Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics.

Importance: The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals. Objective: To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics. Design, Setting, and Participants: This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological whole-slide images prospectively acquired at 6 German university hospitals between April 2021 and February 2023 and benchmarked it using both a holdout and an external test dataset. Data analysis was performed from February to April 2023. Exposures: All whole-slide images were retrospectively analyzed by an AI-based classifier without influencing routine clinical care. Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUROC) served as the primary end point for evaluating the diagnostic performance. Secondary end points included balanced accuracy, sensitivity, and specificity. Results: The study included 1025 whole-slide images of clinically melanoma-suspicious skin lesions from 923 patients, consisting of 388 histopathologically confirmed invasive melanomas and 637 nevi. The median (range) age at diagnosis was 58 (18-95) years for the training set, 57 (18-93) years for the holdout test dataset, and 61 (18-95) years for the external test dataset; the median (range) Breslow thickness was 0.70 (0.10-34.00) mm, 0.70 (0.20-14.40) mm, and 0.80 (0.30-20.00) mm, respectively. The federated approach (0.8579; 95% CI, 0.7693-0.9299) performed significantly worse than the classical centralized approach (0.9024; 95% CI, 0.8379-0.9565) in terms of AUROC on a holdout test dataset (pairwise Wilcoxon signed-rank, P < .001) but performed significantly better (0.9126; 95% CI, 0.8810-0.9412) than the classical centralized approach (0.9045; 95% CI, 0.8701-0.9331) on an external test dataset (pairwise Wilcoxon signed-rank, P < .001). Notably, the federated approach performed significantly worse than the ensemble approach on both the holdout (0.8867; 95% CI, 0.8103-0.9481) and external test dataset (0.9227; 95% CI, 0.8941-0.9479). Conclusions and Relevance: The findings of this diagnostic study suggest that federated learning is a viable approach for the binary classification of invasive melanomas and nevi on a clinically representative distributed dataset. Federated learning can improve privacy protection in AI-based melanoma diagnostics while simultaneously promoting collaboration across institutions and countries. Moreover, it may have the potential to be extended to other image classification tasks in digital cancer histopathology and beyond.

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma.

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists' decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists' diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists' confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists' willingness to adopt such XAI systems, promoting future use in the clinic.

[Blastic plasmacytoid dendritic cell neoplasm (BPDCN) : A rare hematologic neoplasm with frequent cutaneous involvement]. / Blastische plasmazytoide dendritische Zellneoplasie (BPDCN) : Eine seltene hämatologische Neoplasie mit häufiger kutaner Beteiligung.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematological malignancy that derives from precursors of plasmacytoid dendritic cells and is characterized by disseminated, erythematous or bluish-livid plaques or nodi. Because of the disease's rarity the diagnosis and treatment still pose a significant challenge. We present a case of a patient with BPDCN and show clinical and diagnostic characteristics as well as potential treatment regimes.

Model soups improve performance of dermoscopic skin cancer classifiers.

BACKGROUND: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. OBJECTIVE: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. METHODS: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. RESULTS: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. CONCLUSIONS: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency.

[Laugier-Hunziker syndrome : A rare differential diagnosis of mucocutaneous hyperpigmentation]. / Laugier-Hunziker-Syndrom : Eine seltene Differenzialdiagnose der mukokutanen Hyperpigmentierung.

Laugier-Hunziker syndrome (LHS) is characterized by lentiginous hyperpigmentation of the oral mucosa and lips. In addition, longitudinal melanonychia and palmoplantar hyperpigmented lesions may occur. LHS is a clinical diagnosis of exclusion. Herein, we report the case of a 66-year-old woman with LHS. The clinical and histopathologic features of LHS are presented and important differential diagnoses are discussed.

Inhibiting receptor tyrosine kinase AXL with small molecule inhibitor BMS-777607 reduces glioblastoma growth, migration, and invasion in vitro and in vivo.

PURPOSE: Receptor tyrosine kinase AXL (RTK-AXL) is regarded as suitable target in glioma therapy. Here we evaluate the anti-tumoral effect of small molecule inhibitor BMS-777607 targeting RTK-AXL in a preclinical glioma model and provide evidence that RTK-AXL is expressed and phosphorylated in primary and recurrent glioblastoma multiforme (GBM). EXPERIMENTAL DESIGN: We studied the impact of BMS-777607 targeting RTK-AXL in GBM models in vitro and in vivo utilizing glioma cells SF126 and U118MG. Impact on proliferation, apoptosis and angiogenesis was investigated by immunohistochemistry (IHC) and functional assays in vitro and in vivo. Tumor growth was assessed with MRI. Human GBM tissue was analyzed in terms of RTK-AXL phosphorylation by immunoprecipitation and immunohistochemistry. RESULTS: BMS-777607 displayed various anti-cancer effects dependent on increased apoptosis, decreased proliferation and migration in vitro and ex vivo in SF126 and U118 GBM cells. In vivo we observed a 56% tumor volume reduction in SF126 xenografts and remission in U118MG xenografts of more than 91%. The tube formation assay confirmed the anti-angiogenic effect of BMS-777607, which became also apparent in tumor xenografts. IHC of human GBM tissue localized phosphorylated RTK-AXL in hypercellular tumor regions, the migratory front of tumor cells in pseudo-palisades, and in vascular proliferates within the tumor. We further proved RTK-AXL phosphorylation in primary and recurrent disease state. CONCLUSION: Collectively, these data strongly suggest that targeting RTK-AXL with BMS-777607 could represent a novel and potent regimen for the treatment of primary and recurrent GBM.