Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck

Autor: Miranda Frizzelle, Athanasia Pediaditaki, Christopher Thomas, Christopher South, Reynald Vanderstraeten, Wolfgang Wiessler, Elizabeth Adams, Surendran Jagadeesan, Narinder Lalli

Publikováno v: Physics and Imaging in Radiation Oncology, Vol 21, Iss , Pp 18-23 (2022)

Background and purpose: Knowledge-based radiotherapy planning models have been shown to reduce healthy tissue dose and optimisation times, with larger training databases delivering greater robustness. We propose a method of combining knowledge-based

Externí odkaz: https://doaj.org/article/a9589d2019224967938d33282dedea8a

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning

Autor: Roni Hytönen, Reynald Vanderstraeten, Max Dahele, Wilko F. A. R. Verbakel

Publikováno v: Cancers, 14(12):2849. Multidisciplinary Digital Publishing Institute (MDPI)
Hytönen, R, Vanderstraeten, R, Dahele, M & Verbakel, W F A R 2022, ' Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning ', Cancers, vol. 14, no. 12, 2849 . https://doi.org/10.3390/cancers14122849
Cancers; Volume 14; Issue 12; Pages: 2849

Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from thos

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77b729a8c20da13912acf9b2fb16f7bd
https://pubmed.ncbi.nlm.nih.gov/35740515

Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck

Autor: Miranda Frizzelle, Athanasia Pediaditaki, Christopher Thomas, Christopher South, Reynald Vanderstraeten, Wolfgang Wiessler, Elizabeth Adams, Surendran Jagadeesan, Narinder Lalli

Publikováno v: Physics and Imaging in Radiation Oncology, Vol 21, Iss, Pp 18-23 (2022)

Background and purpose: Knowledge-based radiotherapy planning models have been shown to reduce healthy tissue dose and optimisation times, with larger training databases delivering greater robustness. We propose a method of combining knowledge-based

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba0f86ced62197e6e75fa97937c65ddf
https://pubmed.ncbi.nlm.nih.gov/35391782

Selective robust optimization: A new intensity-modulated proton therapy optimization strategy

Autor: Li Liao, Shengpeng Jiang, Sami Siljamaki, Michael Gillin, Narayan Sahoo, Xiaodong Zhang, Falk Poenisch, X. Ronald Zhu, Perttu Niemela, Yupeng Li, Reynald Vanderstraeten, Heng Li

Publikováno v: Medical Physics. 42:4840-4847

Purpose: To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications. Methods: In selective robust optimization, a wo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c9fa8ee7fd5de226fa10d42f209bc90b
https://doi.org/10.1118/1.4923171

Selective robust optimization: A new intensity-modulated proton therapy optimization strategy

Autor: Yupeng, Li, Perttu, Niemela, Li, Liao, Shengpeng, Jiang, Heng, Li, Falk, Poenisch, X Ronald, Zhu, Sami, Siljamaki, Reynald, Vanderstraeten, Narayan, Sahoo, Michael, Gillin, Xiaodong, Zhang

Publikováno v: Medical physics. 42(8)

To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications.In selective robust optimization, a worst-case-based robu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::f5d599cb78e2d5b8940a576d5c4a7b3a
https://pubmed.ncbi.nlm.nih.gov/26233211