Prognostic Value of a Nomogram Model Based on Tumor Immune Markers and Clinical Factors for Adult Primary Gliomas

Prognostic Value of a Nomogram Model Based on Tumor Immune Markers and Clinical Factors for Adult Primary Gliomas | 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 Prognostic Value of a Nomogram Model Based on Tumor Immune Markers and Clinical Factors for Adult Primary Gliomas Junpeng Wen, Jiangwei Yuan, Ziling Zhang, Yuxiang Wang, Yan Zhao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6995164/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 Objective: This study aimed to analyze the factors associated with overall survival (OS) in adult patients with primary gliomas, develop a nomogram prediction model, and optimize its predictive performance. Methods: Clinical data were retrospectively collected from adult patients newly diagnosed with gliomas who underwent surgical treatment at the Department of Neurosurgery, Fourth Hospital of Hebei Medical University, between January 2019 and December 2023. External validation was performed using data from the Chinese Glioma Genome Atlas (CGGA) database. Data analysis and visualization were performed using Statistical Package for the Social Sciences (SPSS) 26.0 and R software (Version 4.4.1). Results: A total of 257 adult patients were included in this study. Multivariate Cox regression analysis revealed that age, Karnofsky Performance Status score, tumor diameter, World Health Organization grade, postoperative radiotherapy and chemotherapy, and the expression of tumor immune markers (ATRX, IDH1, and Ki-67) were all associated with patient prognosis. Factors with P < 0.05 in the multivariate analysis and those included in the CGGA external database were used to construct a nomogram for predicting 1-, 2-, and 3-year survival rates. Multiple validations demonstrated that the model exhibited excellent generalizability and clinical applicability. Conclusion: The nomogram model constructed based on clinical factors, tumor immune markers, and other parameters exhibited strong predictive efficacy and may serve as an effective alternative to molecular testing. tumor immune markers immunohistochemistry glioma nomogram model prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Glioma is the most common primary malignant tumor of the central nervous system (CNS), accounting for approximately 40–50% of all CNS tumors, with an increasing annual incidence rate in adults [ 1 ]. These tumors exhibit highly heterogeneous and invasive growth characteristics, leading to significantly different prognoses among patients with different subtypes [ 2 ]. In the molecular era, genetic testing has become indispensable for diagnosing CNS tumors. However, in patients with gliomas who undergo stereotactic biopsy, have tumors located in the brainstem, or are in poor general conditions such that secondary surgery is not feasible, the surgical samples may contain too little tumor tissue or a low proportion of tumor cells. As a result, it may be impossible to obtain sufficient DNA/RNA for genetic testing. In such cases, immunohistochemistry (IHC) represents a more feasible alternative [ 3 ]. Previous studies have revealed that mutations in ATRX, IDH1, and TP53 genes are significant in predicting the prognosis of gliomas [ 4 – 6 ]. The Ki-67 expression level intuitively reflects the proliferative activity of tumor cells [ 7 ]. Although molecular typing has improved the diagnostic accuracy, dynamic prognostic assessment tools integrating molecular characteristics and clinical parameters into clinical practice remain lacking. Nomograms have recently demonstrated unique value in tumor prognosis models; however, current prediction models based on radiomics or genomics are generally limited to single data dimensions and fail to fully integrate the systematic associations among immunophenotypes, molecular characteristics, and clinicopathological parameters [ 8 , 9 ]. This study aimed to innovatively construct a multidimensional prognostic prediction model, which will provide a reference for the clinical treatment of gliomas in clinical applications. Materials and Methods 1. Study design and participants This study retrospectively analyzed the clinical data of patients newly diagnosed with gliomas who underwent surgical treatment at the Department of Neurosurgery, Fourth Hospital of Hebei Medical University, from January 2019 to December 2023. Clinical data, including sex, age, smoking history, head trauma history, preoperative Karnofsky Performance Status (KPS) score, presenting symptoms, tumor diameter, tumor location and distribution, extent of surgical resection, WHO grade, postoperative treatment regimen, and protein expression levels (ATRX, IDH1, and Ki-67), were collected from patients’ medical records. Inclusion criteria were as follows: (1) patients who underwent surgical treatment for gliomas for the first time at our hospital, (2) availability of complete clinical data, (3) informed consent obtained from patients and their families, and (4) patients aged ≥ 18 years. Exclusion criteria were as follows: (1) presence of other concomitant malignant tumors; (2) insufficient tumor tissue for immunohistochemical testing; (3) history of prior glioma treatment; (4) pathological diagnosis of ependymoma, subependymoma, or pilocytic astrocytoma; and (5) patients aged < 18 years. To further confirm the reliability and generalizability of the overall survival (OS) prediction model for patients with gliomas developed in this study, data from 100 patients with balanced baseline characteristics from the Chinese Glioma Genome Atlas (CGGA) database were selected for external validation. 2. Histopathological detection methods All tumor tissue samples were fixed in formalin and embedded in paraffin. After reviewing the slides, two experienced pathologists from our hospital jointly determined all pathological results. The following comprised the positive criteria: under the premise of normal negative and positive controls, ATRX protein expression was localized in the nucleus, with > 10% positive cells defined as positive; IDH1 protein expression was localized in the cytoplasm, with ≥ 10% positive cells defined as positive; and Ki-67 and p53 protein expressions were localized in the nucleus. Positive p53 was defined as > 10% of nuclei containing p53-positive protein granules. Ki-67–positive cells of ≤ 20% were classified as weak positive (+), whereas > 20% were categorized as strong positive (++). 3. WHO grading criteria for gliomas The diagnosis of gliomas is based on the 2016 edition of the WHO Classification of Central Nervous System Tumors [ 10 ]. WHO grade II gliomas encompass IDH-mutant diffuse astrocytomas and oligodendrogliomas; WHO grade III gliomas are anaplastic gliomas, including anaplastic astrocytomas and anaplastic oligodendrogliomas; and WHO grade IV gliomas are mostly glioblastomas. 4. Treatment of gliomas Experienced neurosurgeons formulated surgical plans and determined the extent of resection on the basis of contrast-enhanced cranial MRI images within 1 week. According to the residual tumor volume revealed on cranial CT/MRI images at 24–72h postoperatively, the extent of surgical resection was classified into subtotal resection (resection degree, > 80%) and partial resection (resection degree, ≤ 80%). Radiotherapy, concurrent temozolomide chemotherapy, and adjuvant chemotherapy were the postoperative adjuvant treatments. 