Skin of color representation in dermoscopy studies distinguishing benign from malignant skin lesions: A scoping review.
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ABSTRACT:
SUBMITTER: Fahmy LM
PROVIDER: S-EPMC10570117 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature
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Skin of color representation in dermoscopy studies distinguishing benign from malignant skin lesions: A scoping review.
Fahmy Lauren M LM Karantza Ioanna Maria IM Schreidah Celine M CM Lapolla Brigit A BA Geskin Larisa J LJ
JAAD international 20230909
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Project description:Detection of microscopic skin lesions presents a considerable challenge in diagnosing early-stage malignancies as well as in residual tumor interrogation after surgical intervention. In this study, we established the capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin. We analyzed 86 human specimens collected during Mohs micrographic surgery for BCC to cross-examine spatial distributions of numerous lipids and metabolites in BCC aggregates versus adjacent skin. Statistical analysis using the least absolute shrinkage and selection operation (Lasso) was employed to categorize each 200-µm-diameter picture element (pixel) of investigated skin tissue map as BCC or normal. Lasso identified 24 molecular ion signals, which are significant for pixel classification. These ion signals included lipids observed at m/z 200-1,200 and Krebs cycle metabolites observed at m/z < 200. Based on these features, Lasso yielded an overall 94.1% diagnostic accuracy pixel by pixel of the skin map compared with histopathological evaluation. We suggest that DESI-MSI/Lasso analysis can be employed as a complementary technique for delineation of microscopic skin tumors.
| S-EPMC6016785 | biostudies-literature
Project description:SignificanceAccurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones.AimTo develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy.ApproachWe tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy.ResultsClassification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered.ConclusionsSimple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.
| S-EPMC9196925 | biostudies-literature
Project description: Not available
| S-EPMC10718124 | biostudies-literature
Project description:Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method. Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.
| S-EPMC9395205 | biostudies-literature
Project description:IntroductionWorldwide, the incidence and mortality of lung cancer are at the highest levels, and the most lesions are located in the lung periphery. Despite extensive screening and diagnosis, the pathologic types of peripheral pulmonary lesions (PPLs) are difficult to diagnose by noninvasive examination. This study aimed to identify a novel index-time difference of arrival (TDOA)-to discriminate between benign inflammation and malignant PPLs.MethodsUsing contrast-enhanced ultrasound (CEUS), we retrospectively analyzed 96 patients with PPLs who had undergone biopsy to confirm the pathologic types. All data were collected from Dazhou Central Hospital between December 2012 and July 2019. The parameters of CEUS were analyzed by two assistant chief physicians of ultrasound diagnosis. Area under the receiver operating characteristic curve analysis, sensitivity, specificity, positive predictive value, and negative predictive value were calculated to assess the diagnostic ability of different indices.ResultsWe found that the TDOA significantly distinguished benign inflammation from malignant lesions. The TDOA was markedly increased in patients with malignant lesions than benign inflammation lesions (P < 0.001). Compared with conventional time-intensity curve (TIC) indices, TDOA showed high diagnostic accuracy (area under the curve = 0.894). Moreover, conventional diagnostic indices did not affect the diagnostic performance of TDOA by adjusting the receiver operating characteristic curve.ConclusionTDOA is feasible for the diagnosis of benign inflammation and malignant PPLs.
| S-EPMC7689010 | biostudies-literature
Project description:ObjectiveInadequate diversity in dermatologic images has been associated with diagnostic delays and poorer health outcomes. This underrepresentation of darker skin tones has also been demonstrated across various fields of medicine, including rheumatology, urology, and in the COVID-19 pandemic. The distribution of skin tones has not been examined in educational ophthalmology texts. The authors aimed to quantify the representation of skin tones across three leading ophthalmology textbooks.MethodsTwo independent investigators utilized the Fitzpatrick's skin phototype scale to code images containing skin as either "light" (Fitz. I-IV) or "dark" (Fitz. V-VI) in three fundamental ophthalmology textbooks: Clinical Ophthalmology (Salmon and Kanski), Ophthalmology (Yanoff and Duker), and the 13 Basic and Clinical Science Course texts by the American Academy of Ophthalmology. Images without discernible skin color were excluded.ResultsOf the 9766 images reviewed, 2305 images met inclusion criteria. The three textbooks combined were found to have 2123 (92.1%) images of light skin tones and 182 images (7.9%) of dark skin tones. When compared to national data that found 12.6% of individuals to have dark skin tones, the proportion of images with darker skin tones in ophthalmology textbooks was statistically significantly lower (χ 2 corr(1, N = 4996) = 211.7, p < 0.001).ConclusionsDarker skin tones are statistically significantly underrepresented in textbooks that are central to education of trainees in ophthalmology. Acknowledgement and inclusion of skin tone diversity in ophthalmology educational materials are necessary to ensure that physicians in the field are equipped with the knowledge and training to provide the highest level of care to all patients.Supplementary informationThe online version contains supplementary material available at 10.1007/s40670-022-01636-4.
