Skin Cancer

ChatGPT-4o as a diagnostic tool for skin cancer: Diagnostic accuracy in melanoma and non-melanoma detection.

INTRODUCTION: The global incidence of skin cancer is rising, emphasizing the need for early detection tools. Artificial intelligence (AI) models, including multimodal systems like ChatGPT-4o, can analyze visual data to assist clinicians in diagnosis. This study evaluated ChatGPT-4o's diagnostic accuracy in detecting melanoma and non-melanoma skin cancers from macroscopic and dermoscopic images. ME...

Immunocto: A massive immune cell database auto-generated for histopathology.

With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on prognosis and understand potential response to therapeutic agents. A key approach to characterising the TIME involves combining digitised images of haematoxylin and eosin (H&E) stained tissue sections obtained in routine histopathology examination wit...

Unraveling the microbiome's role in breast cancer progression and treatment response.

The human microbiome, encompassing microbial communities in the gut and breast tissue, has emerged as a critical modulator of breast cancer (BC) initi...

Development of a lactylation-related molecular classification and machine learning-based gene signature to predict survival, response to immunotherapy for ovarian cancer.

BACKGROUND: Lactylation is acknowledged as a regulator of numerous biological processes related to cancer. Research on the ability of lactylation-rela...

Turmeric extracellular vesicles-derived "all-in-one" nanoagent enables full-cycle synergistic immunomodulation of lung cancer.

Lung cancer's metastatic propensity and recurrence prevalence necessitate innovative immunotherapy strategies beyond conventional single-mode regulati...

AI-assisted literature screening: A hybrid approach using large language models and retrieval-augmented generation.

OBJECTIVE: Current systematic literature reviews largely rely on manual screening of articles retrieved through keyword search, which is time-consumin...

Explainable artificial intelligence for multi-modal cancer analysis: From genomics to immunology.

Cancer remains one of the most biologically intricate and heterogeneous diseases, with variability spanning genetic, epigenetic, metabolic, spatial, a...

Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study.

OBJECTIVES: To develop and evaluate the predictive efficacy of a combined model incorporating clinical parameters and PET-based radiomics signature (R...

Molecular targeted therapies, omics, and AI based theranostics approaches for the treatment of prostate cancer.

Cancer is a disease that begins with genetic and epigenetic alterations occurring in specific cells, some of which can spread and migrate to other tis...

Integrated multi-omics analyses identified the H3K18la-based spatiotemporal characteristics and risk-stratified treatment strategy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, characterized by high mortality, recurrence, and metastasis. Despite a...

Drug Repurposing in Oncology: A Strategic Pathway to Unlocking New Therapeutic Potential.

The growing number of cancer cases and deaths highlights the urgent need for innovative treatment approaches. One technique that has lately been recog...

Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.

Oropharyngeal cancer (OPC) is increasingly driven by human papillomavirus (HPV), particularly HPV16, marking a shift in its epidemiology, prognosis, a...

Emerging options and future strategies in immunotherapy for advanced lung squamous-cell carcinoma.

Advanced-stage lung squamous-cell carcinoma (LUSC) remains a therapeutic challenge. Although immune checkpoint inhibitors (ICIs) have revolutionized L...

A deep learning-based radiomics model for noninvasive diagnosis of melanoma.

To develop a noninvasive diagnostic model integrating deep learning and radiomics for improving the accuracy and clinical utility of early melanoma di...

Machine learning-derived serum protein signatures as biomarkers for checkpoint inhibition in melanoma.

An interactive and explainable AI approach to improve human-machine teaming in cancer subtyping from digital cytopathology.

Algorithmic decision support is rapidly becoming a staple of personalized medicine, particularly for high-stakes recommendations such as cancer subtyp...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...

Machine learning-based integration develops a lactate metabolism related gene signature for improving outcomes in pancreatic ductal adenocarcinoma.

OBJECTIVES: Given its high global mortality rate, pancreatic ductal adenocarcinoma (PDAC) remains a significant area of investigation. However, a robu...

CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.

BACKGROUND: Colony-stimulating factor-1 receptor (CSF1R) signaling is crucial for the ability of tumor-associated macrophages (TAMs) to establish an i...

Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.

PURPOSE: To develop and evaluate a deep learning model that integrates ultra-widefield fundus photography and B-scan ultrasonography for automated cla...