Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jul;22(3):329-352.
doi: 10.1007/s12021-024-09671-9. Epub 2024 Jun 20.

Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space

S M Boelders  1   2 , W De Baene  3 , E Postma  2 , K Gehring  4   5 , L L Ong  2
Affiliations

Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space

S M Boelders et al. Neuroinformatics. 2024 Jul.

Abstract

Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.

Keywords: Cognitive functioning; Dimensionality reduction; High-grade glioma; Individual predictions; Lesion location; Parcellations.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of tumors and the first ten PCA components
Fig. 2
Fig. 2
Overview of all prediction results
None
Cumulative amount of variance explained versus the number of dimensions used by PCA
None
Example of the mass effect of a A small, B medium, and C large glioma. 1) shows the midline shift where the midline is shown as a red line, 2) shows the compression of the ventricles.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Acevedo-Vergara, K., Perez-Florez, M., Ramirez, A., Torres-Bayona, S., Dau, A., Salva, S., et al. (2022). Cognitive deficits in adult patients with high-grade glioma: A systematic review. Clinical Neurology and Neurosurgery. 10.1016/j.clineuro.2022.107296 10.1016/j.clineuro.2022.107296 - DOI - PubMed
    1. Adewole, M., Rudie, J. D., Gbdamosi, A., Toyobo, O., Raymond, C., Zhang, D., Omidiji, O., Akinola, R., Suwaid, M. A., Emegoakor, A., Ojo, N., Aguh, K., Kalaiwo, C., Babatunde, G., Ogunleye, A., Gbadamosi, Y., Iorpagher, K., Calabrese, E., Aboian, M., Linguraru, M., … Anazodo, U. C. (2023). The brain tumor segmentation (BraTS) Challenge 2023: glioma segmentation in sub-saharan Africa patient population (BraTS-Africa). ArXiv, arXiv:2305.19369v1.
    1. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., et al. (2017). Advancing the cancer genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data,4(1), 170117. 10.1038/sdata.2017.117 10.1038/sdata.2017.117 - DOI - PMC - PubMed
    1. Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., et al. (2019). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv:1811.02629 [cs, stat]. http://arxiv.org/abs/1811.02629. Accessed 7 October 2020
    1. Banerjee, P., Leu, K., Harris, R. J., Cloughesy, T. F., Lai, A., Nghiemphu, P. L., et al. (2015). Association between lesion location and language function in adult glioma using voxel-based lesion-symptom mapping. NeuroImage: Clinical,9, 617–624. 10.1016/j.nicl.2015.10.010 10.1016/j.nicl.2015.10.010 - DOI - PMC - PubMed

LinkOut - more resources