Currently, most intensity-modulated radiation therapy systems use dose-volume (DV)-based objectives. Although acceptable plans can be generated using these objectives, much trial and error is necessary to plan complex cases with many structures because numerous parameters need to be adjusted. An objective function that makes use of a generalized equivalent uniform dose (gEUD) was developed recently that has the advantage of involving simple formulae and fewer parameters. In addition, not only does the gEUD-based optimization provide the same coverage of the target, it provides significantly better protection of critical structures. However, gEUD-based optimization may not be superior once dose distributions and dose-volume histograms (DVHs) are used to evaluate the plan. Moreover, it is difficult to fine-tune the DVH with gEUD-based optimization. In this paper, we propose a method for combining the gEUD-based and DV-based optimization approaches to overcome these limitations. In this method, the gEUD optimization is performed initially to search for a solution that meets or exceeds most of the treatment objectives. Depending on the requirements, DV-based optimization with a gradient technique is then used to fine-tune the DVHs. The DV constraints are specified according to the gEUD plan, and the initial intensities are obtained from the gEUD plan as well. We demonstrated this technique in two clinical cases: aprostate cancer and ahead and neck cancer case. Compared with the DV-optimized plan, the gEUD plan provided better protection of critical structures and the target coverage was similar. However, homogeneities were slightly poorer. The gEUD plan was then fine-tuned with DV constraints, and the resulting plan was superior to the other plans in terms of the dose distributions. The planning time was significantly reduced as well. This technique is an effective means of optimizing individualized treatment plans.
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http://dx.doi.org/10.1088/0031-9155/48/3/301 | DOI Listing |
Med Dosim
November 2024
Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC. Electronic address:
This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries.
View Article and Find Full Text PDFPhys Med
March 2024
Division of Radiation Oncology, Small Animal Department, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland.
Background: Classical radiation protocols are guided by physical dose delivered homogeneously over the target. Protocols are chosen to keep normal tissue complication probability (NTCP) at an acceptable level. Organs at risk (OAR) adjacent to the target volume could lead to underdosage of the tumor and a decrease of tumor control probability (TCP).
View Article and Find Full Text PDFJ Appl Clin Med Phys
April 2024
Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.
Purpose: To investigate bolus design and VMAT optimization settings for total scalp irradiation.
Methods: Three silicone bolus designs (flat, hat, and custom) from .decimal were evaluated for adherence to five anthropomorphic head phantoms.
J Appl Clin Med Phys
February 2024
Department of Radiation Oncology, Kyorin University, Mitaka, Tokyo, Japan.
Optimizing the positional accuracy of multileaf collimators (MLC) for radiotherapy is important for dose accuracy and for reducing doses delivered to normal tissues. This study investigates dose sensitivity variations and complexity metrics of MLC positional error in volumetric modulated arc therapy and determines the acceptable ranges of MLC positional accuracy in several clinical situations. Treatment plans were generated for four treatment sites (prostate cancer, lung cancer, spinal, and brain metastases) using different treatment planning systems (TPSs) and fraction sizes.
View Article and Find Full Text PDFJ Appl Clin Med Phys
October 2023
Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, USA.
Purpose: Knowledge-based planning (KBP) offers the ability to predict dose-volume metrics based on information extracted from previous plans, reducing plan variability and improving plan quality. As clinical integration of KBP is increasing there is a growing need for quantitative evaluation of KBP models. A .
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