Skin Cancer

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

Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.

Urothelial carcinoma, predominantly appearing as non-muscle-invasive papillary urothelial carcinoma (NMIPUC), exhibits wide clinical variability. Accurate pathological staging and grading are essential for effective risk stratification and treatment decisions. Advancements in artificial intelligence (AI) open new opportunities to improve predictive models; however, their generalizability across di...

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

Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogeneity limits therapeutic benefit to specific patient...

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 (DCB) from immune checkpoint inhibitors (ICIs), under...

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-initiating capacity and have been implicated in metast...

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 cancer detection. While most clinically approved ...

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 control trajectories. Accurate prediction of time...

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 progression of cancer. The need to identify biomark...

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 options for resectable non-small cell lung cancer ...

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

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despite combined treatments, including surgical removal...

Early Immunological Biomarkers for Personalized Treatment Selection in Severe COVID-19: Post Hoc Machine Learning Analysis of a Randomized Clinical Trial.

BACKGROUND: Severe COVID-19 is a global health concern despite continuous vaccination campaigns because current therapies, such as dexamethasone and r...

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-related death among women worldwide. The therapeutic li...

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 key regulator in regulating inflammatory responses...

Prospective germline exome and machine learning-based risk score identify predictive and prognostic biomarkers of immunotherapy outcomes in advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, a su...

Associations of serum per- and polyfluoroalkyl substances (PFAS) mixture with the risk of chronic diseases: evidence from single‑ , multi-pollutant, and machine learning models.

Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals commonly found in consumer products. Despite growing concern over their health eff...

Nanomedicine in precision treatment of gastric cancer: current clinical landscape.

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with therapeutic efficacy often hindered by late-stage diagnosis, c...

Spatial omics of immunity: Mapping cellular landscapes in tissue microenvironments.

The tumor microenvironment (TME) is composed of diverse heterogeneous components and plays a crucial role in immune cell infiltration, immune evasion,...

Landscape of T-cell bispecific antibodies in cancer therapy: therapeutic strategies, challenges and future prospection.

T-cell bispecific antibodies (TCBs), a specialized subclass of bispecific antibodies (BsAbs), are engineered to simultaneously engage T cells and tumo...

Ferroptosis in cancer: integrating multi-omics, AI-driven biomarkers, and translational challenges.

Ferroptosis, an iron-dependent regulated cell death driven by lipid peroxidation, has emerged as a potential target in cancers resistant to apoptosis ...

Supervised deep learning with gene functional annotation for cell classification.

Gene-by-gene differential expression analysis is a widely used supervised approach for interpreting single-cell RNA-sequencing (scRNA-seq) data. Howev...