Clinical and genetic characteristics of BAP1-mutated non-uveal and uveal melanoma.

Background: Screening for gene mutations has become routine clinical practice across numerous tumor entities, including melanoma. BAP1 gene mutations have been identified in various tumor types and acknowledged as a critical event in metastatic uveal melanoma, but their role in non-uveal melanoma remains inadequately characterized. Methods: A retrospective analysis of all melanomas sequenced in our department from 2014-2022 (n=2650) was conducted to identify BAP1 mutated samples. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome. Results: BAP1 mutations were identified in 129 cases and distributed across the entire gene without any apparent hot spots. Inactivating BAP1 mutations were more prevalent in uveal (55%) compared to non-uveal (17%) melanomas. Non-uveal BAP1 mutated melanomas frequently exhibited UV-signature mutations and had a significantly higher mutation load than uveal melanomas. GNAQ and GNA11 mutations were common in uveal melanomas, while MAP-Kinase mutations were frequent in non-uveal melanomas with NF1, BRAF V600 and NRAS Q61 mutations occurring in decreasing frequency, consistent with a strong UV association. Survival outcomes did not differ among non-uveal melanoma patients based on whether they received targeted or immune checkpoint therapy, or if their tumors harbored inactivating BAP1 mutations. Conclusion: In contrast to uveal melanomas, where BAP1 mutations serve as a significant prognostic indicator of an unfavorable outcome, BAP1 mutations in non-uveal melanomas are primarily considered passenger mutations and do not appear to be relevant from a prognostic or therapeutic perspective.

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ensure optimal treatment and best possible patient outcome. Artificial intelligence (AI) in the form of deep neural networks has recently shown enormous potential in the field of medicine including pathology, where we and others have found common cutaneous tumors can be detected with high sensitivity and specificity. To become a widely applied tool, AI approaches will also need to reliably detect and distinguish less common tumor entities including the diverse group of cutaneous adnexal tumors. METHODS: To assess the potential of AI to recognize cutaneous adnexal tumors, we selected a diverse set of these entities from five German centers. The algorithm was trained with samples from four centers and then tested on slides from the fifth center. RESULTS: The neural network was able to differentiate 14 different cutaneous adnexal tumors and distinguish them from more common cutaneous tumors (i.e. basal cell carcinoma and seborrheic keratosis). The total accuracy on the test set for classifying 248 samples into these 16 diagnoses was 89.92 %. Our findings support AI can distinguish rare tumors, for morphologically distinct entities even with very limited case numbers (< 50) for training. CONCLUSION: This study further underlines the enormous potential of AI in pathology which could become a standard tool to aid pathologists in routine diagnostics in the foreseeable future. The final diagnostic responsibility will remain with the pathologist.

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS: In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS: In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS: AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

Deep learning detection of melanoma metastases in lymph nodes.

BACKGROUND: In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. METHODS: In this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). RESULTS: The established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to -1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. CONCLUSIONS: Our results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.

Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.

Background: Some of the most common cutaneous neoplasms are Bowen's disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists' workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen's disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen's diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model's robustness. In one of the centers, the distinction between Bowen's disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen's disease remained challenging. Conclusions: Bowen's disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists.