# Quantification of fractional tumor burden for the early detection of post-treatment glioblastoma progression

## TL;DR

This study shows that fractional tumor burden maps from MRI scans can help distinguish tumor growth from treatment effects in glioblastoma patients.

## Contribution

The study introduces FTB quantification as a method to improve early detection of glioblastoma progression after treatment.

## Key findings

- FTBhigh voxel percentages significantly differ between tumor progression and treatment-related abnormalities.
- Using FTBhigh cutoffs yields 63% sensitivity and 79% specificity for detecting tumor progression.
- FTB maps provide fair accuracy in differentiating tumor growth from treatment effects.

## Abstract

Quantitative postprocessing of perfusion-weighted magnetic resonance imaging, including fractional tumor burden (FTB) maps, provides better visualization of the heterogeneous nature of glioblastomas. This study aimed to determine whether FTB maps help in distinguishing tumor progression (TP) from treatment-related abnormalities (TRA) in post-treatment glioblastoma patients.

Unenhanced and contrast-enhanced T1-weighted and perfusion-weighted sequences of patients with new contrast-enhancing lesions were retrospectively included. Semiautomatic segmentation of these lesions was performed. Using predefined relative cerebral blood volume (rCBV) thresholds, voxels within this segmentation were classified as FTBlow, FTBmid, or FTBhigh. Patient outcome was determined by clinical and radiological follow-up. Non-parametric statistics were used to compare the FTB quantification. Diagnostic accuracy was evaluated with the area under the receiver operating characteristic curve (AUROC) and Youden’s J. The difference between AUROCs was tested using bootstrapping.

Fifty-nine patients were included, 35 of them showing TP (59%). The percentages of voxels classified as FTBlow and FTBhigh were significantly different between the groups (p = 0.031 and p = 0.010, respectively). Using the percentage of voxels classified as FTBhigh as a cutoff to differentiate TP from TRA yielded an AUROC of 0.70 (95% confidence interval: 0.56‒0.84), while FTBlow yielded 0.67 (0.52–0.82), without a significant difference (p = 0.466). The highest sensitivity and specificity based on the cutoff of 24% of voxels classified as FTBhigh coverage, were 63% and 79%, respectively.

FTB quantification yielded fair accuracy in the early detection of glioblastoma TP. Future research is needed to investigate how to use FTB maps in clinical practice.

Early discrimination between TP and TRA, even with fair accuracy, can help in alleviating some uncertainty in glioblastoma patients. A clear visualization of lesion heterogeneity provided by FTB-maps could allow for more targeted treatment options and targeted follow-up.

Follow-up of patients with glioblastoma is complicated by the similar appearance of treatment effects and tumor growth on MRI.Perfusion imaging provides a basis for the creation of FTB maps. These visualize the heterogeneity of brain lesions.Quantitative analysis of FTB maps can help differentiate tumor growth from treatment effect with reasonable accuracy.

Follow-up of patients with glioblastoma is complicated by the similar appearance of treatment effects and tumor growth on MRI.

Perfusion imaging provides a basis for the creation of FTB maps. These visualize the heterogeneity of brain lesions.

Quantitative analysis of FTB maps can help differentiate tumor growth from treatment effect with reasonable accuracy.