Other Cancers

OncoEducate: A Pilot Study of Generative AI to Enhance Patient-Clinician Communication in Genitourinary Cancer Care.

OncoEducate is a clinician-supervised generative artificial intelligence (AI) application designed to deliver standardized, regimen-level, plain-language education at the point of care. We developed a templated handout generator using a locked prompt and predefined content structure that incorporates diagnosis, treatment regimen, and treatment intent, with clinician review prior to patient distrib...

Predicting hepatocellular carcinoma in people with hepatitis B: a comparison between Cox proportional hazard and machine learning models.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with chronic hepatitis B virus (HBV) infection being a major risk factor. To date, existing predictive scores of HCC are mainly based on traditional Cox proportional hazard (CPH) models. This study aimed to compare the variable selection process and performance of CPH models with those of machi...

Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review.

Metabolic dysfunction-associated steatotic liver disease (MASLD) poses significant diagnostic challenges. We conducted a systematic review of artifici...

Dual-model deep learning for Alzheimer's prognostication.

Disease-modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal clinical obser...

Machine learning-based radiomics analysis of PET imaging for early prediction of concurrent chemoradiotherapy response in stage II-III cervical squamous cell carcinoma.

BACKGROUND: Cervical squamous cell carcinoma is a major global health burden, with many patients presenting with locally advanced disease requiring co...

PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.

PURPOSE: Preoperative identification of lymph node metastasis (LNM) in cervical cancer is crucial for guiding therapeutic strategies but remains clini...

Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.

The tumor microenvironment (TME), composed of tumor cells together with stromal cells, immune cells, vascular networks, and other components, constitu...

Improved tumor-only variant calling and mutation burden estimation with VarNet-T.

Somatic variant calling algorithms typically detect mutations in cancer genomes by comparing sequence data from a tumor sample against a matched norma...

Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.

Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive ...

Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...

Targeting cholesterol metabolism: a core regulator of tumor-associated macrophage plasticity and immunotherapy response.

Tumor-associated macrophages (TAMs) are one of the main immunosuppressive components in the tumor microenvironment (TME). Growing evidence indicates t...

Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.

Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and t...

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions.

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chr...

Digital Enabled Diagnosis System for Mammogram Images Classification Using Machine Learning.

Breast tumor is the most commonly detected tumors and remain one of the leading causes of cancer-related mortality among women worldwide. Although mam...

Computerized diagnosis of brain tumor using graph based CNN classification.

Magnetic resonance imaging (MRI) is essential in the accurate diagnosis of brain tumors so that sound treatment planning can be done, however, clinica...

Machine learning-based clustering differentiates bilateral hepatocellular carcinoma characteristics and prognosis.

BACKGROUND: Bilateral hepatocellular carcinoma represents a biologically heterogeneous disease with uncertain optimal surgical selection criteria. Alt...

Multiparametric MRI-Based Integrated Analysis of Clinical, Radiomics, Deep Learning, and Machine Learning for Predicting Tumor Proliferation and Prognosis in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a predictive model integrating clinical, radiomics, deep learning (DL), and machine...

Network toxicology and single-cell transcriptomics nominate candidate pyrethroid-associated targets and pathways in clear cell renal cell carcinoma.

Pyrethroid insecticides are widely used in agricultural and domestic settings. Increasing evidence suggests that pyrethroid exposure may harm multiple...

Uncertainty-Aware Multi-Class Brain Tumor Segmentation Using Bayesian U-Net Variants.

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and surgical guidance. Altho...

Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort.

OBJECTIVE: This study explored the relationship between clinical phenotypes and immuno-molecular features of systemic lupus erythematosus (SLE) using ...