Indirect functional connectivity does not predict overall survival in glioblastoma.

BACKGROUND: Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM). METHODS: A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable. RESULTS: The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration. CONCLUSIONS: Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

White Matter Tract Density Index Prediction Model of Overall Survival in Glioblastoma.

Importance: The prognosis of overall survival (OS) in patients with glioblastoma (GBM) may depend on the underlying structural connectivity of the brain. Objective: To examine the association between white matter tracts affected by GBM and patients' OS by means of a new tract density index (TDI). Design, Setting, and Participants: This prognostic study in patients with a histopathologic diagnosis of GBM examined a discovery cohort of 112 patients who underwent surgery between February 1, 2015, and November 30, 2020 (follow-up to May 31, 2023), in Italy and 70 patients in a replicative cohort (n = 70) who underwent surgery between September 1, 2012, and November 30, 2015 (follow-up to May 31, 2023), in Germany. Statistical analyses were performed from June 1, 2021, to May 31, 2023. Thirteen and 12 patients were excluded from the discovery and the replicative sets, respectively, because of magnetic resonance imaging artifacts. Exposure: The density of white matter tracts encompassing GBM. Main Outcomes and Measures: Correlation, linear regression, Cox proportional hazards regression, Kaplan-Meier, and prediction analysis were used to assess the association between the TDI and OS. Results were compared with common prognostic factors of GBM, including age, performance status, O6-methylguanine-DNA methyltransferase methylation, and extent of surgery. Results: In the discovery cohort (n = 99; mean [SD] age, 62.2 [11.5] years; 29 female [29.3%]; 70 male [70.7%]), the TDI was significantly correlated with OS (r = -0.34; P < .001). This association was more stable compared with other prognostic factors. The TDI showed a significant regression pattern (Cox: hazard ratio, 0.28 [95% CI, 0.02-0.55; P = .04]; linear: t = -2.366; P = .02). and a significant Kaplan-Meier stratification of patients as having lower or higher OS based on the TDI (log-rank test = 4.52; P = .03). Results were confirmed in the replicative cohort (n = 58; mean [SD] age, 58.5 [11.1] years, 14 female [24.1%]; 44 male [75.9%]). High (24-month cutoff) and low (18-month cutoff) OS was predicted based on the TDI computed in the discovery cohort (accuracy = 87%). Conclusions and Relevance: In this study, GBMs encompassing regions with low white matter tract density were associated with longer OS. These findings indicate that the TDI is a reliable presurgical outcome predictor that may be considered in clinical trials and clinical practice. These findings support a framework in which the outcome of GBM depends on the patient's brain organization.

Patterns of gray and white matter functional networks involvement in glioblastoma patients: indirect mapping from clinical MRI scans.

Background: Resting-state functional-MRI studies identified several cortical gray matter functional networks (GMNs) and white matter functional networks (WMNs) with precise anatomical localization. Here, we aimed at describing the relationships between brain's functional topological organization and glioblastoma (GBM) location. Furthermore, we assessed whether GBM distribution across these networks was associated with overall survival (OS). Materials and methods: We included patients with histopathological diagnosis of IDH-wildtype GBM, presurgical MRI and survival data. For each patient, we recorded clinical-prognostic variables. GBM core and edema were segmented and normalized to a standard space. Pre-existing functional connectivity-based atlases were used to define network parcellations: 17 GMNs and 12 WMNs were considered in particular. We computed the percentage of lesion overlap with GMNs and WMNs, both for core and edema. Differences between overlap percentages were assessed through descriptive statistics, ANOVA, post-hoc tests, Pearson's correlation tests and canonical correlations. Multiple linear and non-linear regression tests were employed to explore relationships with OS. Results: 99 patients were included (70 males, mean age 62 years). The most involved GMNs included ventral somatomotor, salient ventral attention and default-mode networks; the most involved WMNs were ventral frontoparietal tracts, deep frontal white matter, and superior longitudinal fasciculus system. Superior longitudinal fasciculus system and dorsal frontoparietal tracts were significantly more included in the edema (p < 0.001). 5 main patterns of GBM core distribution across functional networks were found, while edema localization was less classifiable. ANOVA showed significant differences between mean overlap percentages, separately for GMNs and WMNs (p-values<0.0001). Core-N12 overlap predicts higher OS, although its inclusion does not increase the explained OS variance. Discussion and conclusion: Both GBM core and edema preferentially overlap with specific GMNs and WMNs, especially associative networks, and GBM core follows five main distribution patterns. Some inter-related GMNs and WMNs were co-lesioned by GBM, suggesting that GBM distribution is not independent of the brain's structural and functional organization. Although the involvement of ventral frontoparietal tracts (N12) seems to have some role in predicting survival, network-topology information is overall scarcely informative about OS. fMRI-based approaches may more effectively demonstrate the effects of GBM on brain networks and survival.