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Skin cancer (SC) is one of the three most common cancers worldwide. Melanoma has the deadliest potential to spread to other parts of the body among all SCs. For SC treatments to be effective, early detection is essential. The high degree of similarity between tumor and non-tumors makes SC diagnosis difficult even for experienced doctors. To address this issue, authors have developed a novel Deep Learning (DL) system capable of automatically classifying skin lesions into seven groups: actinic keratosis (AKIEC), melanoma (MEL), benign keratosis (BKL), melanocytic Nevi (NV), basal cell carcinoma (BCC), dermatofibroma (DF), and vascular (VASC) skin lesions. Authors introduced the Multi-Grained Enhanced Deep Cascaded Forest (Mg-EDCF) as a novel DL model. In this model, first, researchers utilized subsampled multigrained scanning (Mg-sc) to acquire micro features. Second, authors employed two types of Random Forest (RF) to create input features. Finally, the Enhanced Deep Cascaded Forest (EDCF) was utilized for classification. The HAM10000 dataset was used for implementing, training, and evaluating the proposed and Transfer Learning (TL) models such as ResNet, AlexNet, and VGG16. During the validation and training stages, the performance of the four networks was evaluated by comparing their accuracy and loss. The proposed method outperformed the competing models with an average accuracy score of 98.19%. Our proposed methodology was validated against existing state-of-the-art algorithms from recent publications, resulting in consistently greater accuracies than those of the classifiers.

Introduction: Because of different therapeutic regimens for superficial and non-superficial basal cell carcinomas (BCCs), accurate histopathological examination of a punch biopsy to determine its subtype is essential. The aim of the current study was to evaluate the additional yield of a more extensive step-section method to that of a standard histologic examination at 4 levels., Material and Methods: Data for this prospective study was obtained from the Pathology department of a Dutch tertiary hospital. Biopsy specimens of subsequent patients from March 2019 to June 2020 were sectioned to 8-levels instead of the regular 4-levels. Only patients with a superficial BCC subtype in the first 4-levels of sectioning were included (n = 100). After 8-level sectioning, it was recorded in which level (5-8) a more aggressive BCC component was found (i.e. nodular, infiltrative, or micronodular). Patients were followed-up to evaluate further treatment, and in case of excision, the excision specimen was reviewed to determine the BCC subtype. A logistic regression was performed to assess characteristics associated with a more aggressive BCC component in levels 5-8., Results: In 14 patients (14%) a more aggressive component was found in levels 5-8, all with a nodular component. Thirteen of these patients underwent excision, confirming a more aggressive BCC subtype. Of the 86 patients that had no deeper BCC component in levels 5-8, 26 (30.2%) underwent excision; In 4 patients no residual BCC was found, in 15 patients superficial BCC, and in 7 a more aggressive BCC subtype (1 nodular and 6 a combination of superficial/nodular/infiltrative). In multivariable analysis, head&neck localization was associated with finding a more aggressive BCC subtype in levels 5-8 (OR 6.41 (95%CI 1.56-26.30), p = 0.01))., Conclusions: More extensive sectioning of superficial BCC biopsy specimens, especially in the head&neck area, leads to a more accurate BCC subtype diagnosis requiring different clinical management strategies., Competing Interests: The authors have declared that no competing interests exist.

Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data., (© 2021. The Author(s).)

Background: Basal cell carcinoma (BCC) is the most common skin cancer in the general population. Treatments vary from Mohs surgery to topical therapy, depending on the subtype. Dermoscopy, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) have gained a foothold in daily clinical practice to optimize diagnosis and subtype-oriented treatment. The new technique of line-field confocal OCT (LC-OCT) allows imaging at high resolution and depth, but its use has not yet been investigated in larger studies., Aim: To evaluate the main LC-OCT criteria for the diagnosis and subtyping of BCC compared with histopathology, OCT and RCM., Methods: In total, 52 histopathologically confirmed BCCs were evaluated for imaging criteria. Their frequency, predictive values and ROC curves were calculated. A multinominal regression with stepwise variables selection to distinguish BCC subtypes was performed., Results: Nodular BCCs were mainly characterized by atypical keratinocytes, altered dermoepidermal junction (DEJ), tumour nests in the dermis, dark clefting, prominent vascularization and white hyper-reflective stroma. Superficial BCCs showed a thickening of the epidermis due to a series of tumour lobules with clear connection to the DEJ (string of pearls pattern). Infiltrative BCCs were characterized by elongated hyporeflective tumour strands, surrounded by bright collagen (shoal of fish pattern). The overall BCC subtype agreement between LC-OCT and conventional histology was 90.4% (95% CI 79.0-96.8)., Conclusion: LC-OCT allows noninvasive, real-time identification of BCCs and their subtypes in vertical, horizontal and three-dimension mode compared with histology, RCM and OCT. Further larger studies are needed to better explore the clinical applications of this promising device., (© 2021 The Authors. Clinical and Experimental Dermatology published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.)

Little is known regarding the molecular differences between basal cell carcinoma (BCC) subtypes, despite clearly distinct phenotypes and clinical outcomes. In particular, infiltrative BCCs have poorer clinical outcomes in terms of response to therapy and propensity for dissemination. In this project, we aimed to use exome sequencing and RNA sequencing to identify somatic mutations and molecular pathways leading to infiltrative BCCs. Using whole-exome sequencing of 36 BCC samples (eight infiltrative) combined with previously reported exome data (58 samples), we determine that infiltrative BCCs do not contain a distinct somatic variant profile and carry classical UV-induced mutational signatures. RNA sequencing on both datasets revealed key differentially expressed genes, such as POSTN and WISP1, suggesting increased integrin and Wnt signaling. Immunostaining for periostin and WISP1 clearly distinguished infiltrative BCCs, and nuclear β-catenin staining patterns further validated the resulting increase in Wnt signaling in infiltrative BCCs. Of significant interest, in BCCs with mixed morphology, infiltrative areas expressed WISP1, whereas nodular areas did not, supporting a continuum between subtypes. In conclusion, infiltrative BCCs do not differ in their genomic alteration in terms of initiating mutations. They display a specific type of interaction with the extracellular matrix environment regulating Wnt signaling., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The acid-sensing ion channels ASIC1 and ASIC2, as well as the transient receptor potential vanilloid channels TRPV1 and TRPV4, are proton-gated cation channels that can be activated by low extracellular pH (pH e ), which is a hallmark of the tumor microenvironment in solid tumors. However, the role of these channels in the development of skin tumors is still unclear. In this study, we investigated the expression profiles of ASIC1, ASIC2, TRPV1 and TRPV4 in malignant melanoma (MM), squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and in nevus cell nevi (NCN). We conducted immunohistochemistry using paraffin-embedded tissue samples from patients and found that most skin tumors express ASIC1/2 and TRPV1/4. Striking results were that BCCs are often negative for ASIC2, while nearly all SCCs express this marker. Epidermal MM sometimes seem to lack ASIC1 in contrast to NCN. Dermal portions of MM show strong expression of TRPV1 more frequently than dermal NCN portions. Some NCN show a decreasing ASIC1/2 expression in deeper dermal tumor tissue, while MM seem to not lose ASIC1/2 in deeper dermal portions. ASIC1, ASIC2, TRPV1 and TRPV4 in skin tumors might be involved in tumor progression, thus being potential diagnostic and therapeutic targets.

Cutaneous basal cell carcinoma (cBCC) is an economic burden to health services, due to its great morbidity and increasing incidence in old people. Infiltrative cBCCs and cBCCs with micronodular pattern are considered as more aggressive. The role of p53 expression and TERTp mutation on cBCC behavior remains to be clarified. We aimed to assess TERTp mutations and p53 expression in relation to the cBCC histological subtype in a cohort of patients referred to an ENT Department of a tertiary Hospital of Northern Portugal. We performed a retrospective clinicopathological and histological review of the head and neck cBCCs followed-up at the otorhinolaryngology department of Trás-os-Montes e Alto Douro hospital (January 2007-June 2018). We assessed TERTp mutations in 142 cBCCs and p53 protein expression, through immunohistochemistry, in 157 cBCCs. We detected TERTp mutations in 43.7% of cBCCs and p53 overexpression in 60.5% of cBCCs. We spotted association of p53 overexpression and TERTp mutation with necrosis. In the infitrative-growth pattern cBCCs, there was no significant association with the clinical and histological features evaluated, except for necrosis. In the indolent-growth cBCCs, we identified a significant association of TERTp mutation status with female sex, necrosis, multiple cBCCs, and p53 positive expression. Our results suggest that TERTp mutation may be useful to identify more aggressive features in the indolent-growth pattern cBCCs (nodular and superficial subtypes). Further studies with larger cohorts are warranted to clarify the relevance of TERTp mutation in cBCCs.

Background: The term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression of estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for TNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology and gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression data but also expression data for microRNAs., Results: In this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained microRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses integrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key networks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of members of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including inositol polyphosphate 4-phosphatase type II, INPP4B., Conclusions: These data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be applied to future biomarker and therapeutic development studies.

Background: The histological subtype of basal-cell carcinoma (BCC) is often based on a punch biopsy; only a small part is evaluated, possibly leading to misclassification. Consensus on the optimal approach to process punch biopsies is lacking, though accurate subtyping is important for appropriate treatment., Objective: The aim is to investigate whether evaluating 4 levels of a punch biopsy instead of 1 or 2 levels leads to more accurate subtyping of BCC., Methods: In a retrospective study we evaluated 87 punch biopsies of histologically confirmed BCCs. The primary outcome was the proportion of "more aggressive" BCCs (nonsuperficial vs. superficial, infiltrative vs. nodular subtype) that was missed by evaluation on 1 or 2 levels, using 4-level diagnosis as reference standard., Results: Eighty-five cases were available for analysis. Subtyping based on 1 level resulted in discrepancies with 4-level diagnosis in 16.5% of all cases. Underdiagnosis occurred in 14 of 58 nonsuperficial BCCs (24.1%, 95% CI: 13.9-37.2). Seven of 38 nodular BCCs (18.4%, 95% CI: 7.74-34.3) were diagnosed as superficial in 1 level, and 7 of 20 infiltrative BCCs (35%, 95% CI: 15.4-59.2) were diagnosed as superficial (n = 2) or nodular (n = 5) in 1 level., Conclusion: In order to maximize correct subtyping and plan appropriate treatment, we advise to evaluate at least 2, but preferably more, levels of a punch biopsy to determine the BCC subtype., (© 2019 The Author(s) Published by S. Karger AG, Basel.)

Objective: Basal cell carcinoma (BCC) is the most common malignancy in humans and represents a growing public health care problem. The major etiological factors contributing to BCC development are exposure to ultraviolet radiation and genetic alterations. BCC is primarily caused by dysregulation of sonic Hedgehog (HH) signaling pathway in basal cells of the skin. BCC can be classified into low risk non-aggressive and high risk aggressive subtypes. BCC subtypes differentiation is essential for prognosis and for better disease management and treatment strategies. The aim of this study was to assess the correlation between PTCH1 protein expression level and the aggressiveness of BCC histopathology. Methods: Archival paraffin embedded blocks containing BCC were retrieved from a cohort of 101 patients. Immunohistochemistry staining was performed to assess the expression level of PTCH1 which is a key component of Hedgehog pathway. Results: 101 paraffin embedded samples were evaluated and classified as high risk and low risk BCC subtypes by histopathological finding. High risk BCC subtypes were found in 40 samples (39.6%) and low risk subtypes were identified in 61 samples (60.4%). Nodular was the most frequent subtype which was found in (56/ 101), followed by infiltrative (22/101) and micronodular (14/ 101) subtypes. Positive PTCH1 expression was found highest in nodular subtypes (46.5%). Conclusion: In this study, the correlation between low risk or high risk BCC subtypes and PTCH1 expression level was not statistically significant (p>0.05), but the frequency of positive PTCH1 expression was found to be higher in low risk subtypes than high risk BCC subtypes.

We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.

Background: Atypical basal cell carcinoma (BCC), characterized by equivocal dermoscopic features typical of malignant melanoma (MM), can be difficult to diagnose. Reflectance confocal microscopy (RCM) enables in vivo imaging at nearly histological resolution., Objectives: To evaluate with RCM atypical melanocytic lesions identified in dermoscopy, according to common RCM criteria for the differential diagnosis of BCC, and to identify representative RCM parameters for superficial (sBCCs) and nonsuperficial (nsBCCs) basal cell carcinomas (BCCs)., Methods: A retrospective analysis of consecutive patients evaluated with RCM, selecting excised lesions classified at dermoscopy with ≥1 score from the re visited 7-point checklist, mimicking melanoma, registered between 2010 and 2016. Cluster analysis identified BCC subclassifications., Results: Of 178 atypical lesions, 34 lesions were diagnosed as BCCs with RCM. Lesions were confirmed BCCs with histopathology. Dermoscopic features included atypical network (55.9%) and regression structures (35.5%) associated with sBCCs, and an atypical vascular pattern (58.8%) and irregular blotches (58.8%) with nsBCCs. Hierarchical cluster analysis identified 2 clusters: cluster 1 (100% sBCCs) was characterized by the presence of cords connected to the epidermis (90%, p < 0.001), tumor islands located in the epidermis (100%, p < 0.001), smaller vascular diameter (100%, p < 0.001) and solar elastosis (90%, p = 0.017), and cluster 2 (nsBCCs 85%) was defined by the dermic location of tumor islands (87.5%, p < 0.001) with branch-like structures (70.8%, p = 0.007) and surrounding collagen (83.3%, p = 0.012), peripheral palisading (83.3%, p = 0.012) and coiled vascular morphology (79.2%, p < 0.001) with a larger vascular diameter (50%, p < 0.001)., Conclusions: RCM is able to diagnose BCCs mimicking melanoma at dermoscopy and seems able to identify sBCCs and nsBCCs., (© 2018 S. Karger AG, Basel.)

Background and Objective: Melanoma is one of the major death causes while basal cell carcinoma (BCC) is the utmost incident skin lesion type. At their early stages, medical experts may be confused between both types with benign nevus and pigmented benign keratoses (BKL). This inspired the current study to develop an accurate automated, user-friendly skin lesion identification system., Methods: The current work targets a novel discrimination technique of four pre-mentioned skin lesion classes. A novel proposed texture feature, named cumulative level-difference mean (CLDM) based on the gray-level difference method (GLDM) is extracted. The asymmetry, border irregularity, color variation and diameter are summed up as the ABCD rule feature vector is originally used to classify the melanoma from benign lesions. The proposed method improved the ABCD rule to also classify BCC and BKL by using the proposed modified-ABCD feature vector. In the modified set of ABCD features, each border feature, such as compact index, fractal dimension, and edge abruptness is considered a separate feature. Then, the composite feature vector having the pre-mentioned features is ranked using the Eigenvector Centrality (ECFS) feature ranking method. The ranked features are then classified by a cubic support vector machine for different numbers of selected features., Results: The proposed CLDM texture features combined with the ranked ABCD features achieved outstanding performance to classify the four targeted classes (melanoma, BCC, nevi and BKL). The results report 100% outstanding performance of the sensitivity, accuracy and specificity per each class compared to other features when using the highest seven ranked features., Conclusions: The proposed system established that Melanoma, BCC, nevus and BKL are efficiently classified using cubic SVM with the new feature set. In addition, the comparative studies proved the superiority of the cubic SVM to classify the four classes., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Long non-coding RNAs (lncRNAs) can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as competing endogenous RNAs (ceRNAs) or as sponges. Previous studies identified lncRNA-mediated sponge interactions in various cancers including the breast cancer. However, breast cancer subtypes are quite distinct in terms of their molecular profiles; therefore, ceRNAs are expected to be subtype-specific as well., Results: To find lncRNA-mediated ceRNA interactions in breast cancer subtypes, we develop an integrative approach. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions. Functional enrichment of mRNAs that participate in these interactions highlights distinct biological processes for different subtypes. Interestingly, some of the ceRNAs also reside in close proximity in the genome; for example, those involving HOX genes, HOTAIR, miR-196a-1 and miR-196a-2. We also discover subtype-specific sponge interactions with high prognostic potential. We found that patients differ significantly in their survival distributions if they are group based on the expression patterns of specific ceRNA interactions. However, it is not the case if the expression of individual RNAs participating in ceRNA is used., Conclusion: These results can help shed light on subtype-specific mechanisms of breast cancer, and the methodology developed herein can help uncover sponges in other diseases.

