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Kaplan – Meier survival curves according to age-groups (A) age group 1 classi fi cation: ≤ 45 years, 46 – 64 years, and ≥ 65 years; (B) age group 2 classi fi cation: ≤ 50 years and N 50 years; and (C) age group 3 classi fi cation: ≤ 60 years and N 60 years. Log-rank tests were performed to compare survival rates among age-groups.
Source publication
Although age is thought to correlate with the prognosis of glioma patients, the most appropriate age-group classification standard to evaluate prognosis had not been fully studied. This study aimed to investigate the influence of age-group classification standards on the prognosis of patients with high-grade hemispheric glioma (HGG). This retrospec...
Contexts in source publication
Context 1
... 1). The mean follow-up time was 23.2 months (SD = 21.9 months). A total of 86 subjects (68.8%) died during the study period. The median survival time was 19 months and the one-year and two-year survival rates were 64.8% and 40.0%, respectively (data not shown). Survival rates were analyzed in the different age-groups using Kaplan-Meier curves (Fig. 1). Regardless of the definition of age groups, results of log-rank test consistently showed that patients in the older age-group had significantly lower survival rates compared with patients in the younger age-groups (all p b 0.001). Kaplan-Meier survival curves were also plotted for demographic and clinical characteristics. We showed ...
Context 2
... in the younger age-groups (all p b 0.001). Kaplan-Meier survival curves were also plotted for demographic and clinical characteristics. We showed that survival outcomes were better in patients who were fe- male, had received regular chemotherapy and radiotherapy, had lower grade of tumor based on WHO classification, or had lobar-involved gli- oma (Fig. ...
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Citations
... This finding is consistent with findings in patients with highgrade glioma. Inconsistent results have been reported regarding the age cut-off for predicting clinical outcomes in glioma patients (22)(23)(24). In our study, the limits of the age categories ranged from 50 years, which is also used in recursive partitioning analysis classes (25,26) to 65 years, which is a common cut-off point for age categories. ...
Aim
To investigate the prognostic factors of survival in patients with high-grade gliomas without isocitrate dehydrogenase-1 (IDH) mutation and O 6 -methylguanine-DNA methyltransferase (MGMT) methylation.
Methods
The study enrolled Slovenian patients with high-grade gliomas. Postoperatively, they completed a battery of neuropsychological tests. Demographics and clinical data were collected. The results of cognitive tests were converted to standardized scores and dichotomized based on impairment. A univariate Cox proportional hazard regression model was used to determine clinical predictors, and a multivariate Cox model was used to determine the prognostic value of cognitive test results. Kaplan-Meier curves were constructed, and survival was compared with the log rank test.
Results
The study enrolled 49 patients with IDH wild-type, MGMT-unmethylated high-grade gliomas. The median time to progression was 9.92 months (7.25, 12.59) and the overall median survival was 12.19 months (8.95, 15.4). Age and the extent of surgery were significant prognostic factors for survival. After controlling for these factors, cognitive functioning in the domain of verbal fluency remained a significant predictor of survival outcomes.
Conclusion
Cognitive functioning in the domain of verbal fluency was associated with overall survival independently of age and the extent of surgery. Cognitive functioning could be an important stratifying tool in this group of patients lacking other predictors.
... 32 Previous studies have confirmed that age plays an important role in the prognosis of glioma. 33,34 In addition to WHO grade and age, our study also shows that IDH mutation and 1p/19q co-deletion are independent prognostic factors. Many previous studies have confirmed that IDH mutation indicates prolonged survival. ...
Abstract An accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1‐weighted images and T2 fluid‐attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression‐free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1‐, 2‐, and 3‐year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.
... It seems that the age stratification for children, adolescents, and young adults should be reconsidered, challenging the applicability of current age groups for gliomas. Published data in recent years have questioned the applicability of age grouping of patients with glioma; however, the issue remains controversial given the inconsistencies in published studies [5,16,18]. Chen et al. [18] analyzed clinical and follow-up data of 125 patients with high-grade gliomas (HGG) who underwent surgery and were pathologically diagnosed at a single-center medical facility between 2002 and 2012. Their study investigated the relationship between different age classification criteria and HGG prognosis. ...
