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Quantification of Tissue Compression Identifies High-Grade Glioma Patients with Reduced Survival.

Authors :
Fuster-Garcia E
Thokle Hovden I
Fløgstad Svensson S
Larsson C
Vardal J
Bjørnerud A
Emblem KE
Source :
Cancers [Cancers (Basel)] 2022 Mar 28; Vol. 14 (7). Date of Electronic Publication: 2022 Mar 28.
Publication Year :
2022

Abstract

The compression of peritumoral healthy tissue in brain tumor patients is considered a major cause of the life-threatening neurologic symptoms. Although significant deformations caused by the tumor growth can be observed radiologically, the quantification of minor tissue deformations have not been widely investigated. In this study, we propose a method to quantify subtle peritumoral deformations. A total of 127 MRI longitudinal studies from 23 patients with high-grade glioma were included. We estimate longitudinal displacement fields based on a symmetric normalization algorithm and we propose four biomarkers. We assess the interpatient and intrapatient association between proposed biomarkers and the survival based on Cox analyses, and the potential of the biomarkers to stratify patients according to their survival based on Kaplan−Meier analysis. Biomarkers show a significant intrapatient association with survival (p < 0.05); however, only compression biomarkers show the ability to stratify patients between those with higher and lower overall survival (AUC = 0.83, HR = 6.30, p < 0.05 for CompCH). The compression biomarkers present three times higher Hazard Ratios than those representing only displacement. Our study provides a robust and automated method for quantifying and delineating compression in the peritumoral area. Based on the proposed methodology, we found an association between lower compression in the peritumoral area and good prognosis in high-grade glial tumors.

Details

Language :
English
ISSN :
2072-6694
Volume :
14
Issue :
7
Database :
MEDLINE
Journal :
Cancers
Publication Type :
Academic Journal
Accession number :
35406497
Full Text :
https://doi.org/10.3390/cancers14071725