Skin Cancer

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular car...

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to...

Mitochondrial Metabolic Biomarkers in Periodontitis: Discovery and Clinical Validation.

INTRODUCTION AND AIMS: Mitochondrial metabolic dysregulation is associated with periodontitis (PD); ...

Toward generalizable prediction of cancer signal using a cell-free DNA language model.

Cell-free DNA can be used for early cancer detection, minimal residual disease monitoring, and post-...

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyro...

New Frontiers of Skin Ultrasound in Precision Medicine.

Ultrasound has emerged as a versatile, non-invasive imaging technique in dermatology, offering real-...

A Single-Cell Guided Machine Learning Model Predicts Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Resistance to immune checkpoint inhibitors is a major clinical obstacle in the treatment of gastric ...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilit...

Increased Dendritic Cell Density and Altered Melanin Patterns Distinguish Melanoma from Nevi on CRFF-OCT: A Pilot Study Toward Noninvasive Diagnosis.

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogene...

Dynamic urinary proteomics integrates single-cell and spatial transcriptomics to reveal tumour microenvironment and predict immunotherapy response in biliary tract cancer.

BACKGROUND: Most patients with biliary tract cancer (BTC) do not derive durable clinical benefit (DC...

Targeting colorectal cancer stem-like cells: Overcoming therapeutic resistance and reprogramming the immunosuppressive niche.

Colorectal cancer stem-like cells (CRC-SCs) represent a tumor subpopulation with enhanced tumor-init...

Multiclass convolutional neural network vs. 96 dermatologists in skin lesion diagnosis, an international study.

BACKGROUND AND OBJECTIVES: Artificial intelligence was shown to improve diagnostic accuracy for skin...

Predicting time to local failure after gamma knife radiosurgery for melanoma brain metastases using survival machine learning.

BACKGROUND: Melanoma brain metastases treated with Gamma Knife radiosurgery show heterogeneous local...

Machine learning models predict the immunotherapy response in tumors on the basis of DNA methylation.

BACKGROUND: The epigenetic control of immune responses plays a crucial role in the development and p...

Explainable AI reveals squamous histology and U-shaped PD-L1 patterns as primary subgroup predictors of neoadjuvant and perioperative immunotherapy benefit in NSCLC: a machine learning analysis.

BACKGROUND: The introduction of neoadjuvant and perioperative immunotherapy has broadened treatment ...

Understanding resistance in glioblastoma: insights into personalized and targeted therapeutic strategies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despi...

Contrastive adapter training and consensus knowledge distillation for multi-source-free domain adaptation in skin cancer diagnosis.

Skin cancer diagnosis, particularly the differentiation of melanoma from benign nevi, is a vital yet...

Impact of nanocarrier mediated drug delivery systems: a deep dive into therapeutics and clinical management of breast cancer.

Breast cancer (BC) is the most frequently diagnosed malignancy and the leading cause of cancer-relat...

An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.

BACKGROUND: SERPING1, which encodes the C1 inhibitor (C1-INH) of the complement system, and plays a ...

ViGNet: A clinical data-supported deep learning approach for NSCLC immunotherapy response prediction in digital pathology.

Histopathology is the cornerstone of oncology diagnosis, while whole-slide images (WSIs) enable the ...