Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features.

Morgan, Howard E; Wang, Kai; Dohopolski, Michael; Liang, Xiao; Folkert, Michael R; Sher, David J; Wang, Jing

Morgan, Howard E; Wang, Kai; Dohopolski, Michael; Liang, Xiao; Folkert, Michael R; Sher, David J; Wang, Jing.

Afiliação

Morgan HE; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Wang K; Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Dohopolski M; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Liang X; Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Folkert MR; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Sher DJ; Medical Artificial Intelligence and Automation Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Wang J; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Quant Imaging Med Surg ; 11(12): 4781-4796, 2021 Dec.

Article em En | MEDLINE | ID: mdl-34888189

Palavras-chave

Delta radiomics; ensemble learning; head and neck squamous cell carcinoma (HNSCC)

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo

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PubMed Links

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article