Other Cancers

Machine Learning-Driven SERS Analysis Platform for Accurate and Rapid Diagnosis of Peritoneal Metastasis from Gastric Cancer.

BACKGROUND: Peritoneal metastasis (PM) is the most common form of distant metastasis in gastric canc...

Comprehensive Characterization of the Immune Microenvironment Based on Nested Resampling Machine Learning Framework Identifies TRAF3 Interacting Protein 3 as a Promising Regulator to Improve the Resistance to Immunotherapy in Glioma.

Diffuse glioma, the most prevalent and malignant intracranial tumor, presents a formidable challenge...

Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study.

BACKGROUND: Neoplasms are a major cause of mortality globally, where early diagnosis is essential fo...

Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort.

BACKGROUND: The variability in treatment response in people with rheumatoid arthritis (RA) warrants ...

Comprehensive Insights into APOBEC Mutations in Thyroid Cancer: Prognostic and Therapeutic Discoveries.

BACKGROUND: The APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) family pla...

Artificial intelligence for multi-time-point arterial phase contrast-enhanced MRI profiling to predict prognosis after transarterial chemoembolization in hepatocellular carcinoma.

PURPOSE: Contrast-enhanced magnetic resonance imaging (CE-MRI) monitoring across multiple time point...

Detection of breast cancer using machine learning and explainable artificial intelligence.

Breast cancer is characterized by the proliferation of abnormal breast cells that eventually turn in...

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy ...

Vox-MMSD: Voxel-wise Multi-scale and Multi-modal Self-Distillation for Self-supervised Brain Tumor Segmentation.

Many deep learning methods have been proposed for brain tumor segmentation from multi-modal Magnetic...

Minimal Ablative Margin Quantification Using Hepatic Arterial Versus Portal Venous Phase CT for Colorectal Metastases Segmentation: A Dual-center, Retrospective Analysis.

OBJECTIVE: To compare the predictive value of minimal ablative margin (MAM) quantification using tum...

MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging.

BACKGROUND: Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bon...

Malignancy classification of thyroid incidentalomas using 18F-fluorodeoxy-d-glucose PET/computed tomography-derived radiomics.

BACKGROUND: Thyroid incidentalomas (TIs) are incidental thyroid lesions detected on fluorodeoxy-d-gl...

Biological tumor volume predicts survival in recurrent High-Grade glioma: A multiparametric [F]FET PET/MRI study.

BACKGROUND AND PURPOSE: Single-session, multiparametric [¹⁸F]FET PET/MRI is used to detect tumor rec...

Dynamic Early Survival Prediction Model for Hepatocellular Carcinoma Patients Treated With Atezolizumab and Bevacizumab: A Longitudinal Deep Learning Analysis.

BACKGROUND: Atezolizumab combined with bevacizumab has become the standard first-line systemic thera...

CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study.

BACKGROUND: Noninvasive and precise tools for treatment response estimation in patients with high-ri...

T cell engagers: expanding horizons in oncology and beyond.

BACKGROUND/INTRODUCTION: T cell engagers (TCEs) are engineered immunotherapeutic molecules designed ...

Joint Fine-Grained Representation Learning and Masked Relational Modeling for EEG-Based Automatic Sleep Staging in Fabric Space.

Sleep staging is a crucial method for the evaluation of sleep quality and the diagnosis of sleep dis...

Letter to the editor: commentary on a multimodal artificial intelligence model for early detection of hepatocellular carcinoma.

Comment on "Artificial Intelligence-based multi-modal prediction for nuclear grading status and prognosis of clear cell renal cell carcinoma: a multicenter cohort study".

Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images.

Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefo...

Proteomic technologies for profiling cell-membrane/secretome interactions in brain metastatic cancer progression.

INTRODUCTION: The ability of cancer cells to disseminate from the primary tumor and form metastatic ...