Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Physics > Medical Physics

arXiv:2405.03916 (physics)
[Submitted on 7 May 2024]

Title:Robust Optimization for Spot Scanning Proton Therapy based on Dose-Linear Energy Transfer (LET) Volume Constraints

Authors:Jingyuan Chen, Yunze Yang, Hongying Feng, Lian Zhang, Carlos E. Vargas, Nathan Y. Yu, Jean-Claude M. Rwigema, Sameer R. Keole, Sujay A. Vora, Jiajian Shen, Wei Liu
View a PDF of the paper titled Robust Optimization for Spot Scanning Proton Therapy based on Dose-Linear Energy Transfer (LET) Volume Constraints, by Jingyuan Chen and 10 other authors
View PDF
Abstract:Purpose: Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events (AEs).
Methods: DLVCRO treats DLVC as soft constraints controlling the joint distribution of dose and LET. Ten prostate cancer patients were included with rectum and bladder as OARs. DLVCRO was compared with the conventional robust optimization (RO) method using the worst-case analysis method. Besides the dose-volume histogram (DVH) indices, the analogous LETVH and extra-biological-dose (xBD)-volume histogram indices were also used. The Wilcoxon signed rank test was used to measure statistical significance.
Results: In nominal scenario, DLVCRO significantly improved dose, LET and xBD distributions to protect OARs (rectum: V70Gy: 3.07\% vs. 2.90\%, p = .0063, RO vs. DLVCRO; $\text{LET}_{\max}$ (keV/um): 11.53 vs. 9.44, p = .0101; $\text{xBD}_{\max}$ (Gy$\cdot$keV/um): 420.55 vs. 398.79, p = .0086; bladder: V65Gy: 4.82\% vs. 4.61\%, p = .0032; $\text{LET}_{\max}$ 8.97 vs. 7.51, p = .0047; $\text{xBD}_{\max}$ 490.11 vs. 476.71, p = .0641). The physical dose distributions in targets are comparable (D2%: 98.57\% vs. 98.39\%; p = .0805; CTV D2% - D98%: 7.10\% vs. 7.75\%, p = .4624). In the worst-case scenario, DLVCRO robustly enhanced OAR while maintaining the similar plan robustness in target dose coverage and homogeneity.
Conclusion: DLVCRO upgrades 2D DVH-based to 3D DLVH-based treatment planning to adjust dose/LET distributions simultaneously and robustly. DLVCRO is potentially a powerful tool to improve patient outcomes in SSPT.
Subjects: Medical Physics (physics.med-ph)
Cite as: arXiv:2405.03916 [physics.med-ph]
  (or arXiv:2405.03916v1 [physics.med-ph] for this version)
  https://doi.org/10.48550/arXiv.2405.03916
arXiv-issued DOI via DataCite

Submission history

From: Jingyuan Chen Ph.D. [view email]
[v1] Tue, 7 May 2024 00:24:54 UTC (1,041 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Robust Optimization for Spot Scanning Proton Therapy based on Dose-Linear Energy Transfer (LET) Volume Constraints, by Jingyuan Chen and 10 other authors
  • View PDF
license icon view license

Current browse context:

physics.med-ph
< prev   |   next >
new | recent | 2024-05
Change to browse by:
physics

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences