The role of chemotherapy in patients with H3K27M-mutant diffuse midline gliomas: a SEER-based propensity scored matching study

The role of chemotherapy in patients with H3K27M-mutant diffuse midline gliomas: a SEER-based propensity scored matching study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The role of chemotherapy in patients with H3K27M-mutant diffuse midline gliomas: a SEER-based propensity scored matching study Jin Zhang, Shanshan Wang, Sichen Wang, Haowen Jiang, Yuanli Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5432895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Diffuse Midline Gliomas (DMGs) represent a category of rare brain tumors with an exceedingly poor prognosis. Anatomical constraints make complete surgical resection challenging. Conventional radiotherapy is widely regarded as a means to enhance patient survival. Currently, while chemotherapy is frequently employed in clinical practice for DMGs, its full therapeutic efficacy remains incompletely understood. Methods We conducted a SEER-based propensity scored matching (PSM) study on patients with H3K27M-mutant DMGs to evaluate the role of chemotherapy in the treatment benefit of DMGs. Univariate and multivariate Cox regression model were used to evaluate the relevant factors affecting cancer specific survival (CSS). Stratification and interaction analyses were conducted to delineate the impact of demographic and clinicopathological variables. Results Patients underwent both radiotherapy and chemotherapy concurrently achieved notably longer survival times compared to those who received only radiotherapy. The CSS among patients who received chemotherapy regimens was significantly prolonged in both the PSM and non PSM cohort. Univariable Cox regression suggested that age, primary site and chemotherapy were potential prognostic factors for CSS. Multivariate Cox regression indicated patients who received radiotherapy or chemotherapy exhibited a reduced risk of mortality. Multitude demographic factors, including gender, race, marital status, household income and rural urban, as well as clinicopathological variables could affect the chemotherapy benefits of DMGs patients. Conclusion Chemotherapy as an adjuvant therapy could significantly improve the prognosis of DMGs patients under comprehensive treatment conditions. The nature of multiple factors affecting chemotherapy benefits emphasizes the necessity of tailored treatment strategies. Diffuse midline gliomas DMGs Chemotherapy H3K27M Figures Figure 1 Figure 2 Figure 3 Introduction Diffuse midline gliomas (DMGs) are defined by the 2021 WHO Central Nervous System (CNS) Tumor Classification as a high-grade glioma with histone H3K27 mutation, which have been indicated that predominantly occurs in the pediatric population. Irrespective of histology and morphology, DMGs with H3K27 mutation are classified as WHO grade IV[ 1 ]. DMGs exhibit extremely high malignancy and are prone to recurrence with a dismal prognosis. The median overall survival (OS) of DMGs only last for 12 months, and the 2-year survival rate after diagnosis is less than 10% [ 2 , 3 ]. As the hallmark of the diagnosis of DMGs, the histone H3K27 mutation mainly occurs the somatic mutations at point 27 of the H3 gene including HIST1H3B/C (H3.1), HIST1H3B/D (H3.2) and H3F3A (H3.3) [ 4 ]. This mutation leads to the lysine (K27) at the 27th position of histone H3 to be replaced by the methionine (M) (K27M), disrupting the histone H3 methylation modification site and altering the histone methylation state [ 5 , 6 ]. In addition, H3K27M interacts with methyltransferase EZH2 to inhibit the activity of polycomb repressive complex 2 (PRC2) and reduce the methylation level of histone H3 and surrounding genes DNA, causing an increase in cell proliferation potential and a decrease in differentiation ability, thereby enabling tumor cells to achieve high malignancy [ 7 , 8 ]. There are no guidelines recommended for the treatment of DMGs, surgery and adjuvant radiotherapy and chemotherapy are still the main methods for comprehensive treatment. Due to the anatomical location of DMGs almost occurring in the midline of the central nervous system, such as the brainstem, thalamus, spinal cord, cerebellum and third ventricle, safe total removal of tumors is nearly impractical, which makes pathological diagnosis largely dependent on biopsy [ 4 , 9 , 10 ]. Therefore, adjuvant therapy is particularly important for DMGs. It is widely recognized that radiotherapy can prolong the survival of DMGs patients [ 11 – 14 ], but it also brings risks of long-term complications, such as radiation-induced brain necrosis and secondary tumors[ 13 , 15 ]. Even for patients who are sensitive to radiotherapy, radiotherapy can only prolong their survival by a few months [ 14 , 16 ]. However, as the main adjuvant therapy for malignant brain tumors such as glioblastoma, although many studies suggest that chemotherapy is ineffective against DMGs [ 12 , 13 , 17 , 18 ], there is no conclusive evidence that chemotherapy is completely ineffective for DMGs [ 11 , 19 ]. Some studies and clinical doctors still incorporate chemotherapy into the treatment practice of DMGs [ 11 , 19 – 22 ]. Therefore, it is of great practical significance to clarify whether DMGs patients can benefit from chemotherapy regimens, which is crucial for the clinical treatment of DMGs. In this study, we conducted a retrospective analysis of clinical factors such as survival time, epidemiology, and treatment interventions in 278 DMGs patients in the SEER database to explore the risk factors affecting their survival. Specifically, we focused on evaluating the effectiveness of chemotherapy in prolonging the survival of DMGs patients. We found that the majority of DMGs patients received radiotherapy, while those who received chemotherapy generally had a longer survival time. This finding provides evidence and new insights that chemotherapy may benefit DMGs patients, which will be conducive to evaluate treatment options and improve prognosis of H3K27M-mutant DMGs patients in clinical practice. Materials and methods Patients The Surveillance, Epidemiology, and End Result Program (SEER) (17 registries, 2000–2021, November 2023 submission) was utilized for analysis, accessed through SEER*Stat (Version 8.4.3). Based on the 2020 census, it covers approximately 26.5% of the U.S. population and provides 9,750,718 primary tumors. Given the public availability and de-identification of the SEER dataset, it eliminates the necessity for Institutional Review Board (IRB) approval when utilizing it for data analysis. Within the SEER incidence database, we identified patients diagnosed with H3K27M-mutant DMGs between 2018 and 2021. We excluded all patients without survival time. The following data were gathered: demographic characteristics (year, age at diagnosis, gender, race, marital status, household income, rural urban); clinicopathological information (primary site, laterality, tumor size, diagnostic confirmation, surgery, chemotherapy, radiotherapy) and follow-up data (cause-specific death and survival time). Age at diagnosis as a continuous variable was separated into pediatric group (≤ 18 years) and adult group (> 18 years). Median household income inflation-adjusted to 2022 was used to classify individuals into the low-income ( $ 80,000 or less) and high-income ( $ 80,001 or more) groups. Surgical interventions were systematically documented using surgery codes in SEER program coding and staging manual. Cancer-specific survival (CSS) is defined as the interval between the initial diagnosis and the date of death attributable to H3K27M-mutant DMGs. Statistical Analysis Patients with H3K27M-mutant DMGs were again two stratified into distinct cohorts using PSM: those who received chemotherapy (chemo) and those who did not undergo chemotherapy (non-chemo). Descriptive statistics were presented as median [interquartile range (IQR)] and frequency (%) for continuous and categorical variables, respectively. Wilcoxon’s rank-sum test was used for non-normal distribution variables, and Fisher’s exact or Pearson's Chi-squared test were used to compare categorical variables (“gtsummary” package). The baseline characteristics between the two groups were matched using PSM with the nearest-neighbor method, employing a 1:1 ratio and a caliper width of 0.1 (“Matchit” package). The log-rank test and Kaplan-Meier survival curves were implemented to compare CSS between different groups. Univariate and Multivariate analysis was performed using the Cox proportional hazards regression model for calculating hazard ratio (HR) with its 95% CI. Stratification and interaction analyses were conducted to delineate the impact of demographic and clinicopathological variables, utilizing likelihood ratio tests to investigate potential interactions. Two-tailed test with a P -value < 0.05 was considered statistically significant. Statistical analyses were performed using R software (version 4.3.2). Results Demographic and clinicopathological characteristics of DMGs Based on the predetermined inclusion and exclusion criteria, a cohort of 278 patients diagnosed with H3K27M-mutant DMGs was retrieved from the database for analysis (Fig. 1 ). The median age at diagnosis was 13 years (interquartile range 7–29 years), with 170 (61%) pediatric. The vast majority of patients were white (74%), single (81%) and resided in metropolitan areas (91%). The primary tumor site was the brainstem in 419 patients (50%), followed by the cerebrum (44%) and spinal cord (6.1%). Additionally, the diagnoses of patients were confirmed through histological examinations (88%) and radiological assessments (12%) (Supplementary Table S1). In terms of therapeutic interventions, radiotherapy was administered to 235 patients (85%), while surgical intervention was contraindicated in 137 cases (49%). Of the patient cohort, 147 individuals (53%) received chemotherapy (Chemo group), whereas the remaining 131 (60.8%) did not (non-chemo group). Compared with non-chemo group, patients receiving chemotherapy were predominantly older, married, metropolitan residents, and had tumors located in the cerebrum and spinal cord ( P < 0.05). Additionally, these patients frequently received radiation therapy and surgical interventions ( P < 0.05) (Supplementary Table S1). Based on the data analyzed, patients were categorized into four groups according to their treatment regimens: radiotherapy alone (RT), chemotherapy alone (Chemo), combined radiotherapy and chemotherapy (RT + Chemo), and no adjuvant therapy (None). We then assessed the prognostic benefits associated with each treatment modality. The findings indicated that compared with the group that received no adjuvant therapy, those treated with either radiotherapy alone (RT) ( P = 0.52) or chemotherapy alone (Chemo) ( P = 0.81) did not experience significant survival advantages (Fig. 2 a-b). Conversely, patients who underwent both radiotherapy and chemotherapy concurrently (RT + Chemo) achieved notably longer survival times (Fig. 2 c, P = 0.0057). Notably, while there was no significant difference in survival between patients receiving only radiotherapy and those receiving only chemotherapy (Fig. 2 d, P = 0.8), those subjected to both treatments concurrently exhibited a marked increase in survival compared to those who received only radiotherapy (Fig. 2 e, P = 0.019). In contrast, the survival of patients undergoing concurrent chemoradiotherapy with an enhanced radiotherapy regimen did not demonstrate superiority over those treated with chemotherapy alone. (Fig. 2 f, P = 0.48). The overall comparison of patient outcomes among the four treatment options suggested that the use of radiotherapy alone as the standard treatment for DMGs may warrant reconsideration, given the unexpectedly beneficial effects of chemotherapy in DMGs management (Fig. 2 g). Consequently, a reevaluation of the significance and impact of chemotherapy in DMGs therapy was necessary. The role of chemotherapy in survival after propensity score matching To address the potential impact of imbalanced baseline characteristics between the Chemo and Non-chemo groups on statistical power, we employed PSM to minimize the influence of confounding variables and establish a balanced cohort. Subsequently, 78 patients with H3K27M-mutant DMGs were precisely matched in both the Chemo and Non-chemo groups. The P - values for all covariates after matching were more than 0.1, indicating that the PSM diminished the selection bias within the two groups. Demographics and clinicopathological variables for all 278 patients before and after PSM are delineated in Table 1 . Table 1 Characteristics of H3K27 mutated diffuse midline gliomas patients before and after propensity score matching. Characteristic Before matching P -value 2 After matching P -value 2 Overall , N = 278 1 Non-chemo , N = 131 1 Chemo , N = 147 1 Non-chemo , N = 78 1 Chemo , N = 78 1 Year, n (%) 0.6 0.5 2018–2019 145 (52%) 66 (50%) 79 (54%) 41 (53%) 37 (47%) 2020–2021 133 (48%) 65 (50%) 68 (46%) 37 (47%) 41 (53%) Age, Median (IQR) 13 (7, 29) 9 (6, 21) 18 (9, 34) < 0.001 11 (6, 16) 10 (6, 20) 0.8 Age, n (%) < 0.001 0.4 Adult 108 (39%) 36 (27%) 72 (49%) 17 (22%) 22 (28%) Pediatric 170 (61%) 95 (73%) 75 (51%) 61 (78%) 56 (72%) Gender, n (%) 0.2 0.7 Female 151 (54%) 77 (59%) 74 (50%) 47 (60%) 45 (58%) Male 127 (46%) 54 (41%) 73 (50%) 31 (40%) 33 (42%) Race, n (%) 0.3 0.4 Others 71 (26%) 37 (28%) 34 (23%) 22 (28%) 17 (22%) White 207 (74%) 94 (72%) 113 (77%) 56 (72%) 61 (78%) Marital status, n (%) 0.001 0.15 Married 52 (19%) 14 (11%) 38 (26%) 4 (5.1%) 9 (12%) Single 226 (81%) 117 (89%) 109 (74%) 74 (95%) 69 (88%) Household income, n (%) 0.085 0.4 < 80000 100 (36%) 54 (41%) 46 (31%) 31 (40%) 26 (33%) 80000+ 178 (64%) 77 (59%) 101 (69%) 47 (60%) 52 (67%) Rural urban, n (%) 0.028 0.3 Metropolitan 253 (91%) 114 (87%) 139 (95%) 69 (88%) 73 (94%) Nonmetropolitan 25 (9.0%) 17 (13%) 8 (5.4%) 9 (12%) 5 (6.4%) Primary site, n (%) 0.016 0.8 Brain stem 139 (50%) 77 (59%) 62 (42%) 47 (60%) 45 (58%) Cerebrum 122 (44%) 49 (37%) 73 (50%) 27 (35%) 27 (35%) Spinal cord 17 (6.1%) 5 (3.8%) 12 (8.2%) 4 (5.1%) 6 (7.7%) Laterality, n (%) 0.017 0.3 Not a paired site 186 (67%) 97 (74%) 89 (61%) 60 (77%) 54 (69%) Right/Left 92 (33%) 34 (26%) 58 (39%) 18 (23%) 24 (31%) Tumor size, n (%) 0.7 0.5 < 4.5cm 124 (45%) 59 (45%) 65 (44%) 36 (46%) 33 (42%) ≥ 4.5cm 97 (35%) 43 (33%) 54 (37%) 26 (33%) 33 (42%) Unknown 57 (21%) 29 (22%) 28 (19%) 16 (21%) 12 (15%) Diagnostic confirmation, n (%) 0.064 0.6 Histology 246 (88%) 111 (85%) 135 (92%) 66 (85%) 68 (87%) Radiography 32 (12%) 20 (15%) 12 (8.2%) 12 (15%) 10 (13%) Surgery, n (%) 0.004 0.8 No surgery 137 (49%) 75 (57%) 62 (42%) 43 (55%) 39 (50%) Biopsy 63 (23%) 19 (15%) 44 (30%) 14 (18%) 14 (18%) STR 45 (16%) 25 (19%) 20 (14%) 12 (15%) 12 (15%) GTR 33 (12%) 12 (9.2%) 21 (14%) 9 (12%) 13 (17%) Radiotherapy, n (%) 0.9 No 43 (15%) 40 (31%) 3 (2.0%) 3 (3.8%) 3 (3.8%) Beam radiation 235 (85%) 91 (69%) 144 (98%) 75 (96%) 75 (96%) 1 n (%); Median (IQR) 2 Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test STR, Subtotal resection; GTR, Gross total resection; Chemo, Chemotherapy. The Kaplan-Meier survival curves of the CSS for H3K27M-mutant DMGs stratified by Chemo or non-chemo before and after PSM are shown in Fig. 2 h-i. Compared with the CSS of patients who did not receive chemotherapy, the CSS among patients who received chemotherapy regimens was significantly prolonged in both the PSM cohort ( P = 0.012) and non PSM cohort ( P = 0.00063). Furthermore, in the PSM cohort, univariable Cox regression model suggested that age, primary site and chemotherapy were potential prognostic factors for CSS ( P 0.05). Table 2 Univariate and multivariate analyses of cancer-specific survival (CSS) in the cohort after propensity score matching Characteristic Univariate Multivariate HR 1 95% CI 1 P - value HR 1 95% CI 1 P - value Year 2018–2019 — — 2020–2021 1.10 0.70, 1.74 0.686 Gender Female — — Male 1.14 0.76, 1.71 0.536 Age 0.98 0.96, 1.00 0.054 Adult — — — — Pediatric 1.86 1.10, 3.15 0.021 1.72 0.95, 3.10 0.075 Race Others — — — — White 1.13 0.71, 1.81 0.601 1.34 0.80, 2.26 0.3 Household income < 80000 — — 80000+ 1.20 0.79, 1.82 0.385 Marital status Married — — Single 1.88 0.81, 4.38 0.143 Primary site Brain stem — — — — Cerebrum 0.60 0.38, 0.96 0.034 0.69 0.40, 1.20 0.2 Spinal cord 0.74 0.30, 1.84 0.511 3.34 0.70, 16.0 0.13 Tumor size < 4.5 — — 4.5+ 0.67 0.42, 1.08 0.099 Unknown 1.32 0.79, 2.22 0.295 Laterality Not a paired site — — Right/Left 0.63 0.38, 1.04 0.069 Diagnostic confirmation Histology — — — — Radiography 1.43 0.85, 2.43 0.181 1.22 0.67, 2.21 0.5 Surgery No surgery — — — — Biopsy 1.11 0.64, 1.95 0.705 1.20 0.64, 2.22 0.6 STR 0.83 0.46, 1.49 0.536 1.11 0.57, 2.15 0.8 GTR 0.52 0.24, 1.15 0.105 0.29 0.07, 1.15 0.078 Chemotherapy No — — — — Yes 0.60 0.40, 0.90 0.013 0.63 0.41, 0.97 0.037 Radiotherapy No — — — — Beam radiation 0.78 0.28, 2.12 0.621 0.23 0.06, 0.90 0.034 1 HR, Hazard Ratio; CI, Confidence Interval; STR, Subtotal resection; GTR, Gross total resection. Based on Cox stepwise regression and previous clinical research findings, a multivariate Cox proportional hazards regression model was constructed, incorporating variables such as age, race, diagnostic confirmation, primary tumor site, surgery, radiotherapy, and chemotherapy. After controlling for other prognostic factors, patients underwent chemotherapy were demonstrated a significant association with better CSS (HR = 0.63; 95% CI, 0.41–0.97; P = 0.037) (Table 2 ). Similarly, patients who received radiotherapy exhibited a reduced relative risk of mortality (HR = 0.23; 95% CI, 0.06–0.90; P = 0.034) (Table 2 ). Consistent with univariate regression analysis, the primary tumor site, diagnostic confirmation, surgery failed to demonstrate a significant association with prognosis in the multivariable Cox regression model (Table 2 ). Subgroup interaction analysis Subgroup analyses were conducted to assess the prognostic value of chemotherapy for patients stratified by demographic and clinicopathological variables after PSM. The chemotherapy was a significant predictor of CSS within subgroups, notably among females (HR = 0.50; 95% CI, 0.29–0.87), those of white race (HR = 0.48; 95% CI, 0.30–0.77), single individuals (HR = 0.64; 95% CI, 0.42–0.97), individuals residing in metropolitan areas (HR = 0.50; 95% CI, 0.32–0.77), and those with high household income (HR = 0.47; 95% CI, 0.28–0.80) (Fig. 3 a). In term of clinicopathological variables, chemotherapy demonstrated a significant association with a reduced mortality risk in individuals diagnosed histologically (HR = 0.57; 95% CI, 0.36–0.89). This association was also observed in patients with tumors situated in the cerebrum (HR = 0.44; 95% CI, 0.20–0.94), those with unknown or unmeasured tumor size (HR = 0.31; 95% CI: 0.11–0.87), those who underwent biopsy (HR = 0.35; 95% CI, 0.13–0.96), and those who received beam radiation therapy (HR = 0.59; 95% CI, 0.39 = 0.89) (Fig. 3 b). Discussion Diffuse midline gliomas (DMGs) garners significant attention due to their aggressive nature and poor prognosis. The management of DMGs requires a comprehensive consideration of various factors, including tumor location, size, and molecular characteristics. Currently, gross total resection is deemed the ideal treatment modality for DMGs. However, due to the unique locations of these tumors, fewer than 20% of patients are eligible for this approach [ 9 , 19 , 23 ]. Consequently, adjunctive treatment strategies are crucial for extending the survival of DMGs patients. Conventional high-dose radiotherapy and temozolomide chemotherapy currently represent the principal treatment options [ 22 , 24 , 25 ]. Nevertheless, some studies have suggested that DMGs patients might only benefit from radiation therapy, while chemotherapy appears to be ineffective for DMGs [ 12 , 13 , 17 , 18 , 26 , 27 ]. This has sparked controversy regarding the role of chemotherapy in DMGs management [ 3 , 28 – 30 ]. Hence, whether chemotherapy should be routinely incorporated into treatment protocols and in which patient populations it could be beneficial remains an unresolved issue. Our research findings highlight the significant protective effect of chemotherapy on patients with DMGs. We observed no statistically significant difference in survival rates between patients receiving chemotherapy alone and those subjected exclusively to radiotherapy. However, the combination of chemotherapy and radiotherapy significantly increased patient survival compared to radiotherapy alone, but did not bring additional benefits compared to chemotherapy alone. These findings suggest that chemotherapy can be recommended as a standard treatment for DMGs in clinical practice. Why does chemotherapy exhibit variable efficacy in patients with DMGs? Based on our research findings, both radiotherapy and chemotherapy exerted a protective effect on patients with DMGs. Specifically focusing on chemotherapy, our stratified analysis revealed that certain demographic and clinical pathological variables could impact its efficacy. For example, in subgroups characterized by demographic factors such as female gender, Caucasian ethnicity, single marital status, residence in metropolitan areas, and higher family income, chemotherapy significantly improved survival outcomes in DMGs patients. These populations share common attributes: they are from potentially more developed regions or high-income groups, and therefore are more likely to seek medical care, exhibit better compliance and can afford the most advanced or appropriate treatment plans. Correspondingly, chemotherapy will demonstrate its value as an effective treatment option. On the contrary, individuals from lower-income groups may only have access to outdated or suboptimal treatment regimens, potentially leading to an inaccurate assessment of the efficacy of chemotherapy. This finding is novel and comprehensible. Certain clinical pathological variables can also influence the efficacy of chemotherapy. For instance, a significant association exists between chemotherapy and individual mortality risk reduction based on histological diagnosis, underscoring the dual significance of histological diagnosis in both accurate diagnosis and treatment of DMGs. Relying solely on imaging for pathology assessment and treatment planning might subject patients to increased therapeutic risks. Furthermore, in comparison to their respective cohorts, patients within the groups characterized by cerebrum (primary site), undetermined tumor size, biopsy-based surgery intervention, and beam radiation therapy demonstrated a significant benefit from chemotherapy for DMGs. Compared to midline anatomy such as the brainstem and spinal cord, cerebrum tumor resection is relatively less challenging and associated with fewer complications which may be a crucial reason why chemotherapy yields significant benefits in cerebrum DMGs. Considering that surgical trauma can weaken the patient's immune system, leading to increased side effects during chemotherapy, and that non-surgical patients often have poor physical conditions, the patients who receive biopsies tend to exhibit more effective response to chemotherapy. Among the patients receiving radiotherapy, the addition of chemotherapy can improve survival rates, which is consistent with our previous findings and represents one of the most important discoveries of this study. The combination of radiotherapy and chemotherapy appears to benefit the vast majority of patients. In addition to the aforementioned factors, the genetic mutation status of DMGs patients themselves also influence the selection of chemotherapy [ 31 , 32 ]. For instance, patients with CpG island methylation in the O6-methylguanine DNA methyltransferase (MGMT) gene promoter region are generally considered more sensitive to chemotherapy compared to wild-type patients [ 32 , 33 ]. The heightened sensitivity is attributed to the fact that most chemotherapy drugs are alkylating agents (such as temozolomide, TMZ), which possess strong mutagenic properties and induces a significant number of base mismatches during DNA replication by alkylating the O6 position of guanine, and subsequently lead to further induction of cell DNA double-strand breaks and apoptosis through the mismatch repair (MMR) mechanism [ 33 – 35 ]. MGMT is a DNA repair protein that counteracts the cytotoxicity and mutagenic activity of TMZ by removing alkyl groups from the O6 position of guanine, leading to drug resistance. Methylation of the MGMT promoter results in gene silencing, reduced protein expression, and diminished DNA repair function, thereby increasing the drug sensitivity of tumor cells [ 33 , 36 , 37 ]. However, the methylation status of MGMT is relative and non-permanent with respect to chemotherapy sensitivity. Because once a single alkyl group is removed, MGMT becomes irreversibly inactivated [ 32 , 33 , 38 ]. Consequently, the cellular concentration of MGMT protein is a critical determinant for repairing damage induced by TMZ. When TMZ treatment depletes the MGMT protein, cells regain their sensitivity to chemotherapy. Additionally, high-grade gliomas treated with TMZ often exhibit evolutionary escape mechanisms such as MMR inactivation [ 39 , 40 ]. In the absence of MMR function, cells are unable to detect base pair mismatches, ultimately resulting in an accumulation of thousands of mutations and consequently causing tumor cells to undergo hypermutation. In prior studies investigating the recurrence of low-grade gliomas (LGG) treated with TMZ, it was noted that the methylation level of the MGMT promoter was elevated in hypermutated recurrent tumors compared to non-hypermutated ones [ 41 , 42 ]. Other researches have also documented the concurrent presence of hypermutation and MGMT promoter methylation in recurrent tumors of both LGG and glioblastoma multiforme (GBM) patients undergoing TMZ therapy [ 43 – 45 ]. These findings implied that TMZ and related alkylating agents not only deplete MGMT proteins but also induce hypermutations that may encompass MGMT promoter methylation, facilitating a transition in tumor cells from a chemotherapy-insensitive wild-type or hypomethylated state to a chemotherapy-sensitive methylated state, thereby enhancing patient response to chemotherapy. Consequently, the mutational status, including MGMT methylation, should not be regarded as the exclusive factor determining the efficacy of chemotherapy. A comprehensive assessment and judgment of the effectiveness of chemotherapeutic agents are warranted. Based on the results presented, it is inferred that the efficacy of chemotherapy transcends mere therapeutic intervention; it potentially modulates drug sensitivity through epigenetic mechanisms, thereby substantially enhancing treatment benefits for DMGs patients. Nonetheless, acknowledging the multifactorial nature influencing chemotherapy benefits underscores the necessity of tailored therapeutic strategies. A nuanced assessment incorporating patients’ clinical profiles and inherent factors forms the cornerstone for devising personalized treatments aimed at augmenting survival rates among DMGs populations. In essence, our study contributes novel insights and empirical support to the notion that chemotherapy plays a pivotal role in benefiting DMGs patients and offering clinicians valuable guidance in evaluating, managing, and optimizing the prognosis of H3K27M-mutant DMGs patients. Declarations Funding This work was supported by Natural Science Foundation of Beijing Municipality (grant number: 7222053), National Natural Science Foundation of China (grant number: 82003134), and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Authors’ Contributions All authors contributed to the study design and conception. Data collection and analysis were performed by Hui Shen and Jin Zhang. The first draft of the manuscript was written by Jin Zhang and Shanshan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. Ethics Approval Given the public availability and de-identification of the SEER dataset, it eliminates the necessity for Institutional Review Board (IRB) approval when utilizing it for data analysis. References López-Pérez CA, Franco-Mojica X, Villanueva-Gaona R, Díaz-Alba A, Rodríguez-Florido MA, Navarro VG (2022) Adult diffuse midline gliomas H3 K27-altered: review of a redefined entity. Journal of neuro-oncology 158: 369-378 doi:10.1007/s11060-022-04024-5 Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-oncology 23: 1231-1251 doi:10.1093/neuonc/noab106 Gong X, Kuang S, Deng D, Wu J, Zhang L, Liu C (2023) Differences in survival prognosticators between children and adults with H3K27M-mutant diffuse midline glioma. CNS neuroscience & therapeutics 29: 3863-3875 doi:10.1111/cns.14307 Di Nunno V, Lombardi G, Simonelli M, Minniti G, Mastronuzzi A, Di Ruscio V, Corrà M, Padovan M, Maccari M, Caccese M, Simonetti G, Berlendis A, Farinotti M, Pollo B, Antonelli M, Di Muzio A, Dipasquale A, Asioli S, De Biase D, Tosoni A, Silvani A, Franceschi E (2024) The role of adjuvant chemotherapy in patients with H3K27 altered diffuse midline gliomas: a multicentric retrospective study. Journal of neuro-oncology 167: 145-154 doi:10.1007/s11060-024-04589-3 Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, Tönjes M, Sill M, Bender S, Kool M, Zapatka M, Becker N, Zucknick M, Hielscher T, Liu XY, Fontebasso AM, Ryzhova M, Albrecht S, Jacob K, Wolter M, Ebinger M, Schuhmann MU, van Meter T, Frühwald MC, Hauch H, Pekrun A, Radlwimmer B, Niehues T, von Komorowski G, Dürken M, Kulozik AE, Madden J, Donson A, Foreman NK, Drissi R, Fouladi M, Scheurlen W, von Deimling A, Monoranu C, Roggendorf W, Herold-Mende C, Unterberg A, Kramm CM, Felsberg J, Hartmann C, Wiestler B, Wick W, Milde T, Witt O, Lindroth AM, Schwartzentruber J, Faury D, Fleming A, Zakrzewska M, Liberski PP, Zakrzewski K, Hauser P, Garami M, Klekner A, Bognar L, Morrissy S, Cavalli F, Taylor MD, van Sluis P, Koster J, Versteeg R, Volckmann R, Mikkelsen T, Aldape K, Reifenberger G, Collins VP, Majewski J, Korshunov A, Lichter P, Plass C, Jabado N, Pfister SM (2012) Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer cell 22: 425-437 doi:10.1016/j.ccr.2012.08.024 Castel D, Philippe C, Calmon R, Le Dret L, Truffaux N, Boddaert N, Pagès M, Taylor KR, Saulnier P, Lacroix L, Mackay A, Jones C, Sainte-Rose C, Blauwblomme T, Andreiuolo F, Puget S, Grill J, Varlet P, Debily MA (2015) Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta neuropathologica 130: 815-827 doi:10.1007/s00401-015-1478-0 Piunti A, Hashizume R, Morgan MA, Bartom ET, Horbinski CM, Marshall SA, Rendleman EJ, Ma Q, Takahashi YH, Woodfin AR, Misharin AV, Abshiru NA, Lulla RR, Saratsis AM, Kelleher NL, James CD, Shilatifard A (2017) Therapeutic targeting of polycomb and BET bromodomain proteins in diffuse intrinsic pontine gliomas. Nature medicine 23: 493-500 doi:10.1038/nm.4296 Schwartzentruber J, Korshunov A, Liu XY, Jones DT, Pfaff E, Jacob K, Sturm D, Fontebasso AM, Quang DA, Tönjes M, Hovestadt V, Albrecht S, Kool M, Nantel A, Konermann C, Lindroth A, Jäger N, Rausch T, Ryzhova M, Korbel JO, Hielscher T, Hauser P, Garami M, Klekner A, Bognar L, Ebinger M, Schuhmann MU, Scheurlen W, Pekrun A, Frühwald MC, Roggendorf W, Kramm C, Dürken M, Atkinson J, Lepage P, Montpetit A, Zakrzewska M, Zakrzewski K, Liberski PP, Dong Z, Siegel P, Kulozik AE, Zapatka M, Guha A, Malkin D, Felsberg J, Reifenberger G, von Deimling A, Ichimura K, Collins VP, Witt H, Milde T, Witt O, Zhang C, Castelo-Branco P, Lichter P, Faury D, Tabori U, Plass C, Majewski J, Pfister SM, Jabado N (2012) Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482: 226-231 doi:10.1038/nature10833 Meyronet D, Esteban-Mader M, Bonnet C, Joly MO, Uro-Coste E, Amiel-Benouaich A, Forest F, Rousselot-Denis C, Burel-Vandenbos F, Bourg V, Guyotat J, Fenouil T, Jouvet A, Honnorat J, Ducray F (2017) Characteristics of H3 K27M-mutant gliomas in adults. Neuro-oncology 19: 1127-1134 doi:10.1093/neuonc/now274 Schreck KC, Ranjan S, Skorupan N, Bettegowda C, Eberhart CG, Ames HM, Holdhoff M (2019) Incidence and clinicopathologic features of H3 K27M mutations in adults with radiographically-determined midline gliomas. Journal of neuro-oncology 143: 87-93 doi:10.1007/s11060-019-03134-x Desai J, Rajkumar S, Shepard MJ, Herbst J, Karlovits SM, Hasan S, Horne ZD, Wegner RE (2024) National trends in the treatment of adult diffuse midline gliomas: a rare clinical scenario. Journal of neuro-oncology 168: 269-274 doi:10.1007/s11060-024-04663-w Argersinger DP, Rivas SR, Shah AH, Jackson S, Heiss JD (2021) New Developments in the Pathogenesis, Therapeutic Targeting, and Treatment of H3K27M-Mutant Diffuse Midline Glioma. Cancers 13 doi:10.3390/cancers13215280 Al Sharie S, Abu Laban D, Al-Hussaini M (2023) Decoding Diffuse Midline Gliomas: A Comprehensive Review of Pathogenesis, Diagnosis and Treatment. Cancers 15 doi:10.3390/cancers15194869 Persson ML, Douglas AM, Alvaro F, Faridi P, Larsen MR, Alonso MM, Vitanza NA, Dun MD (2022) The intrinsic and microenvironmental features of diffuse midline glioma: Implications for the development of effective immunotherapeutic treatment strategies. Neuro-oncology 24: 1408-1422 doi:10.1093/neuonc/noac117 Miguel Llordes G, Medina Pérez VM, Curto Simón B, Castells-Yus I, Vázquez Sufuentes S, Schuhmacher AJ (2023) Epidemiology, Diagnostic Strategies, and Therapeutic Advances in Diffuse Midline Glioma. Journal of clinical medicine 12 doi:10.3390/jcm12165261 Bai J, Varghese J, Jain R (2020) Adult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know. Topics in magnetic resonance imaging : TMRI 29: 71-82 doi:10.1097/rmr.0000000000000234 Price G, Bouras A, Hambardzumyan D, Hadjipanayis CG (2021) Current knowledge on the immune microenvironment and emerging immunotherapies in diffuse midline glioma. EBioMedicine 69: 103453 doi:10.1016/j.ebiom.2021.103453 Cacciotti C, Wright KD (2023) Advances in Treatment of Diffuse Midline Gliomas. Current neurology and neuroscience reports 23: 849-856 doi:10.1007/s11910-023-01317-8 Yoon HI, Wee CW, Kim YZ, Seo Y, Im JH, Dho YS, Kim KH, Hong JB, Park JS, Choi SH, Kim MS, Moon J, Hwang K, Park JE, Cho JM, Yoon WS, Kim SH, Kim YI, Kim HS, Sung KS, Song JH, Lee MH, Han MH, Lee SH, Chang JH, Lim DH, Park CK, Lee YS, Gwak HS (2021) The Korean Society for Neuro-Oncology (KSNO) Guideline for Adult Diffuse Midline Glioma: Version 2021.1. Brain tumor research and treatment 9: 1-8 doi:10.14791/btrt.2021.9.e8 Li YA, Zhao C, Ge JJ, Li C, Xue FJ, Qi SP, Zhao C, Kong CC, Zhang JP (2024) Apatinib combined with temozolomide in diffuse midline glioma: a novel and effective therapy. BMC cancer 24: 754 doi:10.1186/s12885-024-12373-9 Heravi Shargh V, Luckett J, Bouzinab K, Paisey S, Turyanska L, Singleton WGB, Lowis S, Gershkovich P, Bradshaw TD, Stevens MFG, Bienemann A, Coyle B (2021) Chemosensitization of Temozolomide-Resistant Pediatric Diffuse Midline Glioma Using Potent Nanoencapsulated Forms of a N(3)-Propargyl Analogue. ACS applied materials & interfaces 13: 35266-35280 doi:10.1021/acsami.1c04164 Liu C, Kuang S, Huang T, Wu J, Zhang L, Gong X (2024) Radiotherapy plus temozolomide with or without anlotinib in H3K27M-mutant diffuse midline glioma: A retrospective cohort study. CNS neuroscience & therapeutics 30: e14730 doi:10.1111/cns.14730 Wang L, Li Z, Zhang M, Piao Y, Chen L, Liang H, Wei Y, Hu Z, Zhao L, Teng L, Lu D (2018) H3 K27M-mutant diffuse midline gliomas in different anatomical locations. Human pathology 78: 89-96 doi:10.1016/j.humpath.2018.04.015 El-Khouly FE, Veldhuijzen van Zanten SEM, Santa-Maria Lopez V, Hendrikse NH, Kaspers GJL, Loizos G, Sumerauer D, Nysom K, Pruunsild K, Pentikainen V, Thorarinsdottir HK, Rutkauskiene G, Calvagna V, Drogosiewicz M, Dragomir M, Deak L, Kitanovski L, von Bueren AO, Kebudi R, Slavc I, Jacobs S, Jadrijevic-Cvrlje F, Entz-Werle N, Grill J, Kattamis A, Hauser P, Pears J, Biassoni V, Massimino