Background: The behaviour of each basal cell carcinoma is known to be different according to the histological growth pattern. Among these aggressive lesions, sclerodermiform basal cell carcinomas are the most common type. This is a challenging-to-treat lesion due to its deep tissue invasion, rapid growth, risk of metastasis and overall poor prognosis if not diagnosed in early stages., Objective: To investigate if sclerodermiform basal cell carcinomas are diagnosed later compared to non-sclerodermiform basal cell carcinoma Method: All lesions excised from 2000 to 2010 were included. A pathologist classified the lesions in two cohorts: one with specimens of non-aggressive basal cell carcinoma (superficial, nodular and pigmented), and other with sclerodermiform basal cell carcinoma. For each lesion, we collected patient's information from digital medical records regarding: gender, age when first attending the clinic and the tumor location., Results: 1256 lesions were included, out of which 296 (23.6%) corresponded to sclerodermiform basal cell carcinoma, whereas 960 (76.4%) were non-aggressive subtypes of basal cell carcinoma. The age of diagnosis was: 72.78±12.31 years for sclerodermiform basal cell and 69.26±13.87 years for non-aggressive basal cell carcinoma (P<.0001). Sclerodermiform basal cell carcinomas are diagnosed on average 3.52 years later than non-aggressive basal cell carcinomas. Sclerodermiform basal cell carcinomas were diagnosed 3.40 years and 2.34 years later than non-aggressive basal cell carcinomas in younger and older patients respectively (P=.002 and P=.03, respectively)., Study Limitations: retrospective design., Conclusion: The diagnostic accuracy and primary clinic conjecture of sclerodermiform basal cell carcinomas is quite low compared to other forms of basal cell carcinoma such as nodular, superficial and pigmented. The dermoscopic vascular patterns, which is the basis for the diagnosis of non-melanocytic nonpigmented skin tumors, may not be particularly useful in identifying sclerodermiform basal cell carcinomas in early stages. As a distinct entity, sclerodermiform basal cell carcinomas show a lack of early diagnosis compared to less-aggressive subtypes of BCC, and thus, more accurate diagnostic tools apart from dermatoscopy are required to reach the goal of early-stage diagnosis of sclerodermiform basal cell carcinomas.

Background: Histological diagnosis of a clinically suspected nonmelanoma skin cancer (NMSC) is recommended before treatment. For NMSC, concordance between the histological subtype of the preoperative biopsy and the excision specimen of basal cell carcinoma (BCC) has been reported to range from 10% to 81%. No large study on the concordance between NMSC histology seen in a preoperative biopsy with the following tumour specimen from Mohs micrographic surgery (MMS) has been performed in a Latin American population., Objective: The aim of this study was to analyse and compare the histological subtype of the incisional biopsies reviewed by the dermatopathologist with the histological subtype of the tumour specimen obtained during MMS interpreted by the dermatopathologist and the Mohs surgeon., Methods: A retrospective analysis of 320 NMSC was performed. The interobserver correlation was based on kappa values., Results: The mean weighted kappa value between the preoperative NMSC biopsy and intraoperative histological subtype of the tumour specimen from MMS analysed by the Mohs surgeon and the dermatopathologist was 0.22 and 0.24, respectively. The correlation in the histologic subtype of the intraoperative tumour specimen from MMS that was interpreted by the dermatopathologist and Mohs surgeon was 0.58., Conclusions: Dermatologists need to be aware of the limited value of incisional biopsies to accurately diagnose the histological subtype of a NMSC. The concordance rate in the histological diagnosis of the tumour specimens that were obtained from MMS between the Mohs surgeon and the dermatopathologist is moderate. However, the correlation is low compared with incisional biopsy subtypes., (Copyright © 2017 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.)

Purpose: Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation., Methods: Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio., Results: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics., Conclusion: The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work., (© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: Incisional biopsy may not always provide a correct classification of histologic subtypes of basal cell carcinoma (BCC). High-frequency ultrasound (HFUS) imaging of the skin is useful for the diagnosis and management of this tumor., Objectives: The main aim of this study was to compare the diagnostic value of HFUS compared with punch biopsy for the correct classification of histologic subtypes of primary BCC. We also analyzed the influence of tumor size and histologic subtype (single subtype vs. mixed) on the diagnostic yield of HFUS and punch biopsy., Methods: Retrospective observational study of primary BCCs treated by the Dermatology Department of Hospital Costa del Sol in Marbella, Spain, between october 2013 and may 2014. Surgical excision was preceded by HFUS imaging (Dermascan C © , 20-MHz linear probe) and a punch biopsy in all cases. We compared the overall diagnostic yield and accuracy (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of HFUS and punch biopsy against the gold standard (excisional biopsy with serial sections) for overall and subgroup results., Results: We studied 156 cases. The overall diagnostic yield was 73.7% for HFUS (sensitivity, 74.5%; specificity, 73%) and 79.9% for punch biopsy (sensitivity, 76%; specificity, 82%). In the subgroup analyses, HFUS had a PPV of 93.3% for superficial BCC (vs. 92% for punch biopsy). In the analysis by tumor size, HFUS achieved an overall diagnostic yield of 70.4% for tumors measuring 40mm 2 or less and 77.3% for larger tumors; the NPV was 82% in both size groups. Punch biopsy performed better in the diagnosis of small lesions (overall diagnostic yield of 86.4% for lesions ≤40mm 2 vs. 72.6% for lesions >40mm 2 )., Conclusions: HFUS imaging was particularly useful for ruling out infiltrating BCCs, diagnosing simple, superficial BCCs, and correctly classifying BCCs larger than 40mm 2 ., (Copyright © 2016 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.)

Importance: Keratinocyte carcinoma (nonmelanoma skin cancer) accounts for substantial burden in terms of high incidence and health care costs but is excluded by most cancer registries in North America. Administrative health insurance claims databases offer an opportunity to identify these cancers using diagnosis and procedural codes submitted for reimbursement purposes., Objective: To apply recursive partitioning to derive and validate a claims-based algorithm for identifying keratinocyte carcinoma with high sensitivity and specificity., Design, Setting, and Participants: Retrospective study using population-based administrative databases linked to 602 371 pathology episodes from a community laboratory for adults residing in Ontario, Canada, from January 1, 1992, to December 31, 2009. The final analysis was completed in January 2016. We used recursive partitioning (classification trees) to derive an algorithm based on health insurance claims. The performance of the derived algorithm was compared with 5 prespecified algorithms and validated using an independent academic hospital clinic data set of 2082 patients seen in May and June 2011., Main Outcomes and Measures: Sensitivity, specificity, positive predictive value, and negative predictive value using the histopathological diagnosis as the criterion standard. We aimed to achieve maximal specificity, while maintaining greater than 80% sensitivity., Results: Among 602 371 pathology episodes, 131 562 (21.8%) had a diagnosis of keratinocyte carcinoma. Our final derived algorithm outperformed the 5 simple prespecified algorithms and performed well in both community and hospital data sets in terms of sensitivity (82.6% and 84.9%, respectively), specificity (93.0% and 99.0%, respectively), positive predictive value (76.7% and 69.2%, respectively), and negative predictive value (95.0% and 99.6%, respectively). Algorithm performance did not vary substantially during the 18-year period., Conclusions and Relevance: This algorithm offers a reliable mechanism for ascertaining keratinocyte carcinoma for epidemiological research in the absence of cancer registry data. Our findings also demonstrate the value of recursive partitioning in deriving valid claims-based algorithms.

