Developing automated tools in radiation therapy to streamline treatment planning and improve plan quality
Abstract/Contents
- Abstract
- By recent estimates, just under 2 million cancer cases are diagnosed in the US each year, with a majority of those patients receiving radiation therapy. For each patient, a team of personnel manually designs a treatment plan with the intent to treat diseased tissue while minimizing damage to surrounding healthy organs. Treatment planning for these patients can be both time-consuming and labor intensive, especially for emerging treatment modalities where radiation is delivered from noncoplanar directions or with nonuniform gantry arcs. Radiation therapy treatment planning is a complicated process for determining various machine parameters that allow for the delivery of a desired dose distribution. As treatment planning can involve many conflicting objectives, no single plan can optimize performance on all objectives at once. Instead, planning typically involves the navigation of numerous clinical trade-offs, where planners iteratively adjust certain hyperparameters in order to achieve an acceptable treatment plan. To streamline the treatment planning process, we have developed a fully automated framework, which requires no active planning time. This dissertation will present the components of this framework—including treatment plan utility functions, meta-optimization, content-based image retrieval, etc.—as well as the applications of automated planning to emerging treatment modalities. Were this framework to be adopted in clinical settings, we would expect substantial reductions in planning times and improvements to overall plan quality.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Huang, Charles |
|---|---|
| Degree supervisor | Xing, Lei |
| Thesis advisor | Xing, Lei |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Marsden, Alison (Alison Leslie), 1976- |
| Thesis advisor | Yang, Yong |
| Degree committee member | Altman, Russ |
| Degree committee member | Marsden, Alison (Alison Leslie), 1976- |
| Degree committee member | Yang, Yong |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Charles Huang. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/mb643zx4035 |
Access conditions
- Copyright
- © 2022 by charles huang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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