AIMC Topic: Melanoma

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Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

Frontiers in immunology
BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associate...

Automated assessment of skin histological tissue structures by artificial intelligence in cutaneous melanoma.

Pathology, research and practice
BACKGROUND: Prognostic histopathological features such as mitosis in melanoma are excluded from the staging systems due to inter-observer variability and time constraints. While digital pathology offers artificial intelligence-driven solutions, exist...

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data.

Nature communications
Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular...

Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

Clinica chimica acta; international journal of clinical chemistry
A standard for reporting genetic pathology results currently does not exist as a consensus. While effective reports are produced, there is lack of consistency on which details to present or to emphasise, and the ultimate report often reflects an indi...

Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study.

JMIR dermatology
ChatGPT is increasingly used in healthcare. Fields like dermatology and radiology could benefit from ChatGPT's ability to help clinicians diagnose skin lesions. This study evaluates the accuracy of ChatGPT in diagnosing melanoma. Our analysis indicat...

Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

The American Journal of dermatopathology
BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supported by immunohistochemical staining, is the gold standard for detecting LN metastases....

GatedSegDiff: a gated fusion diffusion model for skin lesion segmentation.

Medical & biological engineering & computing
Skin lesion segmentation is a vital process in skin disease diagnosis, crucial for maintaining diagnostic precision. Despite progress in existing image segmentation methods, challenges remain in handling the fuzzy boundaries of skin lesion areas. To ...

Skin cancer detection using dermoscopic images with convolutional neural network.

Scientific reports
Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and...