The Analytical Hierarchy Process (AHP) to Score Plan Quality of Intact Prostate Treatment Plans

Delgado, A. Brito; Rasmussen, Klaus; Shi, Zhiming; Pesqueira, T. Medrano; Kauweloa, K.; Cohen, Donna R.; Eng, Tony Y.; Kirby, Nigel; Saenz, Daniel; Stathakis, S; Papanikolaou, Niko; Gutiérrez, Alonso N.

doi:10.1016/j.ijrobp.2018.07.1497

Cited by 2 publications

(2 citation statements)

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“…Cloud model algorithm. Brito et al shown that in the real world, everything has qualitative characteristics that are fuzzy and random, so are often difficult to quantify with accurate numbers [18]. In this study, the cloud model comprehensively considers the fuzziness and randomness of indicators through a specific calculation process, enabling a transformation from qualitative to quantitative.…”

Section: Improved Cloud Model Risk Assessment Modelmentioning

confidence: 99%

Virtual Power Plant Operation Based on Entropy-Weight-Improved Cloud Risk Assessment Model

Guo¹,

Wang²,

Liu³

et al. 2023

Pol. J. Environ. Stud.

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Many virtual power plant demonstration projects are currently in operation in China, but research on the risk assessment of virtual power plants is insufficiently deep. Virtual power plants are complex systems operated by multiple participants and can provide a variety of products or services. Based on the operation practices of virtual power plants, in this study, we considered the characteristics of different components of virtual power plants and their impact on the operation process. We developed a risk assessment model for virtual power plants from many aspects involved in their operation processes. First, we analyzed the operation modes that can be selected during the operation of a virtual power plant, including the scenario in which electric vehicles participate and considering the comprehensive demand response. Second, we considered the risk faced by a virtual power plant from five dimensions: external policy, participation, coupling technology, bidding transaction, and credit management risk; we then designed an operation risk indicator system for virtual power plants with 29 indicators. Third, based on the entropy weight order relation method and cloud model for determining the index uncertainty in the process of risk assessment, we developed a cloud risk assessment model and specific algorithm flow based on the entropy weight order relation method are proposed. Finally, we compared the optimal economic operation strategies of a virtual power plant under different operating characteristics. The results showed that the comprehensive risk in the operation of gas virtual power plants can be effectively reduced when considering various uncertainties, electric vehicles, and comprehensive demand response

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Section: Improved Cloud Model Risk Assessment Modelmentioning

confidence: 99%

Virtual Power Plant Operation Based on Entropy-Weight-Improved Cloud Risk Assessment Model

Guo¹,

Wang²,

Liu³

et al. 2023

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

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“…In the past, there have been extensive studies in this direction. For example, Delgado et al ( 2018 ) developed an analytical hierarchy process that was able to select a treatment plan from a set of options according to the preferences of the respondent. Knowledge-based planning was successfully developed to extract ‘knowledge’ about physician-preferred dose distributions by analyzing a library of previously approved plans (Tol et al 2015 , Ge and Wu 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning

Gao,

Gonzalez,

Nwachukwu

et al. 2024

Phys. Med. Biol.

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Objective: Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).Approach: The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN). DPN predicts OAR D2cc and CTV D90% of the current fraction from the patient's current anatomy and prescription dose of HDRBT. PPN outputs the probability of a given plan being acceptable to the physician based on the patient’s anatomy and the total dose combining HDRBT and external beam radiotherapy sessions. Training of the networks was achieved by first training them separately for a good initialization, and then jointly via an adversarial process. We collected approved treatment plans of 248 treatment fractions from 63 patients. Among them, 216 plans from 54 patients were employed in a four-fold cross validation study, and the remaining 32 plans from other 9 patients were saved for independent testing.Main results: DPN predicted equivalent dose of 2 Gy for bladder, rectum, sigmoid D2cc and CTV D90% with a relative error of 11.51±6.92%, 8.23±5.75%, 7.12±6.00%, and 10.16±10.42%, In a task that differentiates clinically approved plans and disapproved plans generated by perturbing doses in ground truth approved plans by 20%, PPN achieved accuracy, sensitivity, specificity, and area under the curve 0.70, 0.74, 0.65, and 0.74. Significance: We demonstrated the feasibility of developing a novel deep-learning framework that predicts a probability of plan approval for HDRBT of cervical cancer, which is an essential component in automatic treatment planning.

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The Analytical Hierarchy Process (AHP) to Score Plan Quality of Intact Prostate Treatment Plans

Cited by 2 publications

References 0 publications

Virtual Power Plant Operation Based on Entropy-Weight-Improved Cloud Risk Assessment Model

Virtual Power Plant Operation Based on Entropy-Weight-Improved Cloud Risk Assessment Model

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning

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