Steve B. Jiang, 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...

Treating multiple oligometastatic lesions typically requires creating separate SBRT plans, resulting in longer treatment time and an increase in planning complexity. In this work we evaluate biology-guided multi-target treatment (MTT) approach for a PET-linea...

The increasing adoption of MRI-guided online adaptive radiotherapy (oART) has improved treatment personalization but introduced substantial technical complexity and time pressure. Existing QA approaches remain largely manual and retrospective, with limited ab...

To clinically validate an implicit neural representation (INR)-based fitting approach for intravoxel incoherent motion (IVIM) diffusion MRI parameter estimation against conventional non-linear least squares (NLLS), and to evaluate a robustness metric (R-index...

Positron Emission Tomography/Computed Tomography (PET/CT) plays a pivotal role in radiation therapy, particularly in tumor delineation, response assessment, and adaptive therapy planning. However, the reliability of PET/CT depends on key system performance an...

Simulation-omitted prostate SBRT (SO-PRO) has potential to reduce treatment delays and expand access to care but presents unique challenges for SBRT workflows that require careful management of target and organ-at-risk (OAR) dose. A major barrier is diagnosti...

To develop an AI agent framework leveraging large language models (LLMs) for intelligent data extraction and reasoning over radiation oncology data from oncology information systems (OIS) and electronic medical records (EMR), enabling patient-specific queries...

One major bottleneck in MRI-guided online adaptive radiotherapy is the prolonged treatment process, particularly extended delivery times, partly due to the relatively low dose rate and high prescription doses. Efficient radiation delivery is essential for mai...

Online adaptive prostate MR-guided radiotherapy (MRgRT) is time-sensitive, and contouring with structure preparation can require upwards of 15 minutes per fraction. While vendor-TPS provided contours can be useful, performance and consistency vary by site, pr...

Magnetic resonance imaging-guided online adaptive radiotherapy (MRgRT) requires a complex planning process, during which an upstream peer review prior to physician approval plays an essential role in early error detection and supporting safe and efficient onl...

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...

MRI–only radiotherapy planning requires accurate synthetic CT (sCT) generation to enable dose calculation and patient positioning without a planning CT in Linac-based treatment delivery settings. While prior studies have demonstrated promising results for ind...

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...

Predicting tumor radiosensitivity remains a major challenge in precision radiotherapy due to incomplete concordance between transcriptomic alterations and functional protein expression. This study aims to develop an integrated transcriptomic–proteomic framewo...

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...

Monitoring patient-reported outcomes (PROs) for radiation-induced toxicities is critical for providing clinical feedback undergoing radiotherapy (RT). While RT is a highly effective cancer treatment, there are still a minority of patients reporting severe sym...

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...