Enhancing glioma progression prediction: Unsupervised clustering of whole-brain magnetic resonance spectroscopy data...

The inevitable progression of high-grade gliomas has prompted a need for data-backed identification of compromised tissue prior to detection on traditional serial imaging. Whole-brain magnetic resonance spectroscopy (WB-MRS) can fill this role to classify glioma progression prior to contrast enhancement. Voxel-level data can differentiate areas of perilesional tissue under supervised machine learning (ML) and has been shown to predict the likelihood of tumor progression within six months. In this study, we aim to improve the spatial utility of WB-MRS ML through unsupervised ML clustering for utility in intraoperative integration with existing neuronavigation platforms.

This analysis involved 16 adult patients that developed recurrence of high-grade glioma (HGG) on serial imaging, including 13 with glioblastoma (GBM) and three with anaplastic astrocytomas. Postoperative WB-MRS images were used as data inputs. We investigated two unsupervised clustering methods to optimize the identification of compromised perilesional tissue from a previously published supervised model. All voxels within the new region of interest (ROI) are reclassified as future progression regardless of their classification from the supervised model.

After hyperparameter tuning, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) shows an area under the curve (AUC) of 0.942, while K-Means Clustering shows an AUC of 0.874. DBSCAN also shows a superior accuracy, specificity, sensitivity, and F1-score to the original supervised model alone.

The incorporation of unsupervised clustering enhances the utility of WB-MRS for identifying compromised perilesional tissue and predicting high-grade glioma progression. Unsupervised clustering incorporates critical geospatial data and offers a more surgically relevant approach to visualizing tumor progression.

Early detection of high-grade glioma recurrence remains a major limitation of current imaging modalities, restricting the ability to guide timely and precise interventions. While prior work has demonstrated that WB-MRS combined with supervised ML can identify metabolically abnormal tissue at risk for progression, these approaches lack spatial coherence and clinical usability. In this study, we introduce a hybrid pipeline integrating supervised voxel-wise predictions with unsupervised spatial clustering (DBSCAN and K-means) to refine recurrence mapping. This approach significantly improves classification performance and generates spatially contiguous, clinically interpretable regions of interest. Importantly, these outputs can be exported as DICOM overlays, enabling direct integration into neuronavigation systems. While still in pilot phase, this work advances WB-MRS from a predictive tool toward a clinically integrable platform, with potential applications in surgical planning, biopsy targeting, and longitudinal disease monitoring in patients with high-grade glioma.