nevus, keloid, hypertrophic scar, and Spitz nevus, often exhibit similar clinical and dermoscopic features. This similarity extends to some malignant lesions, including basal cell carcinoma, actinic keratosis, and amelanotic melanoma, making differentiation difficult. Recent studies highlight the enhanced diagnostic accuracy of reflectance confocal microscopy (RCM), which offers increased sensitivity and specificity compared to dermoscopy alone for diagnosing skin cancer. This study aims to summarize the application of dermoscopy and RCM in distinguishing between benign and malignant pinkish-reddish skin lesions. The integration of RCM with traditional dermoscopic techniques improves the ability to accurately identify and differentiate these lesions. However, it is crucial to note that for any suspicious lesions, a final diagnosis must be confirmed through surgical excision and histopathological evaluation. This comprehensive approach ensures accurate diagnosis and appropriate treatment, highlighting the importance of combining advanced imaging techniques in clinical practice.

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

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

Vertical Ex Vivo Dermoscopy in Assessment of Malignant Skin Lesions.

The role of vertical ex vivo dermoscopy relevant to clinical diagnosis has not been investigated yet. Study objectives were defining, describing, and determining the importance of the structures visible using vertical ex vivo dermoscopy in the diagnosis of malignant skin lesions, as well as determining their accuracy in the assessment of tumor margins. A prospective, descriptive study was conducted in two University centers. Digital images of completely excised skin lesions, fixed in formalin, before histopathological diagnosis were used for analysis. BCCs had the most diverse dermoscopic presentation on the vertical section, while SCCs showed a similar presentation in most cases. Vertical dermoscopy of thin melanomas was almost identical, unlike nodular melanomas. Thickness accuracy assessed by dermatologist was 0.753 for BCC, 0.810 for SCC, and 0.800 for melanomas, whereas assessment by pathologist was 0.654, 0.752, and 0.833, respectively. The accuracy of tumor width assessment was 0.819 for BCCs, 0.867 for SCCs and 1.000 for melanoma as estimated by a Dermatologist. Interobserver agreement was 0.71 for BCC, 0.799 for SCC and 0.832 for melanomas. Vertical ex vivo dermoscopy may contribute to the distinction between BCCs, SCCs, and melanomas. Moreover, regardless of the doctor's specialty, it enables a good assessment of the tumor's margins.

Deep Learning Algorithms for the Detection of Suspicious Pigmented Skin Lesions in Primary Care Settings: A Systematic Review and Meta-Analysis.

Early detection of suspicious pigmented skin lesions is crucial for improving the outcomes and survival rates of skin cancers. However, the accuracy of clinical diagnosis by primary care physicians (PCPs) is suboptimal, leading to unnecessary referrals and biopsies. In recent years, deep learning (DL)algorithms have shown promising results in the automated detection and classification of skin lesions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of DL algorithms for the detection of suspicious pigmented skin lesions in primary care settings. A comprehensive literature search was conducted using electronic databases, including PubMed, Scopus, IEEE Xplore, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science. Data from eligible studies were extracted, including study characteristics, sample size, algorithm type, sensitivity, specificity, diagnostic odds ratio (DOR), positivelikelihood ratio (PLR), negative likelihood ratio(NLR), and receiver operating characteristic curve analysis. Three studies were included. The results showed that DL algorithms had a high sensitivity (90%, 95% CI: 90-91%) and specificity (85%, 95% CI: 84-86%) for detecting suspicious pigmented skin lesions in primary care settings. Significant heterogeneity was observed in both sensitivity (p = 0.0062, I2 = 80.3%) and specificity (p < 0.001, I2 = 98.8%). The analysis of DOR and PLR further demonstrated the strong diagnostic performance of DL algorithms. The DOR was 26.39, indicating a strong overall diagnostic performance of DL algorithms. The PLR was 4.30, highlighting the ability of these algorithms to influence diagnostic outcomes positively. The NLR was 0.16, indicating that a negative test result decreased the odds of misdiagnosis. The area under the curve of DL algorithms was 0.95, indicating excellent discriminative ability in distinguishing between benign and malignant pigmented skin lesions. DL algorithms have the potential to significantly improve the detection of suspicious pigmented skin lesions in primary care settings. Our analysis showed that DL exhibited promising performance in the early detection of suspicious pigmented skin lesions. However, further studies are needed.

Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging.

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.

New technology and working methods to improve healthcare for suspected malignant skin lesions: Implementation of teledermatoscopy in comprehensive healthcare

AbstractBackgroundSuspected malignant skin lesions are common and challenging in terms of diagnostic accuracy and lead time. Efficient use of teledermatoscopy has the potential to improve these factors.ObjectivesThis report aims to explore the implementation of teledermatoscopy for quality improvement in a comprehensive healthcare system.MethodsA stepwise implementation started in 2012 in the Stockholm Region, Sweden, serving 2.4 million inhabitants across approximately 240 primary healthcare centres. The full‐scale implementation occurred from May 2021 to February 2023, utilising a double‐reading teledermatoscopy system. The RE‐AIM framework (reach, effectiveness, adoption, implementation, maintenance) guided the evaluation, including measures such as participant engagement, referrals, diagnoses, staff participation, and centres going on post‐implementation. Descriptive statistics were used.ResultsIn the full‐scale implementation period, 10,862 referrals were transmitted, with 17% recommended for biopsy or excision. Among these, 282 (15%) were confirmed melanomas and 516 (28%) non‐melanoma skin cancers. Challenges were observed during the stepwise phase, particularly in engaging primary healthcare and establishing dermatologic consultation capacity. However, all centres and 1263 primary care physicians participated in the fully scaled implementation. Challenges included contractual compliance, adopting a collective system perspective, effective management transition of referrals, cost allocation, and communication improvements. Six months post‐study, most centres used the system, with around 1500 monthly referrals.ConclusionsThe evaluation revealed the overall satisfactory performance of the teledermatoscopy system in terms of reach, effectiveness, adoption, implementation, and maintenance during the fully scaled phase. Results underscore the effectiveness of a stepwise implementation to successfully address contextual challenges. The findings emphasise the system's potential for a broader patient outreach. In conclusion, implementation of teledermatoscopy for quality improvement in a comprehensive healthcare system is an organisational challenge but has the potential to improve healthcare quality for suspected malignant skin lesions.

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful. This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset. As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features. Therefore, two stage prediction model achieved better results with feature fusion.

Molecular mechanisms of human papilloma virus related skin cancers: A review.

The human papillomavirus (HPV) belongs to the Papillomaviridae family of viruses which includes small, double-stranded DNA viral agents. Approximately 90% of HPV infections occur asymptomatically and resolve spontaneously. However, infection with high-risk viral strains can lead to the development of preneoplastic lesions, with an increased propensity to become cancerous. The location of these malignancies includes the oral cavity, cervix, vagina, anus, and vulva, among others. The role of HPV in carcinogenesis has already been demonstrated for the aforementioned neoplasia. However, regarding skin malignancies, the mechanisms that pinpoint the role played by HPV in their initiation and progression still elude our sight. Until now, the only fully understood mechanism of viral cutaneous oncogenesis is that of human herpes virus 8 infection in Kaposi sarcoma. In the case of HPV infection, however, most data focus on the role that beta strains exhibit in the oncogenesis of cutaneous squamous cell carcinoma (cSCC), along with ultraviolet radiation (UVR) and other environmental or genetic factors. However, recent epidemiological investigations have highlighted that HPV could also trigger the onset of other non-melanocytic, for example, basal cell carcinoma (BCC), and/or melanocytic skin cancers, for example, melanoma. Herein, we provide an overview of the role played by HPV in benign and malignant skin lesions with a particular focus on the main epidemiological, pathophysiological, and molecular aspects delineating the involvement of HPV in skin cancers.

Pattern Analysis of Benign and Malignant Atypical Melanocytic Skin Lesions of Palms and Soles: Variations of Dermoscopic Features According to Anatomic Site and Personal Experience.

