Other Cancers

Identification of PANoptosis-Related Biomarkers in Head and Neck Squamous Cell Carcinoma.

BACKGROUND: The early detection and diagnosis of head and neck squamous cell carcinoma (HNSCC) pose significant challenges. OBJECTIVES: This study explores the diagnostic and prognostic role of PANoptosis-related genes (PANRGs) in HNSCC. METHODS: In this study, machine learning was used to identify PANoptosis-related biomarkers in HNSCC, and a corresponding prognostic risk model was constructed. R...

Toward clinical value, not just accuracy: deep learning segmentation of soft tissue sarcoma.

Artificial intelligence enhances cancer cell selection for HER2 gene amplification assessment in breast carcinoma.

The evaluation of HER2 gene amplification is a time-consuming process that requires quantifying a large number of signals in cancer cells to ensure re...

Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, Channel Attention, and ASPP.

Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study,...

Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment ...

Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.

Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has...

Development and Validation of an Interpretable Machine Learning Model for Predicting Distant Metastasis in Tongue Squamous Cell Carcinoma: A Multicentre Study.

INTRODUCTION AND AIMS: Distant metastasis (DM) in tongue squamous cell carcinoma is associated with poor prognosis. However, reliable tools for immedi...

DNA methylation-based classification of hematolymphoid neoplasms.

Accurate pathological diagnosis of hematolymphoid neoplasms (HLN) is often challenging due to their complexity and heterogeneity. Genome-wide DNA meth...

Machine learning identifies prognosticators of intracranial metastatic disease in patients with breast or lung cancer.

BACKGROUND: Intracranial metastatic disease is a severe complication of cancer that confers substantial morbidity and mortality. Patients with breast ...

Using tree-based ensemble methods to produce a population-based mortality risk score in Ontario, Canada.

INTRODUCTION: Risk adjustment is critical in observational epidemiology to control for confounding of the exposure-outcome relationship. Accurate pred...

Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals.

Accurate sleep stage classification in animal models is crucial for translational sleep research, enabling the study of mechanistic pathways and thera...

Integration of AHRR methylation, heavy metals, and clinical characteristics for urothelial carcinoma risk stratification: An explainable artificial intelligence approach.

The short metabolic half-life of conventional tobacco biomarkers often limits their ability to reflect cumulative toxicological damage, which may comp...

Robust validation of neuroimaging and clinical models via the SAR method: A case study based on the ADNI dataset.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and substantial brain atrophy. Early and accur...

Precision nutrition in breast cancer: Towards patient- and tumour-informed dietary strategies.

Dietary strategies are increasingly recognized as important modulators of breast cancer outcomes, acting through effects on metabolic regulation, weig...

Predicting amyotrophic lateral sclerosis stage based on multi-parameter ultrasound: development and validation of an interpretable machine learning model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks sensitive, objective staging tools to guide clinical management and trials. Existing methods hav...

Protocol for an interpretable self-attention artificial neural network framework to predict cancer risk in oral potentially malignant disorders.

Oral potentially malignant disorders (OPMDs) can directly progress to cancer, necessitating accurate risk prediction to guide clinical intervention. H...

Identification and molecular typing of disulfidptosis-related biomarkers in anaplastic thyroid carcinoma.

Increasing evidence suggests that disulfidptosis plays a crucial role in tumorigenesis and progression. This study aimed to identify biomarkers closel...

A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.

Certain characteristics, such as high heterogeneity, a complex tumor microenvironment, metastatic potential, and drug resistance, render Lung cancer (...

The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.

Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marke...

ThyroFusion: A Multi-modal Deep Learning Framework Integrating Vision and Language for Thyroid Nodule Malignancy Risk Assessment.

Accurate differentiation between benign and malignant thyroid nodules remains challenging in clinical practice. Current deep learning approaches predo...