Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data.

Naser, Mohamed A; Wahid, Kareem A; Mohamed, Abdallah S R; Abdelaal, Moamen Abobakr; He, Renjie; Dede, Cem; van Dijk, Lisanne V; Fuller, Clifton D

Naser, Mohamed A; Wahid, Kareem A; Mohamed, Abdallah S R; Abdelaal, Moamen Abobakr; He, Renjie; Dede, Cem; van Dijk, Lisanne V; Fuller, Clifton D.

Afiliación

Naser MA; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
Wahid KA; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
Mohamed ASR; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
Abdelaal MA; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
He R; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
Dede C; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
van Dijk LV; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.
Fuller CD; Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX 77030, USA.

Head Neck Tumor Segm Chall (2021) ; 13209: 287-299, 2022.

Article en En | MEDLINE | ID: mdl-35399868

Palabras clave

CT; Deep learning; Head and neck cancer; Oropharyngeal cancer; Outcome prediction model; PET; Progression-free survival

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Head Neck Tumor Segm Chall (2021) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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