{"paper_id":"2c1770f2-cedb-4397-b030-3ed80ef5ba50","body_text":"Short-term progression risk stratification in glioblastoma using post-resection structural connectivity biomarkers | 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 Short-term progression risk stratification in glioblastoma using post-resection structural connectivity biomarkers Quanzhi Feng, Xinjun Suo, Xiyue Jing, Pan Wang, Shuang Xia, Tong Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8614285/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 21 You are reading this latest preprint version Abstract ‌ Background ‌: While structural connectome analysis enables preoperative mapping of glioblastoma (GBM) infiltration, mass effect-induced distortion compromises the accuracy of peritumoral tract assessment. We aimed to investigate fiber disruption characteristics and predict short-term progression based on structural connectivity features after eliminating mass effect. Methods : We retrospectively analyzed 113 GBM patients with ≥ 90% resection and 65 healthy controls. Diffusion tensor imaging (DTI) data were processed to construct structural connectomes, which were segmented into three compartments relative to the resection cavity: Tumor disrupted cerebral regions, anatomically confined to FLAIR hyperintense areas and direct fiber disruption; Distant disrupted cerebral regions, outside FLAIR hyperintense areas but exhibiting direct fiber disruption; Indirect disrupted cerebral regions, remote from FLAIR lesions with indirect fiber disruption. The patterns of differential disruption across compartments and progression timelines were quantified, along with their correlations to the Karnofsky performance status (KPS). The Area Under the Curve (AUC) evaluated how well disrupted fibers predict progression time. Patients with fiber disruption counts exceeding the Youden index were classified as high-risk versus low-risk for progression, validated by Kaplan-Meier analysis and Chi-square test. Structural connectivity disruption were used to predict short-term progression via Cox regression. Results : After eliminating mass effects, widespread structural connectome disruption was observed. Among 49 within 1-year progressers, tumor-disrupted regions showed more severe fiber disruption than later-progressing patients (F = 32.5, P < 0.001). Fiber disruption in tumor-disrupted compartment negatively correlated with pre-radiotherapy KPS score (r=-0.349, P < 0.001), and best predicted progression time (AUC = 0.803, P < 0.001). High-risk patients progressed faster (10 months) than low-risk patients (15 months) ( P < 0.001). 81% of low-risk and 71% of high-risk patients were correctly identified (χ²=30.29, P < 0.001). Incorporating structural connectivity disruption significantly improved multivariable Cox regression performance over clinical/imaging variables alone ( P < 0.001). Conclusions : Structural connectivity quantitatively maps postoperative regional cerebral disruption in GBM. Fiber disruption within the tumor-disrupted compartment may identify patients for short-term progression. glioblastoma short-term progression structural connectivity DTI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 ‌Background The high recurrence rate and poor response to treatment of glioblastoma (GBM) have been attributed to its heterogeneity, which has therefore garnered considerable research attention [ 1 ] . Despite achieving ‌gross total resection‌ (GTR) and administering ‌concurrent radiochemotherapy‌, GBM recurrence‌ remains the predominant clinical challenge in neuro-oncological practice. GBM exhibits widespread infiltration into white matter tracts, which remains undetectable on conventional MRI [2 − 3] . And postoperative recurrence correlates closely with the spatial locations of fiber tract disruption [4 − 6] . Therefore, it is crucial to accurately identify disrupted fiber tracts. Recently, the structural connectivity method based on preoperative diffusion magnetic resonance imaging can reveal and quantify GBM invasion that is not visible on conventional MRI [ 7 ] . This approach models brain regions as nodes while the white matter connections among brain regions as edges, forming a complex network. Therefore, studying