The specialty of the treating physician affects the likelihood of tumor-free resection margins for basal cell carcinoma: Results from a multi-institutional retrospective study

2001 ◽  
Vol 44 (2) ◽  
pp. 224-230 ◽  
Author(s):  
Alan B. Fleischer ◽  
Steven R. Feldman ◽  
James O. Barlow ◽  
Beiyao Zheng ◽  
Holly B. Hahn ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Resection Margins

scholarly journals Dermoscopic Features of Basal Cell Carcinoma

2018 ◽  
Vol 35 (4) ◽  
pp. 273-281
Author(s):  
Danica Tiodorović ◽  
Andrija Jović ◽  
Danijela Popović ◽  
Hristina Kocić ◽  
Aleksandra Ignjatović ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Basal Cell ◽  
Skin Tumors ◽  
Clinical Type ◽  
Reliable Diagnosis ◽  
The Moment

Abstract The aim of this study was to analyze the dermoscopic features in patients with pathohistologically confirmed basal cell carcinoma (BCC). Our retrospective study included 54 patients with 76 BCCs in total, diagnosed in 2016 and 2017. All lesions were classified into four clinical types: nodular, pigmented, superficial and infiltrative. Digital dermoscopic images were evaluated by three observers. We selected five dermoscopic features for analysis, including: the absence of pigment network, the presence of arborizing vessels, blue-gray globules and ovoid nests, leaf-like areas and ulcerations. In the total of 54 patients, there were 22 females and 32 males. At the moment of establishing the diagnosis, the patients’ age was in the range from 31 to 84 years (median age 67 years). The most frequent clinical type was the nodular type with 28 confirmed diagnoses. Nodular BCC was more frequently localized on the head and neck areas compared to the trunk and limbs (p < 0.01). Dermoscopically, the absence of pigmented network was verified in all cases. Arborizing vessels were present in 71 (93.4%) lesions, blue-gray globules and ovoid nests in 33 (42.1%), ulcerations in 44 (57.9%), and leaf-like areas in 5 (6.6%) lesions. Blue-gray globules and ovoid nests were significantly frequent in pigmented BCC in comparison to other clinical types of BCC (p < 0.01). In conclusion, using dermoscopy, it is entirely possible to make a reliable diagnosis of BCC as well as to differentiate it from others skin tumors.

scholarly journals Single versus Multiple Level Sectioning for the Subtyping of Basal-Cell Carcinoma: A Retrospective Study

Dermatology ◽  
2019 ◽  
Vol 236 (3) ◽  
pp. 237-240 ◽  
Author(s):  
Lieke C.J. van Delft ◽  
Patty J. Nelemans ◽  
Myrurgia Abdul Hamid ◽  
Nicole W.J. Kelleners-Smeets
Keyword(s):  
Retrospective Study ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Primary Outcome ◽  
Punch Biopsy ◽  
Appropriate Treatment ◽  
Treatment Objective ◽  
Optimal Approach ◽  
Single Versus

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.

scholarly journals Use of Cytology in the Diagnosis of Basal Cell Carcinoma Subtypes

2020 ◽  
Vol 9 (3) ◽  
pp. 612
Author(s):  
Paola Pasquali ◽  
Gonzalo Segurado-Miravalles ◽  
Mar Castillo ◽  
Ángeles Fortuño ◽  
Susana Puig ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Inverse Correlation ◽  
Basal Cells ◽  
White Population ◽  
First Line ◽  
Common Skin ◽  
Cytological Features