5. Follow-up All patients received a follow-up visit (combining outpatient review and telephone follow-up) at 3–6 months postoperatively. December 30, 2024 was the follow-up deadline, with a 31.17-month median follow-up duration. OS was defined as the time from the surgery date to the date of death or the last follow-up. 6. Statistical analysis Data analysis and visualization were performed using SPSS (version 26, IBM, Chicago, IL, USA) and R language (version 4.4.1). To determine differences, the chi-squared test was used for categorical variables between groups. The “linkET” package of the R language was used for visualizing correlation heatmaps, and Spearman correlation analysis was applied for correlation analysis. SPSS 26.0 was utilized for Cox univariate and multivariate analyses [ 11 ]. The predictive efficacy of the nomogram was evaluated using ROC curve, consistency index (C-index), calibration curve, and decision curve analysis (DCA), followed by internal and external validations. Results 1. Comparison of the clinical data of adult patients with gliomas of different WHO grades This study collected clinical data from 302 patients with gliomas who underwent surgical resection at our hospital. After excluding 45 patients who did not meet the inclusion criteria (4 patients aged < 18 years; 8 with ependymoma; 4 with subependymoma; 9 with other concomitant tumors; and 20 with missing ATRX, p53, IDH1, or Ki-67 test results), 257 patients were finally included in the study (Fig. 1 ). Significant differences were observed among patients with gliomas of different WHO grades in terms of age at diagnosis, presenting symptoms, KPS score, and tumor location. However, no significant differences were found in sex, smoking history, head trauma history, or tumor diameter (Table 1 ). Table 1 Comparison of clinical data among adult patients with different grades of gliomas of the brain n WHO Ⅱ WHO Ⅲ WHO Ⅳ χ 2 P value Sex Male 143 23 35 85 1.372 0.503 Female 114 23 22 69 Age (years) <60 137 39 38 60 35.130 < 0.001 ≥60 120 7 19 94 Smoking history Yes 86 16 22 48 1.075 0.584 No 171 30 35 106 Head trauma history Yes 20 4 3 13 0.651 0.722 No 237 42 54 141 KPS score ≥80 237 46 56 135 11.221 0.004 <80 20 0 1 19 Diameter (cm) < 5 157 32 34 91 1.699 0.428 ≥5 100 14 23 63 Symptom Intracranial hypertension 100 15 22 63 29.090 < 0.001 Neurological dysfunction 100 10 22 68 Epilepsy 39 16 10 13 Mental disorders 8 1 0 7 No obvious symptoms 10 4 3 3 Tumor distribution Left side 123 24 27 72 3.695 0.718 Right side 97 15 25 57 Bilateral 21 4 4 13 Middle 16 3 1 12 Tumor location Frontal lobe 101 28 24 49 25.461 0.001 Temporal lobe 63 4 11 48 Parietooccipital lobe 53 8 8 37 Insular lobe 14 0 6 8 Others 26 6 8 12 Surgical type Subtotal resection 154 33 30 91 5.371 0.251 Partial resection 80 10 23 47 Stereotactic biopsy 23 3 4 16 Postoperative chemotherapy Yes 204 32 43 129 5.058 0.080 No 53 14 14 25 Postoperative radiotherapy Yes 185 3 12 57 18.090 < 0.001 No 72 43 45 97 ATRX Positive 171 10 26 135 83.536 < 0.001 Negative 86 36 31 19 IDH1 Positive 62 24 17 21 30.038 < 0.001 Negative 195 22 40 133 p53 Positive 106 29 31 46 21.304 < 0.001 Negative 151 17 26 108 Ki-67 Weak+ positive 84 42 32 10 134.111 < 0.001 Strong++ 173 4 25 144 Note: KPS: Karnofsky Performance Status; ATRX: alpha thalassemia/mental retardation syndrome X-linked; IDH1: isocitrate dehydrogenase 1. 2. Treatment of gliomas Standard treatment for patients with gliomas include maximal surgical resection, followed by adjuvant radiotherapy and/or adjuvant chemotherapy with temozolomide, as recommended by clinical guidelines [ 12 , 13 ]. In this study, 154 (59.9%), 80 (31.1%), and 23 (9.0%) patients underwent subtotal resection, partial resection, and stereotactic biopsy, respectively (Table 1 ). A total of 204(79.4%) and 185(72.0%) patients received postoperative chemotherapy and postoperative radiotherapy, respectively. 3. Mutation and correlation analysis of common immune markers in various gliomas grades In this study, the mutation-positive rates of ATRX, IDH1, and p53 expression levels as well as the differentiation degree of Ki-67 significantly varied among patients with gliomas of different WHO grades (Table 1 ). Correlation analysis of ATRX, IDH1, p53, and Ki-67 protein expression levels revealed that ATRX was positively correlated with Ki-67, whereas ATRX was negatively correlated with IDH1, ATRX was negatively correlated with p53, and Ki-67 was negatively correlated with IDH1 (Fig. 2 ). 4. Cox regression analysis of prognostic factors Univariate Cox regression analysis (Table 2 ) revealed that age at diagnosis, KPS score, WHO grade, postoperative adjuvant radiotherapy, and the expression of tumor immune markers were all associated with the prognosis of patients with gliomas. Variables with statistical significance in the univariate Cox analysis and postoperative chemotherapy were included in the multivariate Cox analysis. The results indicated that postoperative adjuvant chemotherapy was an influencing factor for glioma prognosis, whereas P53 (−) was not a risk factor for poor prognosis in gliomas (Fig. 3). Table 2 One-way Cox prognostic analysis of overall survival and progression-free survival in 257 patients with gliomas OS HR 95% CI P value Sex (male/female) 0.913 0.683–1.220 0.540 Age (< 60/≥60 years) 3.502 2.555–4.800 < 0.001 Smoking history (no/yes) 0.799 0.585–1.092 0.159 KPS score (< 80/≥80) 0.114 0.069–0.188 < 0.001 Diameter (< 5/≥5 cm) 1.514 1.129–2.030 0.006 Head trauma history (no/yes) 1.145 0.674–1.944 0.616 WHO grade WHO Ⅱ 1 * 0 * WHO Ⅲ 3.477 1.784–6.775 < 0.001 WHO Ⅳ 14.739 7.995–27.170 < 0.001 Postoperative radiotherapy (no/yes) 0.281 0.204–0.388 < 0.001 Postoperative chemotherapy (no/yes) 0.739 0.512–1.068 0.107 ATRX (−/+) 11.042 7.083–17.213 < 0.001 IDH1 (−/+) 0.221 0.140–0.350 < 0.001 P53 (−/+) 0.475 0.349–0.646 < 0.001 Ki-67 (weak+/strong++) 6.611 4.402–9.929 < 0.001 *: control group 5. Construction and validation of the nomogram prognostic model 5.1 Construction of the nomogram prognostic model Overall, 257 patients were randomly divided into a training set and a validation set at a 6:4 ratio. Additionally, 100 patients with balanced baseline characteristics were selected from the CGGA database to serve as an external validation set. No significant differences in the proportion of sex, age, WHO grade, postoperative chemotherapy, or expression levels of ATRX, IDH1, and Ki-67 were noted among the three groups ( P > 0.05). However, a significant difference was observed in the proportion of patients who received postoperative radiotherapy among the three groups ( P < 0.05). The proportion of patients receiving postoperative radiotherapy in the external validation set was 89%, which was significantly higher than that in the training and validation sets (Table 3 ). Table 3 Comparison of clinical data between the training, validation, and CGGA external validation sets Training set (n = 154) Validation set (n = 103) CGGA (n = 100) χ2 P value Sex Male 91 (59.1%) 50 (48.5%) 61 (61.0%) 3.898 0.142 Female 63 (40.9%) 53 (51.1%) 39 (39.0%) Age (years) <60 78 (50.6%) 56 (54.4%) 52 (52.0%) 0.343 0.843 ≥60 76 (49.4%) 47 (45.6%) 48 (48.0%) WHO grade WHO Ⅱ 28 (18.2%) 19 (18.4%) 18 (18.0%) 1.038 0.904 WHO Ⅲ 31 (20.1%) 26 (25.2%) 22 (22.0%) WHO Ⅳ 95 (61.7%) 58 (56.3%) 60 (60.0%) Postoperative radiotherapy Yes 112 (72.7%) 75 (72.8%) 89 (89.0%) 10.821 0.004 No 42 (27.3%) 28 (27.2%) 11 (11.0%) Postoperative chemotherapy Yes 121 (78.6%) 77 (74.8%) 69 (69.0%) 2.946 0.229 No 33 (21.4%) 26 (25.2%) 31 (31.0%) ATRX Negative 45 (29.2%) 38 (36.9%) 37 (37.0%) 2.342 0.310 Positive 109 (70.8%) 65 (63.1%) 63 (63.0%) IDH1 Negative 113 (73.4%) 86 (71.8%) 70 (70.0%) 0.344 0.842 Positive 41 (26.6%) 171 (28.2%) 30 (30.0%) Ki-67 Weak+ 50 (32.5%) 35 (34.0%) 30 (30.0%) 0.376 0.829 Strong++ 104 (67.5%) 68 (66.0%) 70 (70.0%) The nomogram for predicting 1-, 2-, and 3-year survival rates was constructed using variables with P < 0.05 in the multivariate analysis and included in the CGGA external database (Fig. 4 ). The following was the scoring system for each factor: age ≥ 60 years, 25.1 points; WHO grade III, 43.3 points; WHO grade IV, 86.6 points; no postoperative radiotherapy, 38.4 points; no postoperative chemotherapy, 29.8 points; IDH1 (−), 48.2 points; ATRX (+), 100 points; and Ki-67 (strong+), 67.0 points. The total score was calculated by summing the points of all applicable factors. A higher total score indicates a worse prognosis (e.g., lower survival probability at 1, 2, and 3 years). 