Dataset Information

Predicting overall survival in diffuse glioma from the presurgical connectome.

ABSTRACT: Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients diagnosed with diffuse glioma. These features were entered into a Cox proportional hazards model to predict overall survival with 10-folds cross-validation. The mean time-dependent area under the curve (AUC) of the connectome model was compared with the mean AUCs of clinical and radiomic models using a pairwise t-test with Bonferroni correction. One clinical model included only features that are known presurgery (clinical) and another included an advantaged set of features that are not typically known presurgery (clinical +). The median survival time for all patients was 134.2 months. The connectome model (AUC 0.88 ± 0.01) demonstrated superior performance (P < 0.001, corrected) compared to the clinical (AUC 0.61 ± 0.02), clinical + (AUC 0.79 ± 0.01) and radiomic models (AUC 0.75 ± 0.02). These findings indicate that the connectome is a feasible and reliable early biomarker for predicting survival in patients with diffuse glioma. Connectome and other whole-brain models could be valuable tools for precision medicine by informing patient risk stratification and treatment decision-making.

SUBMITTER: Kesler SR

PROVIDER: S-EPMC9637134 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Predicting overall survival in diffuse glioma from the presurgical connectome.

Kesler Shelli R SR Harrison Rebecca A RA Rao Vikram V Dyson Hannah H Petersen Melissa M Prinsloo Sarah S

Scientific reports 20221105 1

Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients ...[more]

PMID: 36335224

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Project description:Background: Diffuse large B-cell lymphoma (DLBCL) is a well-differentiated disease, which makes the diagnosis and therapeutic strategy a difficult problem. While ferroptosis, as an iron-dependent form of regulated cell death, it plays an important role in causing several types of cancer. This study is aimed at exploring the prognostic value of ferroptosis-related genes in DLBCL. Methods: In our study, mRNA expression and matching clinical data of DLBCL patients were derived from Gene Expression Omnibus (GEO) database. First, multivariate cox regression model and nomogram which can predict the DLBCL patients' prognosis were built and validated. The multigene signature was constructed and optimized by the least absolute shrinkage and selection operator (LASSO) cox regression model. Also, ferroptosis-related subtypes were developed by consistent cluster. Last but not least, we explored the association between categories of infiltrating immune cells and model genes' expression. Results: Our results showed that 27 gene expressions were correlated with overall survival (OS) in the univariate cox regression analysis. A 4-gene signature was constructed through these genes to stratify patients into high-low risk groups using risk score derived from model (model 1:gene expression model). The OS of patients in the high-risk group was shorter than that of patients in the low-risk group in the TNM stage and clinically distinct subtypes (activated B cell [ABC], germinal center B cell [GCB]) (P < .001). Furthermore, it was shown that the risk score was an independent factor in clinical cox regression model for OS (model 2:clinical model) (HR>1, P < .010). Besides, in consistent cluster analysis, ferroptosis prognosis status was different among 3 subtypes. Moreover, the correlation analysis between 4-gene with immune cells showed dendritic cells may be significantly associated with DLBCL. Conclusion: This research constructed an innovative ferroptosis-related gene signature for prognostic estimation of DLBCL patients. Solutions targeting ferroptosis could be an important therapeutic intervention for DLBCL.

Project description:BackgroundDiffuse gliomas (WHO grade II-IV) are progressive primary brain tumors with great variability in prognosis. Cognitive deficits are of important prognostic value for survival in diffuse gliomas. Until now, few studies focused on domain-specific neuropsychological assessment and rather used MMSE as a measure for cognitive functioning. Additionally, these studies did not take WHO 2016 diagnosis into account. We performed a retrospective cohort study with the aim to investigate the independent relationship between cognitive functioning and survival in treatment-naive patients undergoing awake surgery for a diffuse glioma.MethodsIn patients undergoing awake craniotomy between 2010 and 2017, we performed pre-operative neuropsychological assessments in five cognitive domains, with special attention for the domains executive functioning and memory. We evaluated the independent relation between these domains and survival, in a Cox proportional hazards model that included state-of-the-art integrated histomolecular ('layered' or WHO-2016) classification of the gliomas and other known prognostic factors.ResultsWe included 197 patients. Cognitive impairments (Z-values ≦ - 2.0) were most frequent in the domains memory (18.3%) and executive functioning (25.9%). Impairments in executive functioning and memory were significantly correlated with survival, even after correcting for the possible confounders. Analyses with the domains language, psychomotor speed, and visuospatial functioning yielded no significant results. Extensive domain-specific neuropsychological assessment was more strongly correlated to survival than MMSE.ConclusionCognitive functioning is independently related to survival in diffuse glioma patients. Possible mechanisms underlying this relationship include the notion of cognitive functioning as a marker for diffuse infiltration of the tumor and the option that cognitive functioning and survival are determined by overlapping genetic pathways and biomarkers.

Dataset Information

Predicting overall survival in diffuse glioma from the presurgical connectome.

Publications

Predicting overall survival in diffuse glioma from the presurgical connectome.

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