Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning

Selena I Huisman, Arthur T J van der Boog, Fia Cialdella, Joost J C Verhoeff, Szabolcs David

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Abstract

Background and purpose: Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework. Materials and methods: BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients’ pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT. Results: The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54–3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction. Conclusion: We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy.

Original languageEnglish
Pages (from-to)18-25
Number of pages8
JournalRadiotherapy & Oncology
Volume175
Early online date10 Aug 2022
DOIs
Publication statusPublished - Oct 2022

Keywords

  • Deep learning
  • Glioma
  • Radiation Therapy

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