... Published data in recent years have questioned the applicability of age grouping of patients with glioma; however, the issue remains controversial given the inconsistencies in published studies [5,16,18]. Chen et al. [18] analyzed clinical and follow-up data of 125 patients with high-grade gliomas (HGG) who underwent surgery and were pathologically diagnosed at a single-center medical facility between 2002 and 2012. Their study investigated the relationship between different age classification criteria and HGG prognosis. ...
... Our results challenge the current stratification method of changing age into a classification variable as a predictor of glioma prognosis. Various age cutoff points are being spread among people, and these data suggest that the mortality rate of patients with glioma increases dramatically with age [18,20]. However, it should be emphasized that the results of previous studies are mostly based on small sample size and lack of adjustment of covariates. ...
Background
The age of glioma plays a unique role in prognosis. We hypothesized that age is not positively correlated with survival prognosis and explored its exact relationship.
Methods
Glioma was identified from the SEER database (between 2000 and 2018). A multivariate Cox proportional regression model and restricted cubic spline (RCS) plot were used to assess the relationship between age and prognosis.
Results
A total of 66465 patients with glioma were included. Hazard ratios (HR) for ten-year by age: 0–9 years, HR 1.06 (0.93–1.20); 10–19 years: reference; 20–29 years, HR 0.90 (0.82–1.00); 30–39 years, HR 1.14 (1.04–1.25); 40–49 years, HR 2.09 (1.91–2.28); 50–59 years, HR 3.48 (3.19–3.79); 60–69 years, HR 4.91 (4.51–5.35);70–79 years, HR 7.95 (7.29–8.66); 80–84 years, HR 12.85 (11.74–14.06). After adjusting for covariates, the prognosis was not positively correlated with age. The smooth curve of RCS revealed this non-linear relationship: HR increased to 10 years first, decreased to 23 years, reached its lowest point, and became J-shaped.
Conclusion
The relationship between age and glioma prognosis is non-linear. These results challenge the applicability of current age groupings for gliomas and advocate the consideration of individualized treatment guided by precise age.
... The clinical outcome of GBM patients remained poor even after the gross total resection of the tumor followed by adjuvant radio-and chemotherapy [1]. The prognosis of GBM patients also worsens as age increases [9,19]. Elderly patients have a median overall survival of 6 months when compared with younger patients with GBM patients [2,14]. ...
This study assesses the effect of extent of resection (EOR) on the longer-term survival and early mortality of elderly patients (≥ 75 years old) with glioblastoma. We used the Surveillance, Epidemiology, and End Results (SEER) database and data from our center to evaluate the effect of EOR on the long-term survival and early mortality of patients with glioblastoma. We included 50 elderly patients (≥ 75 years old) with glioblastoma visiting our hospital. The median overall survival of the patients who underwent a gross total resection, a subtotal resection, and a partial resection were 278, 200, and 83 days, respectively. The multivariate analysis showed that gross total resection (HR: 0.100; 95% CI: 0.015–0.671, p < 0.001) and subtotal reresection (HR: 0.134, 95% CI: 0.022–0.831, p < 0.001) were independent predictors of favorable prognosis when compared with partial resection. The data extracted from the SEER database also indicated that EOR was an independent predictor of OS, CCS, and early mortality. The stratification analysis revealed that gross total resection was the best protective factor of OS, early mortality, and CCS. Radical resection may improve the OS and CCS of glioblastoma patients aged ≥ 75 years and decrease early mortality.
... Besides, the manual methods existed are tedious and expensive and need time. Therefore, divers systems were suggested to assist the radiologist to make a decision [6][7][8][9][10][11][12][13][14]. These systems can take a significant role to facilitate radiologist's diagnosis, prognosis, pre-surgical and postsurgical processes, etc. [1,15]. ...
Various computer systems have attracted more researchers' attention to arrive at a qualitative diagnosis in a few times. Different brain tumor classification approaches are proposed due to lesion complexity. This complexity makes the early tumor diagnosis using magnetic resonance images (MRI) a hard step. However, the accuracy of these techniques requires a significant amelioration to meet the needs of real-world diagnostic situations. We aim to classify three brain tumor types in this paper. A new technique is suggested which provides excellent results and surpasses the previous schemes. The proposed scheme makes use of the normalization, dense speeded up robust features, and histogram of gradient approaches to ameliorate MRI quality and generate a discriminative feature set. We exploit support vector machine in the classification step. The suggested system is benchmarked on an important dataset. The accuracy achieved based on this scheme is 90.27%. This method surpassed the most recent system according to experimental results. The results were earned through a strict statistical analysis (k-fold cross-validation), which proves the reliability and robustness of the suggested method.
