Diagnosis Of Basal Cell Carcinoma Research Articles

Dermatoscopy and reflectance confocal microscopy for basal cell carcinoma diagnosis and diagnosis prediction score: A prospective and multicenter study on 1005 lesions

BackgroundBasal cell carcinoma is usually diagnosed by clinical and dermoscopy examination, but diagnostic accuracy may be suboptimal. Reflectance confocal microscopy (RCM) imaging increases skin cancer diagnostic accuracy. ObjectiveTo evaluate additional benefit in diagnostic accuracy of handheld RCM in a prospective controlled clinical setting MethodsA prospective, multicenter study in 3 skin cancer reference centers in Italy enrolling consecutive lesions with clinical-dermoscopic suspicion of basal cell carcinoma. Clinicaltrials.gov; NCT04789421 ResultsA total of 1005 lesions were included, of which 474 histopathologically confirmed versus 531 diagnosed by clinical-dermoscopic-RCM correlation, confirmed with 2 years of follow up. Specifically, 740 were confirmed basal cell carcinomas. Sensitivity and specificity for dermoscopy alone was 93.2% (CI 95% 91.2 - 94.9) and 51.7% (CI 95% 45.5 - 57.9); PPV was 84.4 (CI 95% 81.7 – 86.8) and NPV 73.3 (66.3 - 79.5). Adjunctive reflectance confocal microscopy reported higher rates: 97.8 (CI 95% 96.5 - 98.8) sensitivity and 86.8 (CI 95% 82.1 - 90.6) specificity, with PPV of 95.4 (CI 95% 93.6 – 96.8) and NPV 93.5 (89.7 - 96.2). LimitationsStudy conducted in one single Country ConclusionAdjunctive handheld RCM assessment of lesions clinically suspicious for basal cell carcinoma permits higher diagnostic accuracy with minimal false negative lesions.

Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.

A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.

Initial clinical manifestations and early diagnosis of basal cell skin carcinoma

Background/Aim: Basal cell carcinoma (BCC) is the most common cancer of the skin. It is believed that increased UV radiation from the sun accounts for almost 90 % of the risk of BCC. There is a growing trend in the incidence of BCC in a younger population. The aim of study was to analyse the initial clinical symptoms of BCC that may be important for the early detection of this skin tumour. Method: The study was a prospective, multicentre study performed in the period from March 2017 to February 2022. A total of 69 respondents with BCC were analysed. Respondents applied for a targeted examination to examine a suspicious skin lesion (due to certain symptoms) or were diagnosed with BCC by accident, when examining other skin changes. Respondents were divided into 2 groups. The first, Group I (35 respondents), consisted of respondents with nodular BCC. The second, Group II (34 respondents), consisted of respondents with superficial spreading BCC. Initially, a careful history and data on the characteristics, shape and character of the tumour were taken from all respondents. Data on all (even the smallest) initial symptoms and relevant signs of evolution, as well as subjective problems related to the tumour were noted. All respondents underwent dermoscopy of suspected skin changes. Results: A significant difference was found between the examined groups in the characteristics of bleeding, crust formation and tendency to injury in lesions, where they occur more often in patients with nodular BCC. Symptoms such as burning and flaking occurred significantly more often in patients with superficial spreading BCC (p < 0.01), as well as the diameter of lesions over 5 mm (p < 0.05). Conclusion: Early clinical diagnosis of BCC is possible with a tumour diameter of only a few mm. The predominant initial (highly susceptible) symptoms of nodular BCC were initial bleeding and / or scab formation on the lesions, as well as propensity to injury. The superficial spreading form of BCC was often larger than 5 mm in diameter, with more frequent scaling of the lesion, as well as burning and stinging sensations in the tumour area. Itching was observed to be a very common previous occurrence in the BCC initial focus zone in subjects of both study groups. Dermsocopy is a highly reliable diagnostic method for early detection of BCC.