5.2 Validation and evaluation of the nomogram prognostic model Based on the calculation results of the regression equation, ROC curves were drawn using data from the training, validation, and CGGA external validation sets. The AUC values of 1-, 2-, and 3-year OS in the training, validation, and CGGA external validation sets were all > 0.75, indicating that the model had good discrimination ability for OS (Fig. 5). The C-index of the training set was 0.861. Calibration plots were drawn using data from the training, validation, and CGGA external validation sets. The slopes of the calibration curves in the three cohorts were all close to 1, indicating that the predicted 1-, 2-, and 3-year OS rates of the model for patients with gliomas were consistent with the actual values (Fig. 6). The Hosmer–Lemeshow test was used for evaluating the calibration ability of the prediction model. The results indicated χ 2 = 5.980 and P = 0.650 for the model, suggesting no statistical difference between the predicted values and actual values of the prediction model, indicating good calibration of the prediction model. DCA was constructed using data from the training, validation, and CGGA external validation sets (Fig. 7). The 1–3-year OS prediction model for patients with gliomas developed in this study exhibited a high net benefit within specific risk threshold ranges. Compared with nonintervention strategies and other comprehensive strategies, the constructed model demonstrates practical utility in informing clinical decisions for patients with gliomas, serving as a valuable reference for both doctors and patients. Discussion The 2021 fifth edition of the WHO classification introduced an integrated diagnostic model that emphasizes the essential role of molecular markers in the diagnosis, subtyping, and prognostic assessment of gliomas. This approach enables a more accurate diagnostic classification by combining histopathological and molecular features [ 14 ]. Synhaeve et al. [ 15 ] demonstrated the value of next-generation sequencing (NGS) in identifying glioma subtypes with different prognostic outcomes. Among patients histologically diagnosed with WHO grade II astrocytoma, 16.9% were molecularly diagnosed with WHO grade IV glioblastoma. Some challenges remain despite significant progress in the application of molecular characteristics in glioma classification [ 16 ]. The survival rate of patients with gliomas has not been considerably improved in the molecular era, and the high cost of NGS has restricted the popularity of routine large-panel gene testing for patients with gliomas in China postoperatively. IHC detection methods, owing to their high efficiency and accessibility, remain an indispensable molecular typing tool in clinical practice [ 3 ]. Regulating the glioma immune microenvironment involves the synergistic action of multiomics biomarkers. As early as 2009, Yan et al. [ 17 ] reported that patients with IDH-mutated gliomas had significantly better prognoses. Low-grade gliomas and secondary glioblastomas more frequently exhibit IDH mutations [ 18 ]. Several studies have investigated the use of IDH inhibitors in the management of gliomas [ 19 , 20 ], and these therapies are expected to influence future treatment regimen for IDH-mutated gliomas. In adult primary gliomas, IDH-mutant gliomas are frequently accompanied by ATRX mutations [ 21 ]. Olar et al. [ 22 ] reported that patients with comutations had higher survival rates. Hu et al. [ 23 ] revealed that ATRX mutations activate a BRD-dependent immunosuppressive transcriptome, contributing to immune escape mechanisms in IDH1 R132H-mutated astrocytoma cells. Furthermore, Murnyak et al. [ 24 ] showed that gliomas with IDH1 mutations are frequently accompanied by TP53 mutations. The TP53 gene plays a key role in various processes, including cell cycle regulation, DNA damage repair, and apoptosis [ 25 ]. However, the association between the TP53 gene mutation status and survival outcomes remains inconclusive. Some studies [ 26 , 27 ] have reported that patients with TP53 mutations have better survival rates, whereas others found no such correlation [ 28 ]. Xie et al. [ 6 ] reported that ATRX mutations frequently coexist with IDH1 and TP53 mutations in low-grade gliomas, suggesting the involvement of ATRX, TP53, and IDH1 in interactions with ferredoxin reductase. However, the synergistic mechanisms involving ATRX, IDH1, and TP53 gene mutations remain incompletely understood, and the specific molecular mechanisms require further comprehensive investigation. Hu et al. [ 29 ] demonstrated that IDH1 was negatively correlated with the Ki-67 expression. Moreover, this study observed the same result in the correlation analysis of common tumor markers. The Ki-67 expression level reflects the proliferative activity of tumor cells [ 7 ], and tumor cells with high proliferative activity are more likely to cause tumor recurrence and metastasis [ 30 ]. Several studies [ 31 , 32 ] have shown that patients with gliomas with weak + Ki-67 expression tend to have a longer OS. Therefore, the proliferative status of tumor cells can be evaluated by detecting the positive expression rate of Ki-67 in tumor tissues. However, a single immune marker does not determine glioma prognosis. Factors including patient age, KPS score, tumor size and location, extent of surgical resection, postoperative treatment regimen, and immune marker expression interact with each other and collectively determine the patient’s prognosis [ 33 , 34 ]. This study constructed a nomogram model integrating tumor immune markers (ATRX, IDH1, and Ki-67) and clinical characteristics (age, WHO grade, postoperative radiotherapy, and chemotherapy) to comprehensively evaluate the prognosis of adult patients with primary gliomas. Compared with traditional evaluation methods, the constructed nomogram model considers the interaction of multiple factors and offers more accurate and comprehensive prognostic information. ROC curves were drawn using data from the training, validation, and CGGA external validation sets, with all AUC values < 0.75, indicating that the model has good discriminative ability for OS. Calibration curves for the training, validation, and CGGA external validation sets were plotted, and the slopes of the calibration curves were all close to 1, suggesting that the predicted values of the model for the 1-, 2-, and 3-year OS of patients with gliomas align with the actual values. Furthermore, the DCA curve demonstrated that the model has certain practicality in guiding clinical decisions for patients with gliomas, providing valuable reference for both clinicians and patients. This study had some limitations. First, the sample size was relatively small, which may affect the accuracy and universality of the nomogram model. Although strict inclusion and exclusion criteria were adopted in the model construction, a certain selection bias may still exist. Second, this study only used the CGGA database to validate the nomogram model. Although the validation results show that the model has good predictive performance, it still needs to be further verified by prospective clinical studies. Future research could further explore novel prognostic factors for adult primary gliomas. To date, integrating pathological factors and molecular characteristics remains the optimal reference for diagnosis and treatment. Nomogram models constructed on the basis of clinical factors, tumor markers, and other parameters have demonstrated good predictive efficacy, excellent generalization ability, and clinical practicability, serving as effective alternative strategies to molecular testing. Declarations Acknowledgments: We appreciated Hebei Tumor Hospital’s support and help. Funding: None. Competing Interests: The authors declare no competing interests. Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of The Fourth Hospital of Hebei Medical University (Hebei Tumor Hospital) (No. 2025KY346) and the requirement for individual consent for this retrospective analysis was waived. Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). Author Contribution: (I) Conception and design:J Wen; (II) Administrative support: J Li; (III) Provision of study materials or patients:J Yuan, Y Wang, Y Liu, J Li; (IV) Collection and assembly of data: Z Zhang, Y Zhao,A Sui,X Ma,J Yang; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. References Weller M, Wen P Y, Chang S M, et al. Glioma[J]. Nature Reviews. Disease Primers, 2024, 10(1): 33. Ostrom Q T, Price M, Neff C, et al. 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Ji X, Ding W, Wang J, et al. Application of Intraoperative Radiotherapy for Malignant Glioma[J]. Cancer Radiotherapie: Journal De La Societe Francaise De Radiotherapie Oncologique, 2023, 27(5): 425–433. Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6995164","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482772042,"identity":"574413a3-f1fb-4f03-871a-4d018d7dd68c","order_by":0,"name":"Junpeng Wen","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junpeng","middleName":"","lastName":"Wen","suffix":""},{"id":482772043,"identity":"e438d5a3-54d6-4405-b06b-e44fad418d12","order_by":1,"name":"Jiangwei Yuan","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiangwei","middleName":"","lastName":"Yuan","suffix":""},{"id":482772044,"identity":"886673cd-48b4-4edf-9c06-8964d1719757","order_by":2,"name":"Ziling Zhang","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziling","middleName":"","lastName":"Zhang","suffix":""},{"id":482772045,"identity":"08f011b8-73fa-4e3b-8d1f-3ff332dc0017","order_by":3,"name":"Yuxiang Wang","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Wang","suffix":""},{"id":482772046,"identity":"347a73b0-3997-4648-bf04-684498f82145","order_by":4,"name":"Yan Zhao","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical 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University","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-28 03:23:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6995164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6995164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86668532,"identity":"e88b036d-d246-4773-8c35-22fd33914966","added_by":"auto","created_at":"2025-07-14 11:21:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25850,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of eligible adult patients with primary gliomas enrolled in this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/431fb04f656e3a45b82b4906.png"},{"id":86668529,"identity":"08872310-7f9d-43ef-8d3d-e8cfcaef2d19","added_by":"auto","created_at":"2025-07-14 11:21:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81314,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the correlation of common tumor immune markers across WHO grades\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/c859d3a174f9faf598cf83ad.png"},{"id":86668530,"identity":"e050f09c-0830-432d-ac0b-2f886b499b0e","added_by":"auto","created_at":"2025-07-14 11:21:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113863,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multifactorial Cox regression in 257 patients with gliomas\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/51e6617f237c195aec4ccfb5.png"},{"id":86673286,"identity":"8180b0dc-edf3-47bf-b4c6-c615901f51c0","added_by":"auto","created_at":"2025-07-14 11:45:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64776,"visible":true,"origin":"","legend":"\u003cp\u003eColumnar plot of the prognostic OS in patients with gliomas of the brain\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/05f5fe714d4f481121316e0c.png"},{"id":86670513,"identity":"e0aab551-2da4-41b1-a503-d68d704db567","added_by":"auto","created_at":"2025-07-14 11:29:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63388,"visible":true,"origin":"","legend":"\u003cp\u003eColumn line graphs predicting ROC curves for 1-, 2-, and 3-year OS in patients with gliomas\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/f0d351f54fdc18714ec72d15.png"},{"id":86668537,"identity":"d0261254-aba3-4c01-b249-3c47aa1bf2cb","added_by":"auto","created_at":"2025-07-14 11:21:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64018,"visible":true,"origin":"","legend":"\u003cp\u003eColumn line graphs predicting calibration curves for 1-, 2-, and 3-year OS in patients with gliomas\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/c57e9d3054c4f0f66f573697.png"},{"id":86668540,"identity":"b2a7fe7c-20e6-4de0-9185-15ae7a045c9e","added_by":"auto","created_at":"2025-07-14 11:21:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":59134,"visible":true,"origin":"","legend":"\u003cp\u003eColumn line graphs predicting decision curves for 1-, 2-, and 3-year OS in patients with gliomas\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/4b846ec8962ce948c5db7034.png"},{"id":90973396,"identity":"40e28b89-f3b7-4385-af6f-79f2972c3032","added_by":"auto","created_at":"2025-09-10 08:09:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1309656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6995164/v1/3256ae71-e04a-40b6-b1bd-6668348901de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of a Nomogram Model Based on Tumor Immune Markers and Clinical Factors for Adult Primary Gliomas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma is the most common primary malignant tumor of the central nervous system (CNS), accounting for approximately 40\u0026ndash;50% of all CNS tumors, with an increasing annual incidence rate in adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These tumors exhibit highly heterogeneous and invasive growth characteristics, leading to significantly different prognoses among patients with different subtypes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the molecular era, genetic testing has become indispensable for diagnosing CNS tumors. However, in patients with gliomas who undergo stereotactic biopsy, have tumors located in the brainstem, or are in poor general conditions such that secondary surgery is not feasible, the surgical samples may contain too little tumor tissue or a low proportion of tumor cells. As a result, it may be impossible to obtain sufficient DNA/RNA for genetic testing. In such cases, immunohistochemistry (IHC) represents a more feasible alternative [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Previous studies have revealed that mutations in ATRX, IDH1, and TP53 genes are significant in predicting the prognosis of gliomas [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Ki-67 expression level intuitively reflects the proliferative activity of tumor cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough molecular typing has improved the diagnostic accuracy, dynamic prognostic assessment tools integrating molecular characteristics and clinical parameters into clinical practice remain lacking. Nomograms have recently demonstrated unique value in tumor prognosis models; however, current prediction models based on radiomics or genomics are generally limited to single data dimensions and fail to fully integrate the systematic associations among immunophenotypes, molecular characteristics, and clinicopathological parameters [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This study aimed to innovatively construct a multidimensional prognostic prediction model, which will provide a reference for the clinical treatment of gliomas in clinical applications.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e1. Study design and participants\u003c/p\u003e\u003cp\u003eThis study retrospectively analyzed the clinical data of patients newly diagnosed with gliomas who underwent surgical treatment at the Department of Neurosurgery, Fourth Hospital of Hebei Medical University, from January 2019 to December 2023. Clinical data, including sex, age, smoking history, head trauma history, preoperative Karnofsky Performance Status (KPS) score, presenting symptoms, tumor diameter, tumor location and distribution, extent of surgical resection, WHO grade, postoperative treatment regimen, and protein expression levels (ATRX, IDH1, and Ki-67), were collected from patients\u0026rsquo; medical records.