Advancing Radiotherapy Treatment Through Artificial Intelligence-Driven Approaches
type: CreativeWork
description: Radiotherapy is a critical treatment regimen for cancer patients. Modern radiotherapy aims to deliver precise radiation doses to specific tumor target volumes while minimizing exposure to surrounding healthy tissues and organs. To achieve precision and high treatment quality, accurate target delineation, streamlined workflows, and timely follow-up are necessary. However, the current clinical standard of radiotherapy has limitations that need to be addressed. These include the slow manual delineation of targets and organs-at-risk, a lack of tools for early prediction of treatment outcomes, and the potential radiation toxicity and side effects. Fortunately, ultra-high dose rates (FLASH) irradiation emerges as a promising new modality of radiotherapy that has the potential to reduce the radiation toxicity to surrounding normal tissues while maintaining tumor control. However, the clinical translation of FLASH is challenging, and precise quantification of radiobiology is urgently needed. To address these above limitations and needs, this dissertation aims to develop artificial intelligence (AI)-driven techniques to advance current radiotherapy treatment. Recent breakthroughs in AI, including mathematical algorithms and high-performance computing technologies, have led to transformative impacts in healthcare. Various studies have shown significant improvements in the quality and efficiency of image segmentation and treatment outcome analysis with AI approaches. Therefore, this dissertation specifically focuses on leveraging the power of AI to improve the target delineation, predict treatment outcomes, and assist the clinical translation of FLASH in the following three parts: (1) Streamlining and standardizing Stereotactic Radiosurgery (SRS) workflow with AI. In this part, an AI-driven auto-segmentation and labeling platform was firstly developed for SRS patients with multiple brain metastases (mBMs). This platform can automatically segment out mBMs and auto-label each se…
abstract: Radiotherapy is a critical treatment regimen for cancer patients. Modern radiotherapy aims to deliver precise radiation doses to specific tumor target volumes while minimizing exposure to surrounding healthy tissues and organs. To achieve precision and high treatment quality, accurate target delineation, streamlined workflows, and timely follow-up are necessary. However, the current clinical standard of radiotherapy has limitations that need to be addressed. These include the slow manual delineation of targets and organs-at-risk, a lack of tools for early prediction of treatment outcomes, and the potential radiation toxicity and side effects. Fortunately, ultra-high dose rates (FLASH) irradiation emerges as a promising new modality of radiotherapy that has the potential to reduce the radiation toxicity to surrounding normal tissues while maintaining tumor control. However, the clinical translation of FLASH is challenging, and precise quantification of radiobiology is urgently needed. To address these above limitations and needs, this dissertation aims to develop artificial intelligence (AI)-driven techniques to advance current radiotherapy treatment. Recent breakthroughs in AI, including mathematical algorithms and high-performance computing technologies, have led to transformative impacts in healthcare. Various studies have shown significant improvements in the quality and efficiency of image segmentation and treatment outcome analysis with AI approaches. Therefore, this dissertation specifically focuses on leveraging the power of AI to improve the target delineation, predict treatment outcomes, and assist the clinical translation of FLASH in the following three parts: (1) Streamlining and standardizing Stereotactic Radiosurgery (SRS) workflow with AI. In this part, an AI-driven auto-segmentation and labeling platform was firstly developed for SRS patients with multiple brain metastases (mBMs). This platform can automatically segment out mBMs and auto-label each se…
author: Yang, Zi
language: en
keywords: Brain Neoplasms, Outcome Assessment, Health Care, Radiosurgery, Radiotherapy, Artificial Intelligence
date published: 2025-06-03T19:48:30Z
date modified: 2026-07-24T05:52:22Z
provider: University of Texas Southwestern Medical Center
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Texas Southwestern Medical Center

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2152.5/10584