Jeroen Blommaert1, Ahmed Radwan2, Charlotte Sleurs1, Ron Peeters2,3, Stefan Sunaert2,3, An-Sofie Gorissen1, Kristel Van Calsteren4,5, Frédéric Amant1,6,7, and Sabine Deprez2
1Oncology, KU Leuven, Leuven, Belgium, 2Imaging & Pathology, KU Leuven, Leuven, Belgium, 3Radiology, University Hospitals Leuven, Leuven, Belgium, 4Development and regeneration, KU Leuven, Leuven, Belgium, 5Gynaecology and Obstetrics, University Hospitals Leuven, Leuven, Belgium, 6Gynecological Oncology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, Netherlands, 7Gynaecological oncology, Amsterdam university medical centers, Amsterdam, Netherlands
Synopsis
Currently, data are scarce on the long-term
outcome of prenatal exposure to cancer treatment. In this preliminary analysis,
we investigated white matter development in 9-year-old children who were
prenatally exposed to chemotherapy, using multi-shell diffusion MRI and
fixel-based analysis. We found indications of lower within-voxel (reflected by
Fibre Density), macroscopic (reflected by Fibre Cross-section) and total (reflected
by Fibre Density and Cross-section) intra-axonal volume of the splenium,
isthmus and tapetal fibres of the corpus callosum in children with prenatal
chemotherapy exposure compared to controls. This suggests prenatal chemotherapy
exposure to potentially impact white matter development.
Introduction
The diagnosis of cancer during pregnancy poses difficult
medical-ethical decisions, concerning both maternal and fetal health. While
short-term outcomes after prenatal cancer treatment exposure are reassuring,
long-term outcome data remain scarce1,2. In a recent event-related
potentials study3, we demonstrated altered
neural signaling after prenatal chemotherapy exposure in 9-year-old children,
affecting executive functioning. This further emphasizes the need for long-term
follow-up of children born after maternal cancer.
In this preliminary analysis, we investigate
white matter development in 9-year-old children who were prenatally exposed to
chemotherapy, compared to healthy controls, using multi-shell diffusion MRI and
fixel-based analysis.Methods
Children in the prenatal-exposed group were born to mothers
that were diagnosed with cancer during pregnancy and treated with chemotherapy
after the first trimester of pregnancy. Children in the control group, born to
healthy mothers, were matched on group level with regards to age, prematurity
and gender. Children having major prenatal or neonatal complications known to possibly
cause neurological sequelae, except for prematurity or intra-uterine growth
restriction, were excluded. All children underwent MRI scanning at the age of
nine years old, between 2015-2019, using the same scanner (3T Philips Achieva,
32-channel phased-array head coil).
Echo-planar, Diffusion weighted MR images were obtained
using b-values of 0,700 and 2000, acquired with respectively 6,30 and 60
uniformly distributed gradient directions. All volumes were acquired with the following
set-up: resolution=2.5x2.5x2.5mm, FOV=240x240x125mm, TR/TE/FA=7000ms/72ms/90°,
Phase encoding=AP, halfscan=0.766, SENSE=2 and total acquisition time=12:01min.
Additionally, one b0 image was acquired with reversed phase-encoding.
Diffusion images were analyzed with MRtrix4 (v3.0) following the
recommended fixel-based analysis pipeline5, which was adapted for this
dataset (Figure 1)6–8. Image quality was assessed
by both visual inspection and quantitative reports generated using FSL’s
automated quality control9.
Differences in fixel-based parameters of fibre
density (FD), fibre cross-section (FC, logarithmically scaled) and their
combined metric of fibre density and cross-section (FDC) were assessed at each
individual fixel using non-parametric connectivity-based fixel enhancement10
(CFE), using 2 million streamlines. Fixels traversed by less than 150
streamlines were excluded from the final analysis. Group differences and effects
of prematurity were tested with two general linear models. The first model only
included group and normalized (z-score) gestational age at birth (GA, defined
in days) as factors. In the second model, normalized Intracranial volume (ICV) was
added as a covariate. ICV was calculated by back-transforming the group mask to
subject-space, as described by Pannek et al.11 Significance
was inferred at p<.05, corrected for multiple comparisons using Bonferroni
correctionResults
We included 22 prenatal-exposed children (11 boys, 13 born
prematurely) and 39 healthy controls (20 boys, 21 born prematurely). Both
groups did not significantly differ on GA, ICV or sex, as respectively tested using
Mann-Whitney U-test or Fisher’s exact test. However, there was a small, though
significant, difference in age between prenatal-exposed (median 9.24 years,
range [9.01-9.94 years]) and control children (median 9.50 years, range [8.95-9.98years]).
FDC was significantly lower in the prenatal-exposed group,
bilaterally in the splenium, isthmus and the bilateral occipital, parietal and
left temporal tapetal fibres of the corpus callosum (CC) (Figure
2:b).
A smaller subset of bilateral fixels in the isthmus showed significantly
smaller FD in the prenatal-exposed group (Figure
2:a).
GA was significantly positively correlated with FD in a small set of fibres
within the right cortico-spinal tract (CST) (Figure
3:a).
FC was negatively correlated with GA in the right side of the splenium and parietal
tapetal fibres of the CC (Figure 3:b). All
results remained significant when corrected for ICV, except for FD with GA,
which was marginally significant (p=.0506). Additionally, when corrected for ICV,
FC was significantly lower bilaterally in the splenium of the CC of
prenatal-exposed children compared to controls.Discussion
The
results of this study suggest that within-voxel (reflected by FD), macroscopic
(reflected by FC) and total (reflected by FDC) intra-axonal volume of the
splenium, isthmus and tapetal fibres of the CC to be lower in children who were
prenatally exposed to chemotherapy, compared to controls. Previous research
showed FD and FC in this region to be impacted in adult childhood cancer survivors
treated with intravenous chemotherapy12. On the other hand, FD,FC and
FDC were previously found to increase during prepubertal development13.
