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Introduction: Being able to recognize high-risk facial basal cell carcinoma (BCC) may lead to fewer incomplete excisions and inappropriate treatments. Objectives: We sought to investigate clinical and dermoscopic criteria for predicting facial BCC subtypes, analyze the interobserver agreement between readers, and develop a diagnostic algorithm to predict high-risk histopathological subtype. Methods: In this single-center, retrospective investigation, 6 independent readers evaluated predefined clinical and dermoscopic criteria in images of histopathologically verified primary facial BCCs including: topography, border demarcation, vessels, ulceration, white porcelain areas, shiny white blotches and strands, and pigmented structures and vessels within ulceration. Results: Overall, 297 clinical and dermoscopic image pairs were analyzed. The strongest associations with high-risk subtype were: “bumpy” topography (OR 3.8, 95% CI, 3.1-4.7), ill-defined borders (OR 3.4, 95% CI 3.1-4.7), white porcelain area (OR 3.5, 95% CI 2.8-4.5), and vessels within ulceration (OR 3.1, 95% CI 2.4-4.1). Predominantly focused vessels were a positive diagnostic criterium for either nodular (OR 1.7, 95% CI 1.3-2.2) or high-risk (OR 2.0, 95% CI 1.6-2.5) subtypes and a strong negative diagnostic criterium for superficial BCC (OR 14.0, 95% CI 9.6-20.8). Interobserver agreement ranged from fair to substantial (κ=0.36 to 0.72). A diagnostic algorithm based on these findings demonstrated a sensitivity of 81.4% (95% CI, 78.9-83.7%) and a specificity of 53.3% (95% CI, 49.7-56.9%) for predicting high-risk BCC subtype. Conclusions: Integration of both clinical and dermoscopic features (including novel features such as topography and vessels within ulceration) are essential to improve subtype prediction of facial BCCs and management decisions.

Several melanoma-specific dermoscopic features have been described, some of which have been reported as indicative of in situ or invasive melanomas. To assess the usefulness of these features to differentiate between these 2 categories, a retrospective, single-centre investigation was conducted. Dermoscopic images of melanomas were reviewed by 7 independent dermatologists. Fleiss’ kappa (κ) was used to analyse interobserver agreement of predefined features. Logistic regression and odds ratios were used to assess whether specific features correlated with melanoma in situ or invasive melanoma. Overall, 182 melanomas (101 melanoma in situ and 81 invasive melanomas) were included. The interobserver agreement for melanoma-specific features ranged from slight to substantial. Atypical blue-white structures (κ=0.62, 95% confidence interval 0.59–0.65) and shiny white lines (κ=0.61, 95% confidence interval 0.58–0.64) had a substantial interobserver agreement. These 2 features were also indicative of invasive melanomas >1.0 mm in Breslow thickness. Furthermore, regression/peppering correlated with thin invasive melanomas. The overall agreement for classification of the lesions as invasive or melanoma in situ was moderate (κ=0.52, 95% confidence interval 0.49–0.56).

Background: Melanomas are often easy to recognize clinically but determining whether a melanoma is in situ (MIS) or invasive is often more challenging even with the aid of dermoscopy. Recently, convolutional neural networks (CNNs) have made significant and rapid advances within dermatology image analysis. The aims of this investigation were to create a de novo CNN for differentiating between MIS and invasive melanomas based on clinical close-up images and to compare its performance on a test set to seven dermatologists.Methods: A retrospective study including clinical images of MIS and invasive melanomas obtained from our department during a five-year time period (2016–2020) was conducted. Overall, 1,551 images [819 MIS (52.8%) and 732 invasive melanomas (47.2%)] were available. The images were randomized into three groups: training set (n = 1,051), validation set (n = 200), and test set (n = 300). A de novo CNN model with seven convolutional layers and a single dense layer was developed.Results: The area under the curve was 0.72 for the CNN (95% CI 0.66–0.78) and 0.81 for dermatologists (95% CI 0.76–0.86) (P < 0.001). The CNN correctly classified 208 out of 300 lesions (69.3%) whereas the corresponding number for dermatologists was 216 (72.0%). When comparing the CNN performance to each individual reader, three dermatologists significantly outperformed the CNN.Conclusions: For this classification problem, the CNN was outperformed by the dermatologist. However, since the algorithm was only trained and validated on 1,251 images, future refinement and development could make it useful for dermatologists in a real-world setting.

Research has shown higher rates of incomplete excision among high-risk than low-risk basal cell carcinomas, but data is limited. A single-centre, retrospective study including excised high-risk basal cell carcinomas (type II–III according to the Swedish classification) was performed to determine incomplete excision rates and associated clinicopathological risk factors. Overall, 987 consecutive cases were included. Of these, 203 (20.6%) were incompletely excised. Incomplete excision rates were higher for type III basal cell carcinomas (27.0% vs 17.6% for type II, p

Background: The preoperative prediction of whether melanomas are invasive or in situ can influence initial management. Objectives: This study evaluated the accuracy rate, interobserver concordance, sensitivity and specificity in determining if a melanoma is invasive or in situ, as well as the ability to predict invasive melanoma thickness based on clinical and dermoscopic images. Methods: In this retrospective, single-center investigation, 7 dermatologists independently reviewed clinical and dermoscopic images of melanomas to predict if they were invasive or in situ and, if invasive, their Breslow thickness. Fleiss’ and Cohen’s kappa (κ) were used for interobserver concordance and agreement with histopathological diagnosis. Results: We included 184 melanomas (110 invasive and 74 in situ). Diagnostic accuracy ranged from 67.4% to 76.1%. Accuracy rates for in situ and invasive melanomas were 57.5% (95% confidence interval [CI], 53.1%-61.8%) and 81.7% (95% CI, 78.8%-84.4%), respectively. Interobserver concordance was moderate (κ = 0.47; 95% CI, 0.44-0.51). Sensitivity for predicting invasiveness ranged from 63.6% to 91.8% for 7 observers, while specificity was 32.4%-82.4%. For all correctly predicted invasive melanomas, agreement between predictions and correct thickness over or under 1.0 mm was moderate (κ = 0.52; 95% CI, 0.45-0.58). All invasive melanomas incorrectly predicted by any observer as in situ had a thickness 1.0 mm were correctly predicted to be invasive by all observers. Conclusions: Accuracy rates for predicting thick melanomas were excellent, melanomas inaccurately predicted as in situ were all thin, and interobserver concordance for predicting in situ or invasive melanomas was moderate. Preoperative dermoscopy of suspected melanomas is recommended for choosing appropriate surgical margins.

Facial high-risk basal cell carcinomas are preferably treated with Mohs micrographic surgery, but only 10% of patients are offered Mohs micrographic surgery in Sweden. The aim of this retrospective study was to examine the differences between primary and recurrent or incompletely excised facial high-risk basal cell carcinomas undergoing Mohs micrographic surgery, with regard to the number of stages, final defect sizes, reconstructive techniques and other consequences. The study was performed during the period 2012 to 2019 at our centre. A total of 903 basal cell carcinomas in 813 patients (70.1% primary, 10.4% incompletely excised and 19.5% recurrences) were included. The mean number of Mohs micrographic surgery stages was significantly lower for primary basal cell carcinomas compared with recurrences (p = 0.03), and the mean final defect size was significantly smaller in primary basal cell carcinomas compared with both recurrent (p

Background: Early detection of melanoma is vital for treatment outcome and survival. Short-term sequential digital dermoscopic monitoring (ST-SDDM) involves the capture and assessment of dermoscopic images of one or more atypical melanocytic lesions (AMLs), at baseline and after four months, in order to detect early morphologic changes. Electrical impedance spectroscopy (EIS) is a diagnostic tool with high sensitivity for the detection of malignant melanocytic lesions. Objectives: The aim of this study was to assess whether EIS, in addition to ST-SDDM, could improve the selection of AMLs requiring surgery. Methods: In this retrospective descriptive study, 22 AMLs in 19 patients were monitored with both ST-SDDM and EIS. A modified EIS decision-making algorithm was established. AMLs were excised if any dermoscopic changes were seen and/or if the EIS score had increased significantly at follow-up. Statistical analyses were made including sensitivity, specificity, PPV and NPV. Results: A total of seven lesions (32%) were excised. Four lesions (57%) were excised solely because of dermoscopic changes including a 0.4 mm-thick melanoma and three benign nevi. Three benign lesions (43%) were excised because of increased EIS scores without any dermoscopic changes. The EIS scores at follow-up showed high variability as compared to the initial scores. Conclusion: The addition of EIS to ST-SDDM did not identify additional malignant lesions. There was no correlation between dermoscopic changes seen with ST-SDDM and increased EIS scores. Three histopathologically benign lesions were needlessly excised. Moreover, the low reproducibility and the possible interoperator variability of the method raised concerns.