Back

Directional interface mechanics using magnetic resonance elastography predicts focal tumor recurrence in glioblastomas

Aunan-Diop, J. S.; Friismose, A. I.; Yin, Z.; Hojo, E.; Ganji, S.; Le, Y.; Harbo, F.; Halle, B.; Poulsen, F. R.

2026-05-15 radiology and imaging
10.64898/2026.05.06.26352294 medRxiv
Show abstract

Glioblastoma progression is spatially heterogeneous, but conventional imaging provides limited information about where subsequent tumor progression is likely to occur. We developed a directional magnetic resonance elastography framework to test whether local post-treatment tumor-brain interface mechanics are associated with later spatial tumor progression. In a secondary analysis of a prospectively acquired glioblastoma cohort, wedge-level viscoelastic instability features were extracted from the first post-treatment MRE scan and related to novel tumor burden on the second post-treatment scan after excluding tumor already present on pretreatment or first post-treatment imaging. Nine patients had longitudinal imaging suitable for spatial comparison; six lesions showed net interval growth and were included in the primary wedge-level directional analysis, while three non-growing lesions were retained for descriptive comparison. In growing lesions, several directional mechanical features were descriptively associated with later novel tumor burden. In cluster-aware models accounting for within-patient dependence among wedges, mean {Delta}tan{delta} ; showed the most consistent association with later wedge-level novel tumor fraction across mixed-effects and generalized estimating equation analyses. Associations were directionally stable across wedge-width sensitivity analyses. These findings provide proof of principle that post-treatment glioblastoma interface mechanics contain spatially resolved information related to where later tumor emergence occurs, supporting further validation of directional MRE as a framework for longitudinal mapping of progression geometry.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
NeuroImage: Clinical
132 papers in training set
Top 0.1%
21.7%
2
Scientific Reports
3102 papers in training set
Top 11%
8.1%
3
Nature Communications
4913 papers in training set
Top 24%
8.1%
4
NeuroImage
813 papers in training set
Top 1%
8.1%
5
Neuro-Oncology Advances
24 papers in training set
Top 0.1%
8.1%
50% of probability mass above
6
Science Translational Medicine
111 papers in training set
Top 0.5%
4.7%
7
Brain Communications
147 papers in training set
Top 0.8%
3.5%
8
Imaging Neuroscience
242 papers in training set
Top 1%
3.5%
9
Clinical Cancer Research
58 papers in training set
Top 0.6%
3.1%
10
Human Brain Mapping
295 papers in training set
Top 2%
3.0%
11
eLife
5422 papers in training set
Top 32%
2.6%
12
Annals of Neurology
57 papers in training set
Top 0.8%
2.5%
13
eBioMedicine
130 papers in training set
Top 0.7%
2.3%
14
BMC Medicine
163 papers in training set
Top 3%
1.7%
15
Brain
154 papers in training set
Top 3%
1.4%
16
Communications Medicine
85 papers in training set
Top 0.6%
1.1%
17
The Journal of Neuroscience
928 papers in training set
Top 7%
0.9%
18
npj Precision Oncology
48 papers in training set
Top 1%
0.8%
19
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 44%
0.8%
20
npj Digital Medicine
97 papers in training set
Top 3%
0.8%
21
Biomaterials Advances
20 papers in training set
Top 0.7%
0.7%
22
PLOS ONE
4510 papers in training set
Top 70%
0.7%
23
Cell Reports
1338 papers in training set
Top 35%
0.7%
24
JCI Insight
241 papers in training set
Top 9%
0.6%
25
Cell Reports Medicine
140 papers in training set
Top 10%
0.6%