AIMC Topic: Melanoma

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Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis.

Frontiers in immunology
BACKGROUND: The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification o...

Automated segmentation for early detection of uveal melanoma.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
OBJECTIVE: Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncol...

Artificial Intelligence in Dermatology: A Systematic Review of Its Applications in Melanoma and Keratinocyte Carcinoma Diagnosis.

Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]
BACKGROUND: Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in skin cancer diagnosis may alleviate potential care gaps.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning.

Apoptosis : an international journal on programmed cell death
The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently...

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

Histopathology
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms tha...

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Journal of theoretical biology
Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep lea...

A probabilistic knowledge graph for target identification.

PLoS computational biology
Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone....

The selective deployment of AI in healthcare: An ethical algorithm for algorithms.

Bioethics
Machine-learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Giv...

GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

PloS one
Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been prima...