Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are both the most common methods and they display the best performance with the currently available limited datasets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult.

Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice. The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians. PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included. A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images. All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice.

Hotspot mutations of the oncogenes BRAF and NRAS are the most common genetic alterations in cutaneous melanoma. Specific inhibitors of BRAF and MEK have shown significant survival benefits in large phase III trials. However, the prognostic significance of BRAF and NRAS mutations outside of clinical trials remains unclear. The mutational status of BRAF (exon 15) and NRAS (exon 2 and 3) was determined in melanoma samples of 217 patients with pyrosequencing and Sanger sequencing. The genotypes were correlated with clinical outcomes and pathologic features of the primary tumors. Time to disease progression was calculated with the cumulative incidence function. Survival analyses were performed with Kaplan-Meier estimates and Cox proportional hazards regression analysis. Relative survival was calculated with the Ederer-II method. Treatment with BRAF and MEK inhibitors and immune checkpoint blockade (ICB) was allowed. Mutations in BRAF and NRAS were identified in 40.1 and 24.4% of cases, respectively. Concurrent mutations in both genes were detected in further 2.3%. The remaining 33.2% were wild type for the investigated exons (WT). BRAF mutations were significantly associated with younger age at first diagnosis (p < 0.001) and truncal localization of the culprit primary (p = 0.002). The nodular subtype was most common in the NRAS cohort. In addition, NRAS-mutant melanoma patients showed a higher frequency of nodal relapse (p = 0.013) and development of metastatic disease (p = 0.021). The time to loco-regional nodal relapse was shortest in NRAS-mutant melanoma (p = 0.002). Presence of NRAS mutation was an independent risk factor for disease progression in multivariate analysis (HR 2.01; 95% CI 1.02 – 3.98). BRAF-mutant melanoma patients showed a tendency for better overall and relative survival. Genotype was not a consistent risk factor in multivariate analysis. Instead, positive sentinel lymph node status (HR 2.65; 95% CI 1.15 – 6.10) and treatment with ICB in stage IV disease (HR 0.17; 95% CI 0.06–0.48) were significant multivariate risk factors. NRAS-mutant tumors tended to behave more aggressively particularly in early stages of the disease in this high-risk melanoma population. Treatment with immune checkpoint blockade improved survival in stage IV disease in a real-world setting.

Uveal melanoma (UM) is highly refractory to treatment with dismal prognosis in advanced stages. The value of the combined checkpoint blockade with CTLA-4 and PD-1 inhibition in metastatic UM is currently unclear. Patients with metastatic or unresectable UM treated with ipilimumab in combination with a PD-1 inhibitor were collected from 16 German skin cancer centers. Patient records of 64 cases were analyzed for response, progression-free survival (PFS), overall survival (OS), and safety. Clinical parameters and serum biomarkers associated with OS and treatment response were determined with Cox regression modelling and logistic regression. The best overall response rate to combined checkpoint blockade was 15.6% with 3.1 and 12.5% complete and partial response, respectively. The median duration of response was 25.5 months (range 9.0–65.0). Stable disease was achieved in 21.9%, resulting in a disease control rate of 37.5% with a median duration of the clinical benefit of 28.0 months (range 7.0–65.0). The median PFS was 3.0 months (95% CI 2.4–3.6). The median OS was estimated to 16.1 months (95% CI 12.9–19.3). Regarding safety, 39.1% of treated patients experienced a severe, treatment-related adverse event according to the CTCAE criteria (grade 3: 37.5%; grade 4: 1.6%). The most common toxicities were colitis (20.3%), hepatitis (20.3%), thyreoiditis (15.6%), and hypophysitis (7.8%). A poor ECOG performance status was an independent risk factor for decreased OS (p = 0.007). The tolerability of the combined checkpoint blockade in UM may possibly be better than in trials on cutaneous melanoma. This study implies that combined checkpoint blockade represents the hitherto most effective treatment option available for metastatic UM available outside of clinical trials.