Other Cancers

Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification.

OBJECTIVES: Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors w...

T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.

BACKGROUND: T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their vari...

Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases.

The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify...

NA_mCNN: Classification of Sodium Transporters in Membrane Proteins by Integrating Multi-Window Deep Learning and ProtTrans for Their Therapeutic Potential.

Sodium transporters maintain cellular homeostasis by transporting ions, minerals, and nutrients acro...

Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.

BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortali...

Federated learning with integrated attention multiscale model for brain tumor segmentation.

Brain tumors are an extremely deadly condition and the growth of abnormal cells that have formed ins...

Complex-valued neural networks to speed-up MR thermometry during hyperthermia using Fourier PD and PDUNet.

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treat...

Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients.

Muscle-Invasive Bladder Cancer (MIBC) is a more aggressive disease than non-muscle-invasive bladder ...

Correction to "Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis".

Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA neutrophil prognostic model.

BACKGROUND: Neutrophils play a key role in the tumor microenvironment (TME); however, their function...

Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.

BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcription...

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the dige...

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provid...

Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model.

PURPOSE: This study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative ca...

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care.

PURPOSE: The use of real-world data (RWD) in oncology is becoming increasingly important for clinica...

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the cen...

LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer.

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer...

Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.

BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for t...

Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and experiments.

INTRODUCTION: Early diagnosis of Ewing sarcoma (ES) is critical for improving patient prognosis. How...

Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research.

In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambie...

An extension to the OVH concept for knowledge-based dose volume histogram prediction in lung tumor volumetric-modulated arc therapy.

PURPOSE: Volumetric-modulated arc therapy (VMAT) treatment planning allows a compromise between a su...