Artificial Intelligence-Based MRI Segmentation in Glioblastoma and Single Brain Metastasis: An Exploratory Study of Diagnostic and Prognostic Value.

(1)

Background

Differentiating between glioblastoma (GB) and single brain metastasis (SBM) on conventional MRI remains a clinical challenge, and manual tumor volumetry often fails to provide a robust prognostic signal for survival. This study evaluates whether AI-based automated segmentation and the derivation of multi-compartment volumetric ratios can improve diagnostic accuracy and survival prediction. (2)

Methods

A retrospective study was conducted on 123 patients (n = 84 GB; n = 39 SBM) who underwent 1.5 T MRI. Automated segmentation was performed using a CE-certified deep learning tool to quantify total tumor, contrast-enhancing, necrotic, and peritumoral edema volumes. AI volumes were validated against the manual ellipsoidal method estimates. Three machine learning (ML) models were developed to predict 6-month overall survival: Model 1 (manual volumetry), Model 2 (AI sub-compartment volumes), and Model 3 (enhanced feature model with normalized ratios). (3)

Results

AI-derived total volumes showed excellent correlation with manual measurements (r = 0.955, p < 0.001). GB exhibited a significantly higher AI Model Necrosis Volume/AI Model Total Volume Ratio (p < 0.001), while SBM showed a higher AI Model Edema Volume/AI Model Total Volume Ratio (p = 0.003). In survival tasks, the ML enhanced feature model showed improved discrimination over manual methods, reaching an AUC-ROC of 0.783 compared to 0.571 for manual volumetry-a +21.2% improvement. AI Model Contrast Volume/AI Model Total Volume Ratio and AI Model Edema Volume/AI Model Total Volume Ratio were identified as the leading prognostic predictors within this exploratory cohort. (4)

Conclusions

In this exploratory analysis, AI-assisted multi-compartment segmentation appears to provide granular, biologically relevant imaging biomarkers that may complement traditional manual volumetry in the prognostic assessment of GB and SBM. Given the retrospective design and the absence of molecular profiling, these preliminary findings should be interpreted with caution and warrant prospective, multicenter validation.