Nishan Shrestha, PhD

CyberKnife stereotactic treatments are delivered using a step-and-shoot technique, with patient-specific QA verification plans for diode array measurements typically generated and evaluated in a perpendicular composite geometry. This study investigates whethe...

To evaluate the feasibility of treating a large superficial skin (scalp) cancer target with a 3-field static IMPT technique and to assess the potential impact of elevated LET in high-dose regions.

We share our institutional experience upgrading Varian ARIA to v18.1/18. 2 in a multi-vendor radiation therapy environment, highlighting practical challenges, mitigations, and actionable lessons for centers planning similar upgrades.

Couch shift accuracy and image-registration performance are central to MR-guided radiotherapy, yet QA is often interpreted with fixed tolerances alone. This study applied statistical process control (SPC) to derive machine-specific 2σ (warning) and 3σ (action...

To assess the feasibility of prostate stereotactic body radiation therapy (SBRT) in patients with unilateral and bilateral hip prostheses across multiple treatment platforms, focusing on image quality, planning feasibility, delivery characteristics, motion ma...

To develop and evaluate an in-house .NET/C# Eclipse Scripting API (ESAPI) solution with a dynamic universal graphical user interface (UGUI) to automate optimization-structure creation across disease sites and reduce treatment planning time.

Deep-learning (DL) auto-segmentation may improve efficiency in head-and-neck (HN) planning, but clinical use depends on how consistently experts judge contour acceptability and how performance varies by organ. We evaluated a DL-based auto-segmentation system...

Daily QA tolerances are often implemented as fixed pass/fail thresholds, yet MR-Linac performance can be facility-specific and may evolve over time. This study used statistical process control (SPC) to establish global, machine-specific 2σ warning and 3σ acti...

To compare head-and-neck (HN) IMPT robustness using a conventional systematic setup uncertainty model versus a probabilistic random (interfractional) setup uncertainty model.