... However, there is no uniform age criterion for grouping glioma patients for personalized treatment [14]. Some glioma patient cohorts were divided into different age groups according to fixed age intervals [15], some were divided into two groups based on a certain age point [16], and others were divided based on the overall survival (OS) of the patients [17]. Different criteria for age grouping have led to inconsistent conclusions regarding the prognostic value of age. ...
... These contradictory conclusions could be partly explained by the difference in age classification criteria between different studies. In one study, a multivariate Cox regression model with different cutoff points was used to analyze the effect of age on OS, but only three age groups were compared, and univariate analysis was performed using prognostic factors as a classification criterion [17]. OS is a good indicator for evaluating patient outcomes, but confounding factors such as tumor size, tumor location, surgical resection extent, and patient compliance, might impair the accuracy of the relationship between age and OS. ...
Background:
Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients.
Methods:
1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, P53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, PR, CD3, H3K27M, TS, and 1p/19q status.
Results:
The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14 years old (pediatric group), 15-47 years old (youth group), 48-63 years old (middle-aged group), and ≥ 64 years old (elderly group).The proportions of glioblastoma and large tumor size (4-6 cm) increased with age (p = 0.000, p = 0.018, respectively). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively).
Conclusions:
Appropriate classifications of the age group for risk stratification are 0-14 years old (pediatric group), 15-47 years old (young group), 48-63 years old (middle age group) and ≥ 64 years old (elderly group). This age group classification is effective in evaluating the risk of glioblastoma in glioma patients.
... Growing evidence indicates that IDH1, MGMT, TERT, P53, EGFR, and age at diagnosis are associated with clinical outcome in patients with glioblastoma [3][4][5][6]. The age at diagnosis has a pivotal role in predicting the clinical outcome of several malignant tumors, including hormone receptor-positive breast cancer, glioma, thyroid cancer, and cervical cancer [7][8][9][10][11], while the prognostic role of age in glioblastoma is conflicting [7,8,12,13]. Chen et al. [8] conducted a retrospective analysis with 125 high-grade gliomas to evaluate the prognostic effect of 3 age groups (£50 and >50 years old; £60 and >60 years old; £45 and 45-65 and ³65 years old) and their results showed that older patients had worse clinical survival. ...
... Growing evidence indicates that IDH1, MGMT, TERT, P53, EGFR, and age at diagnosis are associated with clinical outcome in patients with glioblastoma [3][4][5][6]. The age at diagnosis has a pivotal role in predicting the clinical outcome of several malignant tumors, including hormone receptor-positive breast cancer, glioma, thyroid cancer, and cervical cancer [7][8][9][10][11], while the prognostic role of age in glioblastoma is conflicting [7,8,12,13]. Chen et al. [8] conducted a retrospective analysis with 125 high-grade gliomas to evaluate the prognostic effect of 3 age groups (£50 and >50 years old; £60 and >60 years old; £45 and 45-65 and ³65 years old) and their results showed that older patients had worse clinical survival. ...
... The age at diagnosis has a pivotal role in predicting the clinical outcome of several malignant tumors, including hormone receptor-positive breast cancer, glioma, thyroid cancer, and cervical cancer [7][8][9][10][11], while the prognostic role of age in glioblastoma is conflicting [7,8,12,13]. Chen et al. [8] conducted a retrospective analysis with 125 high-grade gliomas to evaluate the prognostic effect of 3 age groups (£50 and >50 years old; £60 and >60 years old; £45 and 45-65 and ³65 years old) and their results showed that older patients had worse clinical survival. However, Gately et al. reviewed the clinical data of 165 glioblastoma multiforme (GBM) patients to assess the influence of age group (£60 and 60-70 and ³70 years old) on the prognosis of patients, and they found that age was not associated with survival of GBM patients [13]. ...
