Evaluating Generalizability of Oncology Trial Results to Real-World Patients Using Machine Learning-Based Trial Emulations

Information Science, Artificial Intelligence, Medicine, Oncology,
oncology   randomized controlled trials   machine learning   generalizability   real-world data   prognostic models   survival analysis  

Evaluation of the Generalizability of Oncology Trial Results Using Machine Learning-Based Trial Emulations Academic Background Randomized Controlled Trials (RCTs) are the gold standard for evaluating the efficacy of anti-cancer drugs, but their results often cannot be directly generalized to real-world oncology patients. RCTs typically employ stric...

A Two-Phase Epigenome-Wide Four-Way Gene–Smoking Interaction Study of Overall Survival for Early-Stage Non-Small Cell Lung Cancer

Medicine, Respiratory System, Oncology,
epigenomics   four-way interaction   gene–smoking interaction   non-small cell lung cancer   survival analysis   DNA methylation   molecular mechanisms  

Study on the Four-Way Gene-Smoking Interaction and Survival in Early-Stage Non-Small Cell Lung Cancer Research Background Lung cancer is one of the most prevalent malignancies worldwide and a leading cause of cancer-related mortality. According to global cancer statistics, approximately 2.5 million new cases are diagnosed annually, with 1.8 million...

Analysis of HER2 Expression Changes from Breast Primary to Brain Metastases and the Impact of HER2-Low Expression on Overall Survival

Medicine, Neurosurgery, Oncology,
breast cancer   brain metastases   HER2 expression   survival analysis   noninvasive diagnostics  

HER2 Expression Dynamics in Breast Cancer Brain Metastases and Its Impact on Survival Background Breast cancer is one of the most common cancers among women worldwide, and brain metastases (BrMs) pose a significant challenge for patients. In recent years, with the prolonged survival of breast cancer patients, the incidence of brain metastases has g...

Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Oncology,
pediatric low-grade gliomas   deep learning   postoperative recurrence prediction   multimodal model   magnetic resonance imaging   risk stratification   survival analysis  

Application of Deep Learning in Postoperative Recurrence Prediction for Pediatric Low-Grade Gliomas Background Pediatric Low-Grade Gliomas (PLGGs) are one of the most common types of brain tumors in children, accounting for 30%-50% of all central nervous system tumors in children. Although the prognosis of PLGGs is relatively favorable, the risk of...