Objectives 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. Materials and methods 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. Results 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. Conclusion 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. Relevance statement 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. Key Points <UnorderedList Mark="Bullet"> <ItemContent> Follow-up of patients with glioblastoma is complicated by the similar appearance of treatment effects and tumor growth on MRI. </ItemContent> <ItemContent> Perfusion imaging provides a basis for the creation of FTB maps. These visualize the heterogeneity of brain lesions. </ItemContent> <ItemContent> Quantitative analysis of FTB maps can help differentiate tumor growth from treatment effect with reasonable accuracy. </ItemContent> </UnorderedList> Graphical Abstract

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

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2026年3月2日

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Siem Herings,
Rebecca de Wit,
Baris Saglik,
Manoj Mannil,
Rik van den Elshout,
Anne Arens,
Anja van der Kolk,
Tom Scheenen,
Dylan Henssen

摘要

Objectives

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.

Materials and methods

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 FTB_low, FTB_mid, or FTB_high. 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.

Results

Fifty-nine patients were included, 35 of them showing TP (59%). The percentages of voxels classified as FTB_low and FTB_high were significantly different between the groups (p = 0.031 and p = 0.010, respectively). Using the percentage of voxels classified as FTB_high as a cutoff to differentiate TP from TRA yielded an AUROC of 0.70 (95% confidence interval: 0.56‒0.84), while FTB_low 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 FTB_high coverage, were 63% and 79%, respectively.

Conclusion

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.

Relevance statement

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.

Key Points

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.

Graphical Abstract