Background
The present study aimed to assess the association of various age groups with survival in patients with glioblastoma.
Material/Methods
The Surveillance, Epidemiology, and End Results (SEER) database was used to extracted data on new diagnoses of glioblastoma between 2005 and 2015. Four age models were constructed according to the age at diagnosis.
Results
A total of 28 734 patients with glioblastoma (16 823 men and 11 911 women) were enrolled in the study. In multivariate analysis, variables including sex, race, tumor, and clinical information were identified as confounding factors to adjust 4 age models. In model 1, ages 39–58, 59–78, and 79+ years were risk factors of survival compared with age 0–18 years. In model 2, ages 18–65, 66–79, and 80+ years were prognostic factors of shorter survival compared with ages 0–17 years. In model 3, ages 45–59, 60–74, and 75+ years were associated with poor prognosis, while ages 18–44 years was associated with favorable clinical outcomes compared with ages 0–17 years. In model 4, ages 18–53, 54–64, and 65+ years were associated with poor prognosis.
Conclusions
The differences in prognoses in different age groups of glioblastoma patients suggest that clinicians should incorporate age into routine clinical assessments and develop appropriate treatment strategies.
... In the nomogram, several factors including, MATH level, gene IDH1/2, gene TTN, age, sex, WHO grade and histological classification, were indicated to have a substantial effect on glioma recurrence. Age has been identified as an independent prognostic factor in high-grade glioma (35), and elderly patients with glioma exhibit abnormal repair functions, resulting in gene mutations and impaired DNA metabolic functions. Therefore, compared with young patients with glioma, older patients are more likely to have higher tumor ITH levels. ...
Intra-tumor heterogeneity (ITH) is one of the most important causes of therapy resistance, which eventually leads to the poor outcomes observed in patients with glioma. Mutant-allele tumor heterogeneity (MATH) values are based on whole-exon sequencing and precisely reflect genetic ITH. However, the significance of MATH values in predicting glioma recurrence remains unclear. Information of patients with glioma was obtained from The Cancer Genome Atlas database. The present study calculated the MATH value for each patient, analyzed the distributions of MATH values in different subtypes and investigated the rates of clinical recurrence in patients with different MATH values. Gene enrichment and Cox regression analyses were performed to determine which factors influenced recurrence. A nomogram table was established to predict 1-, 2- and 5-year recurrence probabilities. MATH values were increased in patients with glioma with the wild-type isocitrate dehydrogenase (NADP(+)) (IDH)1/2 (IDH-wt) gene (P=0.001) and glioblastoma (GBM; P=0.001). MATH values were negatively associated with the 2- and 5-year recurrence-free survival (RFS) rates in patients with glioma, particularly in the IDH1/2-wt and GBM cohorts (P=0.001 and P=0.017, respectively). Furthermore, glioma cases with different MATH levels had distinct patterns of gene mutation frequencies and gene expression enrichment. Finally, a nomogram table that contained MATH values could be used to accurately predict the probabilities of the 1-, 2- and 5-year RFS of patients with glioma. In conclusion, the MATH value of a patient may be an independent predictor that influences glioma recurrence. The nomogram model presented in the current study was an appropriate method to predict 1-, 2- and 5-year RFS probabilities in patients with glioma.
... This study analyses relationships between age factor and lateral angle of the internal acoustic canal in three age groups: young and middle adults (21-50 years); young-old adults (51-70 years), and old adults (71-90 years). Age groups were established taking also into account the most recent debates regarding the definition of the elderly categories in Western contemporary populations (Chang et al, 2019;Chen et al, 2015;Orimo et al, 2006). Significant differences in LA values were found among the three age-groups within each sex (Table 2). ...
... L IFE EXPECTANCY HAS INCREASED SIGNIFICANTLY IN the past few decades, 1 thus increasing the interest in studying the potential impact of age on human functions. In plastic surgery, orthopedics, and cancer, the influence of age was investigated in terms of outcome of surgeries, 2 prognosis and prediction of treatments, 3,4 and decisions for the most appropriate treatments. 5 In ophthalmology, intraocular pressure (IOP) increases with age, 6 especially in the decades from the 60s to the 80s, which if not managed, leads to serious effects on vision. ...
