Oncology/Hematology

Machine Learning Models for Individualized Osteoradionecrosis Risk Prediction in Head and Neck Cancer.

To develop and validate predictive models for osteoradionecrosis (ORN) after head and neck radiation...

Prognostic Significance and Immune Landscape of Migrasome-Related Genes in Pancreatic Cancer.

Pancreatic cancer is characterized by early metastasis and high aggressiveness, highlighting the nee...

Doctor-patient communication in Chinese cancer care: a qualitative exploration.

Patient-centred care (PCC) receives less attention in developing countries like China. The aim of ou...

Fairness aware subset selection for advancing equity in skin cancer detection.

OBJECTIVES: Skin cancer is the most common malignancy in the United States, with more than five mill...

The prevalence and clinical significance of clonal monocytosis.

The terms clonal monocytosis of undetermined significance (CMUS) and clonal cytopenia and monocytosi...

Combined hepatocellular-cholangiocarcinoma: a contemporary pathologic and molecular perspective.

Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare primary liver carcinoma characterize...

Integrative network pharmacology and machine learning identify potential targets of indole-3-lactic acid in colorectal cancer.

The treatment of colorectal cancer (CRC) remains challenging due to chemotherapy resistance and gene...

Identification of core genes mediating the association between obesity and hepatocellular carcinoma: A bioinformatics study based on mitochondrial metabolism and immune pathways.

PURPOSE: Obesity is strongly associated with hepatocellular carcinoma (HCC), yet the molecular mecha...

Epitranscriptomic signatures of malignancy: how RNA modifications shape breast and ovarian tumor progression.

Breast and ovarian cancers are still one of the most prevalent causes of cancer death among the wome...

Profiling extracellular matrix-driven heterogeneity of single cell migration and morphology.

Cell migration and morphology are fundamental to various biological processes, including tissue deve...

Decoding the pancreatic duct: Multi-structure radiomics redefines risk stratification and early cancer detection in intraductal papillary mucinous neoplasm.

Tumor burden in metastatic colorectal cancer quantified using deep learning models: Prognostic value and maintenance treatment benefit in the CAIRO3 trial.

INTRODUCTION: Total tumor number (TTN) and total tumor volume (TTV) reflect tumor burden and have be...

Increased Prevalence of Extrathymic Neoplasms in Myasthenia Gravis Patients-A Population-Based, Matched Case-Control Study.

INTRODUCTION/AIMS: Myasthenia gravis (MG) is associated with thymic neoplasms. However, an increased...

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in M...

Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.

To address the challenges of cross-modal information fusion in high-dimensional multimodal medical d...

Adaptive diagnostic reasoning framework for pathology with multimodal large language models.

BACKGROUND: Artificial intelligence enhances pathology screening efficiency, yet clinical adoption r...

Multimodal Deep Learning-Based Screening of Degenerative Temporomandibular Joint Disease Using 2D Radiography: A Cost-Effective and Low-Radiation Approach.

OBJECTIVES: This study aimed to develop and preliminarily validate a multimodal deep learning model ...

Combining AI to reveal CCDC3-mediated pathways of colorectal cancer liver metastasis.

In colorectal cancer liver metastases, chromosomal instability (CIN) serves as a critical hallmark l...

Role of the gut microbiome in shaping drug response in immunocompromised hosts.

BACKGROUND: There is an increasing amount of evidence on microbiome-drug interactions in several cli...

Integrating single-cell atlases and machine learning to construct 'in silico patients' for predicting individualized drug responses.

Intratumoral cellular heterogeneity is a core challenge that drives drug resistance and hinders the ...

Enhancing adverse drug event extraction and summarization for cancer drugs through large language models.

PURPOSE: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively...