Computational models of melanoma

Albrecht, Marco; Lucarelli, Philippe; Kulms, Dagmar; Sauter, Thomas

doi:10.1186/s12976-020-00126-7

Cited by 13 publications

(4 citation statements)

References 173 publications

(219 reference statements)

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“…The impact of the underlying anatomy such as vasculature and local lymphatic system has not been thoroughly investigated for their influence on the pathogenesis of BCCs and melanomas. In recent years, there have been advancements in various in silico, in vitro and in vivo techniques such as vasculature-on-chip devices 19 and computational models 20,21 which may enable us to test some of these hypotheses. Further investigations such as computational modelling of skin cancer progression and cancer-on-chip are still required to elucidate the underlying mechanisms to explain the observed heterogeneity of these tumours.…”

Section: Discussionmentioning

confidence: 99%

MORPHOMETRIC IMAGE ANALYSIS COMPARISON OF BASAL CELL CARCINOMAS & MELANOMAS OF THE HEAD & NECK VERSUS OTHER SITES

Hue,

Ekanayake,

Dehmeshki

et al. 2024

Preprint

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BackgroundManagement of skin cancers are heavily dependent on clinical diagnosis via dermoscopy. Dermoscopic morphology of basal cell carcinomas (BCC) and melanomas influence diagnosis and may be influenced by anatomic location.ObjectiveIn this study, we aimed to investigate the morphologic differences between anatomic sites of the head and neck (H&N) versus other body sites in basal cell carcinomas and melanomas.Patients and methodsThe publicly available HAM10000 dataset was used in this study. Morphometric image analysis of the BCCs (n=422) and melanomas (n=868) in this dataset was performed using an open-source image analysis software. Univariate and multivariate statistical analysis was done to identify differences between H&N and other anatomic sites. The multifactorial data was further interrogated with dimensionality reduction techniques, linear discriminant analysis, principal component analysis and t-distributed stochastic neighbour embedding.ResultsUnivariate and multivariate statistical analyses revealed significant differences between H&N and other sites for both BCCs and melanomas (P<0.05). Fifty-three univariate and 11 multivariate features were found to be statistically significant in the BCC group. Thirteen univariate and 8 multivariate features were statistically significant in the melanoma group. Dimensionality reduction via linear discriminant analysis of the BCC groups revealed modest separation of the data by anatomical site. However, melanomas appeared to be more homogenous across H&N and other body sites.ConclusionBCCs of the H&N may be morphologically different to BCCs of other body sites. This may influence the accuracy of computer-assisted diagnostic tools and specialist clinicians working predominantly in the H&N region should exercise caution when employing these tools in clinical practice.

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Section: Discussionmentioning

confidence: 99%

MORPHOMETRIC IMAGE ANALYSIS COMPARISON OF BASAL CELL CARCINOMAS & MELANOMAS OF THE HEAD & NECK VERSUS OTHER SITES

Hue,

Ekanayake,

Dehmeshki

et al. 2024

Preprint

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“…The use of computational approaches for anticancer drug discovery and development has become increasingly popular in the last few decades [48]. Computational tools require the implementation of an interdisciplinary approach, where mathematical and computational methods provide new and useful information [49,50]. For instance, the FDA-approved binimetinib and encorafenib antimelanoma drugs benefited from in silico methods during their development [51].…”

Section: In Silico Modelsmentioning

confidence: 99%

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Matias

Pinho

Penetra

et al. 2021

Cells

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Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

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“…The challenge here is amplified by the heterogeneity of Melanoma subtypes, necessitating detailed lesion characterization. Albrecht et al highlight the critical importance of feature standardization in mathematical models for an accurate assessment of Melanoma, a testament to the complexity of this disease [2]. To address these challenges, emerging graph-based methods are integrating biological parameters with diverse domain features, offering a more universal approach to skin cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks in Multi-Stained Pathological Imaging: Extended Comparative Analysis of Radiomic Features

Monroy,

Rist,

Ostalecki

et al. 2024

Preprint

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Purpose: This study investigates Radiomics features for Graph Neural Networks (GNNs) in MELC pathology sample classification focusing on often misdiagnosed skin diseases. Methods: GNNs processing multiple pathological slides together as cell-level graphs are compared to XGBoost and Random Forest. The analysis assesses the use of MELC vs. Radiomics features, their dimensionality reduction with UMAP or tSNE and the graph connectivity based on spatial and feature closeness. Results: Integrating Radiomics features in a spatially connected graph markedly outperforms standard models when classifying pathologically similar diseases. Additionally, the UMAP dimensionality reduction techniques improves GNN classification performance. Conclusion: Radiomics, processed with GNNs, shows promise for multi-disease classification, enhancing diagnosis accuracy. Considering the potential, future research should extend these methods to a broader range of diseases.

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Computational models of melanoma

Cited by 13 publications

References 173 publications

MORPHOMETRIC IMAGE ANALYSIS COMPARISON OF BASAL CELL CARCINOMAS & MELANOMAS OF THE HEAD & NECK VERSUS OTHER SITES

MORPHOMETRIC IMAGE ANALYSIS COMPARISON OF BASAL CELL CARCINOMAS & MELANOMAS OF THE HEAD & NECK VERSUS OTHER SITES

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Graph Neural Networks in Multi-Stained Pathological Imaging: Extended Comparative Analysis of Radiomic Features

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