The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Márcio Ajudarte Lopes - One of the best experts on this subject based on the ideXlab platform.
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the impact of an educational video about Radiotherapy and its toxicities in head and Neck cancer patients evaluation of patients understanding anxiety depression and quality of life
Oral Oncology, 2020Co-Authors: Diego Tetzner Fernandes, Ana Carolina Pradoribeiro, Renata Lucena Markman, Karina Morais, Karina Moutinho, Juliana Ono Tonaki, Alan Roger Santossilva, César Rivera, Thaís Bianca Brandão, Márcio Ajudarte LopesAbstract:Abstract Objectives Head and Neck Radiotherapy can cause several toxicities, and its management has important treatment implications. Proper information about treatment is crucial to assist patients by preparing them and enhancing their ability to manage their illness. Thus, this study aimed to verify the impact of an educational video on the improvement of the patient’s understanding, satisfaction, quality of life, and influence on their emotional state in different moments of treatment. Methods A 10 min video about head and Neck Radiotherapy and its toxicities was produced. A prospective randomized clinical trial was performed in two groups: a control group (n = 65), which received standard verbal and written information, and an experimental group (n = 65), which received standard information and the video. Appropriated questionnaires (HADS, UW-QOLv4, IRTU, and Post-RTU) were applied in four different moments in order to evaluate patients’ understanding, anxiety, depression, and quality of life. Results The video improved the understanding of treatment and its side effects. Also, the video group reported better awareness about oral health care during the treatment. Osteoradionecrosis and radiation-related caries were the most unknown side effects. On the other hand, the educational video did not modify the patients’ anxiety, depression, and quality of life. All patients reported high satisfaction with the video. Conclusions Audiovisual tools may improve patients' understanding of Radiotherapy and were shown to be a useful tool when used in association with verbal and written information in cancer centers. In addition, information about osteoradionecrosis and radiation-related caries must be reinforced to patients.
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young head and Neck cancer patients are at increased risk of developing oral mucositis and trismus
Supportive Care in Cancer, 2020Co-Authors: Karina Moraisfaria, Thaís Bianca Brandão, Márcio Ajudarte Lopes, Ana Carolina Prado Ribeiro, Gilberto De Castro, Natalia Rangel Palmier, Jaqueline De Lima Correia, Reinaldo Brito E Dias, Henrique Da Graca Pinto, Alan Roger SantossilvaAbstract:To evaluate cancer treatment–related toxicities in young head and Neck cancer (HNC) patients. A total of 44 patients were included in the present retrospective cohort study, which was designed to access oral toxicities of cancer treatment in young ( 58 years of age, Group II, n = 22) HNC patients with similar tumor stage and treatment protocols. Oral mucositis (OM), xerostomia, dysphagia, dysgeusia, trismus, and radiodermatitis were assessed during days 7th, 21st, and 35th of head and Neck Radiotherapy (HNRT) according to previously validated scales (World Health Organization criteria and the National Cancer Institute and Common Terminology Criteria for Adverse Events version 4.0). Patients from both groups showed high incidence and severity of oral toxicities by the end of the HNRT with OM (81.9% (Group I); 63.6% (Group II)) and xerostomia (72.6% (Group I); 77.2% (Group II)) being the most prevalent toxicities. No differences regarding xerostomia, dysphagia, dysgeusia, and radiodermatitis incidences or severity could be observed between groups. However, higher incidences and severity of OM at 21st and 35th fractions (odds ratio = 2.22 and 5.71, respectively) and trismus at 21st and 35th fractions (odds ratio = 6.17 and 14.5, respectively) were observed throughout the treatment in young patients when compared to older patients (p < 0.01 and p < 0.05, respectively). Young HNC patients are more affected by cancer treatment–related OM and trismus despite the similarities in clinical staging and treatment protocols with elderly patients.
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micromorphology of the dental pulp is highly preserved in cancer patients who underwent head and Neck Radiotherapy
Journal of Endodontics, 2014Co-Authors: Karina Morais Faria, Thaís Bianca Brandão, Ana Carolina Prado Ribeiro, Adriele Ferreira Gouvea Vasconcellos, Icaro Thiago De Carvalho, Fernando Freire De Arruda, Gilberto De Castro, Vanessa Cristina Gross, Oslei Paes De Almeida, Márcio Ajudarte LopesAbstract:Abstract Introduction Teeth are often included in the radiation field during head and Neck Radiotherapy, and recent clinical evidence suggests that dental pulp is negatively affected by the direct effects of radiation, leading to impaired sensitivity of the dental pulp. Therefore, this study aimed to investigate the direct effects of radiation on the microvasculature, innervation, and extracellular matrix of the dental pulp of patients who have undergone head and Neck Radiotherapy. Methods Twenty-three samples of dental pulp from patients who finished head and Neck Radiotherapy were analyzed. Samples were histologically processed and stained with hematoxylin-eosin for morphologic evaluation of the microvasculature, innervation, and extracellular matrix. Subsequently, immunohistochemical analysis of proteins related to vascularization (CD34 and smooth muscle actin), innervation (S-100, NCAM/CD56, and neurofilament), and extracellular matrix (vimentin) of the dental pulp was performed. Results The morphologic study identified preservation of the microvasculature, nerve bundles, and components of the extracellular matrix in all studied samples. The immunohistochemical analysis confirmed the morphologic findings and showed a normal pattern of expression for the studied proteins in all samples. Conclusions Direct effects of Radiotherapy are not able to generate morphologic changes in the microvasculature, innervation, and extracellular matrix components of the dental pulp in head and Neck cancer patients.
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evaluation of an educational video to improve the understanding of Radiotherapy side effects in head and Neck cancer patients
Supportive Care in Cancer, 2013Co-Authors: Marco Aurelio Carvalho De Andrade, Lara Maria Alencar Ramos, Jose Ribamar Sabino Bezerra, Alan Roger Santossilva, Wilfredo Alejandro Gonzalezarriagada, Márcio Ajudarte LopesAbstract:Purpose Side effects of head and Neck Radiotherapy are common and can interfere with treatment. However, scientific information on a patient’s understanding of these complications is scarce and confusing. Therefore, the aim of this study was to assess the effect of an educational video on improving the understanding of head and Neck cancer patients undergoing Radiotherapy about treatment complications.
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patterns of demineralization and dentin reactions in radiation related caries
Caries Research, 2009Co-Authors: Alan Roger Dos Santos Silva, Fabio De Abreu Alves, Alberto Nogueira Da Gama Antunes, Mario Fernando De Goes, Márcio Ajudarte LopesAbstract:Radiation-related caries is a unique form of rampant decay and is a complication of head and Neck Radiotherapy that frequently causes generalized dental destruction and impairs quality of life in canc
Thomas G Purdie - One of the best experts on this subject based on the ideXlab platform.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
Physics in Medicine and Biology, 2017Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12–13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
arXiv: Medical Physics, 2016Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31).
Chris Mcintosh - One of the best experts on this subject based on the ideXlab platform.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
Physics in Medicine and Biology, 2017Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12–13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
arXiv: Medical Physics, 2016Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31).
Mattea Welch - One of the best experts on this subject based on the ideXlab platform.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
Physics in Medicine and Biology, 2017Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12–13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
arXiv: Medical Physics, 2016Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31).
Andrea Mcniven - One of the best experts on this subject based on the ideXlab platform.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
Physics in Medicine and Biology, 2017Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12–13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.
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fully automated treatment planning for head and Neck Radiotherapy using a voxel based dose prediction and dose mimicking method
arXiv: Medical Physics, 2016Co-Authors: Chris Mcintosh, Mattea Welch, Andrea Mcniven, David A Jaffray, Thomas G PurdieAbstract:Recent works in automated Radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and Neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31).