\u003c/p\u003e\u003cp\u003eInclusion criteria were as follows: (1) patients who underwent surgical treatment for gliomas for the first time at our hospital, (2) availability of complete clinical data, (3) informed consent obtained from patients and their families, and (4) patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years. Exclusion criteria were as follows: (1) presence of other concomitant malignant tumors; (2) insufficient tumor tissue for immunohistochemical testing; (3) history of prior glioma treatment; (4) pathological diagnosis of ependymoma, subependymoma, or pilocytic astrocytoma; and (5) patients aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years.\u003c/p\u003e\u003cp\u003eTo further confirm the reliability and generalizability of the overall survival (OS) prediction model for patients with gliomas developed in this study, data from 100 patients with balanced baseline characteristics from the Chinese Glioma Genome Atlas (CGGA) database were selected for external validation.\u003c/p\u003e\u003cp\u003e2. Histopathological detection methods\u003c/p\u003e\u003cp\u003eAll tumor tissue samples were fixed in formalin and embedded in paraffin. After reviewing the slides, two experienced pathologists from our hospital jointly determined all pathological results. The following comprised the positive criteria: under the premise of normal negative and positive controls, ATRX protein expression was localized in the nucleus, with \u0026gt;\u0026thinsp;10% positive cells defined as positive; IDH1 protein expression was localized in the cytoplasm, with \u0026ge;\u0026thinsp;10% positive cells defined as positive; and Ki-67 and p53 protein expressions were localized in the nucleus. Positive p53 was defined as \u0026gt;\u0026thinsp;10% of nuclei containing p53-positive protein granules. Ki-67\u0026ndash;positive cells of \u0026le;\u0026thinsp;20% were classified as weak positive (+), whereas \u0026gt;\u0026thinsp;20% were categorized as strong positive (++).\u003c/p\u003e\u003cp\u003e3. WHO grading criteria for gliomas\u003c/p\u003e\u003cp\u003eThe diagnosis of gliomas is based on the 2016 edition of the WHO Classification of Central Nervous System Tumors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. WHO grade II gliomas encompass IDH-mutant diffuse astrocytomas and oligodendrogliomas; WHO grade III gliomas are anaplastic gliomas, including anaplastic astrocytomas and anaplastic oligodendrogliomas; and WHO grade IV gliomas are mostly glioblastomas.\u003c/p\u003e\u003cp\u003e4. Treatment of gliomas\u003c/p\u003e\u003cp\u003eExperienced neurosurgeons formulated surgical plans and determined the extent of resection on the basis of contrast-enhanced cranial MRI images within 1 week. According to the residual tumor volume revealed on cranial CT/MRI images at 24\u0026ndash;72h postoperatively, the extent of surgical resection was classified into subtotal resection (resection degree, \u0026gt;\u0026thinsp;80%) and partial resection (resection degree, \u0026le;\u0026thinsp;80%). Radiotherapy, concurrent temozolomide chemotherapy, and adjuvant chemotherapy were the postoperative adjuvant treatments.\u003c/p\u003e\u003cp\u003e5. Follow-up\u003c/p\u003e\u003cp\u003eAll patients received a follow-up visit (combining outpatient review and telephone follow-up) at 3\u0026ndash;6 months postoperatively. December 30, 2024 was the follow-up deadline, with a 31.17-month median follow-up duration. OS was defined as the time from the surgery date to the date of death or the last follow-up.\u003c/p\u003e\u003cp\u003e6. Statistical analysis\u003c/p\u003e\u003cp\u003eData analysis and visualization were performed using SPSS (version 26, IBM, Chicago, IL, USA) and R language (version 4.4.1). To determine differences, the chi-squared test was used for categorical variables between groups. The \u0026ldquo;linkET\u0026rdquo; package of the R language was used for visualizing correlation heatmaps, and Spearman correlation analysis was applied for correlation analysis. SPSS 26.0 was utilized for Cox univariate and multivariate analyses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The predictive efficacy of the nomogram was evaluated using ROC curve, consistency index (C-index), calibration curve, and decision curve analysis (DCA), followed by internal and external validations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1. Comparison of the clinical data of adult patients with gliomas of different WHO grades\u003c/h2\u003e\u003cp\u003eThis study collected clinical data from 302 patients with gliomas who underwent surgical resection at our hospital. After excluding 45 patients who did not meet the inclusion criteria (4 patients aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years; 8 with ependymoma; 4 with subependymoma; 9 with other concomitant tumors; and 20 with missing ATRX, p53, IDH1, or Ki-67 test results), 257 patients were finally included in the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSignificant differences were observed among patients with gliomas of different WHO grades in terms of age at diagnosis, presenting symptoms, KPS score, and tumor location. However, no significant differences were found in sex, smoking history, head trauma history, or tumor diameter (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of clinical data among adult patients with different grades of gliomas of the brain\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eWHO Ⅱ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWHO Ⅲ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWHO Ⅳ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e35.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSmoking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHead trauma history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.722\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eKPS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDiameter (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSymptom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIntracranial hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNeurological dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEpilepsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMental disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo obvious symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor distribution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLeft side\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRight side\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFrontal lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTemporal lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eParietooccipital lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInsular lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSurgical type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSubtotal resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePartial resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eStereotactic biopsy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePostoperative chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePostoperative radiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eATRX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIDH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ep53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eKi-67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWeak+\u003c/p\u003e\u003cp\u003epositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e134.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eStrong++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: KPS: Karnofsky Performance Status; ATRX: alpha thalassemia/mental retardation syndrome X-linked; IDH1: isocitrate dehydrogenase 1.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2. Treatment of gliomas\u003c/h3\u003e\n\u003cp\u003eStandard treatment for patients with gliomas include maximal surgical resection, followed by adjuvant radiotherapy and/or adjuvant chemotherapy with temozolomide, as recommended by clinical guidelines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study, 154 (59.9%), 80 (31.1%), and 23 (9.0%) patients underwent subtotal resection, partial resection, and stereotactic biopsy, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 204(79.4%) and 185(72.0%) patients received postoperative chemotherapy and postoperative radiotherapy, respectively.