Objective: : to compare the accuracy of preoperative 2-mm punch biopsy at one site and at two sites in the diagnosis of aggressive subtypes of eyelid basal cell carcinoma (BCC)., Methods: : we randomly assigned patients to Group 1 (biopsy at one site) and Group 2 (biopsy at two sites). We compared the biopsy results to the gold standard (pathology of the surgical specimen). We calculated the sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Kappa coefficient to determine the level of agreement in both groups., Results: : we analyzed 105 lesions (Group 1: n = 44; Group 2: n = 61). The agreement was 54.5% in Group 1 and 73.8% in Group 2 (p = 0.041). There was no significant difference between the groups regarding the distribution of quantitative and qualitative variables (gender, age, disease duration, tumor larger diameter, area and commitment of margins). Biopsy at two sites was two times more likely to agree with the gold standard than the biopsy of a single site., Conclusions: : the accuracy and the performance indicators were better for 2-mm punch biopsy in two sites than in one site for the diagnosis of aggressive subtypes of eyelid BCC., Objetivo: comparar a acurácia da biópsia pré-operatória por trépano de 2mm em um sítio e em dois sítios no diagnóstico dos subtipos agressivos de carcinoma basocelular (CBC) palpebral., Métodos: os pacientes foram distribuídos aleatoriamente em Grupo 1 (biópsia em um sítio) e Grupo 2 (biópsia em dois sítios). Os resultados das biópsias foram comparados com o padrão-ouro (exame anatomopatológico da peça cirúrgica). A sensibilidade, especificidade, valor preditivo positivo, valor preditivo negativo, precisão e coeficiente Kappa foram calculados para determinar o nível de concordância nos dois grupos., Resultados: foram analisadas 105 lesões (Grupo 1: n = 44; Grupo 2: n = 61). A concordância foi de 54,5% no Grupo 1 e 73,8% no Grupo 2 (p-valor = 0,041). Não houve diferença significativa entre os grupos quanto à distribuição das variáveis quantitativas e qualitativas (sexo, idade, duração da doença, maior diâmetro do tumor, área e comprometimento de margens). A biópsia em dois sítios mostrou duas vezes mais chance de concordar com o padrão-ouro do que a biópsia de um sítio., Conclusões: a acurácia e os indicadores de desempenho foram melhores para a biópsia por trépano de 2 mm em dois sítios do que em um sítio para o diagnóstico dos subtipos agressivos de CBC palpebral.

Optical polarimetry was employed for assessment of ex vivo healthy and basal cell carcinoma (BCC) tissue samples from human skin. Polarimetric analyses revealed that depolarization and retardance for healthy tissue group were significantly higher (p<0.001) compared to BCC tissue group. Histopathology indicated that these differences partially arise from BCC-related characteristic changes in tissue morphology. Wilks lambda statistics demonstrated the potential of all investigated polarimetric properties for computer assisted classification of the two tissue groups. Based on differences in polarimetric properties, partial least square (PLS) regression classified the samples with 100% accuracy, sensitivity and specificity. These findings indicate that optical polarimetry together with PLS statistics hold promise for automated pathology classification., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Background: Optical coherence tomography (OCT) is a technique that enables real-time in-vivo examination of tissue. This technology provides the clinician with the potential to use a non-invasive tool in the identification and diagnosis of many skin lesions. However, the diagnostic features of basal cell carcinoma have not yet been described with comparison to their histopathology., , Objectives: To identify and describe key features of basal cell carcinoma (BCC) and its subtypes as they present in multi-beam Swept Source - OCT (MSS-OCT), and to correlate those against conventional histopathology.
, Methods: A total of 40 lesions were assessed by MSS-OCT prior to biopsy. 60-slice OCT images of the lesions were obtained and correlated with histology sections taken in the same plane. OCT scans were assessed retrospectively by a panel to determine the OCT criteria for BCC and its subtypes.
, Results: The following diagnostic criteria were identified: hyporeflective ovoid structures (40/40), dark halo boundaries (38/40), epidermal thinning (28/40), and collagen compression (14/40). Lesional tissue also showed a destruction of layers when compared to the surrounding normal tissue. In addition to the shared criteria, other subtypes showed distinct diagnostic criteria.
, Conclusion: With its higher sensitivity, using MSS-OCT allowed for non-invasive, accurate identification of the key diagnostic features of BCC and its subtypes with high correlation to the histopathologic features found with biopsy. , , J Drugs Dermatol. 2016;15(5):545-550.

Purpose: To determine the accuracy of initial biopsy in the diagnosis of basal cell carcinoma (BCC) histologic subtype., Methods: Retrospective histopathologic review of patients with a diagnosis of primary periocular BCC from 2006 to 2013 inclusive., Results: A total of 174 primary BCCs were identified. BCCs were classified as nodular, superficial, or aggressive (including mixed cases with an aggressive component). Punch biopsies were used in 41% of cases, while the remaining patients underwent shave or incision biopsies. The final histologic subtypes at excision were nodular (59%), superficial (7%), nodular and superficial (7%), and aggressive (51%). The overall concordance between the BCC subtype identified in the biopsy specimen and the subsequent excision specimen was 54%. In total, there were 51 cases (29%) of BCC, which included aggressive subtypes, of which 52% of initial biopsies failed to detect an aggressive component. There were 45 cases (26%) of mixed BCC, and an aggressive histologic subtype was present in 73% of these cases., Conclusions: The accuracy of initial biopsy for BCC histologic subtype at excision is highest for nodular BCC. For aggressive BCC, biopsy was able to detect the aggressive component in only 48% of cases. This may have implications for choice of treatment modality.

