Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithm: Pilot study

Rajković, Katarina; Dabić-Stanković, Kata; Stanković, Jovana; Acimovic, M.; Ðukanović, Nina; Nikolin, Borislava

doi:10.1016/j.compbiomed.2020.104045

Cited by 6 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, the AUC value was 0.961. Rajković et al [17] suggested an ANN model for the treatment of prostate carcinoma, resulting in the therapy dose (TD) of 47.3 Gy and coverage index (CI100%) of 1.4 for the low-risk group and TD of 50.4 Gy and CI100% 1.6 for the high-risk group. In this research, we treated better therapy doses for cervical cancer patients to build up the ANNs model.…”

Section: Discussionmentioning

confidence: 99%

“…[16] In volumetric dose analysis, Rajković et al proposed the arti cial neural network with a genetic algorithm to optimize brachytherapy parameters. [17] An ANNs model was developed for intra-factional OARs dose in Jaberi et al, describing applicator changes to compensate the treatment plan [18] and is also used in business, credit and fraud detection, speech recognition, and image processing. [19] The model was applied for traumatic brain injury [20], the lumbar spine canal stenosis [21], cancer identi cation with outcomes [22], detection of posterior lumbar spine fusion [23], and the diagnosis of my cardinal complexation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of the Artificial Neural Network for the Treatment Outcomes of Single-channel and Tri-channel Applicator Used in Cervical Cancer Based on High Dose Rate Brachytherapy

Hossain

Miah

Ray

et al. 2021

Preprint

View full text Add to dashboard Cite

The study aimed to evaluate the treatment outcomes of single-channel and tri-channel applicators for cervical cancer patients based on high dose rate brachytherapy using an artificial neural network. An artificial neural network (ANNs) model is proposed to predict the treatment outcomes for the single-channel applicator and tri-channel applicator in cervical cancer for high dose rate brachytherapy. Fifty-four patients of cervical cancer who were receiving external beam radiation therapy (EBRT) of 40-50 cGy, with chemotherapy, were selected in this study from 37 patients with cervical cancer being used to train and 17 for testing in this model. A model was developed for intracavitary brachytherapy to estimate the comparison of　treatment outcomes for the single-channel applicator and tri-channel applicators, demonstrating the sensitivity 100% and specificity 100 % and accuracy 100% for training and 87.5%, 77.8%, and 82.4% for testing, respectively, including AUC= 0.961. The survival rate was 85% and 95% for single-channel and tri-channel applicators at 2 years, respectively. A model approach for artificial neural networks based on gynecological brachytherapy is a promising method for patient's treatment, resulting in the dosimetry output of applicators; medical physicists can be decided the appropriate applicator for cervical cancer. The proposed model has the potential accuracy in judging the treatment outcomes for the single-channel applicator and tri-channel applicator in cervical cancer based on survival analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of the Artificial Neural Network for the Treatment Outcomes of Single-channel and Tri-channel Applicator Used in Cervical Cancer Based on High Dose Rate Brachytherapy

Hossain

Miah

Ray

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, many nature-inspired algorithms have suggested enhancing the capacity of optimizers in balancing the exploration and exploitation, such as Monarch butterfly optimization [ 30 ], Slime mould algorithm [ 31 ], Moth search algorithm [ 32 ], Hunger games search [ 33 ], Runge–Kutta method [ 34 ], colony predation algorithm [ 35 ], weighted mean of vectors [ 36 ], and Harris hawks optimization [ 37 ] Among these optimizers, the PSO-hybridized GWO is a relatively effective optimizer in solving the global optimum problems. Besides, the metaheuristic optimization algorithms have been used to improve the regression ability of artificial neural network (ANN), such as GA-ANN [ 38 ] and PSO-ANN [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms

Dang

Nguyen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and constraints of the mechanism are formulated via proposing a combination of artificial neural network (ANN) and particle swarm optimization (PSO) methods. Next, the PSO is hybridized with the grey wolf optimization, namely PSO-GWO, which is applied to three scenarios in handling the single objective function. In order to search the multiple functions for the mechanism, the multiobjective optimization genetic algorithm (MOGA) is applied to the last scenario. The achieved results showed that the fitness functions are well-formulated using the PSO-based ANN method. In the scenario 1, the stroke achieved by the PSO-GWO (1852.9842 μm) is better than that gained from the GWO (1802.8087 μm). In the scenarios 2, the stress gained from the PSO-GWO (243.3183 MPa) is lower than that achieved from the GWO (245.0401 MPa). In the scenario 3, the safety factor retrieved from the PSO-GWO (1.9767) is greater than that achieved from the GWO (1.9278). In the scenario 4, by using MOGA, the optimal results found that the stroke is about (1741.3 μm) and the safety factor is 1.8929. The prediction results are well-fitted with the numerical and experimental verifications. The results of this paper are expected to facilitate the synthesis and analysis of compliant mechanisms and related engineering designs.

show abstract

Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera

et al. 2021

View full text Add to dashboard Cite

Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithm: Pilot study

Cited by 6 publications

References 39 publications

The Use of the Artificial Neural Network for the Treatment Outcomes of Single-channel and Tri-channel Applicator Used in Cervical Cancer Based on High Dose Rate Brachytherapy

The Use of the Artificial Neural Network for the Treatment Outcomes of Single-channel and Tri-channel Applicator Used in Cervical Cancer Based on High Dose Rate Brachytherapy

Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms

Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera

Contact Info

Product

Resources

About