Dermatology

Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.

Accurate ischemic stroke lesion segmentation is useful to define the optimal reperfusion treatment and unveil the stroke etiology. Despite the importance of diffusion-weighted MRI (DWI) for stroke diagnosis, learning from multi-sequence MRI images like apparent diffusion coefficient (ADC) can capitalize on the complementary nature of information from various modalities and show strong potential to...

MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.

Melanoma is a highly malignant skin cancer, and identifying its pathogenic genes is crucial for understanding its pathogenesis and developing treatment strategies. Network-based approaches effectively capture the synergistic interactions among genes and their products within biological systems, yet extracting functional insights from these complex networks remains challenging. Here, we propose a n...

The unmet potential of patient context in melanoma AI: Addressing design and data biases.

A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma.

Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression ...

Lifestyle-associated serum metabolites profiling in relation to risk of late-onset psoriasis.

BACKGROUND: Although healthy lifestyle behaviours are associated with a lower risk of psoriasis, the underlying metabolic mechanisms remain unclear. O...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...

Discovery of natural RORγt inhibitor using machine learning, virtual screening, and in vivo validation.

INTRODUCTION: Retinoic acid receptor-related orphan receptor gamma t (RORγt) is a crucial transcription factor regulating Th17 cells, which secrete th...

A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma.

BACKGROUND: Melanoma is a life-threatening skin malignancy, with sentinel lymph node metastasis (SLNM) serving as a critical prognostic factor. While ...

Detection of Jaw Lesions on Panoramic Radiographs Using Deep Learning Method.

This study aimed to evaluate and compare the performance of state-of-the-art deep learning models for detecting and segmenting both radiolucent and ra...

An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment.

Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan's acquisition duration. Thi...

Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.

INTRODUCTION: Skin lesion segmentation and classification is an active research area in medical imaging for the large number of reported deaths in the...

Histopathological images-based deep-learning for risk stratification of uveal melanoma.

Chronic Ocular Sequelae of Stevens-Johnson Syndrome: Recent Advances in Understanding of Patho-Physiology and Management.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe mucocutaneous reactions primarily triggered by drugs or inf...

Non-invasive prediction of the secondary enucleation risk in uveal melanoma based on pretreatment CT and MRI prior to stereotactic radiotherapy.

PURPOSE: The aim of this study was to develop a radiomic model to non-invasively predict the risk of secondary enucleation (SE) in patients with uveal...

Cancer vaccines: Discovery, development, and challenges for clinical translation.

Cancer vaccines stimulate antitumor immunity by delivering tumor antigens and, in recent years, have emerged as a promising therapeutic strategy again...

Deep Learning Reconstruction for T2 Weighted Turbo-Spin-Echo Imaging of the Pelvis: Prospective Comparison With Standard T2-Weighted TSE Imaging With Respect to Image Quality, Lesion Depiction, and Acquisition Time.

PURPOSE: In pelvic MRI, Turbo Spin Echo (TSE) pulse sequences are used for T2-weighted imaging. However, its lengthy acquisition time increases the po...

MILK10k: A Hierarchical Multimodal Imaging-Learning Toolkit for Diagnosing Pigmented and Nonpigmented Skin Cancer and its Simulators.

In this study, we introduce MILK10k, a multimodal image dataset designed to enhance machine learning and artificial intelligence-driven applications f...

Exploring The Role of Artificial Intelligence in Skin Cancer Management: Perspectives from UK Dermatologists.

Development and validation of a prognostic model for predicting survival and immunotherapy benefits in melanoma based on metabolism-relevant genes.

Skin cutaneous melanoma (SKCM) is a fatal form of skin cancer. Metabolism-related genes (MRGs) comprise a group of genes that possess the ability to m...

SLC3A2 as a key anoikis-related gene for prognosis and tumor microenvironment remodeling in melanoma.

OBJECTIVE: Anoikis, a form of programmed cell death triggered by detachment from the extracellular matrix, plays a crucial role in metastasis and immu...