Pre-surgical connectome features predict IDH status in diffuse gliomas

Kesler, Shelli R.; Harrison, Rebecca; Petersen, Melissa; Rao, Vikram; Dyson, Hannah; Alfaro-Munoz, Kristin; Weathers, Shiao Pei; Groot, John de

doi:10.18632/oncotarget.27301

Cited by 18 publications

(17 citation statements)

References 44 publications

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“…With respect to molecular classification in terms of IDH mutation status, Suchorska et al stratified 301 patients with WHO grade II (n = 181) or grade III glioma (n = 120) according to their molecular profile and reviewed pre-operative MRI and volumetric analyses of contrast-enhanced and T2 volumes, showing that contrast enhancement on initial MRI is a prognostic factor for survival with dependence on volume distinguishing IDH-mutated from IDH-wild type tumors (51). Similarly, with an eye towards predicting IDH mutation pre-operatively, several publications have employed MRI and ML (57,58,61). Zhang et al employed preoperative MRIs of 120 HGG patients with confirmed IDH genotype, to extract 2970 imaging features from pre-and post-contrast T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) maps.…”

Section: Imaging and Tumor Grading-based Survival Predictionmentioning

confidence: 99%

Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao

Krauze

2021

Gliomas

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Gliomas are neurologically devastating tumors with generally poor outcomes. Traditionally, survival prediction in glioma is studied from clinical features using statistical approaches. With the rapid development of artificial intelligence approaches encompassing machine learning and deep learning, there has been a keen interest among researchers to apply these methods to survival prediction in glioma allowing for integrated processes that encompass pathology, histology, molecular, imaging, and clinical features. This chapter provides an overview of the emerging computational approaches that have the potential to revolutionize survival prediction in glioma. Machine learning and deep learning techniques, including support vector machine, random forest, convolutional neural network, and radiomics, are discussed.

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Section: Imaging and Tumor Grading-based Survival Predictionmentioning

confidence: 99%

Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao

Krauze

2021

Gliomas

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“…IDH gene-mutant tumors demonstrated significiantly good clinical outcomes as compared with the wild types. IDH mutation has been recognized to be of central prognostic and biologic importance, and the diagnostic tool has been incorporated in the diagnosis since 2016 (18,30). Notably, IDH gene status has been made a major criterion for tumor classification (1,25,39).…”

Section: Discussionmentioning

confidence: 99%

Predictors of survival in turkish patients with primary glioblastoma

Bilgin¹,

Duman²,

Altıntaş³

et al. 2021

Turkish Neurosurgery

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AIM: An increasing number of biomarkers of primary glioblastoma (GBM) have recently been described. We aimed to investigate the biological and clinical factors that affect survival in Turkish patients with primary GBM. MATERIAL and METHODS: The clinical and demographic data of all patients with primary GBM diagnosed between 2007 and 2016 were evaluated. In all the patients' pathological specimens, O6 methylguanine-DNA methyltransferase (MGMT) methylation and isocitrate dehydrogenase (IDH) 1 mutation were detected retrospectively by immunohistochemistry. Kaplan-Meier survival analysis, log-rank test, and multivariate analyses of the Cox hazard proportional model for all the variables were performed using the SPSS statistical package. The treatment details and other patient-related factors were identified, and their correlations were analyzed. RESULTS: We enrolled 137 primary GBM patients to the study. Median progression free survival (PFS) was 8.57 months (95% CI:6.8-9.5) and median overall survival (OS) was 12 months (95% CI:10.8-13.3). IDH-1 mutations were detected in 21 primary GBMs (15.3%). PFS was 15.43 ± 1.95 months. Survival rates were higher, but no statistically significant difference (p=0.074). MGMT methylation was detected in 40 primary GBMs (29.2%). OS and PFS of MGMT (+) cases were higher than MGMT(-) cases (p=0.001; p=0.001 respectively). Ki67 (%) measurement (10%-90%) average is 32.64 ± 16.56. No statistically significant between higher and lower ki67 levels (p=0.510, p=0.505 respectively). KPS (%) more than 70 at the time of diagnosis statistically significant longer median OS and PFS (p=0.001). PFS and OS were higher in all treatment modalities. CONCLUSION: The most important factors that affected survival were performance score, MGMT methylation status, systemic oncologic therapy, and IDH mutation in the Turkish population with primary GBM. We demonstrated that MGMT methylation and higher KPS levels were associated with significiantly longer OS and PFS.

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“…While surgical treatment is currently the main treatment for patients with glioma, there are certain risks in the operation. The traction and stress reaction of related brain tissue during surgery can cause complications such as cerebral edema, which in turn affects the prognosis of patients (12,13). Postoperative cognitive dysfunction (POCD) refers to the slight damage to the brain's advanced cortical function after anesthesia and surgery, which affects the patient's memory, attention and information processing capabilities, and is a mild neurocognitive disorder.…”

Section: Discussionmentioning

confidence: 99%

Observation of the impact of the eight-step process combined with the four-track crossover quality control applied to patients with glioma surgery: a randomised trial

Zhang

et al. 2021

Ann Transl Med

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Background: At present, surgery is the main treatment for patients with glioma, but there are certain risks in the operation. The traction and stress reaction of related brain tissue during surgery can cause complications such as cerebral edema, which adversely affects the prognosis of patients. The purpose of the present study was to explore the effect of an eight-step process combined with four-track quality control applied to patients undergoing glioma surgery.Methods: A total of 122 patients undergoing glioma surgery admitted to our hospital from March 2017 to March 2020 were selected and divided into two groups according to the random number table method, each with 61 cases. The control group underwent routine intervention after surgery and the observation group underwent an eight-step process combined with four-track cross-over quality control intervention after surgery. The postoperative rehabilitation effects, cancer-related fatigue, changes in quality of life, and the incidence of complications before and after intervention were compared between the two groups.Results: The time of catheter removal, the time of first eating, the time of getting out of bed, and the length of hospital stay of the observation group were shorter than those of the control group (P<0.05). In the observation group cognitive fatigue, physical fatigue, and emotional fatigue scores were lower than those of the control group after intervention (P<0.05) and the quality-of-life scores of the observation group after intervention were higher than those of the control group (P<0.05). The total incidence of complications in the observation group was lower than that of the control group (P<0.05). Conclusions:The eight-step process combined with four-track quality control applied to patients undergoing glioma surgery can reduce cancer-related fatigue, improve quality of life, reduce complications, and promote speedy recovery.

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Pre-surgical connectome features predict IDH status in diffuse gliomas

Cited by 18 publications

References 44 publications

Survival Prediction in Gliomas: Current State and Novel Approaches

Survival Prediction in Gliomas: Current State and Novel Approaches

Predictors of survival in turkish patients with primary glioblastoma

Observation of the impact of the eight-step process combined with the four-track crossover quality control applied to patients with glioma surgery: a randomised trial

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