Latest Articles for Cancer Treatment

A look at the TURBO project improving proton therapy efficiency.

Research reveals potential of TLS in improving glioma treatment outcomes.

Study compares models for predicting positron production in cancer treatment.

Study reveals insights into bone metastasis in cancer patients.

Machine learning offers better predictions for bladder cancer recurrence, enhancing patient care.

Examining beta-blockers and salbutamol's effects on prostate cancer risk and survival.

Nivolumab shows promise in treatment, but cost-effectiveness raises concerns.

Investigating the roles of TAK1 and PLCE1 in esophageal squamous carcinoma.

Using machine learning to customize treatments for Acute Myeloid Leukemia patients.

Study finds ESPB offers better pain relief than QLB post-surgery.

Hydroporation enhances CAR-T cell therapy by improving T-cell viability and targeting effectiveness.

Dynamic scheduling improves tumor targeting during robotic radiation therapy.

A study on the explainability of machine learning models for pancreatic cancer care.

PIM kinases are vital for effective CD8 T cell responses in immunity and cancer.

AI helps create new antibodies targeting PD-1 for better cancer therapies.

Efforts to improve patient access to Precision Oncology therapies in rural areas.

Researchers investigate new treatments for cancer cells resistant to cisplatin.

Study shows how advanced care planning aids end-of-life experience for AML patients.

Research shows gold nanoparticles improve radiation therapy effectiveness by influencing ionization clusters.

Study reveals lipid changes linked to painful chemotherapy side effects.

UnifyImmun predicts antigen binding, enhancing cancer immunotherapy effectiveness.

Enhancing communication between cancer patients and healthcare providers using AI.

UPINN enhances our ability to model and predict chemotherapy drug effects.

Key elements affecting cancer return rates post-surgery are highlighted.

AI technologies are improving thyroid cancer detection and treatment accuracy.

RSTAR-Net reduces streak artifacts in lung cancer images, enhancing treatment precision.

Investigating how radiation dose rates affect hydroxyl radical formation.

DEMO design aims to enhance dose finding in oncology trials using biological markers.

Experts establish clearer criteria for diagnosing and managing ORNJ.

Investigating how cancer cells alter energy use to survive and grow.

A novel Bayesian approach improves sample size estimation in clinical trials.

New insights into how immune therapy improves lung cancer outcomes.

MMIST-ccRCC provides comprehensive data for personalized cancer care.

Bayesian Federated Inference allows privacy-preserving survival analysis in cancer research.

Using hydrogels for precise drug delivery improves cancer treatment outcomes.

A study explores how AI can better predict outcomes for glioblastoma patients.

Exploring the significance of proton interactions with cortical bone in cancer therapy.

AI method enhances radiotherapy dose predictions by estimating uncertainty.

A challenge aims to enhance tumor outlining on brain scans for better treatment planning.

The BraTS Challenge pushes boundaries in automated meningioma segmentation using deep learning.