Basal cell carcinoma: Epidemiological impact of clinical versus histopathological diagnosis.

BACKGROUND: Low-risk basal cell carcinomas (BCCs) are to an increasing extent diagnosed by dermatologists through dermoscopic examination only and treated with non-surgical methods. Reports on increasing incidence as well as trends regarding subtypes, anatomical sites and differences related to gender are based solely on histopathologically verified tumours. How unreported clinically diagnosed BCCs affect the epidemiological data has not been sufficiently investigated. OBJECTIVES: To analyse the tumour and patient characteristics of clinically diagnosed versus histopathologically confirmed primary BCCs and to make a gross estimate on how unreported BCCs could influence the total number of new cases. METHODS: We retrospectively reviewed all primary BCCs diagnosed in 2016 at the Department of Dermatology, Sahlgrenska University Hospital in Gothenburg, Sweden. We also reviewed all histopathologically verified primary BCCs at the two largest pathology laboratories in Western Sweden during the same year to estimate the proportion of BCCs diagnosed by dermatologists. RESULTS: In total, 2365 primary BCCs were diagnosed at our centre. More than half of these tumours were clinically diagnosed (55.8%). Superficial subtype (41.7%), location on the trunk (46.3%) and destructive treatment methods (60.0%) were most common. The reports from the two pathology laboratories showed that histopathologically verified BCCs (n = 5837) were more commonly of the infiltrative or nodular subtype and located in the head and neck area. Dermatologists managed 56.0% of them. CONCLUSIONS: This study indicates that a substantial number of BCCs are not visualized in the official statistics which are solely based on reports from pathology laboratories. When taking clinically diagnosed tumours into account, truncal location and superficial subtype are more common than previously believed. Further, based on the regional calculations, the real burden of BCC in Sweden might be up to 70% higher than what is reported in official statistics.

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.

Smartphone teledermoscopy referrals: a novel process for improved triage of skin cancer patients.

In this open, controlled, multicentre and prospective observational study, smartphone teledermoscopy referrals were sent from 20 primary healthcare centres to 2 dermatology departments for triage of skin lesions of concern using a smartphone application and a compatible digital dermoscope. The outcome for 816 patients referred via smartphone teledermoscopy was compared with 746 patients referred via the traditional paper-based system. When surgical treatment was required, the waiting time was significantly shorter using teledermoscopy for patients with melanoma, melanoma in situ, squamous cell carcinoma, squamous cell carcinoma in situ and basal cell carcinoma. Triage decisions were also more reliable with teledermoscopy and over 40% of the teledermoscopy patients could potentially have avoided face-to-face visits. Only 4 teledermoscopy referrals (0.4%) had to be excluded due to poor image quality. Smartphone teledermoscopy referrals allow for faster and more efficient management of patients with skin cancer as compared to traditional paper referrals.