M, Lopez Aguilar E, Torsvik IK, Joao Gil-da-Costa M, Kumirova E, Cruz-Martinez O, Holm S, Bailey S, Hayden T, Thomale UW, Janssens GOR, Kramm CM, van Vuurden DG (2019) Diagnostics and treatment of diffuse intrinsic pontine glioma: where do we stand? Journal of neuro-oncology 145: 177-184 doi:10.1007/s11060-019-03287-9 Mathew RK, Rutka JT (2018) Diffuse Intrinsic Pontine Glioma : Clinical Features, Molecular Genetics, and Novel Targeted Therapeutics. Journal of Korean Neurosurgical Society 61: 343-351 doi:10.3340/jkns.2018.0008 Long W, Yi Y, Chen S, Cao Q, Zhao W, Liu Q (2017) Potential New Therapies for Pediatric Diffuse Intrinsic Pontine Glioma. Frontiers in pharmacology 8: 495 doi:10.3389/fphar.2017.00495 Frazier JL, Lee J, Thomale UW, Noggle JC, Cohen KJ, Jallo GI (2009) Treatment of diffuse intrinsic brainstem gliomas: failed approaches and future strategies. Journal of neurosurgery Pediatrics 3: 259-269 doi:10.3171/2008.11.peds08281 Bailey S, Howman A, Wheatley K, Wherton D, Boota N, Pizer B, Fisher D, Kearns P, Picton S, Saran F, Gibson M, Glaser A, Connolly DJ, Hargrave D (2013) Diffuse intrinsic pontine glioma treated with prolonged temozolomide and radiotherapy--results of a United Kingdom phase II trial (CNS 2007 04). European journal of cancer (Oxford, England : 1990) 49: 3856-3862 doi:10.1016/j.ejca.2013.08.006 Chassot A, Canale S, Varlet P, Puget S, Roujeau T, Negretti L, Dhermain F, Rialland X, Raquin MA, Grill J, Dufour C (2012) Radiotherapy with concurrent and adjuvant temozolomide in children with newly diagnosed diffuse intrinsic pontine glioma. Journal of neuro-oncology 106: 399-407 doi:10.1007/s11060-011-0681-7 Izzuddeen Y, Gupta S, Haresh KP, Sharma D, Giridhar P, Rath GK (2020) Hypofractionated radiotherapy with temozolomide in diffuse intrinsic pontine gliomas: a randomized controlled trial. Journal of neuro-oncology 146: 91-95 doi:10.1007/s11060-019-03340-7 Park C, Kim TM, Bae JM, Yun H, Kim JW, Choi SH, Lee ST, Lee JH, Park SH, Park CK (2021) Clinical and Genomic Characteristics of Adult Diffuse Midline Glioma. Cancer research and treatment 53: 389-398 doi:10.4143/crt.2020.694 Abe H, Natsumeda M, Kanemaru Y, Watanabe J, Tsukamoto Y, Okada M, Yoshimura J, Oishi M, Fujii Y (2018) MGMT Expression Contributes to Temozolomide Resistance in H3K27M-Mutant Diffuse Midline Gliomas and MGMT Silencing to Temozolomide Sensitivity in IDH-Mutant Gliomas. Neurologia medico-chirurgica 58: 290-295 doi:10.2176/nmc.ra.2018-0044 Daniel P, Sabri S, Chaddad A, Meehan B, Jean-Claude B, Rak J, Abdulkarim BS (2019) Temozolomide Induced Hypermutation in Glioma: Evolutionary Mechanisms and Therapeutic Opportunities. Frontiers in oncology 9: 41 doi:10.3389/fonc.2019.00041 Margison GP, Santibáñez Koref MF, Povey AC (2002) Mechanisms of carcinogenicity/chemotherapy by O6-methylguanine. Mutagenesis 17: 483-487 doi:10.1093/mutage/17.6.483 Karran P, Marinus MG (1982) Mismatch correction at O6-methylguanine residues in E. coli DNA. Nature 296: 868-869 doi:10.1038/296868a0 Esteller M, Herman JG (2004) Generating mutations but providing chemosensitivity: the role of O6-methylguanine DNA methyltransferase in human cancer. Oncogene 23: 1-8 doi:10.1038/sj.onc.1207316 Cabrini G, Fabbri E, Lo Nigro C, Dechecchi MC, Gambari R (2015) Regulation of expression of O6-methylguanine-DNA methyltransferase and the treatment of glioblastoma (Review). International journal of oncology 47: 417-428 doi:10.3892/ijo.2015.3026 Fukushima T, Takeshima H, Kataoka H (2009) Anti-glioma therapy with temozolomide and status of the DNA-repair gene MGMT. Anticancer research 29: 4845-4854 Yu Y, Villanueva-Meyer J, Grimmer MR, Hilz S, Solomon DA, Choi S, Wahl M, Mazor T, Hong C, Shai A, Phillips JJ, Wainer BH, McDermott M, Haas-Kogan D, Taylor JW, Butowski N, Clarke JL, Berger MS, Molinaro AM, Chang SM, Costello JF, Oberheim Bush NA (2021) Temozolomide-induced hypermutation is associated with distant recurrence and reduced survival after high-grade transformation of low-grade IDH-mutant gliomas. Neuro-oncology 23: 1872-1884 doi:10.1093/neuonc/noab081 Mathur R, Zhang Y, Grimmer MR, Hong C, Zhang M, Bollam S, Petrecca K, Clarke J, Berger MS, Phillips JJ, Oberheim-Bush NA, Molinaro AM, Chang SM, Costello JF (2020) MGMT promoter methylation level in newly diagnosed low-grade glioma is a predictor of hypermutation at recurrence. Neuro-oncology 22: 1580-1590 doi:10.1093/neuonc/noaa059 Johnson BE, Mazor T, Hong C, Barnes M, Aihara K, McLean CY, Fouse SD, Yamamoto S, Ueda H, Tatsuno K, Asthana S, Jalbert LE, Nelson SJ, Bollen AW, Gustafson WC, Charron E, Weiss WA, Smirnov IV, Song JS, Olshen AB, Cha S, Zhao Y, Moore RA, Mungall AJ, Jones SJM, Hirst M, Marra MA, Saito N, Aburatani H, Mukasa A, Berger MS, Chang SM, Taylor BS, Costello JF (2014) Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science (New York, NY) 343: 189-193 doi:10.1126/science.1239947 van Thuijl HF, Mazor T, Johnson BE, Fouse SD, Aihara K, Hong C, Malmström A, Hallbeck M, Heimans JJ, Kloezeman JJ, Stenmark-Askmalm M, Lamfers ML, Saito N, Aburatani H, Mukasa A, Berger MS, Söderkvist P, Taylor BS, Molinaro AM, Wesseling P, Reijneveld JC, Chang SM, Ylstra B, Costello JF (2015) Evolution of DNA repair defects during malignant progression of low-grade gliomas after temozolomide treatment. Acta neuropathologica 129: 597-607 doi:10.1007/s00401-015-1403-6 Network TC (2013) Corrigendum: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 494: 506 doi:10.1038/nature11903 Jonsson P, Lin AL, Young RJ, DiStefano NM, Hyman DM, Li BT, Berger MF, Zehir A, Ladanyi M, Solit DB, Arnold AG, Stadler ZK, Mandelker D, Goldberg ME, Chmielecki J, Pourmaleki M, Ogilvie SQ, Chavan SS, McKeown AT, Manne M, Hyde A, Beal K, Yang TJ, Nolan CP, Pentsova E, Omuro A, Gavrilovic IT, Kaley TJ, Diamond EL, Stone JB, Grommes C, Boire A, Daras M, Piotrowski AF, Miller AM, Gutin PH, Chan TA, Tabar VS, Brennan CW, Rosenblum M, DeAngelis LM, Mellinghoff IK, Taylor BS (2019) Genomic Correlates of Disease Progression and Treatment Response in Prospectively Characterized Gliomas. Clinical cancer research : an official journal of the American Association for Cancer Research 25: 5537-5547 doi:10.1158/1078-0432.ccr-19-0032 Wang J, Cazzato E, Ladewig E, Frattini V, Rosenbloom DI, Zairis S, Abate F, Liu Z, Elliott O, Shin YJ, Lee JK, Lee IH, Park WY, Eoli M, Blumberg AJ, Lasorella A, Nam DH, Finocchiaro G, Iavarone A, Rabadan R (2016) Clonal evolution of glioblastoma under therapy. Nature genetics 48: 768-776 doi:10.1038/ng.3590 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5432895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383597499,"identity":"f4825762-f784-442b-8027-b7ae601bb6ee","order_by":0,"name":"Jin Zhang","email":"","orcid":"","institution":"Beijing friendship hospital affiliated capital medical university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zhang","suffix":""},{"id":383597500,"identity":"b7f765d6-f83c-4de6-9fdb-8f709651b8b3","order_by":1,"name":"Shanshan Wang","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Wang","suffix":""},{"id":383597501,"identity":"203498ac-9621-41cc-b577-255c075b4664","order_by":2,"name":"Sichen Wang","email":"","orcid":"","institution":"Beijing friendship hospital affiliated capital medical university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sichen","middleName":"","lastName":"Wang","suffix":""},{"id":383597502,"identity":"4b9a3563-48fa-483b-9556-1a431038f511","order_by":3,"name":"Haowen Jiang","email":"","orcid":"","institution":"Beijing friendship hospital affiliated capital medical university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haowen","middleName":"","lastName":"Jiang","suffix":""},{"id":383597503,"identity":"c39dc0f9-cdf4-469e-90f0-3968514fec3e","order_by":4,"name":"Yuanli Zhao","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuanli","middleName":"","lastName":"Zhao","suffix":""},{"id":383597504,"identity":"b73f6c24-c435-442d-b4b1-3bac9f226f72","order_by":5,"name":"Jianjun Sun","email":"","orcid":"","institution":"Beijing friendship hospital affiliated capital medical university","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Sun","suffix":""},{"id":383597505,"identity":"5cd15e9e-572d-449e-9828-48ee83ee8908","order_by":6,"name":"Hui Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDACCSD+YCDBw8/MfPgB0VoYZ1TYyEm2s6UZEK2FmedMmrHBeR4FCaJ0yM/uMf7A23Y4cfNhHgYDhhqbaIJaDO6cMZOQBGrZdpj3wAOGY2m5DQS1SOSYMRiCtfAlGDA2HCasRX5GjvGHRJDDmnkMJIjSwnAjx0DiAMj7zMRqMbiRVibZAAxkicPAQE4gxi/yM5I3f/4Disr+w4cffKixIcJhDBxIEZhAWDkIsD8gTt0oGAWjYBSMXAAA0atAmW8sbaQAAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University, Beijing Tiantan Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2024-11-11 14:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5432895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5432895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71605937,"identity":"5f50c460-6d23-490a-9e4c-2a73a67cec96","added_by":"auto","created_at":"2024-12-17 06:13:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82794,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating patient selection of this study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5432895/v1/c0caf74029386c3ba07fc252.png"},{"id":71605939,"identity":"18ba45ef-bce7-4185-b307-5e50a21d76e9","added_by":"auto","created_at":"2024-12-17 06:13:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":277899,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of cancer-specific survival (CSS) for patients receiving different treatment regimens (A-F). The overall comparison of patient outcomes among the four treatment options (G). Kaplan-Meier curves of cancer-specific survival (CSS) by chemotherapy before (H) and after (I) propensity score matching (PSM).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5432895/v1/c21f791619ffbff14e53716a.png"},{"id":71605936,"identity":"719e90ff-79e2-47a5-bd17-527ef6ce7af6","added_by":"auto","created_at":"2024-12-17 06:13:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":220986,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses estimating the prognostic value of chemotherapy grouped by patients with different demographic (A) and clinicopathological (B) variables after propensity score matching analysis. STR, Subtotal resection; GTR, Gross total resection.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5432895/v1/e68b1932be7754a83b089199.png"},{"id":71608027,"identity":"16d75c76-bcc2-494c-bd9e-7491c1982ff9","added_by":"auto","created_at":"2024-12-17 06:29:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1647877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5432895/v1/9b9432c0-6e88-4037-87a2-71e31c5c0cec.pdf"},{"id":71605935,"identity":"3e551d69-02ec-4dff-ad9b-efbb4b562b2a","added_by":"auto","created_at":"2024-12-17 06:13:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23530,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5432895/v1/18569a410fde2009d40b02c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of chemotherapy in patients with H3K27M-mutant diffuse midline gliomas: a SEER-based propensity scored matching study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse midline gliomas (DMGs) are defined by the 2021 WHO Central Nervous System (CNS) Tumor Classification as a high-grade glioma with histone H3K27 mutation, which have been indicated that predominantly occurs in the pediatric population. Irrespective of histology and morphology, DMGs with H3K27 mutation are classified as WHO grade IV[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. DMGs exhibit extremely high malignancy and are prone to recurrence with a dismal prognosis. The median overall survival (OS) of DMGs only last for 12 months, and the 2-year survival rate after diagnosis is less than 10% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the hallmark of the diagnosis of DMGs, the histone H3K27 mutation mainly occurs the somatic mutations at point 27 of the H3 gene including HIST1H3B/C (H3.1), HIST1H3B/D (H3.2) and H3F3A (H3.3) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This mutation leads to the lysine (K27) at the 27th position of histone H3 to be replaced by the methionine (M) (K27M), disrupting the histone H3 methylation modification site and altering the histone methylation state [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, H3K27M interacts with methyltransferase EZH2 to inhibit the activity of polycomb repressive complex 2 (PRC2) and reduce the methylation level of histone H3 and surrounding genes DNA, causing an increase in cell proliferation potential and a decrease in differentiation ability, thereby enabling tumor cells to achieve high malignancy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e There are no guidelines recommended for the treatment of DMGs, surgery and adjuvant radiotherapy and chemotherapy are still the main methods for comprehensive treatment. Due to the anatomical location of DMGs almost occurring in the midline of the central nervous system, such as the brainstem, thalamus, spinal cord, cerebellum and third ventricle, safe total removal of tumors is nearly impractical, which makes pathological diagnosis largely dependent on biopsy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, adjuvant therapy is particularly important for DMGs. It is widely recognized that radiotherapy can prolong the survival of DMGs patients [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], but it also brings risks of long-term complications, such as radiation-induced brain necrosis and secondary tumors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Even for patients who are sensitive to radiotherapy, radiotherapy can only prolong their survival by a few months [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, as the main adjuvant therapy for malignant brain tumors such as glioblastoma, although many studies suggest that chemotherapy is ineffective against DMGs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], there is no conclusive evidence that chemotherapy is completely ineffective for DMGs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some studies and clinical doctors still incorporate chemotherapy into the treatment practice of DMGs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, it is of great practical significance to clarify whether DMGs patients can benefit from chemotherapy regimens, which is crucial for the clinical treatment of DMGs.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a retrospective analysis of clinical factors such as survival time, epidemiology, and treatment interventions in 278 DMGs patients in the SEER database to explore the risk factors affecting their survival. Specifically, we focused on evaluating the effectiveness of chemotherapy in prolonging the survival of DMGs patients. We found that the majority of DMGs patients received radiotherapy, while those who received chemotherapy generally had a longer survival time. This finding provides evidence and new insights that chemotherapy may benefit DMGs patients, which will be conducive to evaluate treatment options and improve prognosis of H3K27M-mutant DMGs patients in clinical practice.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThe Surveillance, Epidemiology, and End Result Program (SEER) (17 registries, 2000\u0026ndash;2021, November 2023 submission) was utilized for analysis, accessed through SEER*Stat (Version 8.4.3). Based on the 2020 census, it covers approximately 26.5% of the U.S. population and provides 9,750,718 primary tumors. Given the public availability and de-identification of the SEER dataset, it eliminates the necessity for Institutional Review Board (IRB) approval when utilizing it for data analysis.\u003c/p\u003e \u003cp\u003eWithin the SEER incidence database, we identified patients diagnosed with H3K27M-mutant DMGs between 2018 and 2021. We excluded all patients without survival time. The following data were gathered: demographic characteristics (year, age at diagnosis, gender, race, marital status, household income, rural urban); clinicopathological information (primary site, laterality, tumor size, diagnostic confirmation, surgery, chemotherapy, radiotherapy) and follow-up data (cause-specific death and survival time).\u003c/p\u003e \u003cp\u003eAge at diagnosis as a continuous variable was separated into pediatric group (\u0026le;\u0026thinsp;18 years) and adult group (\u0026gt;\u0026thinsp;18 years). Median household income inflation-adjusted to 2022 was used to classify individuals into the low-income (\u003cspan\u003e$\u003c/span\u003e80,000 or less) and high-income (\u003cspan\u003e$\u003c/span\u003e80,001 or more) groups. Surgical interventions were systematically documented using surgery codes in SEER program coding and staging manual. Cancer-specific survival (CSS) is defined as the interval between the initial diagnosis and the date of death attributable to H3K27M-mutant DMGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003ePatients with H3K27M-mutant DMGs were again two stratified into distinct cohorts using PSM: those who received chemotherapy (chemo) and those who did not undergo chemotherapy (non-chemo). Descriptive statistics were presented as median [interquartile range (IQR)] and frequency (%) for continuous and categorical variables, respectively. Wilcoxon\u0026rsquo;s rank-sum test was used for non-normal distribution variables, and Fisher\u0026rsquo;s exact or Pearson's Chi-squared test were used to compare categorical variables (\u0026ldquo;gtsummary\u0026rdquo; package). The baseline characteristics between the two groups were matched using PSM with the nearest-neighbor method, employing a 1:1 ratio and a caliper width of 0.1 (\u0026ldquo;Matchit\u0026rdquo; package).\u003c/p\u003e \u003cp\u003eThe log-rank test and Kaplan-Meier survival curves were implemented to compare CSS between different groups. Univariate and Multivariate analysis was performed using the Cox proportional hazards regression model for calculating hazard ratio (HR) with its 95% CI. Stratification and interaction analyses were conducted to delineate the impact of demographic and clinicopathological variables, utilizing likelihood ratio tests to investigate potential interactions. Two-tailed test with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were performed using R software (version 4.3.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eDemographic and clinicopathological characteristics of DMGs\u003c/h2\u003e\n \u003cp\u003eBased on the predetermined inclusion and exclusion criteria, a cohort of 278 patients diagnosed with H3K27M-mutant DMGs was retrieved from the database for analysis (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The median age at diagnosis was 13 years (interquartile range 7\u0026ndash;29 years), with 170 (61%) pediatric. The vast majority of patients were white (74%), single (81%) and resided in metropolitan areas (91%). The primary tumor site was the brainstem in 419 patients (50%), followed by the cerebrum (44%) and spinal cord (6.1%). Additionally, the diagnoses of patients were confirmed through histological examinations (88%) and radiological assessments (12%) (Supplementary Table S1).\u003c/p\u003e\n \u003cp\u003eIn terms of therapeutic interventions, radiotherapy was administered to 235 patients (85%), while surgical intervention was contraindicated in 137 cases (49%). Of the patient cohort, 147 individuals (53%) received chemotherapy (Chemo group), whereas the remaining 131 (60.8%) did not (non-chemo group). Compared with non-chemo group, patients receiving chemotherapy were predominantly older, married, metropolitan residents, and had tumors located in the cerebrum and spinal cord (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, these patients frequently received radiation therapy and surgical interventions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Table S1).\u003c/p\u003e\n \u003cp\u003eBased on the data analyzed, patients were categorized into four groups according to their treatment regimens: radiotherapy alone (RT), chemotherapy alone (Chemo), combined radiotherapy and chemotherapy (RT\u0026thinsp;+\u0026thinsp;Chemo), and no adjuvant therapy (None). We then assessed the prognostic benefits associated with each treatment modality. The findings indicated that compared with the group that received no adjuvant therapy, those treated with either radiotherapy alone (RT) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52) or chemotherapy alone (Chemo) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81) did not experience significant survival advantages (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003ea-b). Conversely, patients who underwent both radiotherapy and chemotherapy concurrently (RT\u0026thinsp;+\u0026thinsp;Chemo) achieved notably longer survival times (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003ec, P\u0026thinsp;=\u0026thinsp;0.0057). Notably, while there was no significant difference in survival between patients receiving only radiotherapy and those receiving only chemotherapy (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003ed, P\u0026thinsp;=\u0026thinsp;0.8), those subjected to both treatments concurrently exhibited a marked increase in survival compared to those who received only radiotherapy (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003ee, P\u0026thinsp;=\u0026thinsp;0.019). In contrast, the survival of patients undergoing concurrent chemoradiotherapy with an enhanced radiotherapy regimen did not demonstrate superiority over those treated with chemotherapy alone. (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003ef, P\u0026thinsp;=\u0026thinsp;0.48). The overall comparison of patient outcomes among the four treatment options suggested that the use of radiotherapy alone as the standard treatment for DMGs may warrant reconsideration, given the unexpectedly beneficial effects of chemotherapy in DMGs management (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eg). Consequently, a reevaluation of the significance and impact of chemotherapy in DMGs therapy was necessary.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eThe role of chemotherapy in survival after propensity score matching\u003c/h2\u003e\n \u003cp\u003eTo address the potential impact of imbalanced baseline characteristics between the Chemo and Non-chemo groups on statistical power, we employed PSM to minimize the influence of confounding variables and establish a balanced cohort. Subsequently, 78 patients with H3K27M-mutant DMGs were precisely matched in both the Chemo and Non-chemo groups. The \u003cem\u003eP\u003c/em\u003e - values for all covariates after matching were more than 0.1, indicating that the PSM diminished the selection bias within the two groups. Demographics and clinicopathological variables for all 278 patients before and after PSM are delineated in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of H3K27 mutated diffuse midline gliomas patients before and after propensity score matching.