Histological subtypes of basal cell carcinoma have biological, evolutionary and distinct prognostic behavior. The analysis of characteristics of the nucleus can provide data on their cellular physiology and behavior. The authors of this study evaluated nuclear morphological parameters and textural patterns of chromatin from different subtypes of basal cell carcinoma: nodular (n=37), superficial (n=28) and sclerodermiform (n=28). The parameters were compared between neoplasms' subtypes and with unaffected adjacent basal epithelium. Nuclear area and diameter of sclerodermiform neoplasms were superior to the other subtypes. Chromatin's color intensity and fractal dimension were less intense in superficial subtypes. Nuclear roundness and chromatin's entropy presented lower values in tumors than in normal epithelium. There was significant correlation between morphological and textural variables of normal skin and tumors. Morphometric elements and textural chromatin's homogeneity of basal cell carcinomas may be related to evolutionary, biological and behavior particularities related to each histotype.

Basal cell carcinoma (BCC) is a slow-growing, locally invasive malignant epidermal skin neoplasm that represents the most common malignancy in Caucasians. The clinical presentation of BCC can be extremely variable: nodular, ulcerative, superficial, morpheiform, pigmented, and fibroepithelioma of Pinkus are the main clinical variants described. Clinical factors influencing negatively prognosis of BCC are: anatomic location, recurrence and/or persistance at site after treatment, and tumor size. A precise correlations between clinical and histopathological variants is not always possible, especially in biopsy samples. From a histopathological point of view various subtypes has been described: nodular, superficial, infiltrating, morpheiform, micronodular, fibroepithelial BCC and basosquamous carcinoma. A classification system based by growth pattern allows the identification of high-risk subtypes with potential tumor recurrence and aggressive biologic behavior such as infiltrating, morpheiform, micronodular and basosquamous subtypes. Further histopathological aspects determining high risk clinical morbidity are the level of invasion, perineural and lymphovascular invasion, involved surgical margins. The awareness of these clinicopathological features is helpful to better select the appropriate treatment management.

Objective: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model., Materials and Methods: This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis., Results: The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation., Conclusions: The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The aim of this study was to assess the frequency of classic dermoscopic basal cell carcinoma (BCC) features and the sensitivity of new descriptors, such as light brown nests (homogeneous and structured) only visible employing a high magnification digital videomicroscope. A retrospective analysis of 2,024 highly magnified digital images referring to 400 BCCs was performed by 3 independent observers, who assessed 11 classic BCC descriptors and the new ones. Light brown nests were detected in 40.5% of BCCs. Homogeneous ones were observable in 17.8%, and structured nests in 32.8%. Light brown nests were visible in 14.3% of non-pigmented lesions, whereas in the pigmented groups these were observed in 42-54% of the cases. We suggest that brown nests described in this study may improve early recognition of superficial BCCs and of non-pigmented or slightly pigmented ones that may lack classic dermoscopic patterns.

Background: The current guidelines for the management of basal cell carcinoma (BCC) suggest a different therapeutic approach according to histopathologic subtype. Although dermatoscopic and confocal criteria of BCC have been investigated, no specific studies were performed to evaluate the distinct reflectance confocal microscopy (RCM) aspects of BCC subtypes., Objectives: To define the specific dermatoscopic and confocal criteria for delineating different BCC subtypes., Methods: Dermatoscopic and confocal images of histopathologically confirmed BCCs were retrospectively evaluated for the presence of predefined criteria. Frequencies of dermatoscopic and confocal parameters are provided. Univariate and adjusted odds ratios were calculated. Discriminant analyses were performed to define the independent confocal criteria for distinct BCC subtypes., Results: Eighty-eight BCCs were included. Dermatoscopically, superficial BCCs (n=44) were primarily typified by the presence of fine telangiectasia, multiple erosions, leaf-like structures, and revealed cords connected to the epidermis and epidermal streaming upon RCM. Nodular BCCs (n=22) featured the classic dermatoscopic features and well outlined large basaloid islands upon RCM. Infiltrative BCCs (n=22) featured structureless, shiny red areas, fine telangiectasia, and arborizing vessels on dermatoscopy and dark silhouettes upon RCM., Limitations: The retrospective design., Conclusion: Dermatoscopy and confocal microscopy can reliably classify different BCC subtypes., (Copyright © 2014 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

Basal cell carcinomas are the most common primary cutaneous malignant neoplasms. The diagnosis of basal cell carcinoma represents a common and routine task for pathologists and dermatopathologists. The aim of this review is the clinical and histopathological presentation of the most common subtypes of basal cell carcinoma. Furthermore, the rare variants of basal cell carcinoma and their differential diagnoses are also discussed.

Background: To determine the proportion of different subtypes of periocular BCC in South Australia., Design: Retrospective review., Participants: One thousand seven hundred thirteen consecutive periocular basal cell carcinoma (BCC) excision specimens., Methods: Histological analysis of consecutive periocular BCC specimens., Main Outcome Measures: Date of resection, patient age at resection, gender, tumour location, histological subtype and perineural invasion., Results: From 2006 to 2012, a total of 1713 consecutive periocular BCC excision specimens were analysed. The mean age at resection was 68.8 years (median: 71, range: 21-101). Most specimens (56.4%) were removed from male patients. 52.7% involved the lower eyelid, 29.0% the medial canthus, 10.9% the lateral canthus and 7.5% the upper eyelid. The main histological subtypes identified were nodular (65.7%), infiltrative (17.5%), superficial (12.6%) and micronodular (4.2%). Of the specimens, 25.6% had more than one subtype. The most common subtype combinations were nodular with infiltrative (49.7%), and nodular with superficial (26.0%)., Conclusions: The majority of periocular BCC were located on the lower lid and classified histologically as nodular. Infiltrative BCC occurred more frequently than the superficial subtype. As the proportion of mixed BCC containing aggressive subtypes is high, surgical excision with margin control should be considered for periocular BCC., (© 2014 Royal Australian and New Zealand College of Ophthalmologists.)

