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Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.
Haidar, Ali; Field, Matthew; Batumalai, Vikneswary; Cloak, Kirrily; Al Mouiee, Daniel; Chlap, Phillip; Huang, Xiaoshui; Chin, Vicky; Aly, Farhannah; Carolan, Martin; Sykes, Jonathan; Vinod, Shalini K; Delaney, Geoffrey P; Holloway, Lois.
Afiliação
  • Haidar A; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia.
  • Field M; Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia.
  • Batumalai V; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia.
  • Cloak K; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia.
  • Al Mouiee D; Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia.
  • Chlap P; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia.
  • Huang X; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia.
  • Chin V; GenesisCare, Alexandria, NSW 2015, Australia.
  • Aly F; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia.
  • Carolan M; Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia.
  • Sykes J; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia.
  • Vinod SK; Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia.
  • Delaney GP; Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia.
  • Holloway L; South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia.
Cancers (Basel) ; 15(3)2023 Jan 17.
Article em En | MEDLINE | ID: mdl-36765523
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article