| S-EPMC9465660 | biostudies-literature
Project description:Papillomavirus (PV) mainly infects the squamous epithelium and may potentially lead to benign or even malignant cutaneous lesions. However, the malignant transforming ability has been identified in several types of PVs. In humans, papillomavirus (HPV) type 16 and 18 are the most prevalent causative agents of cervical cancer. Therefore, vaccines are being developed to protect against these types. For dogs, there have been limited investigations into the association of different canine papillomavirus (CPV) genotypes with malignant lesions. Understanding the high-risk CPV genotype(s) responsible for these malignant lesions would contribute to the development of interventions for preventing CPV-induced carcinomas. In the present study, a retrospective cohort of 102 pathologically confirmed papillomas and 212 squamous cell carcinomas (SCCs) were included. The viral genome and antigens in the formalin-fixed paraffin-embedded (FFPE) tissues were detected using PCR targeting pan PV E1 and COPV L1 genes and by immunohistochemistry staining (IHC), respectively. PVs were successfully detected from 11 FFPE cutaneous tissues and four oral tissues using pan PV E1- and COPV L1-based PCR, respectively. After sequencing, CPV 1, CPV 2, and CPV 6 were detected in the benign lesions using PCR and were confirmed through IHC. While CPV 9 and CPV 15 were first detected in the SCCs of dogs, CPV 16 was most often detected in SCC specimens. The association and confirmative demonstration of viral genes and intralesional antigens of CPV 9, CPV 15, and CPV 16 in SCCs highlight the potential risk of these genotypes of CPVs in malignant transformation.
| S-EPMC7077320 | biostudies-literature
Project description:An extremely diverse group of human papillomavirus (HPV) types consisting of epidermodysplasia verruciformis (EV)-associated HPV types and other cutaneous HPV types (e.g., HPV types 2 and 3) is associated with nonmelanoma cancers and benign lesions of the skin. The frequent presence of multiple HPV types in single skin biopsy specimens of renal transplant recipients prompted us to develop PCR techniques for the detection of distinct (sub)groups of genotypically related cutaneous HPV types, i.e., three subgroups of EV-associated HPV types and two groups (A2 and A4) of other cutaneous HPV types. This approach generally allowed a reliable identification of HPV genotypes by direct sequencing of the PCR products, despite the frequent occurrence of multiple infections. The targeted spectrum of HPV types comprises 66 cutaneous HPV types including 21 putative novel HPV types. We also detected 17 putative novel HPV subtypes. We demonstrated that the skin of nearly all renal transplant recipients who developed various benign and (pre)malignant skin lesions was persistently infected with one or more EV-associated HPV types and/or HPV types belonging to groups A2 and A4. The frequency and distribution of EV-associated HPV and HPV types belonging to groups A2 and A4 were similar in biopsy specimens from hyperkeratotic papillomas (77.5%), squamous cell carcinomas (77. 8%), and actinic keratoses (67.9%) but appeared to be lower in specimens of basal cell carcinomas (35.7%), benign lesions (38.5%), and clinically normal skin (32.3%). These findings suggest that renal transplant recipients are prone to persistent cutaneous HPV infection. Our data do not support the existence of high-risk cutaneous HPV types.
| S-EPMC86734 | biostudies-literature
Project description:Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary adenocarcinoma could be distinguished from benign masses and biliary strictures. Sera from 157 patients with malignant and benign pancreatic and periampullary lesions were analyzed using proton nuclear magnetic resonance (¹H-NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS). Multivariate projection modeling using SIMCA-P+ software in training datasets (n = 80) was used to generate the best models to differentiate disease states. Models were validated in test datasets (n = 77). The final ¹H-NMR spectroscopy and GC-MS metabolomic profiles consisted of 14 and 18 compounds, with AUROC values of 0.74 (SE 0.06) and 0.62 (SE 0.08), respectively. The combination of ¹H-NMR spectroscopy and GC-MS metabolites did not substantially improve this performance (AUROC 0.66, SE 0.08). In patients with adenocarcinoma, glutamate levels were consistently higher, while glutamine and alanine levels were consistently lower. Pancreatic and periampullary adenocarcinomas can be distinguished from benign lesions. To further enhance the discriminatory power of metabolomics in this setting, it will be important to identify the metabolomic changes that characterize each of the subclasses of this heterogeneous group of cancers.
| S-EPMC5372206 | biostudies-literature
Project description:ObjectiveTo compare the performance of simulated abbreviated breast MRI (AB-MRI) and full diagnostic (FD)-MRI in distinguishing between benign and malignant lesions detected by MRI and investigate the features of discrepant lesions of the two protocols.Materials and methodsAn AB-MRI set with single first postcontrast images was retrospectively obtained from an FD-MRI cohort of 111 lesions (34 malignant, 77 benign) detected by contralateral breast MRI in 111 women (mean age, 49.8. ± 9.8; range, 28-75 years) with recently diagnosed breast cancer. Five blinded readers independently classified the likelihood of malignancy using Breast Imaging Reporting and Data System assessments. McNemar tests and area under the receiver operating characteristic curve (AUC) analyses were performed. The imaging and pathologic features of the discrepant lesions of the two protocols were analyzed.ResultsThe sensitivity of AB-MRI for lesion characterization tended to be lower than that of FD-MRI for all readers (58.8-82.4% vs. 79.4-100%), although the findings of only two readers were significantly different (p < 0.05). The specificity of AB-MRI for lesion characterization was higher than that of FD-MRI for 80% of readers (39.0-74.0% vs. 19.5-45.5%, p ≤ 0.001). The AUC of AB-MRI was comparable to that of FD-MRI for all readers (p > 0.05). Fifteen percent (5/34) of the cancers were false-negatives on AB-MRI. More suspicious margins or internal enhancement on the delayed phase images were related to the discrepancies.ConclusionThe overall performance of AB-MRI was similar to that of FD-MRI in distinguishing between benign and malignant lesions. AB-MRI showed lower sensitivity and higher specificity than FD-MRI, as 15% of the cancers were misclassified compared to FD-MRI.
| S-EPMC7909852 | biostudies-literature