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Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands.
Li, Yan; van Rijn-Dekker, Maria Irene; de Vette, Suzanne Petronella Maria; van der Schaaf, Arjen; van den Bosch, Lisa; Langendijk, Johannes Albertus; van Dijk, Lisanne Vania; Sijtsema, Nanna Maria.
Afiliación
  • Li Y; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands. Electronic address: y.li05@umcg.nl.
  • van Rijn-Dekker MI; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
  • de Vette SPM; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
  • van der Schaaf A; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
  • van den Bosch L; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
  • Langendijk JA; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
  • van Dijk LV; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
  • Sijtsema NM; Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
Radiother Oncol ; 196: 110319, 2024 07.
Article en En | MEDLINE | ID: mdl-38702014
ABSTRACT
BACKGROUND AND

PURPOSE:

Recently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). MATERIALS AND

METHODS:

Totally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs.

RESULTS:

The final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively.

CONCLUSION:

The composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Xerostomía / Fluorodesoxiglucosa F18 / Tomografía de Emisión de Positrones / Neoplasias de Cabeza y Cuello Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Radiother Oncol Año: 2024 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Xerostomía / Fluorodesoxiglucosa F18 / Tomografía de Emisión de Positrones / Neoplasias de Cabeza y Cuello Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Radiother Oncol Año: 2024 Tipo del documento: Article Pais de publicación: Irlanda