\u003c/p\u003e\n\u003ch3\u003e3. Mutation and correlation analysis of common immune markers in various gliomas grades\u003c/h3\u003e\n\u003cp\u003eIn this study, the mutation-positive rates of ATRX, IDH1, and p53 expression levels as well as the differentiation degree of Ki-67 significantly varied among patients with gliomas of different WHO grades (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Correlation analysis of ATRX, IDH1, p53, and Ki-67 protein expression levels revealed that ATRX was positively correlated with Ki-67, whereas ATRX was negatively correlated with IDH1, ATRX was negatively correlated with p53, and Ki-67 was negatively correlated with IDH1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e4. Cox regression analysis of prognostic factors\u003c/em\u003e\u003c/p\u003e\u003cp\u003eUnivariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that age at diagnosis, KPS score, WHO grade, postoperative adjuvant radiotherapy, and the expression of tumor immune markers were all associated with the prognosis of patients with gliomas. Variables with statistical significance in the univariate Cox analysis and postoperative chemotherapy were included in the multivariate Cox analysis. The results indicated that postoperative adjuvant chemotherapy was an influencing factor for glioma prognosis, whereas P53 (\u0026minus;) was not a risk factor for poor prognosis in gliomas (Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOne-way Cox prognostic analysis of overall survival and progression-free survival in 257 patients with gliomas\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male/female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.683\u0026ndash;1.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026lt;\u0026thinsp;60/\u0026ge;60 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.555\u0026ndash;4.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history (no/yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.585\u0026ndash;1.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKPS score (\u0026lt;\u0026thinsp;80/\u0026ge;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069\u0026ndash;0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter (\u0026lt;\u0026thinsp;5/\u0026ge;5 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.129\u0026ndash;2.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead trauma history (no/yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.674\u0026ndash;1.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO Ⅲ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.784\u0026ndash;6.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.995\u0026ndash;27.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative radiotherapy (no/yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.204\u0026ndash;0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative chemotherapy (no/yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.512\u0026ndash;1.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATRX (\u0026minus;/+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.083\u0026ndash;17.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDH1 (\u0026minus;/+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.140\u0026ndash;0.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP53 (\u0026minus;/+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.349\u0026ndash;0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi-67 (weak+/strong++)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.402\u0026ndash;9.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*: control group\u003c/p\u003e\n\u003ch3\u003e5. Construction and validation of the nomogram prognostic model\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Construction of the nomogram prognostic model\u003c/h2\u003e\u003cp\u003eOverall, 257 patients were randomly divided into a training set and a validation set at a 6:4 ratio. Additionally, 100 patients with balanced baseline characteristics were selected from the CGGA database to serve as an external validation set. No significant differences in the proportion of sex, age, WHO grade, postoperative chemotherapy, or expression levels of ATRX, IDH1, and Ki-67 were noted among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, a significant difference was observed in the proportion of patients who received postoperative radiotherapy among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The proportion of patients receiving postoperative radiotherapy in the external validation set was 89%, which was significantly higher than that in the training and validation sets (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of clinical data between the training, validation, and CGGA external validation sets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eTraining set\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;154)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eValidation set\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCGGA\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e91 (59.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e50 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e61 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e63 (40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e53 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e78 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e56 (54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e52 (52.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.843\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u0026ge;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e76 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e47 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eWHO grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eWHO Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e28 (18.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e19 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (18.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eWHO Ⅲ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e31 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e26 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22 (22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eWHO Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95 (61.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e58 (56.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e60 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePostoperative radiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e112 (72.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e75 (72.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89 (89.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e28 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePostoperative chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e121 (78.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e77 (74.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69 (69.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e33 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e26 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eATRX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e45 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e38 (36.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e109 (70.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e65 (63.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e63 (63.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eIDH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e113 (73.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e86 (71.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e70 (70.