Poromas were originally classified as eccrine tumors which predominantly consist of poroid ductal cells and differentiate in the direction of sweat gland ducts. However, there have now been many reports on poromas with additional differential characteristics differentiating in the direction of sebaceous and/or apocrine glands and/or hair follicles. These tumors have been termed apocrine poromas. Multilineage differentiation within a poroma can be explained by the embryological association of the sweat duct with the so-called folliculo-sebaceous-apocrine unit. The clinical and histopathological features of apocrine poromas are reviewed in comparison to classical eccrine poromas by taking into account seven own cases of apocrine poroma and a review of the literature. It is important for histopathologists not to confuse apocrine poroma with other tumors with multilineage differentiation. Apocrine poroma needs to be distinguished from sebaceoma and from basal cell carcinoma with sebaceous differentiation, in particular, because these tumors have therapeutic consequences for the patient. The main histopathological differences between apocrine poroma, sebaceoma and basal cell carcinoma with sebaceous differentiation are explained.

BRCA1-mutated breast cancer is associated with basal-like disease; however, it is currently unclear if the presence of a BRCA1 mutation depicts a different entity within this subgroup. In this study, we compared the molecular features among basal-like tumors with and without BRCA1 mutations. Fourteen patients with BRCA1-mutated (nine germline and five somatic) tumors and basal-like disease, and 79 patients with BRCA1 non-mutated tumors and basal-like disease, were identified from the cancer genome atlas dataset. The following molecular data types were evaluated: global gene expression, selected protein and phospho-protein expression, global miRNA expression, global DNA methylation, total number of somatic mutations, TP53 and PIK3CA somatic mutations, and global DNA copy-number aberrations. For intrinsic subtype identification, we used the PAM50 subtype predictor. Within the basal-like disease, we observed minor molecular differences in terms of gene, protein, and miRNA expression, and DNA methylation variation, according to BRCA1 status (either germinal or somatic). However, there were significant differences according to average number of mutations and DNA copy-number aberrations, and four amplified regions (2q32.2, 3q29, 6p22.3, and 22q12.2), which are characteristic in high-grade serous ovarian carcinomas, were observed in both germline and somatic BRCA1-mutated breast tumors. These results suggest that minor, but potentially relevant, baseline molecular features exist among basal-like tumors according to BRCA1 status. Additional studies are needed to better clarify if BRCA1 genetic status is an independent prognostic feature, and more importantly, if BRCA1 mutation status is a predictive biomarker of benefit from DNA-damaging agents among basal-like disease.

Background: Basal cell carcinoma (BCC) is the most common skin cancer in humans. The histological subtype reported by punch biopsy may influence the type of treatment. Few studies have investigated the accuracy of punch biopsy in diagnosing the true BCC subtype., Objective: To determine the accuracy, sensitivity and specificity of punch biopsy in BCC subtype diagnosis., Methods: In this retrospective study, 333 biopsy specimens and excisions were reviewed. Histological subtypes present in the initial biopsy were compared with tumour subtypes of the total excision., Results: The concordance between the BCC subtype present in the biopsy specimen and in the subsequent excision specimen was 72.3%. The most common BCC patterns were nodular (158, 47.5%) and mixed subtype (90, 27%). Most mixed tumours contained one or more aggressive subtype (63/90, 70%). In 47/120 (39.1%) aggressive tumours (14.1% of the total), punch biopsy failed to correctly identify the aggressive component. The most commonly missed aggressive subtype was mixed aggressive including nodular/micronodular and nodular/infiltrative (30/47, 63.8%). In 45/213 (21.1%) non-aggressive BCCs (13.5% of total cases), punch biopsy incorrectly reported an aggressive subtype. The most commonly misidentified non-aggressive subtype was nodular (39/45, 86.6). The sensitivity and specificity of punch biopsy in diagnosing aggressive vs. non-aggressive BCC subtypes 60.8% (95% CI, 51.9-69.1) and 78.9% (95% CI, 72.8-83.8), respectively. The positive and negative predictive values were 61.9% and 78.1%, respectively., Conclusion: Punch biopsy has serious pitfalls in differentiating aggressive and non-aggressive BCC subtypes. Dermatologists should consider the possibility of aggressive components within non-aggressive BCCs reported using punch biopsy., (© 2012 The Authors. Journal of the European Academy of Dermatology and Venereology © 2012 European Academy of Dermatology and Venereology.)

Background: There are numerous subtypes of basal cell carcinoma (BCC). Defining the histopathologic subtype is an essential element in patient management, but there is little known data regarding interobserver precision in subtyping BCC., Methods: We studied interobserver variance between six board-certified dermatopathologists who subtyped 100 BCCs in a blinded fashion. We used kappa statistic to calculate the concordance in suggested subtype by different dermatopathologists. Provided diagnoses were then re-categorized into low-risk and high-risk phenotypes, and kappa statistic for concordance on high-risk BCC was determined., Results: The overall κ statistic was 0.301, indicating fair agreement among the six observers. Superficial and fibroepithelial BCC had the highest individual kappa statistics. When subtypes were re-classified into a two-tier system of high-risk and low-risk phenotypes, there was substantial interobserver agreement on high-risk BCC with a κ statistic of 0.699., Conclusion: These results suggest only fair agreement among dermatopathologists on specific BCC subtypes, but substantial agreement on superficial, fibroepithelial and high-risk BCC growth patterns. A simplified classification system comprised of superficial, fibroepithelial, nodular and infiltrative subtypes would increase interobserver precision and facilitate clinical decision-making., (© 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Although, generally BCC growths slowly and is minimally invasive, tumors developed in the head and neck region behave more aggressively with deep tissue invasion, recurrence and even local or distant metastases, causing significant morbidity or mortality. Recently, numerous studies have been conducted in order to identify new prognostic markers of BCC aggressiveness, but the results are not consistent. Thus, we were interested here in the immunohistochemical investigation of p53, D2-40 and α-SMA expression in the aggressive forms (eight infiltrative-morpheaform, six micronodular and six metatypical cases) versus superficial facial BCCs (five cases). As results, we first noticed that p53, D2-40 and α-SMA expression varied between different types of investigated BCCs. The highest reactivity was observed in metatypical subtype for the D2-40. p53 was mainly expressed in the micronodular BCC subtype and on overall, the tumor reactivity to this marker correlated directly with the reactivity for the other two used biomarkers. The infiltrative-morpheaform facial BCCs were peculiar more reactive to α-SMA. For all three investigated markers, regardless the histological subtype, the tumor reactivity was higher at the advancing edge, and in addition, at this level we noticed a D2-40 and α-SMA stromal reactivity for some cases of the more aggressive BCC subtype (peculiar in metatypical subtype). Thus, we concluded that in order to identify the most aggressive forms of facial BCCs it is useful to investigate these three markers, and this is even more important as they can all constitute therapeutic targets.

Background: Basal cell carcinoma (BCC) is the most frequently occurring cancer in humans. Worldwide incidences rise about 10% each year, increasing the burden on dermatologists, general practitioners and pathologists as well as increasing costs for the health care system. Increasingly non-surgical treatment options are used in the treatment of BCC, without histological confirmation of BCC subtype, potentially resulting in under-treatment., Objective: We evaluated the diagnostic accuracy of a punch biopsy for the BCC histological subytpe in a primary BCC and the prevalence of biopsy-based under-diagnosis of aggressive subtypes. Accuracy of a punch biopsy was defined as concordance of the diagnosis of subtype of BCC at punch biopsy and excision., Methods: A retrospective chart-review was performed of primary BCC, which were proven by punch biopsy and subsequently treated by excision. The first 100 consecutive BCCs per year during the years 2004-2009 were included, yielding a total of 500 evaluated BCCs., Results: The overall accuracy of punch biopsy for BCC subtype at excision was 69%, in single-type BCC 83% (n = 343) and in mixed-type BCC 37% (n = 157). Accuracy varied substantially according to BCC subtype, being highest in the superficial subtype (84%) and subsequently in infiltrative (69%), nodular (63%) and micronodular subtype (38%). In 11% of all cases, an unsuspected more aggressive subtype was present., Conclusion: Punch biopsy has a high accuracy in single-type BCCs and a considerably lower accuracy in mixed-type BCCs for establishing BCC subtype compared to excision. The presence of an unsuspected aggressive subtype could explain therapy failure of non-surgical treatments like imiquimod or photodynamic therapy., (© 2012 The Authors. Journal of the European Academy of Dermatology and Venereology © 2012 European Academy of Dermatology and Venereology.)

Background: Diagnosis of clinically suspected basal cell carcinoma (BCC) by histological confirmation with punch biopsy has been recommended before treatment. Even shave biopsy has been proposed as useful to predict the correct subtype in primary BCC in 76-81%, whereas the agreement between histological BCC subtype on punch biopsy and subsequent excision specimens in recurrent BCC is 67.1%. However, no large studies on the agreement between histological BCC subtype seen on punch biopsy and the following surgical excision are performed in primary BCC., Objective: The aims of this study were (i) to establish the agreement between histological BCC subtype on punch biopsy and the subsequent surgical excision of primary BCC and; (ii) to investigate the proportion of primary BCCs in which punch biopsy enables identification of the most aggressive growth pattern., Methods: Retrospective analyses of 243 primary BCCs with both punch biopsy and subsequent surgical excision. Analyses were based on the most aggressive histological subtype of the tumour., Results: The agreement between BCC subtype on punch biopsy and the subsequent surgical excision of primary BCCs was 60.9%. A punch biopsy can predict the most aggressive growth pattern of primary BCCs in 84.4%. Seventy-four percentage of all primary BCCs consisted of more than one histological subtype., Conclusion: Dermatologists and other physicians have to be aware of the limited diagnostic value of a punch biopsy to determine the histological BCC subtype of the whole lesion. Misdiagnosis of the subtype will lead to undertreatment in one of six primary BCCs., (© 2012 The Authors. Journal of the European Academy of Dermatology and Venereology © 2012 European Academy of Dermatology and Venereology.)

Introduction: The special types of breast cancer seem to have not only distinct morphological features but also distinct biological features., Materials and Methods: Women diagnosed with a first primary invasive breast cancer in the 2004-2005 period were identified through Tuscan Cancer Registry. Information on age, tumor size, lymph node status, histological type and grade, hormonal receptors, HER2 immunohistochemical expression were collected. Five subtypes were defined: luminal A, luminal B HER2+, luminal B HER2-, triple negative, and HER2 positive. The association between the histological type and molecular subgroups was assessed by a Fisher's exact test, and a multinomial logistic regression model was used., Results: Out of 1,487 patients, 34 % were luminal A subtype, 25 % luminal B HER2-, 11 % luminal B HER2+, 19 % triple negative, and 10.2 % HER2+; 58.5 % of cancers were ductal NOS types. With luminal A as reference, histological types distribution was significantly different between the subgroups. Mucinous, tubular, and cribriform histotypes were found among luminal A cancers more than in other subgroups; all medullary carcinomas were triple negative cancers. Pathological stage at diagnosis was more advanced, and histological grade was lower among subgroups other than luminal A., Conclusions: Significant association between breast cancer histotypes and molecular subgroups was found.

Background: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the USA. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features., Methods: Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including evolving artificial neural networks (EANNs) and evolving artificial neural network ensembles., Results: Experiment results based on 10-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories., Conclusions: Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process., (© 2012 John Wiley & Sons A/S.)