Background: The differential diagnosis of atypical melanocytic skin lesions localized on palms and soles represents a diagnostic challenge: indeed, this spectrum encompasses atypical nevi (AN) and early-stage melanomas (EN) displaying overlapping clinical and dermoscopic features. This often generates unnecessary excisions or delayed diagnosis. Investigations to date were mostly carried out in specific populations, focusing either on acrolentiginous melanomas or morphologically typical acquired nevi. Aims: To investigate the dermoscopic features of atypical melanocytic palmoplantar skin lesions (aMPPLs) as evaluated by variously skilled dermatologists and assess their concordance; to investigate the variations in dermoscopic appearance according to precise location on palms and soles; to detect the features with the strongest association with malignancy/benignity in each specific site. Methods: A dataset of 471 aMPPLs-excised in the suspect of malignancy-was collected from 10 European Centers, including a standardized dermoscopic picture (17×) and lesion/patient metadata. An anatomical classification into 17 subareas was considered, along with an anatomo-functional classification considering pressure/friction, (4 macroareas). A total of 156 participants (95 with less than 5 years of experience in dermoscopy and 61 with ≥than 5 years) from 17 countries performed a blinded tele-dermoscopic pattern analysis over 20 cases through a specifically realized web platform. Results: A total of 37,440 dermoscopic evaluations were obtained over 94 (20%) EM and 377 (80%) AN. The areas with the highest density of EM compared to AN were the heel (40.3% EM/aMPPLs) of the sole and the "fingers area" (33%EM/aMPPLs) of the palm, both characterized by intense/chronic traumatism/friction. Globally, the recognition rates of 12 dermoscopic patterns were non statistically different between 95 dermatology residents and 61 specialists: aMPPLs in the plantar arch appeared to be the most "difficult" to diagnose, the parallel ridge pattern was poorly recognized and irregular/regular fibrillar patterns often misinterpreted. Regarding the aMPPL of the "heel area", the parallel furrow pattern (p = 0.014) and lattice-like pattern (p = 0.001) significantly discriminated benign cases, while asymmetry of colors (p = 0.002) and regression structures (p = 0.025) malignant ones. In aMPPLs of the "plantar arch", the lattice-like pattern (p = 0.012) was significant for benignity and asymmetry of structures, asymmetry of colors, regression structures, or blue-white veil for malignancy. In palmar lesions, no data were significant in the discrimination between malignant and benign aMPPLs. Conclusions: This study highlights that (i) the pattern analysis of aMPPLs is challenging for both experienced and novice dermoscopists; (ii) the histological distribution varies according to the anatomo-functional classification; and (iii) different dermoscopic patterns are able to discriminate malignant from benign aMPPLs within specific plantar and palmar areas.

Role of high-frequency ultrasound in differentiating benign and malignant skin lesions: potential and limitations.

This study examined the diagnostic value of high-frequency ultrasound (HFUS) features in differentiating between benign and malignant skin lesions. A total of 1,392 patients with 1,422 skin lesions who underwent HFUS examinations were included in an initial dataset (cohort 1) to identify features indicative of malignancy. Qualitative clinical and HFUS characteristics were recorded for all lesions. To determine which HFUS and clinical features were suggestive of malignancy, univariable and multivariable logistic regression analyses were employed. The diagnostic performance of HFUS features combined with clinical information was evaluated. This assessment was validated using internal data (cohort 2) and multicenter external data (cohort 3). Features significantly associated with malignancy included age above 60 years; lesion location in the head, face, and neck or genital regions; changes in macroscopic appearance; crawling or irregular growth pattern; convex or irregular base; punctate hyperechogenicity; blood flow signals; and feeding arteries. The area under the receiver operating characteristic curve, sensitivity, and specificity of HFUS features combined with clinical information were 0.946, 92.5%, and 86.9% in cohort 1; 0.870, 93.1%, and 80.8% in cohort 2 (610 lesions); and 0.864, 86.2%, and 86.6% in cohort 3 (170 lesions), respectively. However, HFUS is not suitable for evaluating lesions less than 0.1 mm in thickness or lesions exhibiting surface hyperkeratosis. In a clinical setting, the integration of HFUS with clinical information exhibited good diagnostic performance in differentiating malignant and benign skin lesions. However, its utility was limited in evaluating extremely thin lesions and those exhibiting hyperkeratosis.

Elastic Scattering Spectroscopy on Patient-Selected Lesions Concerning for Skin Cancer.

Access to dermatologists is limited in parts of the US, making primary care clinicians (PCCs) integral for early detection of skin cancers. A handheld device using elastic scattering spectroscopy (ESS) was developed to aid PCCs in their clinical assessment of skin lesions. In this prospective study, 3 PCCs evaluated skin lesions reported by patients as concerning and scanned each lesion with the handheld ESS device. The comparison was pathology results or a 3-dermatologist panel examining high resolution dermatoscopic and clinical images. PCCs reported their diagnosis, management decision, and confidence level for each lesion. Evaluation of results included sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and Area Under the Curve (AUC). A total of 155 patients and 178 lesions were included in the final analysis. The most commonly patient-reported concerning feature was "new or changing lesion" (91.6%). Device diagnostic sensitivity and specificity were 90.0% and 60.7%, respectively, based on biopsy result or dermatologist panel reference standard; comparatively, PCC sensitivity was 40.0% and 84.8% specificity without the use of the device. Device NPV was 98.9%, and device PPV was 13.6%. The device recommended patient referral to dermatology with 88.2% concordance with the dermatologist panel. AUC for the device and PCCs were 0.815 and 0.643, respectively. The use of the ESS device by PCCs can improve diagnostic and management sensitivity for select malignant skin lesions by correctly classifying most benign lesions of patient concern. This may increase skin cancer detection while improving access to specialist care.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning

Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis.