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e41 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e171 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eKi-67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eWeak+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e50 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e35 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eStrong++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e104 (67.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e68 (66.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e70 (70.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe nomogram for predicting 1-, 2-, and 3-year survival rates was constructed using variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the multivariate analysis and included in the CGGA external database (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The following was the scoring system for each factor: age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, 25.1 points; WHO grade III, 43.3 points; WHO grade IV, 86.6 points; no postoperative radiotherapy, 38.4 points; no postoperative chemotherapy, 29.8 points; IDH1 (\u0026minus;), 48.2 points; ATRX (+), 100 points; and Ki-67 (strong+), 67.0 points. The total score was calculated by summing the points of all applicable factors. A higher total score indicates a worse prognosis (e.g., lower survival probability at 1, 2, and 3 years).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e5.2 Validation and evaluation of the nomogram prognostic model\u003c/h3\u003e\n\u003cp\u003eBased on the calculation results of the regression equation, ROC curves were drawn using data from the training, validation, and CGGA external validation sets. The AUC values of 1-, 2-, and 3-year OS in the training, validation, and CGGA external validation sets were all \u0026gt;\u0026thinsp;0.75, indicating that the model had good discrimination ability for OS (Fig.\u0026nbsp;5). The C-index of the training set was 0.861.\u003c/p\u003e\n\u003cp\u003eCalibration plots were drawn using data from the training, validation, and CGGA external validation sets. The slopes of the calibration curves in the three cohorts were all close to 1, indicating that the predicted 1-, 2-, and 3-year OS rates of the model for patients with gliomas were consistent with the actual values (Fig. 6). The Hosmer\u0026ndash;Lemeshow test was used for evaluating the calibration ability of the prediction model. The results indicated \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.980 and P\u0026thinsp;=\u0026thinsp;0.650 for the model, suggesting no statistical difference between the predicted values and actual values of the prediction model, indicating good calibration of the prediction model.\u003c/p\u003e\n\u003cp\u003eDCA was constructed using data from the training, validation, and CGGA external validation sets (Fig. 7). The 1\u0026ndash;3-year OS prediction model for patients with gliomas developed in this study exhibited a high net benefit within specific risk threshold ranges. Compared with nonintervention strategies and other comprehensive strategies, the constructed model demonstrates practical utility in informing clinical decisions for patients with gliomas, serving as a valuable reference for both doctors and patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe 2021 fifth edition of the WHO classification introduced an integrated diagnostic model that emphasizes the essential role of molecular markers in the diagnosis, subtyping, and prognostic assessment of gliomas. This approach enables a more accurate diagnostic classification by combining histopathological and molecular features [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Synhaeve et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] demonstrated the value of next-generation sequencing (NGS) in identifying glioma subtypes with different prognostic outcomes. Among patients histologically diagnosed with WHO grade II astrocytoma, 16.9% were molecularly diagnosed with WHO grade IV glioblastoma. Some challenges remain despite significant progress in the application of molecular characteristics in glioma classification [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The survival rate of patients with gliomas has not been considerably improved in the molecular era, and the high cost of NGS has restricted the popularity of routine large-panel gene testing for patients with gliomas in China postoperatively. IHC detection methods, owing to their high efficiency and accessibility, remain an indispensable molecular typing tool in clinical practice [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegulating the glioma immune microenvironment involves the synergistic action of multiomics biomarkers. As early as 2009, Yan et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported that patients with IDH-mutated gliomas had significantly better prognoses. Low-grade gliomas and secondary glioblastomas more frequently exhibit IDH mutations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Several studies have investigated the use of IDH inhibitors in the management of gliomas [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and these therapies are expected to influence future treatment regimen for IDH-mutated gliomas. In adult primary gliomas, IDH-mutant gliomas are frequently accompanied by ATRX mutations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Olar et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that patients with comutations had higher survival rates. Hu et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] revealed that ATRX mutations activate a BRD-dependent immunosuppressive transcriptome, contributing to immune escape mechanisms in IDH1 R132H-mutated astrocytoma cells. Furthermore, Murnyak et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] showed that gliomas with IDH1 mutations are frequently accompanied by TP53 mutations. The TP53 gene plays a key role in various processes, including cell cycle regulation, DNA damage repair, and apoptosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the association between the TP53 gene mutation status and survival outcomes remains inconclusive. Some studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] have reported that patients with TP53 mutations have better survival rates, whereas others found no such correlation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Xie et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that ATRX mutations frequently coexist with IDH1 and TP53 mutations in low-grade gliomas, suggesting the involvement of ATRX, TP53, and IDH1 in interactions with ferredoxin reductase. However, the synergistic mechanisms involving ATRX, IDH1, and TP53 gene mutations remain incompletely understood, and the specific molecular mechanisms require further comprehensive investigation.\u003c/p\u003e\u003cp\u003eHu et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] demonstrated that IDH1 was negatively correlated with the Ki-67 expression. Moreover, this study observed the same result in the correlation analysis of common tumor markers. The Ki-67 expression level reflects the proliferative activity of tumor cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and tumor cells with high proliferative activity are more likely to cause tumor recurrence and metastasis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Several studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] have shown that patients with gliomas with weak\u0026thinsp;+\u0026thinsp;Ki-67 expression tend to have a longer OS. Therefore, the proliferative status of tumor cells can be evaluated by detecting the positive expression rate of Ki-67 in tumor tissues.