Purpose: Our aim was to develop a framework to objectively perform treatment planning studies using Pareto fronts. The Pareto front represents all optimal possible tradeoffs among several conflicting criteria and is an ideal tool with which to study the possibilities of a given treatment technique. The framework should require minimal user interaction and should resemble and be applicable to daily clinical practice.
Methods And Materials: To generate the Pareto fronts, we used the native scripting language of Pinnacle(3) (Philips Healthcare, Andover, MA). The framework generates thousands of plans automatically from which the Pareto front is generated. As an example, the framework is applied to compare intensity modulated radiation therapy (IMRT) with volumetric modulated arc therapy (VMAT) for prostate cancer patients. For each patient and each technique, 3000 plans are generated, resulting in a total of 60,000 plans. The comparison is based on 5-dimensional Pareto fronts.
Results: Generating 3000 plans for 10 patients in parallel requires on average 96 h for IMRT and 483 hours for VMAT. Using VMAT, compared to IMRT, the maximum dose of the boost PTV was reduced by 0.4 Gy (P=.074), the mean dose in the anal sphincter by 1.6 Gy (P=.055), the conformity index of the 95% isodose (CI(95%)) by 0.02 (P=.005), and the rectal wall V(65 Gy) by 1.1% (P=.008).
Conclusions: We showed the feasibility of automatically generating Pareto fronts with Pinnacle(3). Pareto fronts provide a valuable tool for performing objective comparative treatment planning studies. We compared VMAT with IMRT in prostate patients and found VMAT had a dosimetric advantage over IMRT.
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http://dx.doi.org/10.1016/j.ijrobp.2012.05.045 | DOI Listing |
Adv Sci (Weinh)
January 2025
Computational Science Research Center, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
Efficiently extracting data from tables in the scientific literature is pivotal for building large-scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule-based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT-based table data extractor from the materials science literature.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
Shandong Rongxin Group Co., Ltd., Zoucheng 273517, China.
In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Energy Engineering & Physics, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Engineering, Helwan University, Cairo, Egypt.
Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability-a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions.
View Article and Find Full Text PDFEvol Comput
January 2025
College of Science, Northwest A&F University, Yangling 712100, China
Decomposition-based multi-objective evolutionary algorithms (MOEAs) are popular methods utilized to address many-objective optimization problems (MaOPs). These algorithms decompose the original MaOP into several scalar optimization subproblems, and solve them to obtain a set of solutions to approximate the Pareto front (PF). The decomposition approach is an important component in them.
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