Background: Basal cell carcinoma (BCC) is the most common skin cancer in the white population. Nonsurgical treatments are first-line alternatives in superficial BCC (sBCC); therefore, differentiating between sBCC and non-sBCC is of major relevance for the clinician. Scraping cytology possesses several advantages, such as an earlier diagnosis and scarring absence, in comparison to a biopsy. Nevertheless, previous studies reported difficulties in differentiating the different BCC subtypes. The objective of this study was to determine the capability and accuracy of scraping cytology to differentiate between sBCC and non-sBCC. Methods: In this retrospective study, cytological samples of histologically confirmed BCC were examined. Select cytological features were correlated to BCC subtypes (sBCC or non-sBCC). Results: A total of 84 BCC samples were included (29 sBCC; 55 non-sBCC). An inverse correlation between the diagnosis of sBCC and the presence of mucin, dehiscence, and grade of atypia in the basal cells was observed. The presence of medium and large basal cell clusters correlated directly to a sBCC diagnosis. The presence of clear cells is strongly associated with sBCC. Therefore, Conclusion: Scraping cytology is reliable in differentiating sBCC from other BCC subtypes.

scholarly journals Vulvar basal cell carcinoma: A retrospective study of 29 cases from Queensland

2012 ◽  
Vol 02 (02) ◽  
pp. 136-139 ◽  
Author(s):  
Ian S. C. Jones ◽  
Alex Crandon ◽  
Karen Sanday
Keyword(s):  
Retrospective Study ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell

scholarly journals Basal cell carcinoma with compromised margins: retrospective study of management, evolution, and prognosis

2020 ◽  
Author(s):  
Maria Carolina Fidelis ◽  
Rafael Fantelli Stelini ◽  
Leonardo Piropo Staffa ◽  
Apareceida Machado de Moraes ◽  
Renata Ferreira Magalhães
Keyword(s):  
Retrospective Study ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell

scholarly journals A Retrospective Study of the Diagnostic Accuracy of In Vivo Reflectance Confocal Microscopy for Basal Cell Carcinoma Diagnosis and Subtyping

2019 ◽  
Vol 8 (4) ◽  
pp. 449 ◽  
Author(s):  
Mihai Lupu ◽  
Iris Popa ◽  
Vlad Voiculescu ◽  
Daniel Boda ◽  
Constantin Caruntu ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Confocal Microscopy ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Tertiary Care ◽  
Reflectance Confocal Microscopy ◽  
Predictive Values ◽  
Tertiary Care Centers

Current national and European guidelines recommend distinct management approaches for basal cell carcinoma (BCC) based on tumor location, size, and histopathological subtype. In vivo reflectance confocal microscopy (RCM) is a non-invasive skin imaging technique which may change the diagnostic pathway for BCC patients. This study aimed to determine the sensitivity and specificity of RCM for BCC diagnosis, assess the predictive values of several confocal criteria in correctly classifying BCC subtypes, and evaluate the intraobserver reliability of RCM diagnosis for BCC. We conducted a retrospective study in two tertiary care centers in Bucharest, Romania. We included adults with clinically and dermoscopic suspect BCCs who underwent RCM and histopathological examination of excision specimens. For RCM examinations, we used the VivaScope 1500 and histopathology of the surgical excision specimen was the reference standard. Of the 123 cases included in the analysis, BCC was confirmed in 104 and excluded in 19 cases. RCM showed both high sensitivity (97.1%, 95% CI (91.80, 99.40)) and specificity (78.95%, 95% CI (54.43, 93.95)) for detecting BCC. Several RCM criteria were highly predictive for BCC subtypes: cords connected to the epidermis for superficial BCC, big tumor islands, peritumoral collagen bundles and increased vascularization for nodular BCC, and hyporefractile silhouettes for aggressive BCC. Excellent intraobserver agreement (κ = 0.909, p < 0.001) was observed. This data suggests that RCM could be used for preoperative diagnosis and BCC subtype classification in patients with suspected BCCs seen in tertiary care centers.

scholarly journals Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma

2021 ◽  
Vol 7 (4) ◽  
pp. 71
Author(s):  
Jean Le’Clerc Arrastia ◽  
Nick Heilenkötter ◽  
Daniel Otero Baguer ◽  
Lena Hauberg-Lotte ◽  
Tobias Boskamp ◽  
...  
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Resection Margins ◽  
Tissue Sections ◽  
Tumor Tissues ◽  
Critical Regions ◽  
Whole Slide Images

Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues’ exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network’s decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set.

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