\u003c/p\u003e\u003cp\u003eHowever, a single immune marker does not determine glioma prognosis. Factors including patient age, KPS score, tumor size and location, extent of surgical resection, postoperative treatment regimen, and immune marker expression interact with each other and collectively determine the patient\u0026rsquo;s prognosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study constructed a nomogram model integrating tumor immune markers (ATRX, IDH1, and Ki-67) and clinical characteristics (age, WHO grade, postoperative radiotherapy, and chemotherapy) to comprehensively evaluate the prognosis of adult patients with primary gliomas. Compared with traditional evaluation methods, the constructed nomogram model considers the interaction of multiple factors and offers more accurate and comprehensive prognostic information. ROC curves were drawn using data from the training, validation, and CGGA external validation sets, with all AUC values\u0026thinsp;\u0026lt;\u0026thinsp;0.75, indicating that the model has good discriminative ability for OS. Calibration curves for the training, validation, and CGGA external validation sets were plotted, and the slopes of the calibration curves were all close to 1, suggesting that the predicted values of the model for the 1-, 2-, and 3-year OS of patients with gliomas align with the actual values. Furthermore, the DCA curve demonstrated that the model has certain practicality in guiding clinical decisions for patients with gliomas, providing valuable reference for both clinicians and patients.\u003c/p\u003e\u003cp\u003eThis study had some limitations. First, the sample size was relatively small, which may affect the accuracy and universality of the nomogram model. Although strict inclusion and exclusion criteria were adopted in the model construction, a certain selection bias may still exist. Second, this study only used the CGGA database to validate the nomogram model. Although the validation results show that the model has good predictive performance, it still needs to be further verified by prospective clinical studies.\u003c/p\u003e\u003cp\u003eFuture research could further explore novel prognostic factors for adult primary gliomas. To date, integrating pathological factors and molecular characteristics remains the optimal reference for diagnosis and treatment. Nomogram models constructed on the basis of clinical factors, tumor markers, and other parameters have demonstrated good predictive efficacy, excellent generalization ability, and clinical practicability, serving as effective alternative strategies to molecular testing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments: We appreciated Hebei Tumor Hospital\u0026rsquo;s support and help.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding: None.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of The Fourth Hospital of Hebei Medical University (Hebei Tumor Hospital) (No. 2025KY346) and the requirement for individual consent for this retrospective analysis was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOpen Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license).\u003c/p\u003e\n\u003cp\u003eAuthor Contribution: (I) Conception and design:J Wen; (II) Administrative support: J Li; (III) Provision of study materials or patients:J Yuan, Y Wang, Y Liu, J Li; (IV) Collection and assembly of data: Z Zhang, Y Zhao,A Sui,X Ma,J Yang; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeller M, Wen P Y, Chang S M, et al. 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Fundamental immune-oncogenicity trade-offs define driver mutation fitness[J]. NATURE, 2022, 606(7912):172-179.\u003c/li\u003e\n\u003cli\u003eChaurasia A, Park S H, Seo J W, et al. Immunohistochemical Analysis of ATRX, IDH1 and P53 in Glioblastoma and Their Correlations with Patient Survival[J]. Journal of Korean Medical Science, 2016, 31(8): 1208–1214.\u003c/li\u003e\n\u003cli\u003eNguyen D N, Heaphy C M, De Wilde R F, et al. Molecular and Morphologic Correlates of the Alternative Lengthening of Telomeres Phenotype in High-Grade Astrocytomas[J]. Brain Pathology (Zurich, Switzerland), 2013, 23(3): 237–243.\u003c/li\u003e\n\u003cli\u003eNewcomb E W, Cohen H, Lee S R, et al. Survival of Patients with Glioblastoma Multiforme Is Not Influenced by Altered Expression of P16, P53, EGFR, MDM2 or Bcl-2 Genes[J]. Brain Pathology (Zurich, Switzerland), 1998, 8(4): 655–667.\u003c/li\u003e\n\u003cli\u003e胡志民,刘金霞,娄金峰.Ki-67及IDH1-R132H在神经胶质瘤中的表达水平及临床意义[J].广东医学,2024,45(08):1039-1042.\u003c/li\u003e\n\u003cli\u003ePriambada D, Thohar Arifin M, Saputro A, et al. Immunohistochemical Expression of IDH1, ATRX, Ki67, GFAP, and Prognosis in Indonesian Glioma Patients[J]. International Journal of General Medicine, 2023, 16: 393–403.\u003c/li\u003e\n\u003cli\u003eYang Z, Ling F, Ruan S, et al. Clinical and Prognostic Implications of 1p/19q, IDH, BRAF, MGMT Promoter, and TERT Promoter Alterations, and Expression of Ki-67 and P53 in Human Gliomas[J]. Cancer Management and Research, 2021, 13: 8755–8765.\u003c/li\u003e\n\u003cli\u003eMalueka R G, Dwianingsih E K, Bayuangga H F, et al. Clinicopathological Features and Prognosis of Indonesian Patients with Gliomas with IDH Mutation: Insights into Its Significance in a Southeast Asian Population[J]. Asian Pacific journal of cancer prevention: APJCP, 2020, 21(8): 2287–2295.\u003c/li\u003e\n\u003cli\u003eHervey-Jumper S L, Zhang Y, Phillips J J, et al. Interactive Effects of Molecular, Therapeutic, and Patient Factors on Outcome of Diffuse Low-Grade Glioma[J]. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 2023, 41(11): 2029–2042.\u003c/li\u003e\n\u003cli\u003eJi X, Ding W, Wang J, et al. Application of Intraoperative Radiotherapy for Malignant Glioma[J]. Cancer Radiotherapie: Journal De La Societe Francaise De Radiotherapie Oncologique, 2023, 27(5): 425–433.\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":"tumor immune markers, immunohistochemistry, glioma, nomogram model, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6995164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6995164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to analyze the factors associated with overall survival (OS) in adult patients with primary gliomas, develop a nomogram prediction model, and optimize its predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eClinical data were retrospectively collected from adult patients newly diagnosed with gliomas who underwent surgical treatment at the Department of Neurosurgery, Fourth Hospital of Hebei Medical University, between January 2019 and December 2023. External validation was performed using data from the Chinese Glioma Genome Atlas (CGGA) database. Data analysis and visualization were performed using Statistical Package for the Social Sciences (SPSS) 26.0 and R software (Version 4.4.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 257 adult patients were included in this study. Multivariate Cox regression analysis revealed that age, Karnofsky Performance Status score, tumor diameter, World Health Organization grade, postoperative radiotherapy and chemotherapy, and the expression of tumor immune markers (ATRX, IDH1, and Ki-67) were all associated with patient prognosis. Factors with \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 in the multivariate analysis and those included in the CGGA external database were used to construct a nomogram for predicting 1-, 2-, and 3-year survival rates. Multiple validations demonstrated that the model exhibited excellent generalizability and clinical applicability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe nomogram model constructed based on clinical factors, tumor immune markers, and other parameters exhibited strong predictive efficacy and may serve as an effective alternative to molecular testing.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of a Nomogram Model Based on Tumor Immune Markers and Clinical Factors for Adult Primary Gliomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:21:54","doi":"10.21203/rs.3.rs-6995164/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":"d3cec179-8d6d-4bfd-aaf4-ccbc36c417c0","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T08:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:21:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6995164","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6995164","identity":"rs-6995164","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}