Skin Cancer

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrate multimodal data to construct predictive models, but the methodological quality, risk of bias and clinical applicability of such models have not been systematically evaluated. OBJECTIVE: This study aims to systematically evaluate the methodological ...

Class-balanced dermoscopic lesion segmentation using MoG-LISA and optimized Swin-UNet via the GM-FDE framework.

Automatic skin lesion segmentation is one of the key pivotal tasks in dermatological image processing, with important consequences in early melanoma diagnosis and treatment planning. Nevertheless, issues of extreme class imbalance, morphological variability, and poor boundary delineation still exist in current deep learning-based approaches. This study proposes a unified framework that combines mo...

Automating classification of treatment responses to combined targeted therapy and immunotherapy in HCC.

Tyrosine kinase inhibitor (TKI) combined with immunotherapy regimens are now widely used for treating advanced hepatocellular carcinoma (HCC), but the...

AI Co-Scientists Move to the Front Lines of Cancer Research.

Artificial intelligence systems are beginning to function as "co-scientists" in cancer research, generating drug candidates, prioritizing immunotherap...

The use of advanced machine learning to predict outcomes after atezolizumab plus bevacizumab for advanced hepatocellular carcinoma: a retrospective cohort study.

BACKGROUND: Combination immune checkpoint inhibitors are recommended as first-line therapy for advanced hepatocellular carcinoma. However, only a thir...

Association of Circulating T Cell and Tumor Microenvironment Profiles with Immune Checkpoint Blockade Outcome in Sarcoma.

PURPOSE: Immune checkpoint blockade (ICB) benefits only a subset of sarcoma patients. Biomarkers of response and resistance are needed to help guide p...

Unraveling immune evasion in the tumor microenvironment: Mechanisms, therapeutic strategies, and future directions in cancer immunotherapy.

The immune evasion that is encouraged by the tumor microenvironment (TME) is a key factor in the failure of cancer immunotherapies. This review addres...

Comparison of an AI-based prognostic assay with gene expression profiling in early-stage melanoma.

epiGPTope: A Machine Learning-Based Epitope Generator and Classifier.

Epitopes are short antigenic peptide sequences that are recognized by antibodies or immune cell receptors. These are central to the development of imm...

Advancing prussian blue nanoparticle-mediated photothermal therapy through machine learning and multiomics integration.

Prussian blue nanoparticles (PBNPs) are a versatile platform for administering photothermal therapy (PTT) in cancer therapy applications. PBNPs combin...

Predictive modeling of molecular activity underlying physical cell-cell interactions.

Interactions between cells are central to tissue organization and function in health and disease. Labeling immune partnerships by sortagging intercell...

Integrative bioinformatics and experiments identify RIBC2 as a key regulator in the esophageal cancer.

Early detection of esophageal cancer (EC) remains a major challenge due to the limited understanding of its initial molecular alterations. Therefore, ...

Integrated LIBS-Raman spectroscopy coupled with explainable machine learning for biochemical characterization of melanoma.

Melanoma remains one of the most aggressive and lethal cutaneous malignancies, underscoring the need for objective and label-free diagnostic methods t...

Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.

The tumor immune microenvironment and intratumoral microbiota play critical roles in cancer progression and immunotherapy response, yet their integrat...

Machine learning-driven computational drug repurposing to identify new tubulin inhibitors against cancer.

Tubulin is a validated anticancer target, yet the clinical translation of colchicine-binding site inhibitors remains limited by toxicity and resistanc...

Integrating Spatial Proteogenomics in Cancer Research.

BACKGROUND: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial pro...

Low resource federated learning for classification of nail disease by deploying cross-silo and heterogeneously dataset distributions.

Nail diseases, including such common conditions as fungus, and more serious issues like melanoma, may be important clues to the overall health and req...

Multidisciplinary Team Diagnosis and Treatment of well-differentiated thyroid carcinoma: Current Landscape and Future Prospects.

BACKGROUND: The incidence of thyroid cancer has increased markedly in recent years, largely driven by well-differentiated thyroid carcinoma (WDTC). WD...

CT-based radiomics for predicting PD-L1 expression status in non-small cell lung cancer using a hybrid machine learning model.

PURPOSE: Programmed cell death ligand-1 (PD-L1) is a key prognostic and predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). ...

Stochasticity in cancer immunotherapy stems from rare but functionally critical Spark T cells.

Cancer immunotherapies trigger highly variable responses in patients and in genetically identical mouse models. To assess the intrinsic stochasticity ...