Dermoscopic Findings in Intraepidermal Carcinoma: an Interobserver Agreement Study.

INTRODUCTION: A wide range of descriptive terms have been used for dermoscopic findings in intraepidermal carcinoma (IEC) and the clinical diagnostic accuracy of IEC can be challenging. Furthermore, dermoscopic findings in IEC have only rarely been evaluated in fair-skinned populations. OBJECTIVES: To measure the interobserver agreement between dermatologists for dermoscopic findings in IEC. Furthermore, to describe the frequency of these findings in a predominantly fair-skinned population. METHODS: One hundred dermoscopic images of histopathologically verified IECs were collected. The 11 most common dermoscopic findings described in previous studies were re-defined in a new terminology in a pre-study consensus meeting. Images were assessed by eight experienced international dermoscopists. The frequency of findings and the interobserver agreement was analyzed. RESULTS: Scales (83%), dotted/glomerular vessels (77%), pinkish-white areas (73%) and hemorrhage (46%) were the most commonly present dermoscopic findings. Pigmented structures were found in 32% and shiny white structures (follicular or stromal) in 54% of the IEC. Vascular structures (vessels and/or hemorrhage) could be seen in 89% of the lesions. Overall, the interobserver agreement for the respective dermoscopic findings was poor to moderate, with the highest kappa values noted for scales (0.55) and hemorrhage (0.54) and the lowest for pinkish-white areas (0.015). CONCLUSION: Our results confirm those of previous studies on dermoscopy in IEC, including the frequency of pigmented structures despite the fair-skinned population. The interobserver agreement was relatively low. The proposed new terminology and our findings can hopefully serve as a guideline for researchers, teachers and students on how to identify IEC.

Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network.

Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 independent dermatologists. The secondary aim was to address which clinical and dermoscopic features dermatologists found to be suggestive of invasive and in situ melanomas, respectively. A retrospective investigation was conducted including 1,578 cases of paired images of invasive (n = 728, 46.1%) and in situ melanomas (n = 850, 53.9%). All images were obtained from the Department of Dermatology and Venereology at Sahlgrenska University Hospital and were randomized to a training set (n = 1,078), a validation set (n = 200) and a test set (n = 300). The area under the receiver operating characteristics curve (AUC) among the dermatologists ranged from 0.75 (95% confidence interval 0.70-0.81) to 0.80 (95% confidence interval 0.75-0.85). The combined dermatologists' AUC was 0.80 (95% confidence interval 0.77-0.86), which was significantly higher than the CNN model (0.73, 95% confidence interval 0.67-0.78, p = 0.001). Three of the dermatologists significantly outperformed the CNN. Shiny white lines, atypical blue-white structures and polymorphous vessels displayed a moderate interobserver agreement, and these features also correlated with invasive melanoma. Prospective trials are needed to address the clinical usefulness of CNN models in this setting.

Interobserver Agreement on Dermoscopic Features and their Associations with In Situ and Invasive Cutaneous Melanomas.

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).

Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network.

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.

Can Dermoscopy Be Used to Predict if a Melanoma Is In Situ or Invasive?

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. All 32 melanomas >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.

Surgery for Bowen Disease: Clinicopathological Factors Associated With Incomplete Excision.

BACKGROUND: One common treatment for Bowen disease (BD) is surgical excision, but there is no international consensus on the appropriate surgical margins. OBJECTIVES: This study examined what factors affect the rate of incomplete excision of BD. METHODS: Clinicopathological data potentially linked to surgical outcome (complete or incomplete excision) were retrospectively collected from medical and histopathological records on all surgically excised BD lesions diagnosed at Sahlgrenska University Hospital in Gothenburg, Sweden during 2014-2015. Data were analyzed with two definitions of incomplete excision: less strict (ie, BD present at the surgical margin) and strict (ie, dysplasia present at the surgical margin). RESULTS: In total, 463 BD lesions among 408 patients were included. With the less strict definition, 3 factors were associated with significantly higher rates of incomplete excision: surgical margins <3 mm, a less experienced surgeon, and use of punch biopsy excision. The same factors plus a tumor location on the head and neck area or upper extremities were associated with significantly higher rates of incomplete excision using the strict definition. After adjustment for confounders, less experience was independently associated with incomplete excision using the less strict definition, whereas less experience and location on the head and neck area or upper extremities were independently associated with incomplete excision using the strict definition. Surgeon specialty was not associated with incomplete excision regardless of the definition. CONCLUSIONS: When removing BD surgically, an elliptical excision with surgical margins ≥3 mm carried out by an experienced surgeon should be recommended. Surgical margins may need to be adjusted depending on body site.

Nonsurgical Options for the Treatment of Basal Cell Carcinoma.

OBJECTIVES: The aim of this review article is to summarize the effectiveness, potential adverse events, and indications of the main nonsurgical treatment alternatives for basal cell carcinoma. METHODS: An extensive literature review was carried out. The most relevant articles were discussed and selected by the authors in order to provide a brief but evidence-based overview of the most common nonsurgical methods used for treating basal cell carcinoma. RESULTS: Although surgery and Mohs micrographic surgery are often considered the optimal treatment options for basal cell carcinoma, these tumors can also be treated successfully with destructive techniques (eg, curettage alone, cryosurgery, or electrodesiccation), photodynamic therapy, topical drugs (eg, 5-fluorouracil, imiquimod, or ingenol mebutate), radiotherapy, or hedgehog pathway inhibitors. When choosing between these alternatives, physicians must take into consideration the tumor's size, location, and histopathological subtype. Special care should be taken when treating recurrent tumors. Furthermore, physician experience is of great importance when using destructive techniques. Finally, patient preference, potential adverse events, and cosmetic outcome should also be considered. CONCLUSIONS: Dermatologists and physicians treating basal cell carcinoma should have knowledge of and experience with the large arsenal of therapeutic alternatives available for the successful, safe, and individualized management of patients with basal cell carcinoma.