University of Texas

Lung cancer is a leading global malignancy with high mortality. Radiotherapy is a critical treatment; however, current planning often suffers from subjective dose settings and side effects. This study aims to use a Conditional Generative Adversarial Network (...

Multiscale treatment response prediction in advanced NSCLC enables spatially informed dose painting, yet prediction point estimates alone do not convey the uncertainty required for adaptive therapy decision support. We developed a multiscale conformal predict...

Accurate identification of high-risk and low-risk tumor subregions enables radiographers to customize radiation dose distributions for biologically adaptive therapies. This study proposes a 3DUNET-GMM model that integrates 3D-UNet feature extracting with Gaus...

Accurate prediction of tumor response during chemoradiotherapy is essential for treatment optimization but remains challenging. We developed a deep learning model based on a Dual Path Network (DPN), which is a hybrid architecture combining elements of ResNet...