Skin Cancer

Boosting Checkpoint Blockade Immunotherapy with T Cell Membrane Redox Homeostasis Regulation and Deep Learning Enhanced NIR-II Imaging.

Immune checkpoint blockade therapy has revolutionized clinical oncotherapy by intercepting immunosuppressive pathways in tumor tissues. Nevertheless, the therapeutic efficacy is limited by a multidimensional immunosuppressive tumor microenvironment, which impedes the activity of tumor-infiltrating immune cells and undermines treatment outcomes. In this study, a zwitterionic polymer is designed for...

Multivariate Screening and Automated Clustering of Macrophage Immunoreactome to Nanoparticles and Photothermal Therapy.

Immunotherapy aims to control the immune system against diseases such as cancer or infections. Nanotechnology is part of the armamentarium to reprogram the immune system in a spatially and temporally controlled manner. However, predicting immune responses using high-throughput tests is challenging due to the immunoreactome's complexity and plasticity. This work presents a fast, machine learning-as...

Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT Radiomic subtypes: a multi-cohort study.

BACKGROUND: Radiomics analyzes quantitative features from medical images to reveal tumor heterogeneity, offering new insights for diagnosis, prognosis...

Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.

PURPOSE: Cancer treatment has been revolutionized by the immune checkpoint inhibitors (ICIs). However, a subset of patients do not respond and/or expe...

Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionall...

Modified Le-Net Model with Multiple Image Features for Skin Cancer Detection.

Computer-based technologies significantly improve melanoma and non-melanoma skin cancer detection by providing non-invasive, cost-effective, and rapid...

Cancer vaccines and the future of immunotherapy.

Vaccines have had a major impact on the control of infectious disease, most recently by helping to combat the COVID-19 pandemic. Prophylactic cancer v...

HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet the response rate remains limited, with only about 30% of solid tumor pa...

Machine Learning Characterization of Immunometabolism in the Tumor Microenvironment and Immunotherapy Responses in Bladder Cancer.

Immune dysregulation and metabolism reprogramming are implicated in bladder cancer (BLCA), the relationships between immunometabolism (IMB) and BLCA r...

Self-Driving and Detachable Lab-Microrobots Tailor Drug Delivery for Closed-Loop Stimulation of the Antitumor Immune Cycle.

Hypoxia arises in most solid tumors with insufficient blood flow, which hinders the delivery and efficacy of therapeutic agents to tumors. In this wor...

Artificial Intelligence in Predicting Efficacy and Toxicity of Immunotherapy: Applications, Challenges, and Future Directions.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, becoming a standard approach for various tumor types. Consequently, accurate...

The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology.

Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to convent...

Advances in molecular pathology and therapy of non-small cell lung cancer.

Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substant...

Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma.

INTRODUCTION: Melanoma is a highly aggressive skin cancer that accounts for a disproportionate number of skin cancer-related deaths due to early metas...

Pretreatment Multi-sequence Contrast-Enhanced MRI to Predict Response to Immunotherapy in Unresectable Hepatocellular Carcinoma Using Transformer: A Multicenter Study.

Targeted combined immunotherapy (TCI) has shown certain antitumor effects in patients with unresectable hepatocellular carcinoma(uHCC), but only a su...

Integrated machine learning based on cuproptosis and RNA methylation regulators to explore the molecular model of prostate cancer and provide novel insights to immunotherapy.

As a highly prevalent tumor in males, prostate cancer (PCa) needs newly developed biomarkers to guide prognosis and treatment. However, few researche...

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Un...

Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study.

Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer ...

Artificial Intelligence for Head and Neck Squamous Cell Carcinoma: From Diagnosis to Treatment.

Head and neck squamous cell carcinoma (HNSCC) remains a globally prevalent malignancy with high morbidity and mortality. Despite therapeutic advances,...

Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.

Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurren...