Background: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test). Conclusion: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Introduction. Neuroanatomic locations of gliomas may influence clinical presentations, molecular profiles, and patients' prognoses.Methods. We investigated our institutional cancer registry to include patients with glioma over a 10-year period. Statistical tests were used to compare demographic, genetic, and clinical characteristics among patients with gliomas in different locations. Survival analysis methods were then used to assess associations between location and overall survival in the full cohort, as well as in relevant subgroups.Results. 182 gliomas were identified. Of the tumours confined to a single lobe, there were 51 frontal (28.0%), 50 temporal (27.5%), 22 parietal (12.1%), and seven occipital tumours (3.8%) identified. Tumours affecting the temporal lobe were associated with reduced overall survival when compared to all other tumours (11 months vs. 13 months, log-rank p = 0.0068). In subgroup analyses, this result was significant for males [HR (95%CI) 2.05 (1.30, 3.24), p = 0.002], but not for females [HR (95%CI) 1.12 (0.65, 1.93), p = 0.691]. Out of 82 cases tested for IDH-1, 10 were mutated (5.5%). IDH-1 mutation was present in six frontal, two temporal, one thalamic, and one multifocal tumour. Out of 21 cases tested for 1p19q deletions, 12 were co-deleted, nine of which were frontal lobe tumours. MGMT methylation was assessed in 45 cases; 7/14 frontal tumours and 6/13 temporal tumours were methylated. Conclusion.Our results support the hypothesis that the anatomical locations of gliomas influence patients' clinical courses. Temporal lobe tumours were associated with poorer survival, though this association appeared to be driven by these patients' more aggressive tumour profiles and higher risk baseline demographics. Independently, female patients who had temporal lobe tumours fared better than males. Molecular analysis was limited by the low prevalence of genetic testing in the study sample, highlighting the importance of capturing this information for all gliomas.Importance of this study. The specific neuroanatomic location of tumours in the brain is thought to be predictive of treatment options and overall prognosis. Despite evidence for the clinical significance of this information, there is relatively little information available regarding the incidence and prevalence of tumours in the different anatomical regions of the brain. This study has more fully characterised tumour prevalence in different regions of the brain. Additionally, we have analysed how this information may affect tumours' molecular characteristics, treatment options offered to patients, and patients' overall survival. This information will be informative both in the clinical setting and in directing future research.

Background: Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods: Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results: Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival. Conclusion: Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

; 2 Purpose: Patient sex is recognized as a significant determinant of outcome but the relative prognostic importance of molecular, imaging, and other clinical features of GBM has not yet been thoroughly explored for male versus female patients. Combining multimodal MR images and patient clinical information, this investigation assesses which pretreatment MRIbased and clinical variables impact sexspecific survivorship in glioblastoma patients. Methods:We considered the multimodal MRI and clinical data of 494 patients newly diagnosed with primary glioblastoma (299 males and 195 females). Patient MR images (T1Gd, T2, and T2FLAIR) were segmented to quantify imageable tumor volumes for each MR sequence. Cox proportional hazard (CPH) models and Student's ttests were used to assess which variables were significantly associated with survival outcomes. We used machine learning algorithms to develop pruned decision trees to integrate the impact of these variables on patient survival.