fiber connectivity may provide valuable tools for stratification and precision treatment of GBM patients. However, for brain tumors, objectively assessing white matter integrity through diffusion tensor imaging (DTI) faces some obstacles in clinical settings. Mass effect and peritumoral edema could complicate the assessment of white matter integrity [ 8 ] . Postoperative research utilizing ‌healthy control-derived templates‌ for tract localization, combined with the ‌voxel projection step of tract-based spatial statistics (TBSS) [9− 10 ] ‌, may provide more reliable progression prediction models in GBM patients. So, further study is needed on the relationship between postoperative DTI assessment and GBM recurrence. Therefore, we hypothesized that focal and global disturbance to the structural connectome after GBM surgery correlated with Karnofsky performance status (KPS) score and impact patient outcomes. First, based on constructed structural networks using DTI data from healthy controls, regional connectome disruption was quantitatively analyzed for each patient, and to characterize patterns of disrupted structural connectivity among three compartments. Subsequently, the differences in disrupted fibers among three compartments at various progression time points and its relationship with the patient's performance before radiotherapy were explored. Finally, investigating the predictive value of disrupted structural connectivity in patient progression time using connectivity disruption characteristics, survival curves for risk stratification were constructed and validating this stratification's efficacy. Methods Participants A retrospective study was conducted on 876 patients with pathologically confirmed GBM and postoperative DTI examination from November 2016 to April 2024. Recruiting age and gender matched healthy control groups as reference for DTI data analysis, a total of 72 volunteers were recruited by the end of this study. The detailed process diagram for inclusion and exclusion is shown in Figure 1 . For GBM patients, the inclusion criteria were as follows: (I) pathological diagnostic criteria meeting the 2021 World Health Organization fifth edition classification of glioma [11] ; (II) no history of preoperative treatment; (III) the enhanced lesion was resected at least 90% (the aim is to effectively remove the effect of the mass on the compression of the peritumoral fibers), the assessment method is that postoperative contrast-enhanced MRI within 72 hours shows ‌no abnormal enhancement‌ or only ‌linear enhancement at the surgical cavity margin. While no literature has confirmed that a tumor resection rate of at least 90% can completely eliminate the mass effect on surrounding tissues, our case observations revealed that a resection rate of at least 90% demonstrated no mass effect signs (e.g., midline shift, brain tissue compression) on one-month postoperative MRI. (IV) DTI was performed within 3 days before radiotherapy. The reason is that our hospital routinely performs MRI navigation within 3 days before radiotherapy for precise treatment planning. To ensure data consistency, cases exceeding this 3-day window (11 cases) were excluded from the dataset. (V) regular follow-up; and (VI) pathology or radiographic evidence supporting the diagnosis of true progression. Meanwhile, the exclusion criteria were as follows: (I) the extent of resection is less than 90% and biopsy; (II) pseudoprogression or radiation necrosis; (III) cerebrospinal fluid dissemination; (IV) assessment criteria with which true progression could not be assessed; (V) poor imaging quality and incomplete images; (VI) loss to follow-up; (VII) registration error during data preprocessing; (VIII) DTI was performed more than 3 days before radiotherapy; and (Ⅺ) the MRI performed within 3 days before radiotherapy revealed significant tumor progression with mass effect (e.g., midline shift, brain tissue compression). For the healthy control group, the inclusion criteria were as follows: (I) Age > 18 years; (II) there is no history of intracranial tumor, inflammation, cerebrovascular events. The exclusion criteria were as follows: (I) Failure to complete MRI examination and poor image quality; (II) claustrophobia. The