Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Nienke Bakx"'
Publikováno v:
Acta Oncologica, Vol 63, Iss 1 (2024)
Background: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically
Externí odkaz:
https://doaj.org/article/965563cefa2e42b48f70b9bd97f92b6a
Autor:
Daniel Portik, Enrico Clementel, Jérôme Krayenbühl, Nienke Bakx, Nicolaus Andratschke, Coen Hurkmans
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 29, Iss , Pp 100539- (2024)
Background and Purpose: To improve radiotherapy (RT) planning efficiency and plan quality, knowledge-based planning (KBP) and deep learning (DL) solutions have been developed. We aimed to make a direct comparison of these models for breast cancer pla
Externí odkaz:
https://doaj.org/article/81c68d1c6c53439882e2e121e14b615f
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 28, Iss , Pp 100496- (2023)
Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after wh
Externí odkaz:
https://doaj.org/article/a8b6f3b775294663b23c10c11e95c6ed
Publikováno v:
Technical Innovations & Patient Support in Radiation Oncology, Vol 26, Iss , Pp 100209- (2023)
Introduction: The development of deep learning (DL) models for auto-segmentation is increasing and more models become commercially available. Mostly, commercial models are trained on external data. To study the effect of using a model trained on exte
Externí odkaz:
https://doaj.org/article/9b36365dc253485fb414ade7eb5c170e
Autor:
Nienke Bakx, Dorien Rijkaart, Maurice van der Sangen, Jacqueline Theuws, Peter-Paul van der Toorn, An-Sofie Verrijssen, Jorien van der Leer, Joline Mutsaers, Thérèse van Nunen, Marjon Reinders, Inge Schuengel, Julia Smits, Els Hagelaar, Dave van Gruijthuijsen, Hanneke Bluemink, Coen Hurkmans
Publikováno v:
Technical Innovations & Patient Support in Radiation Oncology, Vol 26, Iss , Pp 100211- (2023)
Introduction: Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for
Externí odkaz:
https://doaj.org/article/cd8f37c0d8e74b7d9b7e2565ab50192f
Autor:
Esther Kneepkens, Nienke Bakx, Maurice van der Sangen, Jacqueline Theuws, Peter-Paul van der Toorn, Dorien Rijkaart, Jorien van der Leer, Thérèse van Nunen, Els Hagelaar, Hanneke Bluemink, Coen Hurkmans
Publikováno v:
Radiation Oncology, Vol 17, Iss 1, Pp 1-9 (2022)
Abstract Background Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of
Externí odkaz:
https://doaj.org/article/be45651a50c1406da48b6c471bcc53dd
Autor:
Dennis van de Sande, Marjan Sharabiani, Hanneke Bluemink, Esther Kneepkens, Nienke Bakx, Els Hagelaar, Maurice van der Sangen, Jacqueline Theuws, Coen Hurkmans
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 20, Iss , Pp 111-116 (2021)
Background and purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain pla
Externí odkaz:
https://doaj.org/article/07caacea83cd4701b752341561a171a3
Autor:
Nienke Bakx, Hanneke Bluemink, Els Hagelaar, Maurice van der Sangen, Jacqueline Theuws, Coen Hurkmans
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 17, Iss , Pp 65-70 (2021)
Background and purpose: Treatment planning of radiotherapy is a time-consuming and planner dependent process that can be automated by dose prediction models. The purpose of this study was to evaluate the performance of two machine learning models for
Externí odkaz:
https://doaj.org/article/493c808ae9114d979cb118ac19b3bd12
Autor:
Nienke Bakx, Hanneke Bluemink, Els Hagelaar, Jorien van der Leer, Maurice van der Sangen, Jacqueline Theuws, Coen Hurkmans
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 18, Iss , Pp 48-50 (2021)
During breast cancer radiotherapy, sparing of healthy tissue is desired. The effect of automatic beam angle optimization and generic dose fall-off objectives on dose and normal tissue complication probabilities was studied. In all patients, dose to l
Externí odkaz:
https://doaj.org/article/e8c942144a4e489092a36034c8c5d132
Autor:
Nienke Bakx, Dennis van de Sande, E. Hagelaar, Jacqueline Theuws, Hanneke Bluemink, Coen W. Hurkmans, Maurice J.C. van der Sangen, Esther Kneepkens, M. Sharabiani
Publikováno v:
Physics and Imaging in Radiation Oncology, 20, 111-116. Elsevier
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology, Vol 20, Iss, Pp 111-116 (2021)
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology, Vol 20, Iss, Pp 111-116 (2021)
Background and purpose: Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain pla