Publications by authors named "Thomas G Charlton"
Article Synopsis
- Recurrence of lung cancer after radiotherapy occurs in up to 36% of patients, highlighting the need for better prediction of who is at higher risk.
- Researchers developed radiomic classification models using CT scans from over 900 patients with NSCLC to predict overall survival (OS), recurrence-free survival (RFS), and recurrence rates two years post-treatment.
- The models showed promising results in predicting outcomes and could be used to create personalized surveillance strategies, potentially leading to improved patient care in future clinical trials.
Background: Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions.
View Article and Find Full Text PDFObjective: The aim of this study was to assess predicted Down syndrome risk, based on three serum analytes (triple test), with HIV infection status and antiretroviral therapy regimen.
Methods: Screening results in 72 HIV-positive women were compared with results from age-matched and race-matched HIV-negative controls. Mean concentrations of each analyte were compared by serostatus and antiretroviral therapy.