AIMC Topic: Melanoma

Clear Filters Showing 1 to 10 of 372 articles

Automatic classification of uveal melanoma response patterns following ruthenium-106 plaque brachytherapy using ultrasound images and deep convolutional neural network.

Scientific reports
Following uveal melanoma (UM) affected treatment using ruthenium-106 brachytherapy, tumor thickness patterns fall into one of four categories: decrease (regression), increase (recurrence), stop (stable), or other, which are assessed in follow-up A-mo...

3D Total Body Photography as a Promising Innovation for Early Skin Cancer Detection: Scoping Review.

JMIR dermatology
BACKGROUND: Skin cancer (SC) is a global health concern because of its high and still increasing incidence and associated health care cost. Belgium is no exception as 1 in 5 people are diagnosed with SC before the age of 75 years. The VECTRA WB360, a...

Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.

BMC primary care
CONTEXT: Artificial intelligence (AI) technologies are increasingly used for image recognition, especially for skin lesions. Due to what may be long wait times for dermatology appointments, general practitioners (GPs) are the gatekeepers when it come...

Comparison of in vitro migration assays evaluating nintedanib's migration inhibitory effects on melanoma cells.

Scientific reports
Cell migration plays a central role in tumor progression and metastasis, making it a critical parameter in both cancer biology and therapeutic evaluation. A range of in vitro migration assays are commonly used to assess treatment-induced effects on m...

Multimodal AI and tumour microenvironment integration predicts metastasis in cutaneous melanoma.

Nature communications
Accurate prognostication is essential to guide clinical management in localised cutaneous melanoma (CM), the form of skin cancer with the highest mortality. While the tumour microenvironment (TME) plays a key role in disease progression, current stag...

Transformer-aided skin cancer classification using VGG19-based feature encoding.

Scientific reports
Skin cancer is among the most widely distributed, deadliest cancers around the globe, and early diagnosis becomes vital to enhance patient survival. Deep learning has demonstrated high potential for automatic skin lesion classification. However, exis...

Enhanced skin cancer classification using modified efficientNetV2L with adaptive early stopping mechanism.

Scientific reports
The accurate classification of skin cancer types is a critical task in medical diagnostics, requiring robust and reliable models to distinguish between various skin lesions. Despite advancements in deep learning, developing models that generalize wel...

Ligand-receptor interaction profiling as a predictive biomarker for anti-PD-1 therapy response in melanoma.

Clinical and experimental medicine
Cell-to-cell communication through ligand-receptor (LR) interactions can fundamentally shape the tumor microenvironment and immune responses, but the full spectrum of these interactions in anti-PD-1 therapy remains unexplored. We developed a predicti...

Multi-omics identification of RNASE6 as an immune regulatory RNA-binding protein associated with melanoma metastasis.

Autoimmunity
BACKGROUND: Cutaneous melanoma is a highly invasive tumor. It enhances metastasis and resistance to immunotherapy immunosuppressive mechanisms. Understanding RNA-binding proteins (RBPs) in melanoma's immune alterations is limited. This study explore...

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.

Scientific data
Machine learning classification algorithms have emerged as promising tools to support the early detection of skin cancers. Existing algorithms typically assess malignancy of skin lesions based on a single skin image. This is in contrast with how clin...