Dan Nguyen, PhD

Automating post-operative primary clinical target volume (CTV) segmentation in head and neck (H&N) cancers is challenging due to the surgical absence of the primary tumor and anatomical heterogeneity. Without the distinct radiographic boundaries of a gross tu...

Therapy Physics

Cancer treatment planning requires clinicians to rapidly synthesize complex clinical information from detailed patient notes, a process that is time-consuming and cognitively demanding, particularly in multidisciplinary workflows involving non-physician clini...

This work develops an agentic AI framework that bridges the gap between state-of-the-art tumor segmentation models and clinical deployment, where model discovery, data preprocessing, and output QA remain time-intensive and require computational expertise. Thi...

Timely dose verification is essential for quality assurance (QA) in modern radiotherapy (RT), particularly in online adaptive RT, where measurement-based QA is often impractical. Current approaches are limited by machine/energy-specific designs, hindering sca...

Radiation therapy(RT) planning remains a manual, trial-and-error process that consumes significant clinical time and yields inconsistent results. We present a compound AI platform integrated with a commercial treatment-planning-system(TPS), combining autonomo...

While deep learning autosegmentation models are widely integrated into clinical workflows in radiation oncology, a critical gap has emerged: the "static deployment" trap. Once deployed, model performance can deteriorate due to real-world data evolution, makin...

Existing adaptive radiotherapy (ART) only accounts for inter-fraction variations in anatomy. Adapted plans can become suboptimal immediately due to anatomical changes during online planning and treatment delivery, degrading treatment quality and efficacy. To...

Deep learning (DL)-based dose prediction has become an important component of modern radiotherapy treatment planning. However, most existing approaches depend on site-specific models, necessitating separate training for each anatomical site, which limits scal...

Treatment planning for MR-guided adaptive radiotherapy (MRgART) requires extensive time and effort in both preplanning and online adaptation processes. It is a major bottleneck hindering the efficiency and quality of MRgART. Specifically, extended preplanning...

AI-based autosegmentation is widely used in radiation oncology to improve efficiency and consistency; however, these models may silently fail when applied to cases that deviate from their training distribution, placing responsibility on clinicians to detect u...