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBefore matching\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e -value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAfter matching\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e -value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;278\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-chemo\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;131\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemo\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;147\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-chemo\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;78\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemo\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;78\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (7, 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6, 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (9, 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (6, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (6, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePediatric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;80000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e80000+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural urban, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNonmetropolitan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary site, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBrain stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCerebrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSpinal cord\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaterality, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNot a paired site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRight/Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;4.5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;4.5cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic confirmation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRadiography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBiopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBeam radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%); Median (IQR) \u003csup\u003e2\u003c/sup\u003ePearson\u0026apos;s Chi-squared test; Wilcoxon rank sum test; Fisher\u0026apos;s exact test\u003c/p\u003e\n \u003cp\u003eSTR, Subtotal resection; GTR, Gross total resection; Chemo, Chemotherapy.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe Kaplan-Meier survival curves of the CSS for H3K27M-mutant DMGs stratified by Chemo or non-chemo before and after PSM are shown in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eh-i. Compared with the CSS of patients who did not receive chemotherapy, the CSS among patients who received chemotherapy regimens was significantly prolonged in both the PSM cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and non PSM cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00063). Furthermore, in the PSM cohort, univariable Cox regression model suggested that age, primary site and chemotherapy were potential prognostic factors for CSS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Conversely, the primary tumor site, diagnostic confirmation, surgery and radiotherapy did not demonstrate a significant correlation with prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnivariate and multivariate analyses of cancer-specific survival (CSS) in the cohort after propensity score matching\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003e- value\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003e- value\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70, 1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76, 1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePediatric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10, 3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95, 3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71, 1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80, 2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;80000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80000+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79, 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81, 4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38, 0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40, 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpinal cord\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30, 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70, 16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42, 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79, 2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaterality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot a paired site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight/Left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38, 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic confirmation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85, 2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67, 2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64, 1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64, 2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46, 1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57, 2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24, 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07, 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40, 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41, 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeam radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28, 2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06, 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eHR, Hazard Ratio; CI, Confidence Interval; STR, Subtotal resection; GTR, Gross total resection.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on Cox stepwise regression and previous clinical research findings, a multivariate Cox proportional hazards regression model was constructed, incorporating variables such as age, race, diagnostic confirmation, primary tumor site, surgery, radiotherapy, and chemotherapy. After controlling for other prognostic factors, patients underwent chemotherapy were demonstrated a significant association with better CSS (HR\u0026thinsp;=\u0026thinsp;0.63; 95% CI, 0.41\u0026ndash;0.97; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Similarly, patients who received radiotherapy exhibited a reduced relative risk of mortality (HR\u0026thinsp;=\u0026thinsp;0.23; 95% CI, 0.06\u0026ndash;0.90; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Consistent with univariate regression analysis, the primary tumor site, diagnostic confirmation, surgery failed to demonstrate a significant association with prognosis in the multivariable Cox regression model (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eSubgroup interaction analysis\u003c/h2\u003e\n \u003cp\u003eSubgroup analyses were conducted to assess the prognostic value of chemotherapy for patients stratified by demographic and clinicopathological variables after PSM. The chemotherapy was a significant predictor of CSS within subgroups, notably among females (HR\u0026thinsp;=\u0026thinsp;0.50; 95% CI, 0.29\u0026ndash;0.87), those of white race (HR\u0026thinsp;=\u0026thinsp;0.48; 95% CI, 0.30\u0026ndash;0.77), single individuals (HR\u0026thinsp;=\u0026thinsp;0.64; 95% CI, 0.42\u0026ndash;0.97), individuals residing in metropolitan areas (HR\u0026thinsp;=\u0026thinsp;0.50; 95% CI, 0.32\u0026ndash;0.77), and those with high household income (HR\u0026thinsp;=\u0026thinsp;0.47; 95% CI, 0.28\u0026ndash;0.80) (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eIn term of clinicopathological variables, chemotherapy demonstrated a significant association with a reduced mortality risk in individuals diagnosed histologically (HR\u0026thinsp;=\u0026thinsp;0.57; 95% CI, 0.36\u0026ndash;0.89). This association was also observed in patients with tumors situated in the cerebrum (HR\u0026thinsp;=\u0026thinsp;0.44; 95% CI, 0.20\u0026ndash;0.94), those with unknown or unmeasured tumor size (HR\u0026thinsp;=\u0026thinsp;0.31; 95% CI: 0.11\u0026ndash;0.87), those who underwent biopsy (HR\u0026thinsp;=\u0026thinsp;0.35; 95% CI, 0.13\u0026ndash;0.96), and those who received beam radiation therapy (HR\u0026thinsp;=\u0026thinsp;0.59; 95% CI, 0.39\u0026thinsp;=\u0026thinsp;0.89) (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiffuse midline gliomas (DMGs) garners significant attention due to their aggressive nature and poor prognosis. The management of DMGs requires a comprehensive consideration of various factors, including tumor location, size, and molecular characteristics. Currently, gross total resection is deemed the ideal treatment modality for DMGs. However, due to the unique locations of these tumors, fewer than 20% of patients are eligible for this approach [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consequently, adjunctive treatment strategies are crucial for extending the survival of DMGs patients. Conventional high-dose radiotherapy and temozolomide chemotherapy currently represent the principal treatment options [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Nevertheless, some studies have suggested that DMGs patients might only benefit from radiation therapy, while chemotherapy appears to be ineffective for DMGs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This has sparked controversy regarding the role of chemotherapy in DMGs management [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Hence, whether chemotherapy should be routinely incorporated into treatment protocols and in which patient populations it could be beneficial remains an unresolved issue.\u003c/p\u003e \u003cp\u003eOur research findings highlight the significant protective effect of chemotherapy on patients with DMGs. We observed no statistically significant difference in survival rates between patients receiving chemotherapy alone and those subjected exclusively to radiotherapy. However, the combination of chemotherapy and radiotherapy significantly increased patient survival compared to radiotherapy alone, but did not bring additional benefits compared to chemotherapy alone. These findings suggest that chemotherapy can be recommended as a standard treatment for DMGs in clinical practice.\u003c/p\u003e \u003cp\u003eWhy does chemotherapy exhibit variable efficacy in patients with DMGs? Based on our research findings, both radiotherapy and chemotherapy exerted a protective effect on patients with DMGs. Specifically focusing on chemotherapy, our stratified analysis revealed that certain demographic and clinical pathological variables could impact its efficacy. For example, in subgroups characterized by demographic factors such as female gender, Caucasian ethnicity, single marital status, residence in metropolitan areas, and higher family income, chemotherapy significantly improved survival outcomes in DMGs patients. These populations share common attributes: they are from potentially more developed regions or high-income groups, and therefore are more likely to seek medical care, exhibit better compliance and can afford the most advanced or appropriate treatment plans. Correspondingly, chemotherapy will demonstrate its value as an effective treatment option. On the contrary, individuals from lower-income groups may only have access to outdated or suboptimal treatment regimens, potentially leading to an inaccurate assessment of the efficacy of chemotherapy. This finding is novel and comprehensible.\u003c/p\u003e \u003cp\u003eCertain clinical pathological variables can also influence the efficacy of chemotherapy. For instance, a significant association exists between chemotherapy and individual mortality risk reduction based on histological diagnosis, underscoring the dual significance of histological diagnosis in both accurate diagnosis and treatment of DMGs. Relying solely on imaging for pathology assessment and treatment planning might subject patients to increased therapeutic risks. Furthermore, in comparison to their respective cohorts, patients within the groups characterized by cerebrum (primary site), undetermined tumor size, biopsy-based surgery intervention, and beam radiation therapy demonstrated a significant benefit from chemotherapy for DMGs.\u003c/p\u003e \u003cp\u003eCompared to midline anatomy such as the brainstem and spinal cord, cerebrum tumor resection is relatively less challenging and associated with fewer complications which may be a crucial reason why chemotherapy yields significant benefits in cerebrum DMGs. Considering that surgical trauma can weaken the patient's immune system, leading to increased side effects during chemotherapy, and that non-surgical patients often have poor physical conditions, the patients who receive biopsies tend to exhibit more effective response to chemotherapy. Among the patients receiving radiotherapy, the addition of chemotherapy can improve survival rates, which is consistent with our previous findings and represents one of the most important discoveries of this study. The combination of radiotherapy and chemotherapy appears to benefit the vast majority of patients.