GA positively correlated with FD in the right CST, while FC
was found to negatively correlate with GA in the splenium of the CC. The decrease
in FD corroborates earlier findings in literature on prematurity11, while the increase in FC is
in line with literature on late-prematurity, where hypo- and hyper-connectivity
is simultaneously observed14.Conclusions
To our knowledge, this is the first study to investigate
white matter development after prenatal chemotherapy exposure. These
preliminary results suggest that prenatal chemotherapy exposure has an impact
on WM development, which might include decreased axonal diameter and/or a lower
axon count. Future work will include expansion of this dataset as well as
linking DWI-derived parameters to clinical and psychological parameters.Acknowledgements
This
project has received funding from the European Union’s Horizon 2020 research
and innovation program(European Research council, grant no 647047), Foundation
against cancer (Stichting tegen kanker, grant no. 2014-152) and Research
Foundation Flanders (FWO, grant no. 11B9919N), J.B. is an aspirant researcher
for the FWO, F.A. is a senior clinical researcher for the FWO. The
computational resources and services used in this work were provided by the VSC
(Flemish Supercomputer Center), funded by the FWO and the Flemish Government –
department EWI.
The authors
would like to thank: Jorine De Haan, Gunnar Naulaers, Charlotte Maggen, Liesbeth
Leemans, Kaat Philippe, Cettina Schellens, Lara Stroobants, Tineke
Vandenbroucke, Marie-Astrid Van Hoorick, Katrien Van Tornout, Dorothée
Vercruysse, Magali Verheecke, Diane Wolput and all families participating in
this study
References
1.
Maggen C, van Gerwen M, Van
Calsteren K, Vandenbroucke T, Amant F. Management of cancer during pregnancy
and current evidence of obstetric, neonatal and pediatric outcome: a review
article. Int J Gynecol Cancer. January 2019:ijgc-2018-000061.
doi:10.1136/ijgc-2018-000061
2. de Haan
J, Verheecke M, Van Calsteren K, et al. Oncological
management and obstetric and neonatal outcomes for women diagnosed with cancer
during pregnancy: a 20-year international cohort study of 1170 patients. Lancet
Oncol.
2018;19(3):337-346. doi:10.1016/S1470-2045(18)30059-7
3. Blommaert
J, Zink R, Deprez S, et al. Long-term impact of
prenatal exposure to chemotherapy on executive functioning: An ERP study. Clin
Neurophysiol. 2019. doi:10.1016/j.clinph.2019.06.012
4.
Tournier J-D, Smith RE, Raffelt DA,
et al. MRtrix3: A fast, flexible and open software framework for medical image
processing and visualisation. bioRxiv. 2019. doi:10.1101/551739
5.
Raffelt DA, Tournier JD, Smith RE,
et al. Investigating white matter fibre density and morphology using
fixel-based analysis. Neuroimage. 2017;144:58-73.
doi:10.1016/j.neuroimage.2016.09.029
6.
Andersson JLR, Graham MS, Drobnjak
I, Zhang H, Campbell J. Susceptibility-induced distortion that varies due to
motion: Correction in diffusion MR without acquiring additional data. Neuroimage.
2018;171:277-295. doi:10.1016/j.neuroimage.2017.12.040
7.
Andersson JLR, Graham MS, Zsoldos
E, Sotiropoulos SN. Incorporating outlier detection and replacement into a
non-parametric framework for movement and distortion correction of diffusion MR
images. Neuroimage. 2016;141:556-572.
doi:10.1016/j.neuroimage.2016.06.058
8.
Andersson JLR, Graham MS, Drobnjak
I, Zhang H, Filippini N, Bastiani M. Towards a comprehensive framework for
movement and distortion correction of diffusion MR images: Within volume
movement. Neuroimage. 2017;152:450-466.
doi:10.1016/j.neuroimage.2017.02.085
9.
Bastiani M, Cottaar M, Fitzgibbon
SP, et al. Automated quality control for within and between studies diffusion
MRI data using a non-parametric framework for movement and distortion
correction. Neuroimage. 2019;184(May 2018):801-812.
doi:10.1016/j.neuroimage.2018.09.073
10.
Raffelt DA, Smith RE, Ridgway GR, et
al. Connectivity-based fixel enhancement: Whole-brain statistical analysis of
diffusion MRI measures in the presence of crossing fibres. Neuroimage. 2015;117:40-55.
doi:10.1016/j.neuroimage.2015.05.039
11. Pannek
K, Fripp J, George JM, et al. Fixel-based analysis
reveals alterations is brain microstructure and macrostructure of preterm-born
infants at term equivalent age. NeuroImage Clin. 2018;18(September
2017):51-59. doi:10.1016/j.nicl.2018.01.003
12.
Sleurs C, Lemiere J, Christiaens D,
et al. Advanced MR diffusion imaging and chemotherapy-related changes in cerebral
white matter microstructure of survivors of childhood bone and soft tissue
sarcoma? Hum Brain Mapp. 2018. doi:10.1002/hbm.24082
13.
Genc S, Smith RE, Malpas CB, et al.
Development of white matter fibre density and morphology over childhood: A
longitudinal fixel-based analysis. Neuroimage. 2018;183(July):666-676.
doi:10.1016/j.neuroimage.2018.08.043
14. Degnan AJ, Wisnowski JL, Choi S, et al. Altered structural and
functional connectivity in late preterm preadolescence: An anatomic seed-based
study of resting state networks related to the posteromedial and lateral
parietal cortex. PLoS One. 2015;10(6):1-22.
doi:10.1371/journal.pone.0130686