clinical and imaging characteristics of the patients with GBM were collected, including age, gender, extent of resection, interval time from postoperative to radiotherapy, postoperative adjuvant therapy, time to recurrence, recurrence pattern, histological grade, the status of IDH and MGMT promoter methylation, tumor location, KPS score before radiotherapy, and volume of enhanced lesions before radiotherapy. The design flow chart of this study is shown in Figure 2 . MRI Examinations All images were acquired from 3.0T MR scanner with 20 channel coil (Skyra and Prisma, Siemens Healthineers, Erlangen, Germany). For GBM patients, the scanned sequence included T2-weighted imaging (T 2 WI), diffusion weighted imaging (DWI), and T 1 WI enhancement, the detailed parameters were shown Supplementary Table S1 . Functional sequences were used to assess the true progression after GBM treatment and included perfusion-weighted imaging (PWI) and magnetic resonance spectroscopy (MRS). PWI included arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), and dynamic contrast enhancement (DCE), the parameters of functional sequences are provided in Supplementary materials Methods. In addition, GBM patients underwent navigation examination within 3 days before radiotherapy, with scanned sequences including fluid attenuated inversion recovery (FLAIR), T 2 WI, T 1 WI enhancement, and diffusion tensor imaging (DTI). The scanned sequences of the healthy controls included T 2 WI, 3D-T1 MPRAGE, and DTI, please refer to Supplementary Table S2 for specific parameters. Assessment of true progression for GBM All enrolled patients underwent maximal safe resection (the extent of enhanced tumor resection ≥ 90%) via the intraoperative navigation system. Postoperative radiotherapy combined with temozolomide-based chemotherapy and sequential temozolomide-based chemotherapy were performed. If O6-methylguanine-DNA methyltransferase (MGMT) methylation was negative on molecular pathology, platinumbased sensitizers were added. All patients underwent MRI enhancement within 3 days after surgery, 3 days before radiotherapy, and 2-4 weeks after radiotherapy, and followup MRI enhancement was performed every 2-4 months. All patients were followed up to true progression, including local progression and distant recurrence. Local progression included the progression of residual lesion within 2 cm of the surgical cavity and the appearance of new abnormal enhanced lesions, distant recurrence is defined as new lesion more than 2cm from the surgical cavity, excluding cerebrospinal fluid dissemination [12] . All cases of true progression were confirmed by the following: (I) gold standard, the second operation was confirmed by pathology, 18% of all patients; (II) follow-up criteria [Response Assessment in Modified NeuroOncology (RANO) criteria] [13] , continuous follow-up with enhanced MRI, 75% of all patients; and (III) when appearance of a new enhancing lesion appeared and no subsequent follow-up MRI, with assessment of functional MRI (PWI and MRS), 7% of all patients. Lesion progression was confirmed as true progression only when concordant abnormalities were demonstrated concurrently on both PWI and MRS. The specific manifestation is enhanced lesions showed hyperperfusion, increased choline (Cho) level, decreased N-acetyl aspartate (NAA) level, and a Cho:NAA ratio >2.5 were considered indicative of tumor progression. DTI data analysis The initial stage involves DTI data preprocessing, specifically designed to spatially normalize the Automatic Anatomical Labelling (AAL) atlas from standard space to individual subject space , as shown in Supplementary Figure S1 . The names of cerebral regions in the detailed AAL template are shown in Supplementary Table S3 . Subsequently, DSI Studio was utilized to conduct whole-brain fiber tractography, with all successfully tracked fibers saved in .trk format for further analysis . Finally, these .trk fiber tractography data were loaded into MATLAB for analysis, enabling the quantification of inter-regional connectivity across 90 predefined cerebral regions in both patient and healthy control cohorts. The detailed methodology and parameter configurations for DTI data analysis are provided in the