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. 2023 Jul 6;25(7):1323-1330.
doi: 10.1093/neuonc/noad029.

Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning

Nikhil Rammohan  1 , Alexander Ho  1 , Pierre Besson  2 , Tim J Kruser  3 , S Kathleen Bandt  4
Affiliations

Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning

Nikhil Rammohan et al. Neuro Oncol. .

Abstract

Background: Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitive deficits. While dose-dependent injury to individual areas such as the hippocampus has been demonstrated, global structural shape changes after WBRT remain to be studied.

Methods: We studied healthy controls and patients with brain metastases and examined MRI brain anatomic surface data before and after WBRT. We implemented a validated graph convolutional neural network model to estimate patient's "brain age". We further developed a mixed-effects linear model to compare the estimated age of the whole brain and substructures before and after WBRT.

Results: 4220 subjects were analyzed (4148 healthy controls and 72 patients). The median radiation dose was 30 Gy (range 25-37.5 Gy). The whole brain and substructures underwent structural change resembling rapid aging in radiated patients compared to healthy controls; the whole brain "aged" 9.32 times faster, the cortex 8.05 times faster, the subcortical structures 12.57 times faster, and the hippocampus 10.14 times faster. In a subset analysis, the hippocampus "aged" 8.88 times faster in patients after conventional WBRT versus after hippocampal avoidance (HA)-WBRT.

Conclusions: Our findings suggest that WBRT causes the brain and its substructures to undergo structural changes at a pace up to 13x of the normal aging pace, where hippocampal avoidance offers focal structural protection. Correlating these structural imaging changes with neurocognitive outcomes following WBRT or HA-WBRT would benefit from future analysis.

Keywords: MRI; aging; brain metastases; deep learning.

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Conflict of interest statement

TJK serves on an advisory board and speakers bureau for AstraZeneca and is a consultant for Radialogica LLC. Other authors declare they have no conflicts of interest.

Figures

Figure 1:
Figure 1:
Surface-based deep learning pipeline. (A) Outer cortical, inner cortical and individual subcortical surfaces were extracted from T1weighted MR image using Freesurfer. (B) Surface meshes converted to graphs using their triangulations. (C) Pre-trained graph-convolutional neural network (gCNN) predicts brain age using surface nodes’ Cartesian coordinates (X, Y, Z).
Figure 2:
Figure 2:
Spaghetti plot of whole brain “aging” over time for healthy controls (HC) vs. WBRT patients. Estimated aging slope of WBRT patients significantly steeper than best-fit aging slope of healthy controls.
See this image and copyright information in PMC

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