\u003c/p\u003e \u003cp\u003eIn addition to the aforementioned factors, the genetic mutation status of DMGs patients themselves also influence the selection of chemotherapy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For instance, patients with CpG island methylation in the O6-methylguanine DNA methyltransferase (MGMT) gene promoter region are generally considered more sensitive to chemotherapy compared to wild-type patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The heightened sensitivity is attributed to the fact that most chemotherapy drugs are alkylating agents (such as temozolomide, TMZ), which possess strong mutagenic properties and induces a significant number of base mismatches during DNA replication by alkylating the O6 position of guanine, and subsequently lead to further induction of cell DNA double-strand breaks and apoptosis through the mismatch repair (MMR) mechanism [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMGMT is a DNA repair protein that counteracts the cytotoxicity and mutagenic activity of TMZ by removing alkyl groups from the O6 position of guanine, leading to drug resistance. Methylation of the MGMT promoter results in gene silencing, reduced protein expression, and diminished DNA repair function, thereby increasing the drug sensitivity of tumor cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the methylation status of MGMT is relative and non-permanent with respect to chemotherapy sensitivity. Because once a single alkyl group is removed, MGMT becomes irreversibly inactivated [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consequently, the cellular concentration of MGMT protein is a critical determinant for repairing damage induced by TMZ. When TMZ treatment depletes the MGMT protein, cells regain their sensitivity to chemotherapy.\u003c/p\u003e \u003cp\u003eAdditionally, high-grade gliomas treated with TMZ often exhibit evolutionary escape mechanisms such as MMR inactivation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the absence of MMR function, cells are unable to detect base pair mismatches, ultimately resulting in an accumulation of thousands of mutations and consequently causing tumor cells to undergo hypermutation. In prior studies investigating the recurrence of low-grade gliomas (LGG) treated with TMZ, it was noted that the methylation level of the MGMT promoter was elevated in hypermutated recurrent tumors compared to non-hypermutated ones [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Other researches have also documented the concurrent presence of hypermutation and MGMT promoter methylation in recurrent tumors of both LGG and glioblastoma multiforme (GBM) patients undergoing TMZ therapy [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings implied that TMZ and related alkylating agents not only deplete MGMT proteins but also induce hypermutations that may encompass MGMT promoter methylation, facilitating a transition in tumor cells from a chemotherapy-insensitive wild-type or hypomethylated state to a chemotherapy-sensitive methylated state, thereby enhancing patient response to chemotherapy. Consequently, the mutational status, including MGMT methylation, should not be regarded as the exclusive factor determining the efficacy of chemotherapy. A comprehensive assessment and judgment of the effectiveness of chemotherapeutic agents are warranted. Based on the results presented, it is inferred that the efficacy of chemotherapy transcends mere therapeutic intervention; it potentially modulates drug sensitivity through epigenetic mechanisms, thereby substantially enhancing treatment benefits for DMGs patients.\u003c/p\u003e \u003cp\u003eNonetheless, acknowledging the multifactorial nature influencing chemotherapy benefits underscores the necessity of tailored therapeutic strategies. A nuanced assessment incorporating patients\u0026rsquo; clinical profiles and inherent factors forms the cornerstone for devising personalized treatments aimed at augmenting survival rates among DMGs populations. In essence, our study contributes novel insights and empirical support to the notion that chemotherapy plays a pivotal role in benefiting DMGs patients and offering clinicians valuable guidance in evaluating, managing, and optimizing the prognosis of H3K27M-mutant DMGs patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Beijing Municipality (grant number: 7222053), National Natural Science Foundation of China (grant number: 82003134), and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study design and conception. Data collection and analysis were performed by Hui Shen and Jin Zhang. The first draft of the manuscript was written by Jin Zhang and Shanshan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the public availability and de-identification of the SEER dataset, it eliminates the necessity for Institutional Review Board (IRB) approval when utilizing it for data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eL\u0026oacute;pez-P\u0026eacute;rez CA, Franco-Mojica X, Villanueva-Gaona R, D\u0026iacute;az-Alba A, Rodr\u0026iacute;guez-Florido MA, Navarro VG (2022) Adult diffuse midline gliomas H3 K27-altered: review of a redefined entity. Journal of neuro-oncology 158: 369-378 doi:10.1007/s11060-022-04024-5\u003c/li\u003e\n\u003cli\u003eLouis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-oncology 23: 1231-1251 doi:10.1093/neuonc/noab106\u003c/li\u003e\n\u003cli\u003eGong X, Kuang S, Deng D, Wu J, Zhang L, Liu C (2023) Differences in survival prognosticators between children and adults with H3K27M-mutant diffuse midline glioma. CNS neuroscience \u0026amp; therapeutics 29: 3863-3875 doi:10.1111/cns.14307\u003c/li\u003e\n\u003cli\u003eDi Nunno V, Lombardi G, Simonelli M, Minniti G, Mastronuzzi A, Di Ruscio V, Corr\u0026agrave; M, Padovan M, Maccari M, Caccese M, Simonetti G, Berlendis A, Farinotti M, Pollo B, Antonelli M, Di Muzio A, Dipasquale A, Asioli S, De Biase D, Tosoni A, Silvani A, Franceschi E (2024) The role of adjuvant chemotherapy in patients with H3K27 altered diffuse midline gliomas: a multicentric retrospective study. Journal of neuro-oncology 167: 145-154 doi:10.1007/s11060-024-04589-3\u003c/li\u003e\n\u003cli\u003eSturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, T\u0026ouml;njes M, Sill M, Bender S, Kool M, Zapatka M, Becker N, Zucknick M, Hielscher T, Liu XY, Fontebasso AM, Ryzhova M, Albrecht S, Jacob K, Wolter M, Ebinger M, Schuhmann MU, van Meter T, Fr\u0026uuml;hwald MC, Hauch H, Pekrun A, Radlwimmer B, Niehues T, von Komorowski G, D\u0026uuml;rken M, Kulozik AE, Madden J, Donson A, Foreman NK, Drissi R, Fouladi M, Scheurlen W, von Deimling A, Monoranu C, Roggendorf W, Herold-Mende C, Unterberg A, Kramm CM, Felsberg J, Hartmann C, Wiestler B, Wick W, Milde T, Witt O, Lindroth AM, Schwartzentruber J, Faury D, Fleming A, Zakrzewska M, Liberski PP, Zakrzewski K, Hauser P, Garami M, Klekner A, Bognar L, Morrissy S, Cavalli F, Taylor MD, van Sluis P, Koster J, Versteeg R, Volckmann R, Mikkelsen T, Aldape K, Reifenberger G, Collins VP, Majewski J, Korshunov A, Lichter P, Plass C, Jabado N, Pfister SM (2012) Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer cell 22: 425-437 doi:10.1016/j.ccr.2012.08.024\u003c/li\u003e\n\u003cli\u003eCastel D, Philippe C, Calmon R, Le Dret L, Truffaux N, Boddaert N, Pag\u0026egrave;s M, Taylor KR, Saulnier P, Lacroix L, Mackay A, Jones C, Sainte-Rose C, Blauwblomme T, Andreiuolo F, Puget S, Grill J, Varlet P, Debily MA (2015) Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta neuropathologica 130: 815-827 doi:10.1007/s00401-015-1478-0\u003c/li\u003e\n\u003cli\u003ePiunti A, Hashizume R, Morgan MA, Bartom ET, Horbinski CM, Marshall SA, Rendleman EJ, Ma Q, Takahashi YH, Woodfin AR, Misharin AV, Abshiru NA, Lulla RR, Saratsis AM, Kelleher NL, James CD, Shilatifard A (2017) Therapeutic targeting of polycomb and BET bromodomain proteins in diffuse intrinsic pontine gliomas. Nature medicine 23: 493-500 doi:10.1038/nm.4296\u003c/li\u003e\n\u003cli\u003eSchwartzentruber J, Korshunov A, Liu XY, Jones DT, Pfaff E, Jacob K, Sturm D, Fontebasso AM, Quang DA, T\u0026ouml;njes M, Hovestadt V, Albrecht S, Kool M, Nantel A, Konermann C, Lindroth A, J\u0026auml;ger N, Rausch T, Ryzhova M, Korbel JO, Hielscher T, Hauser P, Garami M, Klekner A, Bognar L, Ebinger M, Schuhmann MU, Scheurlen W, Pekrun A, Fr\u0026uuml;hwald MC, Roggendorf W, Kramm C, D\u0026uuml;rken M, Atkinson J, Lepage P, Montpetit A, Zakrzewska M, Zakrzewski K, Liberski PP, Dong Z, Siegel P, Kulozik AE, Zapatka M, Guha A, Malkin D, Felsberg J, Reifenberger G, von Deimling A, Ichimura K, Collins VP, Witt H, Milde T, Witt O, Zhang C, Castelo-Branco P, Lichter P, Faury D, Tabori U, Plass C, Majewski J, Pfister SM, Jabado N (2012) Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482: 226-231 doi:10.1038/nature10833\u003c/li\u003e\n\u003cli\u003eMeyronet D, Esteban-Mader M, Bonnet C, Joly MO, Uro-Coste E, Amiel-Benouaich A, Forest F, Rousselot-Denis C, Burel-Vandenbos F, Bourg V, Guyotat J, Fenouil T, Jouvet A, Honnorat J, Ducray F (2017) Characteristics of H3 K27M-mutant gliomas in adults. Neuro-oncology 19: 1127-1134 doi:10.1093/neuonc/now274\u003c/li\u003e\n\u003cli\u003eSchreck KC, Ranjan S, Skorupan N, Bettegowda C, Eberhart CG, Ames HM, Holdhoff M (2019) Incidence and clinicopathologic features of H3 K27M mutations in adults with radiographically-determined midline gliomas. Journal of neuro-oncology 143: 87-93 doi:10.1007/s11060-019-03134-x\u003c/li\u003e\n\u003cli\u003eDesai J, Rajkumar S, Shepard MJ, Herbst J, Karlovits SM, Hasan S, Horne ZD, Wegner RE (2024) National trends in the treatment of adult diffuse midline gliomas: a rare clinical scenario. Journal of neuro-oncology 168: 269-274 doi:10.1007/s11060-024-04663-w\u003c/li\u003e\n\u003cli\u003eArgersinger DP, Rivas SR, Shah AH, Jackson S, Heiss JD (2021) New Developments in the Pathogenesis, Therapeutic Targeting, and Treatment of H3K27M-Mutant Diffuse Midline Glioma. Cancers 13 doi:10.3390/cancers13215280\u003c/li\u003e\n\u003cli\u003eAl Sharie S, Abu Laban D, Al-Hussaini M (2023) Decoding Diffuse Midline Gliomas: A Comprehensive Review of Pathogenesis, Diagnosis and Treatment. Cancers 15 doi:10.3390/cancers15194869\u003c/li\u003e\n\u003cli\u003ePersson ML, Douglas AM, Alvaro F, Faridi P, Larsen MR, Alonso MM, Vitanza NA, Dun MD (2022) The intrinsic and microenvironmental features of diffuse midline glioma: Implications for the development of effective immunotherapeutic treatment strategies. Neuro-oncology 24: 1408-1422 doi:10.1093/neuonc/noac117\u003c/li\u003e\n\u003cli\u003eMiguel Llordes G, Medina P\u0026eacute;rez VM, Curto Sim\u0026oacute;n B, Castells-Yus I, V\u0026aacute;zquez Sufuentes S, Schuhmacher AJ (2023) Epidemiology, Diagnostic Strategies, and Therapeutic Advances in Diffuse Midline Glioma. Journal of clinical medicine 12 doi:10.3390/jcm12165261\u003c/li\u003e\n\u003cli\u003eBai J, Varghese J, Jain R (2020) Adult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know. Topics in magnetic resonance imaging : TMRI 29: 71-82 doi:10.1097/rmr.0000000000000234\u003c/li\u003e\n\u003cli\u003ePrice G, Bouras A, Hambardzumyan D, Hadjipanayis CG (2021) Current knowledge on the immune microenvironment and emerging immunotherapies in diffuse midline glioma. EBioMedicine 69: 103453 doi:10.1016/j.ebiom.2021.103453\u003c/li\u003e\n\u003cli\u003eCacciotti C, Wright KD (2023) Advances in Treatment of Diffuse Midline Gliomas. Current neurology and neuroscience reports 23: 849-856 doi:10.1007/s11910-023-01317-8\u003c/li\u003e\n\u003cli\u003eYoon HI, Wee CW, Kim YZ, Seo Y, Im JH, Dho YS, Kim KH, Hong JB, Park JS, Choi SH, Kim