Supplementary Materials . Classification of the disrupted fibers and cerebral brain regions Firstly, we calculated the mean and standard deviation (SD) of the fiber connectivity strength between cerebral brain regions in all healthy controls. Then, we compared the fiber connection strength of individual patients with the healthy controls. Finally, we defined the significantly decreased fiber connection in patients as those with a strength of over 2SD (95% confidence) lower than the mean strength of the healthy controls, and the two cerebral brain regions with fiber connections as disrupted brain regions. Next, we imported the disrupted fibers and individual AAL templates back into DSI Studio software, and classify them based on the positional relationship between the disrupted fibers, disrupted cerebral brain regions, and FLAIR abnormal signal areas ( Figure 3 ). The disrupted fibers were divided into two categories ( Figure 3B ). 1) Direct disruption: the disrupted fiber passed through the FLAIR abnormal signal area. 2) Indirect disruption: the disrupted fiber did not pass through the FLAIR abnormal signal area. The disrupted cerebral regions were divided into three categories. 1) Tumor disrupted cerebral regions ( Figure 3C ): the disrupted cerebral regions were located in the FLAIR abnormal signal area, and the disrupted fibers were direct disruption. 2) Distant disrupted cerebral regions ( Figure 3D ): the disrupted cerebral regions were not located in the FLAIR abnormal signal area, and the disrupted fibers were direct disruption. 3) Indirect disrupted cerebral region ( Figure 3E ): the disrupted cerebral regions were not located in the FLAIR abnormal signal area, and the disrupted fibers were indirect disruption. At last, within the files documenting inter-cerebral region fiber connection counts for all patients, we screened for significantly decreased fiber connections and their corresponding disrupted brain regions in each patient. Based on the classification of disrupted cerebral regions, we quantified the number of disrupted fiber connections within three distinct brain regions. Statistical Analysis All data were analyzed by SPSS 26.0, GraphPad Prism 8.0 and R4.4.2 software, and the significance level was set at P < .05. Continuous variables were expressed as mean and SD, or quartiles (P 25 , P 50 and P 75 ), and categorical variables were presented as number. The normality test and variance homogeneity test of data were conducted. Two-sample t-test and Mann-Whitney U test were used to compare continuous data, and chi-square test was used to compare classified data. The number of disrupted fibers in three types of cerebral regions did not conform to the normal distribution, and nonparametric test (Kruskal-Wallis) was used, the Dunn multiple comparison test was used for the post hoc analysis. The relationship between the number of disrupted fibers in three types of cerebral regions and clinical variables such as KPS score before radiotherapy and volume of enhanced lesions before radiotherapy was analyzed using Pearson correlation analysis. Receiver operating characteristic (ROC) curve was used to analyse the ability to predict progression time based on the number of disrupted fibers in three types of cerebral regions. Calculating the Youden index (Youden index=sensitivity+species-1), and define patients with values below the index as the low-risk group, and those above the index as the high-risk group. Kaplan Meier method and log rank test were used to construct progression time curves for GBM patients. The number of fibers in three disrupted cerebral regions were used to build univariate and multivariate Cox proportional hazard models of the progression time for GBM patients. The model was adjusted for potential clinical confounders (age, sex, KPS score before radiotherapy), imaging confounder (volume of enhanced lesions before radiotherapy), and molecular confounder (MGMT promoter methylation status). The ability to predict progression time based on the disrupted fibers was assessed by calculating the concordance probability ( C index) of the different models and tested for significance. Results 1-Demographic Characteristics This study retrospectively analyzed the clinical and imaging data of 876 patients with GBM from a single center between November 2016 and April 2024. A total of 113 patients and 65 healthy controls who met the inclusion and exclusion criteria were included in the final analysis. The clinical and imaging characteristics of the patients with GBM and healthy controls were listed in Table 1 . The whole-brain fiber count was significantly higher in healthy controls compared to GBM patients (t=5.24, P < 0.001)‌. The average progression time of patients was 13.8±8.1 months. The number of patients at 6, ≤12, ≤18, ≤24, and >24 months were respectively 16, 33, 37, 16, and 11. Table 1 Clinical and imaging characteristics of the patients with GBM and healthy controls Characteristics GBM (n = 113) Healthy controls (n = 65) Statistics P Age (Year) 57.4 ± 10.6 58.9 ± 10.5 0.86 0.39 a Gender (M/F) 76/37 36/29 2.49 0.11 b Extent of resection (total/ ≥ 90%) 86/27 Interval time from postoperative to radiotherapy (Day) 30.4 ± 12.4 Postoperative adjuvant therapy radiotherapy 7 radiotherapy and chemotherapy 106 Time to recurrence (≤12 months / > 12 months) 49/64 Recurrent pattern (Local recurrence/distant recurrence) 97/33 Histological grade (IV grade) 113 IDH mutation (-) 113 MGMT promoter methylation (+/-) 37/76 Tumor location Frontal lobe (L/R) 38 (11/27) Temporal lobe (L/R) 33 (15/18) Parietal lobe (L/R) 23 (14/9) Occipital lobe (L/R) 19 (13/6) Involved corpus callosum 36 Involved SVZ 35 Cortical contact 58 The number of fibers in whole cerebral (ten thousand strips) 13.5 ± 1.84 14.8 ± 1.31 5.24 <0.001 a The number of fibers in the distant disrupted cerebral region (strip) 37.3 (28.1, 51.9) 117 <0.001 c The number of fibers in the indirect disrupted cerebral region (strip) 8 (0, 26) The number of fibers in the tumor disrupted cerebral region (strip) 46.1 (35.6, 58.1) Data are presented as means ± standard deviation, number or quartiles. All significant values are bold. GBM, glioblasstoma; M, male; F, female; IDH, isocitrate dehydrogenase; MGMT, O6-methylguanine-DNA methyltransferase; L, left; R, right; SVZ, subventricularzone. a represents two sample t-test, b represents chi-square test, c represents Mann-Whitney U test. 2-Comparison of Disrupted fibers results among the cerebral regions Whole-brain analysis revealed a gradient of fiber disruption severity: highest in tumor disrupted cerebral regions, intermediate in distant disrupted cerebral regions, and lowest in indirect disrupted cerebral regions (F = 117, P < 0.001), refer to Figure 4A . Moreover, we further compared the number of disrupted fibers at different progression time points, the results showed that the number of disrupted fibers in the tumor cerebral regions during the 6-12months were markedly larger than that of other progression time points (F=32.5, P < 0.001) , see the Table 2 , and the post hoc analysis was shown in Figure 4B . According to the number of disrupted fibers, we extracted the top 30 pairs of fiber connections in every type of cerebral regions ( Figure 4C ). Next, we divided the number of disrupted fibers into seven levels, namely <50, 50~100, 101~200, 201~300, 301~600, 601~1000, and >1000strips, and extracted the top 30 pairs of fiber connections in every level for the three types of disrupted cerebral regions, as shown in Supplementary Figure S2 . These results suggest that despite the recovery period of approximately one month after GBM surgery (the median interval from postoperative to radiotherapy of this study was 30.4 days), the disruption of the fiber connection between the two cerebral hemispheres was still prominent. In addition, because of the heterogeneity of GBM occurrence sites, we analyzed the differences in the disrupted fibers in three types of cerebral regions at the different cerebral lobes. As shown in Supplementary Table S4 , there was no significant statistical difference between the number of disrupted fibers in the three types of cerebral regions at the frontal lobe and occipital lobe ( P > 0.05), and a statistically significant difference was found at the temporal lobe and parietal lobe ( P < 0.001). The post hoc analysis was shown in Supplementary Figure S3 . Table 2 Comparison of disrupted fibers at different progression time points Characteristic Disrupted cerebral regions Progression time point P ≤ 6months ≤ 12months ≤ 18months ≤ 24months > 24months Number of cases 16 33 37 16 11 Number of fibers Tumor disrupted cerebral regions 38.5（35.1, 73.8） 49 (42.5, 72.9) *#& 32.2 (22.4, 48.8) * 36 (12.5, 37.8) # 22 (19.7, 36.3) & <0.001 Indirect disrupted cerebral regions 49.1 (46.3, 76) 44.1 (39.2, 50.1) 51 (31.8, 58.3) 51.2 (31.2, 67) 39.8 (24.1, 55.9) 0.30 Distant disrupted cerebral regions 8.59 (0.75, 36.3) 12.4 (7, 26) 7 (0, 22.1) 0 (0, 38.3) 0 (0, 10.2) 0.14 Mann-Whitney U test. All significant values are bold. * , # , and & indicate statistical differences among different progression time points. 3-Clinical significance of the disrupted fibers We compared the disrupted fibers of patient subgroups stratified by the KPS score of 80 as reported [14] . We found that a worse KPS score before radiotherapy was associated with higher disruptions of among three types of cerebral regions (all P < 0.05; Figure 5A ). We also found significant negative correlations between the KPS score before radiotherapy and the number of fibers in tumor disrupted cerebral regions and distant disrupted cerebral regions (r tumor = -0.349, P < 0.001; r distant = -0.236, P = 0.01), the number of fibers in indirect disrupted cerebral regions was not correlated with KPS score before radiotherapy (r = -0.063, P = 0.51), see Figure 5B . However, the correlation between the number of fibers in three types of disrupted cerebral regions and the enhanced lesion volume before radiotherapy was not exist (r tumor = 0.118, P = 0.21; r distant = 0.068, P = 0.47; r indirect = 0.032, P = 0.74), see Figure 5C . The possible reason is that the enhanced lesions were totally resected in 76.1% included cases. Next, we analyzed the ability of the disrupted fibers to predict progression time and the results showed that the number of fibers in tumor disrupted cerebral regions had the highest AUC value ( Figure 5D ), for tumor disrupted cerebral regions, AUC = 0.803, 95% CI (0.727, 0.833), P < 0.001; for distant disrupted cerebral regions, AUC = 0.585, 95% CI (0.477, 0.693), P = 0.12; for indirect disrupted cerebral regions, AUC = 0.567, 95% CI (0.446, 0.671), P = 0.29. Moreover, according to the Youden index of fiber count of tumor disrupted cerebral regions, stratified patients showed that there was a significant statistical difference in median progression time (10 months (95% CI, 9-12) vs 15 months (95%CI, 14-19.5), P < 0.001) ( Figure 5E ). In the low risk group, 81% of patients progressed beyond 1 year, while in the high risk group, 71% of patients progressed within 1 year, with a statistically significant difference (χ² = 30.29, P < 0.001), see Figure 5F . Compared to the low risk group, the high risk group had a higher proportion of female patients (χ² = 10.3, P = 0.001), lower KPS scores before radiotherapy (t = 2.66, P = 0.009), while no significant differences were observed in age, MGMT methylation status, enhanced lesion volume before radiotherapy, or interval time from postoperative to radiotherapy ( Table 3 ). Table 3 Clinical characteristics comparison between High risk and Low risk populations Characteristics High risk (n = 52) Low risk (n = 61) Statistics P Age (Year) 56.4 ± 10.9 58.3 ± 10.4 0.94 0.35 a Gender (M/F) 27/25 49/12 10.3 0.001 b Interval time from postoperative to radiotherapy (Day) 29.2 ± 12.0 31.6 ± 12.6 0.99 0.32 a MGMT promoter methylation (+/-) 39/13 37/24 2.62 0.11 b KPS scores before radiotherapy 77.1 ± 17.6 84.3 ± 10.6 2.66 0.009 a Enhanced lesion volume before radiotherapy (cm 3 ) 0 (0, 16.8) 0 (0.38, 13.9) 1519 0.68 c Data are presented as means ± standard deviation, number or quartiles. All significant values are bold. GBM, glioblasstoma; M, male; F, female; MGMT, O6-methylguanine-DNA methyltransferase. a represents two sample t-test, b represents chi-square test, c represents Mann-Whitney U test. 4- Cox proportional hazard models of the short-term progression Finally, we further constructed Cox proportional hazard models to explore the impact of the disrupted fibers on short-term progression. Univariate Cox regression analysis revealed that only the number of fibers in the tumor disrupted cerebral regions was associated with