Multipli bazocelularni karcinomi nakon primene radioterapije u lečenju tinea capitis u detinjstvu - prikaz slučaja i pregled literature

Introduction: Basal cell carcinoma (BCC) is the most frequent skin cancer with a rising incidence worldwide. Predilection sites are the head and the neck in 80-85% of cases. Leading risk factors are sun exposure and ionizing radiation. In the middle of the 20th century, radiation-induced epilation was used as an efficient treatment method for tinea capitis. Patient review: We report a case of an 80-year-old man with a 16year history of the successive occurrence of multiple BCCs on the scalp, face, and neck. He also had the history of tinea capitis treated with radiotherapy during childhood. At presentation during clinical and dermoscopic examination approximately 25 BCCs were observed, predominantly on the scalp. The most common BCC lesion was the nodular subtype, followed by the pigmented and superficial subtypes. Histopathological examination confirmed the diagnosis of BCC in multiple lesions and one squamous cell carcinoma (SCC). In personal history, the most important comorbidity was hairy cell leukemia. The therapeutic approach included surgery and 5-fluorouracil 5% cream. Conclusion: Radiation-induced epilation was used as an efficient method for treating tinea capitis. One of its side effects was the appearance of BCC in radiation exposed areas. Surgical excision is the gold standard for BCC treatment. Also, 5-fluorouracil 5% cream may be a good option for patients with multiple BCCs. We present satisfactory results after combined treatment in our patient.

Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.

This study has used machine learning algorithms to develop a predictive model for differentiating between dermoscopic images of basal cell carcinoma (BCC) and actinic keratosis (AK). We compiled a total of 904 dermoscopic images from two sources - the public dataset (HAM10000) and our proprietary dataset from the First Affiliated Hospital of Dalian Medical University (DAYISET 1) - and subsequently categorised these images into four distinct cohorts. The study developed a deep learning model for quantitative analysis of image features and integrated 15 machine learning algorithms, generating 207 algorithmic combinations through random combinations and cross-validation. The final predictive model, formed by integrating XGBoost with Lasso regression, exhibited effective performance in the differential diagnosis of BCC and AK. The model demonstrated high sensitivity in the training set and maintained stable performance in three validation sets. The area under the curve (AUC) value reached 1.000 in the training set and an average of 0.695 in the validation sets. The study concludes that the constructed discriminative diagnostic model based on machine learning algorithms has excellent predictive capabilities that could enhance clinical decision-making efficiency, reduce unnecessary biopsies, and provide valuable guidance for further treatment.

Differentiation and risk stratification of basal cell carcinoma with deep learning on histopathologic images and measuring nuclei and tumor microenvironment features.

Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity. To develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high-risk (HR) and low-risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors. The deep learning systems were trained on 161 H&E -stained sections which contained 51 sections of HR-BCC, 50 sections of LR-BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi-head self-attention (MSA) U-Net, nuclei segmentation by HoVer-Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers. MSA-U-Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977±0.0159, 0.955±0.0181, 0.885±0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T-T_meanEdgeLength, T-T_Nsubgraph, S-T_HarmonicCentrality, S-S_Degrees. The risk stratification model can well predict HR-BCC and LR-BCC with 0.920±0.0579, 0.839±0.0176, 0.825±0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR-BCC and LR-BCC comprised IntensityMin, Solidity, T-T_minEdgeLength, T-T_Coreness, T-T_Degrees, T-T_Betweenness, S-T_Degrees. This framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification.

