Other Cancers

Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associated macrophages contribute to disease progression. This study aimed to identify macrophage-associated biomarkers with diagnostic, prognostic, and translational relevance in HCC. METHODS: Single-cell RNA-sequencing datasets were integrated with bulk tra...

Integrative single-cell sequencing and machine learning analyses identify chronic inflammation and cancer-associated fibroblast subsets as drivers of adverse outcomes in ovarian cancer.

BACKGROUND: Ovarian cancer remains the deadliest gynecological malignancy, with neoadjuvant chemotherapy (NACT) often leaving residual fibroblast-enriched disease. METHODS: Single-cell RNA sequencing data from 64,097 cells were analyzed using Seurat (v4.1.3) with Harmony batch correction. CellChat (v1.1.3) mapped intercellular communication, while Monocle3 constructed pseudotime trajectories. High...

Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.

OBJECTIVES: Preoperative prediction of perineural invasion (PNI) in rectal cancer (RC) is challenging due to limited MRI resolution and the neglect of...

FusionNetX: A Deep Feature Fusion Model Leveraging MRI and Deep Learning for Enhanced Brain Tumor Detection.

Brain tumors are considered one of the deadliest diseases in the world, and misdiagnosing them puts patients' lives in danger and lowers the survival ...

A ciliated intravascular soft millirobot for multimodal in-flow collective manipulation.

The laminar nature of blood flow and the resulting boundary layer pose substantial challenges for transverse mass transport in blood vessels, limiting...

Machine Learning in Palliative Care: Scoping Review of Applications.

BACKGROUND: Palliative care is increasingly recognized as essential for an aging population and rising life-limiting illnesses. Machine learning (ML) ...

Comparative evaluation of feature-selection strategies for machine learning-based histological grading of breast cancer using tumor and fibroglandular-tissue DCE-MRI features.

BACKGROUND: Nottingham histological grading is central to breast cancer prognosis and treatment planning, but conventional pathological assessment is ...

A deep learning-derived risk score model from digital pathology to forecast nivolumab outcomes in gastric carcinoma.

BACKGROUND: The PD-L1 combined positive score (CPS) is a biomarker predicting responses in gastric cancer (GC) immunotherapy. OBJECTIVES: We aimed to ...

Integrated Transcriptomic Analysis and Machine Learning Identify THY1 as a Key Regulator of Cancer-Associated Fibroblast Infiltration, Promoting Malignant Progression and Immune Escape in Gastric Cancer.

BACKGROUND: Heterogeneity in cancer-associated fibroblast (CAF) infiltration within the tumor microenvironment is closely associated with gastric canc...

Composite dietary antioxidant index with hepatic indices for predicting mortality in cardiovascular-kidney-metabolic syndrome: Integrated analysis of a nationwide cohort study using machine learning approaches.

Oxidative stress is a pathological driver of cardiovascular-kidney-metabolic (CKM) syndrome, exacerbating inflammation and tissue injury, while metabo...

Associations between sex steroid hormones and female malignancies: A cross-sectional analysis of NHANES data.

Sex steroid hormones, including estradiol (E2), estrone (E1), follicle-stimulating hormone (FSH), and luteinizing hormone (LH), have been implicated i...

Integrative systems biology identifies PSMB2 as a core oxidative stress-associated target in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) represents one of the most aggressive and therapeutically challenging subtypes of breast cancer, characterized by...

RNA splicing in health and disease.

RNA splicing expands the functional output of eukaryotic genomes by enabling individual precursor messenger RNA (pre-mRNA) to generate multiple mature...

Prognostic value of automated CT body composition in hepatocellular carcinoma: a volumetric versus single-slice comparison.

BACKGROUNDS/AIMS: To compare the prognostic value of fully automated volumetric versus single-slice computed tomography (CT)-derived body composition ...

Single-photon emission computed tomography/computed tomography-based radiomics of bone metastases for predicting epidermal growth factor receptor mutation status and targeted therapy efficacy in non-small-cell lung cancer.

BACKGROUND: This study aimed to develop a single-photon emission computed tomography/computed tomography (SPECT/CT)-based radiomics model using bone m...

Dissecting multimodal predictors of psychosis risk and remission in youth at ultra‑high risk of developing psychosis: An exploratory study.

Early identification of individuals at risk of developing psychosis enables timely intervention and better clinical outcomes. Current approach relies ...

Equity in Neurotoxicity Care in the Era of Cancer Immunotherapy.

Immunotherapies such as chimeric antigen receptor T cell (CAR-T) therapy and immune checkpoint inhibitors have improved cancer survival but are associ...

A Multiparametric MRI‑Based Deep Learning Model for Binary Triage of Lung Versus Non‑lung Brain Metastases: A Multicenter Proof‑of‑Concept Study.

Identifying the origin of brain metastases (BM) is essential for personalized treatment, particularly in patients with carcinoma of unknown primary or...

Large Language Models for Oncology Guideline Maintenance: Prospective Case Study.

BACKGROUND: Maintenance of oncology clinical practice guidelines (CPGs) is increasingly challenged by the rapid growth of trial data and therapeutic c...

Breast tumor detection in ultrasound images using adaptive hierarchical pruning of non-tumor regions.

BACKGROUND: The development of deep learning techniques has greatly improved tumor detection and analysis in breast ultrasound images. Despite their i...