MS, Moon J, Hwang K, Park JE, Cho JM, Yoon WS, Kim SH, Kim YI, Kim HS, Sung KS, Song JH, Lee MH, Han MH, Lee SH, Chang JH, Lim DH, Park CK, Lee YS, Gwak HS (2021) The Korean Society for Neuro-Oncology (KSNO) Guideline for Adult Diffuse Midline Glioma: Version 2021.1. Brain tumor research and treatment 9: 1-8 doi:10.14791/btrt.2021.9.e8\u003c/li\u003e\n\u003cli\u003eLi YA, Zhao C, Ge JJ, Li C, Xue FJ, Qi SP, Zhao C, Kong CC, Zhang JP (2024) Apatinib combined with temozolomide in diffuse midline glioma: a novel and effective therapy. BMC cancer 24: 754 doi:10.1186/s12885-024-12373-9\u003c/li\u003e\n\u003cli\u003eHeravi Shargh V, Luckett J, Bouzinab K, Paisey S, Turyanska L, Singleton WGB, Lowis S, Gershkovich P, Bradshaw TD, Stevens MFG, Bienemann A, Coyle B (2021) Chemosensitization of Temozolomide-Resistant Pediatric Diffuse Midline Glioma Using Potent Nanoencapsulated Forms of a N(3)-Propargyl Analogue. ACS applied materials \u0026amp; interfaces 13: 35266-35280 doi:10.1021/acsami.1c04164\u003c/li\u003e\n\u003cli\u003eLiu C, Kuang S, Huang T, Wu J, Zhang L, Gong X (2024) Radiotherapy plus temozolomide with or without anlotinib in H3K27M-mutant diffuse midline glioma: A retrospective cohort study. CNS neuroscience \u0026amp; therapeutics 30: e14730 doi:10.1111/cns.14730\u003c/li\u003e\n\u003cli\u003eWang L, Li Z, Zhang M, Piao Y, Chen L, Liang H, Wei Y, Hu Z, Zhao L, Teng L, Lu D (2018) H3 K27M-mutant diffuse midline gliomas in different anatomical locations. Human pathology 78: 89-96 doi:10.1016/j.humpath.2018.04.015\u003c/li\u003e\n\u003cli\u003eEl-Khouly FE, Veldhuijzen van Zanten SEM, Santa-Maria Lopez V, Hendrikse NH, Kaspers GJL, Loizos G, Sumerauer D, Nysom K, Pruunsild K, Pentikainen V, Thorarinsdottir HK, Rutkauskiene G, Calvagna V, Drogosiewicz M, Dragomir M, Deak L, Kitanovski L, von Bueren AO, Kebudi R, Slavc I, Jacobs S, Jadrijevic-Cvrlje F, Entz-Werle N, Grill J, Kattamis A, Hauser P, Pears J, Biassoni V, Massimino M, Lopez Aguilar E, Torsvik IK, Joao Gil-da-Costa M, Kumirova E, Cruz-Martinez O, Holm S, Bailey S, Hayden T, Thomale UW, Janssens GOR, Kramm CM, van Vuurden DG (2019) Diagnostics and treatment of diffuse intrinsic pontine glioma: where do we stand? Journal of neuro-oncology 145: 177-184 doi:10.1007/s11060-019-03287-9\u003c/li\u003e\n\u003cli\u003eMathew RK, Rutka JT (2018) Diffuse Intrinsic Pontine Glioma : Clinical Features, Molecular Genetics, and Novel Targeted Therapeutics. Journal of Korean Neurosurgical Society 61: 343-351 doi:10.3340/jkns.2018.0008\u003c/li\u003e\n\u003cli\u003eLong W, Yi Y, Chen S, Cao Q, Zhao W, Liu Q (2017) Potential New Therapies for Pediatric Diffuse Intrinsic Pontine Glioma. Frontiers in pharmacology 8: 495 doi:10.3389/fphar.2017.00495\u003c/li\u003e\n\u003cli\u003eFrazier JL, Lee J, Thomale UW, Noggle JC, Cohen KJ, Jallo GI (2009) Treatment of diffuse intrinsic brainstem gliomas: failed approaches and future strategies. Journal of neurosurgery Pediatrics 3: 259-269 doi:10.3171/2008.11.peds08281\u003c/li\u003e\n\u003cli\u003eBailey S, Howman A, Wheatley K, Wherton D, Boota N, Pizer B, Fisher D, Kearns P, Picton S, Saran F, Gibson M, Glaser A, Connolly DJ, Hargrave D (2013) Diffuse intrinsic pontine glioma treated with prolonged temozolomide and radiotherapy--results of a United Kingdom phase II trial (CNS 2007 04). European journal of cancer (Oxford, England : 1990) 49: 3856-3862 doi:10.1016/j.ejca.2013.08.006\u003c/li\u003e\n\u003cli\u003eChassot A, Canale S, Varlet P, Puget S, Roujeau T, Negretti L, Dhermain F, Rialland X, Raquin MA, Grill J, Dufour C (2012) Radiotherapy with concurrent and adjuvant temozolomide in children with newly diagnosed diffuse intrinsic pontine glioma. Journal of neuro-oncology 106: 399-407 doi:10.1007/s11060-011-0681-7\u003c/li\u003e\n\u003cli\u003eIzzuddeen Y, Gupta S, Haresh KP, Sharma D, Giridhar P, Rath GK (2020) Hypofractionated radiotherapy with temozolomide in diffuse intrinsic pontine gliomas: a randomized controlled trial. Journal of neuro-oncology 146: 91-95 doi:10.1007/s11060-019-03340-7\u003c/li\u003e\n\u003cli\u003ePark C, Kim TM, Bae JM, Yun H, Kim JW, Choi SH, Lee ST, Lee JH, Park SH, Park CK (2021) Clinical and Genomic Characteristics of Adult Diffuse Midline Glioma. Cancer research and treatment 53: 389-398 doi:10.4143/crt.2020.694\u003c/li\u003e\n\u003cli\u003eAbe H, Natsumeda M, Kanemaru Y, Watanabe J, Tsukamoto Y, Okada M, Yoshimura J, Oishi M, Fujii Y (2018) MGMT Expression Contributes to Temozolomide Resistance in H3K27M-Mutant Diffuse Midline Gliomas and MGMT Silencing to Temozolomide Sensitivity in IDH-Mutant Gliomas. Neurologia medico-chirurgica 58: 290-295 doi:10.2176/nmc.ra.2018-0044\u003c/li\u003e\n\u003cli\u003eDaniel P, Sabri S, Chaddad A, Meehan B, Jean-Claude B, Rak J, Abdulkarim BS (2019) Temozolomide Induced Hypermutation in Glioma: Evolutionary Mechanisms and Therapeutic Opportunities. Frontiers in oncology 9: 41 doi:10.3389/fonc.2019.00041\u003c/li\u003e\n\u003cli\u003eMargison GP, Santib\u0026aacute;\u0026ntilde;ez Koref MF, Povey AC (2002) Mechanisms of carcinogenicity/chemotherapy by O6-methylguanine. Mutagenesis 17: 483-487 doi:10.1093/mutage/17.6.483\u003c/li\u003e\n\u003cli\u003eKarran P, Marinus MG (1982) Mismatch correction at O6-methylguanine residues in E. coli DNA. Nature 296: 868-869 doi:10.1038/296868a0\u003c/li\u003e\n\u003cli\u003eEsteller M, Herman JG (2004) Generating mutations but providing chemosensitivity: the role of O6-methylguanine DNA methyltransferase in human cancer. Oncogene 23: 1-8 doi:10.1038/sj.onc.1207316\u003c/li\u003e\n\u003cli\u003eCabrini G, Fabbri E, Lo Nigro C, Dechecchi MC, Gambari R (2015) Regulation of expression of O6-methylguanine-DNA methyltransferase and the treatment of glioblastoma (Review). International journal of oncology 47: 417-428 doi:10.3892/ijo.2015.3026\u003c/li\u003e\n\u003cli\u003eFukushima T, Takeshima H, Kataoka H (2009) Anti-glioma therapy with temozolomide and status of the DNA-repair gene MGMT. Anticancer research 29: 4845-4854 \u003c/li\u003e\n\u003cli\u003eYu Y, Villanueva-Meyer J, Grimmer MR, Hilz S, Solomon DA, Choi S, Wahl M, Mazor T, Hong C, Shai A, Phillips JJ, Wainer BH, McDermott M, Haas-Kogan D, Taylor JW, Butowski N, Clarke JL, Berger MS, Molinaro AM, Chang SM, Costello JF, Oberheim Bush NA (2021) Temozolomide-induced hypermutation is associated with distant recurrence and reduced survival after high-grade transformation of low-grade IDH-mutant gliomas. Neuro-oncology 23: 1872-1884 doi:10.1093/neuonc/noab081\u003c/li\u003e\n\u003cli\u003eMathur R, Zhang Y, Grimmer MR, Hong C, Zhang M, Bollam S, Petrecca K, Clarke J, Berger MS, Phillips JJ, Oberheim-Bush NA, Molinaro AM, Chang SM, Costello JF (2020) MGMT promoter methylation level in newly diagnosed low-grade glioma is a predictor of hypermutation at recurrence. Neuro-oncology 22: 1580-1590 doi:10.1093/neuonc/noaa059\u003c/li\u003e\n\u003cli\u003eJohnson BE, Mazor T, Hong C, Barnes M, Aihara K, McLean CY, Fouse SD, Yamamoto S, Ueda H, Tatsuno K, Asthana S, Jalbert LE, Nelson SJ, Bollen AW, Gustafson WC, Charron E, Weiss WA, Smirnov IV, Song JS, Olshen AB, Cha S, Zhao Y, Moore RA, Mungall AJ, Jones SJM, Hirst M, Marra MA, Saito N, Aburatani H, Mukasa A, Berger MS, Chang SM, Taylor BS, Costello JF (2014) Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science (New York, NY) 343: 189-193 doi:10.1126/science.1239947\u003c/li\u003e\n\u003cli\u003evan Thuijl HF, Mazor T, Johnson BE, Fouse SD, Aihara K, Hong C, Malmstr\u0026ouml;m A, Hallbeck M, Heimans JJ, Kloezeman JJ, Stenmark-Askmalm M, Lamfers ML, Saito N, Aburatani H, Mukasa A, Berger MS, S\u0026ouml;derkvist P, Taylor BS, Molinaro AM, Wesseling P, Reijneveld JC, Chang SM, Ylstra B, Costello JF (2015) Evolution of DNA repair defects during malignant progression of low-grade gliomas after temozolomide treatment. Acta neuropathologica 129: 597-607 doi:10.1007/s00401-015-1403-6\u003c/li\u003e\n\u003cli\u003eNetwork TC (2013) Corrigendum: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 494: 506 doi:10.1038/nature11903\u003c/li\u003e\n\u003cli\u003eJonsson P, Lin AL, Young RJ, DiStefano NM, Hyman DM, Li BT, Berger MF, Zehir A, Ladanyi M, Solit DB, Arnold AG, Stadler ZK, Mandelker D, Goldberg ME, Chmielecki J, Pourmaleki M, Ogilvie SQ, Chavan SS, McKeown AT, Manne M, Hyde A, Beal K, Yang TJ, Nolan CP, Pentsova E, Omuro A, Gavrilovic IT, Kaley TJ, Diamond EL, Stone JB, Grommes C, Boire A, Daras M, Piotrowski AF, Miller AM, Gutin PH, Chan TA, Tabar VS, Brennan CW, Rosenblum M, DeAngelis LM, Mellinghoff IK, Taylor BS (2019) Genomic Correlates of Disease Progression and Treatment Response in Prospectively Characterized Gliomas. Clinical cancer research : an official journal of the American Association for Cancer Research 25: 5537-5547 doi:10.1158/1078-0432.ccr-19-0032\u003c/li\u003e\n\u003cli\u003eWang J, Cazzato E, Ladewig E, Frattini V, Rosenbloom DI, Zairis S, Abate F, Liu Z, Elliott O, Shin YJ, Lee JK, Lee IH, Park WY, Eoli M, Blumberg AJ, Lasorella A, Nam DH, Finocchiaro G, Iavarone A, Rabadan R (2016) Clonal evolution of glioblastoma under therapy. Nature genetics 48: 768-776 doi:10.1038/ng.3590\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diffuse midline gliomas, DMGs, Chemotherapy, H3K27M","lastPublishedDoi":"10.21203/rs.3.rs-5432895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5432895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDiffuse Midline Gliomas (DMGs) represent a category of rare brain tumors with an exceedingly poor prognosis. Anatomical constraints make complete surgical resection challenging. Conventional radiotherapy is widely regarded as a means to enhance patient survival. Currently, while chemotherapy is frequently employed in clinical practice for DMGs, its full therapeutic efficacy remains incompletely understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a SEER-based propensity scored matching (PSM) study on patients with H3K27M-mutant DMGs to evaluate the role of chemotherapy in the treatment benefit of DMGs. Univariate and multivariate Cox regression model were used to evaluate the relevant factors affecting cancer specific survival (CSS). Stratification and interaction analyses were conducted to delineate the impact of demographic and clinicopathological variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients underwent both radiotherapy and chemotherapy concurrently achieved notably longer survival times compared to those who received only radiotherapy. The CSS among patients who received chemotherapy regimens was significantly prolonged in both the PSM and non PSM cohort. Univariable Cox regression suggested that age, primary site and chemotherapy were potential prognostic factors for CSS. Multivariate Cox regression indicated patients who received radiotherapy or chemotherapy exhibited a reduced risk of mortality. Multitude demographic factors, including gender, race, marital status, household income and rural urban, as well as clinicopathological variables could affect the chemotherapy benefits of DMGs patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eChemotherapy as an adjuvant therapy could significantly improve the prognosis of DMGs patients under comprehensive treatment conditions. The nature of multiple factors affecting chemotherapy benefits emphasizes the necessity of tailored treatment strategies.\u003c/p\u003e","manuscriptTitle":"The role of chemotherapy in patients with H3K27M-mutant diffuse midline gliomas: a SEER-based propensity scored matching study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 06:13:28","doi":"10.21203/rs.3.rs-5432895/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46e4e640-9ddb-49c8-b615-5b23a5004d85","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-17T06:13:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-17 06:13:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5432895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5432895","identity":"rs-5432895","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}