Incidence of malignant eyelid tumors: A 6-year period review (2015–2021)

Abstract Background: Basal cell carcinoma (BCC), the most common malignant tumor, accounts for 85%–95% of cases of eyelid malignant tumors. Demographic factors such as age, gender, tumor location, and subtype play a crucial role in epidemiological knowledge. We investigated eyelid BCC diagnosis and treatment at the Department of Ophthalmology in Centro Hospitalar de Setúbal (CHS). Methods: A retrospective study was conducted on patient cases with suspected eyelid epithelial tumors who underwent excisional biopsy surgery. The study included data from the CHS database between 2015 and 2021. Data collection and subsequent analysis were based on histopathological results. Results: In our study, were analyzed a total of 47 tumor cases, with 68.1% located on the lower eyelids and 31.9% on the upper eyelids. Among these cases, 25 (53.2%) were classified as benign, while 22 (46.8%) were identified as malignant eyelid tumors. BCCs accounted for 90.9% of all malignant cases, with an average age of 71.7 ± 16.6 years. The nodular subtype was the most prevalent, followed by infiltrative and superficial subtypes. Superficial BCC patients were older than infiltrative BCC patients (median age = 69.5 years vs. 61.5 years). BCC primarily affected the lower eyelids compared to the upper eyelid, and there was a slight female predominance (n = 11). All diagnosed BCCs correlated with sun-exposed regions. Conclusions: Timely intervention is crucial for reducing morbidity. These findings emphasize the need for improved registration and notification systems to enhance the prevention and treatment of eyelid BCCs. Furthermore, despite advancements in ultraviolet-blocking technologies, further development of sunscreen specifically formulated for ocular use remains necessary to reduce the incidence of eyelid tumors.

Management of Nodular Nasal Basal Cell Carcinoma with Wide Excision, Transpositional Median Forehead Flap, Forehead Split Thickness Skin Graft: Case Report

Introduction: Basal cell carcinoma (BCC) is the most common skin type cancer with an incidence rate of around 75-80%. The most common location is in the head and neck area around 70-80% and followed by other body parts around 25% consist of the penis, vulva or perianal area and 5% on the skin. BCC occurs more frequently in light-skinned individuals compared to dark-skinned individuals. The incidence rate is relatively lower in Asians, blacks ethnic, and Hispanics.
 Case Presentation: A 66 years-old woman complained of scabs on the right side of her nose which had become increasingly widespread for the past 2 years. The wound was initially the size of a mole and was scratched until it spread until the patient was hospitalized. Physical examination showed a wound in the nasal area measuring 4 x 5 x 1 cm, irregular wound edges, cartilaginous wound base with necrotic tissue and pus. The first biopsy examination showed poorly differentiated basal cell carcinoma. The patient was treated with Nasal Reconstruction surgery consisting of wide excision was performed by surgical oncologist, transpositional median forehead Flap and forehead Split Thickness Skin Graft were performed by reconstructive plastic surgeon.
 Discussion: The diagnosis of BCC in this patient was based on regarding history taking, physical examination and histopathological examination. However, for defects with a diameter of more than 1.5 to 2 cm, generally recommended using axial pattern flaps such as forehead flaps, nasolabial flaps and dorsal nose flaps. In this case, the patient was treated with wide excision followed by nasal reconstruction with a transpositional forehead flap and forehead split thickness skin graft.
 Conclusion: Nevertheless, regional flaps remain the recommended procedure for nasal reconstruction after wide excision of the tumor. Accurate analysis of the damage combined with evaluation of the patient's clinical condition is necessary to select the best surgical technique. Thorough knowledge of regional vascular anatomy and a comprehensive multidisciplinary approach are important first steps for proper treatment of nasal skin cancer, so that the procedure for wide excision of nasal basal cell carcinoma followed by nasal reconstruction with transpositional forehead flap and split thickness skin graft, can provide satisfactory result.

Advancements in Basal Cell Carcinoma Diagnosis: Non-Invasive Imaging and Multimodal Approach.