Spectrum of malignant and premalignant skin lesions in 505 adult subjects at risk of skin cancers

Patients at risk of skin cancers can develop varying types of cutaneous malignancies. However, some subjects may develop only one type of lesion. In this cross-sectional study, the spectrum of premalignant (PM) and malignant skin lesions and their risk factors were studied. Therefore, 505 adult subjects (aged 21–79 years, 256 males and 249 females, 96 with immunosuppression) at risk of any type of skin cancer were examined for cutaneous malignancies, nevi, actinic keratoses, photodamage, and possible risk factors. First, 12 different groups were identified with a varying set of PM and/or malignant skin lesions. Next, 5 larger groups were formed from them: basal cell carcinoma (BCC) only, malignant melanoma (MM) only, squamous cell carcinoma (SCC) and/or PM, BCC + SCC and/or PM, and MM + keratinocyte carcinoma (KC) and/or PM. The groups with BCC or MM only were younger and showed less photodamage than the mixed groups, while SCC/PM showed similarity with them. In logistic regression analyses, the platelet-to-lymphocyte ratio was associated with an increased risk of concomitant KC (OR 1.028, p = 0.023) or SCC/PM (OR 1.009, p = 0.047) in subjects with MM or BCC, respectively. Actinic keratoses produced ORs 0.246–0.252 (p = 0.008–0.020) for BCC in subjects with SCC/PM. Interestingly, atypical mole syndrome decreased the risk of SCC/PM in subjects with BCC (OR 0.092, p = 0.001). Advanced age was a significant risk factor for an additional type of lesion in all 3 comparisons (ORs 1.088–1.388, p = 0.001). In conclusion, even though there are numerous patients with only one lesion type, advancing age may determine the final lesion multiplicity.

Occupational skin carcinogens

Occupational skin cancer may manifest when employees are under exposure to one specific carcinogenic substance or more in the workplace. Workplaces often have higher concentrations of carcinogens compared to any other setting. The most common causes of skin cancer in the workplace are ultraviolet radiation, ionizing radiation, polycyclic aromatic hydrocarbons, and arsenic. However, there is only a limited number of studies on skin cancer from occupational exposure. Skin cancers that are considered mainly work-related are non-melanoma skin cancers. Their most common variants are basal cell carcinomas, squamous cell carcinomas, and actinic keratosis. Two factors that reduced the risk of occupational carcinogen exposure are as follows: a better understanding of skin cancer risk factors involved in industrial processes and better control of the use of ionizing radiation. However, the exposure risk to ultraviolet radiation at dangerous levels remains. Worse still, this risk is often not considered. Yet, the prevention and risk reduction for occupational skin cancer requires the elimination of the contact of all carcinogens present in the workplace with the employees’ skin. Additionally, to encourage and facilitate the early recognition and management of premalignant and malignant skin lesions, training should be given to those working under higher skin cancer risk, and periodic examinations should be performed.

Artificial Intelligence in Dermoscopy: Enhancing Diagnosis to Distinguish Benign and Malignant Skin Lesions.

This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google's platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google's servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model's proficiency in distinguishing between benign and malignant skin lesions. The use of Google's Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model's robustness and facilitate its integration into clinical practice.

Polarization-Based Digital Histology of Human Skin Biopsies Assisted by Deep Learning

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions.

Enhancing skin cancer classification using a fusion of Densenet and Mobilenet models: a deep learning ensemble approach

Skin cancer is a condition that causes the formation of abnormal cells in the skin tissue. Every year, millions of individuals experience skin cancer. Skin cancer identification in its early stages is an expensive and difficult process. Currently, deep learning models have revealed promising results in automated skin cancer classification. The goal of this study is to identify benign or malignant skin lesion using the Internation Skin Imaging Collaboration (ISIC) dataset and a concatenated model that combines two powerful Convolutional Neural Network (CNN) architectures, DenseNet and MobileNet. The ISIC dataset, which consists of a large collection of dermoscopic images, provides a valuable resource for training and evaluating skin cancer classification models. By concatenating the feature maps of DenseNet and MobileNet, the proposed model capitalizes on their individual strengths. The concatenated model is trained using a combination of techniques called transfer learning and fine-tuning, which leverage the pre-trained weights of the individual models and adapt them to the skin cancer classification task. On the test dataset, our proposed model finally achieves 93.75% accuracy, which is 4.69% higher than DenseNet, 3.13% higher than MobileNet, and 3.13% higher than the combination of ResNet50 and InceptionNetV3. The execution time of our proposed ensemble method is 49.48 min, and the execution times of MobileNet and DenseNet are 178.9 and 170.5 min, which are more than our proposed ensemble approach.