Jiahan Zhang, PhD

Treatment planning for pancreatic SBRT remains challenging due to proximity and overlap of the target with the critical organs-at-risk (OARs). This study uses RapidPlan-predicted dose volume histograms (DVHs) to guide treatment planning in Monaco.

A persistent challenge in medical physics education is the lack of centralized, interactive, and easily accessible training tools that integrate into daily clinical workflows. Many existing resources are static, fragmented, and difficult to use consistently a...

To systematically assess whether commonly proposed architectural enhancements provide measurable benefits for deep learning-based radiotherapy dose prediction, using controlled comparisons of 3D U-Net variants to support evidence-based model selection and est...

Objective assessment of radiotherapy plans is challenging because expert assessment relies on complex, multidimensional tradeoffs that are not fully captured by predefined dose-volume constraints. This study aims to quantitatively interpret expert treatment p...

To implement a full abdominal motion model that combines respiration with gastrointestinal (GI) motility and quantify its interplay impact in pencil-beam scanning (PBS) proton therapy.

Commercial treatment planning systems (TPS), such as Varian Eclipse, cannot perform dose calculations on CBCTs acquired with 6DoF couch corrections. This limitation prevents direct dosimetric re-evaluation of verification scans. This study presents an offline...

Accurate prediction of radiation-induced toxicity is crucial for optimizing radiotherapy outcomes, yet most existing models rely on supervised learning with clinician-graded toxicity scores that are susceptible to patient self-reporting errors and intra-obser...

Accurate real-time tumor tracking is critical for MRI-guided radiotherapy, where geometric uncertainty can significantly increase dose to surrounding critical organs. Continuous cine-MRI enables motion-adaptive treatment. However, accurate tracking under larg...

Consistently automating clinically acceptable plans without human intervention remains a challenge in radiotherapy. While knowledge-based planning (KBP) predicts optimal achievable dose-volume metrics, it often fails to achieve these metrics without manual ad...

Knowledge-based planning (KBP) improves plan quality and efficiency. However, training institution-specific models requires substantial clinical data and expertise, and publicly available models may not align with local clinical objectives. This study evaluat...

To quantify the dosimetric consequences of physiology-composed abdominal motion on pancreatic cancer SBRT.

Existing deep learning-based dose prediction methods primarily learn empirical mappings between anatomy and dose, without modeling beam delivery physics. This gap may limit their robustness and accuracy, especially in heterogeneous regions where dose depositi...

To quantify the impact of gastrointestinal (GI) motility on pencil-beam scanning (PBS) proton therapy for abdominal cancers, and assess how fractionation and motion amplitude mitigate motility-induced interplay effects.