(1) Background: The aim of this study was to correlate the diagnostic criteria described in dermatoscopy, ultrasonography (US), ex vivo confocal microscopy, and histology to the most common subtypes of basal cell carcinoma (BCC). (2) Methods: We conducted a prospective study including 46 BCC cases, which were analyzed with dermatoscopy using the Delta 30 dermatoscope and Vidix 4.0 videodermoscope, with US using a high-resolution 20 MHz linear probe, with confocal microscopy, along with histopathological analysis. (3) Results: This study categorized BCC by histological subtype, with nodular being the most common (84.8%) and various other subtypes represented. US measurements of tumor thickness correlated strongly with the histopathological depth of invasion index (DI). Dermatoscopy analysis revealed significant associations between specific features and BCC subtypes. The DI was directly related to arborized vessels but inversely related to short, fine telangiectasias, maple-leaf-like areas, and spoke-wheel areas. The presence of ulceration was directly related to the DI. Confocal microscopy images exhibited several characteristics, including fluorescence, nuclear crowding, peripheral palisading, clefting, increased nuclear-cytoplasmic (N/C) ratio, and a "cauliflower-like" appearance. (4) Conclusion: The advanced detection of BCC through imagistic techniques like dermatoscopy, confocal microscopy, and ultrasound improves the diagnosis and may offer valuable insights for treatment in the future by evaluating lesion characteristics.

Comparison of optical coherence tomography and in vivo reflectance confocal microscopy with dermoscopy for the diagnosis and management of nonmelanoma skin cancer: A randomized controlled trial

AbstractBackgroundOptical coherence tomography (OCT) and reflectance confocal microscopy (RCM) can yield improved diagnostic accuracy of nonmelanoma skin cancer (NMSC) in comparison with dermoscopy alone.ObjectivesThe aim of this study was to compare the diagnostic performance of OCT and RCM together and individually with that of dermoscopy.MethodsPatients with lesions suspicious for NMSC were randomized into two groups in a prospective, single‐centre study. In the intervention group (IG), every lesion was examined with dermoscopy, OCT and RCM, while in the control group (CG), every lesion was examined with dermoscopy alone.ResultsA total of 365 lesions of 250 patients (105 female, 145 male) were included in the study. A total of 208 basal cell carcinomas (BCCs), 65 squamous cell carcinomas (SCCs) and 31 SCCs in situ were examined histologically. The IG involved significantly fewer biopsies than the CG (14 vs. 27, p = 0.029) and required fewer additional inpatient stays due to positive biopsy results (2 vs. 12, p = 0.001). For the diagnosis of BCC, diagnostic accuracy was 81% with dermoscopy, 84% with OCT, 83% with RCM and 85% with the combination of OCT and RCM (OCT/RCM). The diagnostic accuracy of OCT for BCC subtypes was as follows: superficial, 89%; nodular, 79%; sclerodermiform, 82%; and nodular‐cystic BCC, 75%. For the diagnosis of SCC, diagnostic accuracy was 85% with dermoscopy, 87% with OCT, 89% with RCM and 87% with OCT/RCM. For the diagnosis of in situ SCC, diagnostic accuracy was 87% with dermoscopy, 89% with OCT, 89% with RCM and 91% with OCT/RCM.ConclusionsDiagnostic accuracy increased with OCT and RCM and was even higher when both methods were used together. OCT and RCM could facilitate optimization of lesion management by reducing the number of punch biopsies and reducing additional inpatient stays due to positive biopsy results.

'Pitfalls for differentiating basal cell carcinoma from non-basal cell carcinoma on optical coherence tomography: A clinical series'.

Optical coherence tomography (OCT), a non-invasive diagnostic modality, may replace biopsy for diagnosing basal cell carcinoma (BCC) if a high-confidence BCC diagnosis can be established. In other cases, biopsy remains necessary to establish a histopathological diagnosis and treatment regimen. It is, therefore, essential that OCT assessors have a high specificity for differentiating BCC from non-BCC lesions. To establish high-confidence BCC diagnoses, specific morphological BCC characteristics on OCT are used. This study aimed to review several cases of non-BCC lesions that were misclassified as BCC by experienced OCT assessors, thereby providing insight into the causes of these misclassifications and how they may be prevented. The study population consisted of patients who had a histopathologically-verified non-BCC lesion. Patients from Maastricht University Medical Center+ from February 2021 to April 2021 were included in the study. Two independent OCT assessors assessed OCT scans. One OCT assessor recorded the presence or absence of validated morphological BCC characteristics. A false-positive OCT test result was defined as certainty of BCC presence in a non-BCC lesion. The frequency of misclassifications and the presence or absence of morphological BCC features are discussed. A total of 124 patients with non-BCC lesions were included. Six patients were misclassified by both OCT assessors and are discussed in more detail. Histopathological diagnoses were squamous cell carcinoma (n = 2/21), actinic keratosis (n = 2/29), squamous cell carcinoma insitu/Bowen's disease (n = 1/16), or interphase dermatitis (n = 1/4). In all misclassified cases, multiple, apparent morphological BCC characteristics on OCT were present. Most non-BCC lesions are recognized as such by OCT assessors. However, there remains a small risk that a high-confidence BCC diagnosis is established in non-BCC lesions wherein features mimicking validated BCC characteristics are present. Misclassification may be prevented by careful delineation of epidermal layers and good differentiation between dermal ovoid structures typical of BCC versus squamous cell carcinoma.

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

Multiple aggregated yellow-white globules are a major dermoscopic sign of basal cell carcinoma regardless of their histological subtype.

Multiple aggregated yellow-white globules (MAY globules) have been recently described as dermoscopic structures of high specificity associated with high-risk non-pigmented basal cell carcinoma (BCC). To evaluate the diagnostic accuracy of MAY globules in a cohort of pigmented and non-pigmented BCC of all histological types. This was a retrospective case-control study. Dermoscopic and clinical images were all histopathologically confirmed as BCCs of patients seen consecutively at dermatology consultation. Control cases were benign or malignant tumours randomly selected from the database of 8,250 patients. A total of 389 BCCs were included. MAY globules were present in 192 (49%) cases in the BCC group and in only 25 cases in the control group (6,4%). The odds ratio for the diagnosis of BCC was 14.2 (95%CI: 9.62-20.95]). The presence of MAY globules was significant in three histological subtypes, including superficial BCCs. This study shows that MAY globules are a major dermoscopic sign for the diagnosis of BCC, regardless of their histological subtype and their pigmentation.

Melanoma and basal cell carcinoma: Mimickers of each other

Basal cell carcinoma and malignant melanoma display unique dermoscopic features, at times they can mimic each other. A few clinical and dermoscopic algorithms, including the ABCD rule and the seven-point checklist, have been developed to simplify the diagnostic process. However, these tumors may present ambiguous dermoscopic patterns, lacking typical dermoscopic criteria. We present four cases where dermoscopic structures act as a confounding factor for the incorrect diagnosis of basal cell carcinoma and malignant melanoma. This work aims to improve the differentiation between melanoma and basal cell carcinoma. These tumors can sometimes be clinically similar and this can interfere with treatment if the correct diagnosis is not made.

The molecular genetic aspects of basal cell carcinoma from the position of population health preservation

The exploration of molecular genetic mechanisms that underlie carcinogenesis, hereditary factors of various oncological diseases, including basal cell carcinoma, the most common type of skin cancer is especially actual and significant for target strategies of public health. The diagnosis of basal cell carcinoma is based on complex clinical, radiologic and genetic examination data. The further research in the field of somatic or hereditary mutations in genes associated with basal cell carcinoma, including Patched 1 (PTCH1), Patched 2 (PTCH2), Smoothed (SMO) continue to be topical. The strategies of primary prevention of basal cell carcinoma, discussions of complex issues of decision-making concerning treatment at primary health care level, training courses and development of guidelines for general practitioners and interdisciplinary recommendations for effective early diagnosis and comprehensive care of basal cell carcinoma are to be suggested.

Machine learning identification of patient clusters for cutaneous melanoma.

Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma. A Bayesian Gaussian Mixture ML model was created with data from 2056 patients diagnosed with cutaneous melanoma and then used to group the patients into six Clusters based on a Silhouette Score analysis. A t-distributed stochastic neighbor embedding (t-SNE) model was used to visualize the six Clusters. Statistical analysis revealed that Cluster 4 showed a significantly higher rate of metastatic disease, as well as higher Breslow depth at diagnosis, compared to the other five Clusters. Compared to the other five Clusters, patients represented in Cluster 4 also had lower healthcare utilization, fewer dermatology clinic visits, fewer primary care providers, and less frequent colonoscopies and mammograms, and were more likely to smoke and less likely to have a prior diagnosis of basal cell carcinoma. This study uncovers gaps in healthcare utilization of services among patient groups with cutaneous melanoma as well as possible implications for management of disease progression. Data-driven analyses emphasize the importance of routine clinic visits to dermatologists and/or primary care physicians (PCPs) for early detection and management of cutaneous melanoma. The findings from this study demonstrate that unsupervised ML methodology may serve to define the best candidate patients to benefit from enhanced dermatology/primary care which, in turn, is expected to improve outcomes for cutaneous melanoma.

Rotation flap of the nose.

A 84 years old woman came to our attention with an ulcerated lesion of the nose referred to be present for about 4 months. Clinical diagnosis of basal cell carcinoma was made, and surgical removal was scheduled.

Prevalence and Risk Factors for Recurrence of Basal Cell Carcinoma Following Surgical Excision in a 5-Year Follow-Up Period

Material And Methods: Observational, descriptive, retrospective study in patients with clinical and histopathological diagnosis of basal cell carcinoma, attended at CMN La Raza in a period of 5 years, to determine the postoperative recurrence of CBC. Clinical and histological variables were included, which were analyzed with descriptive statistics and for the comparison of variables was used chi-square or exact test of Fisher and U of Mann Whitney, subsequently, multiple logistic regression was performed to determine the independent risk factors, with the use of the statistical package SPSS v25. The study was approved by the ethics and research committee of CMN La Raza. Results: We included 280 patients with CBC, with a median age of 72 years. The majority were 211 women (75.4%). CBC recurrence was observed in 115 (41.1%) patients. In the univariate analysis, age, sex, smoking, history of sunburn, facial area, CBC subtype, perineural and vascular involvement were found to be statistically significant (p<0.05). In the regression model, the independent risk factors associated with postoperative recurrence of CBC were age, male sex, and smoking. Conclusions: The clinical-pathological characteristics were similar to those reported in the literature. In our population, it was observed as independent risk factors for recurrence, adjusted to those already mentioned in the literature, were male sex, age and smoking, so it is important to closely monitor these patients to take measures regarding surgical treatment.

La dermatoscopia y nuevas tecnologías en el diagnóstico del carcinoma basocelular en atención primaria

La dermatoscopia y nuevas tecnologías en el diagnóstico del carcinoma basocelular en atención primaria

Vulvar basal cell carcinoma: A case report and literature review

Basal cell carcinoma (BCC), is the most prevalent skin cancer with favorable prognosis, and lymphatic or hematogenous metastases are quite rare. Here we report an uncommon case of vulvar BCC. A 68-year-old woman with 5-year history of an asymptomatic lump on the right side of the vulva. The patient complained of progressive itching, pain, and color changes to brown for 3 months before visiting the clinic. Excisional biopsy revealed the lesion to be a BCC. After thorough negative metastasis work up the patient underwent a bilateral inguinofemoral lymphadenectomy and a wide local excision with a one-centimeter margin and final histology report confirmed the diagnosis of BCC. (HPV DNA and viral markers were negative, the bimanual exam was normal, both colposcopy and vaginoscopy were normal. There was no acetowhite lesion). To avoid delayed diagnosis, any persistent lesion even asymptomatic, in the vulvar region, especially when pigmented, irritating, or expanding in size, should be biopsied and investigated histologically, regardless of the patient's age. Any persistent lesion in the vulvar region, especially when pigmented, irritating, or expanding in size, should be biopsied and investigated histologically, regardless of the patient's age.