short-term progression (hazard ratio [HR] = 2.101 [95% CI, 1.43-3.087], P < 0.001, C index = 0.617) . In a Multivariate Cox regression analysis addition of the disrupted fibers revealed a performance increase from a C index of 0.607-0.647 compared to a model with clinical data (age, sex, and KPS score before radiotherapy) alone ( P < 0.001). Also, with the addition of MGMT status, the model with information on the disrupted fibers ( C index 0.661) performed better than the model without ( C index 0.619, P < 0.001). By incorporating the enhanced lesion volume before radiotherapy, the model with the disrupted fibers ( C index 0.662) outperformed the same model without this information ( C index 0.619, P < 0.001). All results are summarized in Table 4 . Table 4 Performance measures of multivariate cox proportional hazard models used to predict the short-term progression Models Parameter HR，95%CI P C index P Model a vs .b Model 1: number of fibers in the disrupted cerebral regions (univariate) tumor disrupted cerebral regions 2.101，1.43-3.087 <0.001 0.617 NA indirect disrupted cerebral regions 1.307，0.842-2.03 0.233 0.567 distant disrupted cerebral regions 1.419，0.969-2.08 0.070 0.559 Model 2a: sex + age + KPS score before radiotherapy sex (F) 1.594，1.052-2.415 0.028 0.607 age 1.018，0.997-1.041 0.096 KPS score before radiotherapy (≤80) 0.86，0.58-1.273 0.452 Model 2b: number of fibers in tumor disrupted cerebral regions + sex + age + KPS score before radiotherapy number of fibers in tumor disrupted cerebral regions 2.428,1.614-3.654 <0.001 0.647 <0.001 sex (F) 1.60,1.05-2.439 0.029 age 1.022,0.999-1.046 0.052 KPS score before radiotherapy (≤80) 0.706,0.47-1.061 0.094 Model 3a: sex + age + KPS score before radiotherapy + MGMT sex (F) 1.741,1.135-2.671 0.011 0.619 age 1.014,0.993-1.035 0.184 KPS score before radiotherapy (≤80) 0.913,0.615-1.355 0.650 MGMT unmethylated 1.607,1.05-2.458 0.029 Model 3b: number of fibers in tumor disrupted cerebral regions + sex + age + KPS score before radiotherapy + MGMT number of fibers in tumor disrupted cerebral regions 2.477,1.637-3.748 <0.001 0.661 <0.001 sex (F) 1.657,1.079-2.543 0.021 age 1.016,0.995-1.039 0.136 KPS score before radiotherapy (≤80) 0.763,0.507-1.148 0.194 MGMT unmethylated 1.645,1.067-2.534 0.024 Model 4a: sex + age + KPS score before radiotherapy + MGMT + enhanced lesion volume before radiotherapy sex (F) 1.742,1.135-2.672 0.011 0.619 age 1.014,0.993-1.035 0.189 KPS score before radiotherapy (≤80) 0.916,0.61-1.376 0.674 MGMT unmethylated 1.615,1.041-2.504 0.032 enhanced lesion volume before radiotherapy 0.999,0.993-1.007 0.931 Model 4b: number of fibers in tumor disrupted cerebral regions + sex + age + KPS score before radiotherapy + MGMT + enhanced lesion volume before radiotherapy number of fibers in tumor disrupted cerebral regions 2.49,1.643-3.774 <0.001 0.662 <0.001 sex (F) 1.655,1.078-2.541 0.021 age 1.016,0.994-1.039 0.145 KPS score before radiotherapy (≤80) 0.773,0.509-1.172 0.225 MGMT unmethylated 1.676,1.073-2.618 0.023 enhanced lesion volume before radiotherapy 0.998,0.992-1.006 0.737 All significant values are bold. KPS, karnofsky performance status. F, female. Discussion The present study employed a connectome approach to investigate the disruption of structural connectivity after GBM surgery. Although GBM involves resection of at least 90% of the enhancing core with a recovery period of approximately one month, there remains extensive disruption of neural connectivity within the focal lesion (abnormal signal area around the surgical cavity), while disruption to neural fibers outside the focal lesion is minimal. The fiber integrity loss in the tumor disrupted cerebral regions was associated with worse patient performance, and patients who progress within one year have significantly more disrupted fibers in this area compared to those who progress after one year. And the number of fibers in the tumor disrupted cerebral regions could stratify patients with short-time progression. Resting-state fMRI has reported that gliomas can cause extensive functional damage and affect patient survival time [ 15 – 16 ] , and the disruption of structural connectivity and topological changes in GBM are associated with patient survival [ 7 ] , indicating the importance of characterizing global neural connectivity. However, there is a problem with these studies, which is the impact of the mass effect of preoperative GBM, particularly in assessing the displacement or disruption of peritumoral fibers. Therefore, we screened GBM patients with the extent of resection at least 90% and a recovery period of about one month (the impact of surgery has basically disappeared, eg cerebral edema, mass effect caused by intra-surgical hemorrhage and inflammation.), explored the characteristics and clinical significance of brain structural connectivity. The results demonstrated widespread disruption of fiber connectivity around the surgical cavity, ‌with patients progressing within one year exhibiting more severe fiber destruction‌ compared to those progressing after one year. ‌This suggests that the severity of fiber disruption may have the capability to stratify progression‌ time of GBM . Our study has important clinical implications. We found that the number of fibers in tumor and distant disrupted cerebral regions were negatively correlated with the KPS score before radiotherapy, indicating that the physical condition and quality of life of postoperative patients were related to the fiber connectivity status of their local and overall brain structure. However, the correlation between the fiber connectivity status of local and overall brain structures and the volume of enhanced lesions before radiotherapy no exist, which is opposite to the findings of Yiran Wei et al. that tumor volume leads to changes in brain tissue topology [ 7 ] . This may be related to our inclusion criteria, as effectively removed the mass effect of GBM may eliminate the topological changes of brain tissue. In addition, postoperative pre-radiotherapy higher enhancing tumour volume were significantly associated with shorter overall survival [ 17 ] , but this relationship may no longer exist when extent to resection of the enhanced lesion > 90%. The peritumoral region is the key focus area postoperation of GBM ‌due to its frequent association with progression [ 18 – 20 ] ‌. Compared to distant disruption and indirect disruption, ‌the quantity of fibers in tumor disrupted cerebral regions surrounding the surgical cavity demonstrated a significant correlation with patients' time to progression. In addition, compared with traditional clinical and molecular factors such as sex, age, tumor volume, and MGMT methylation status, fiber connectivity measurement can provide better biomarkers for GBM stratification. We found that the number of fibers in tumor disrupted cerebral regions can predict short-term progression in patients after GBM surgery. Due to the remarkable heterogeneity of GBM, the development of quantitative prognostic markers is crucial for precise treatment. The structural connectivity confers a novel approach to investigate the systematic changes of neural connectivity in GBM [2 1 – 25 ] . It could enable us to understand the interaction between tumour invasion and neural connectivity, which promises to stratify patients more precisely. Moreover, DTI imaging and analysis are relatively easy to implement in clinical practice. Our study has some limitations. Firstly, old cerebrovascular lesions or demyelinating lesions can also lead to the disruption of fiber. Although our data does not include large lesions with a maximum diameter ≥ 1cm, small lesions may also affect the statistics. Secondly, preoperative GBM can cause local and global changes in brain tissue topology [ 26 ] . However, we did not conduct further graph theory analysis because the significance of analyzing only the topological changes in brain tissue after removing tumor effect may be small, and comparative analysis before and after surgery may have greater clinical significance. Thirdly, studies have reported the presence of functional areas around gliomas during direct intraoperative electrical stimulation at the first resection, but, at the second surgery these areas no longer had function, indicating neural plasticity during the growth of the lesion [ 27 ] . Due to the lack of DTI data for preoperative GBM, further research is needed on the neural plasticity changes before and after GBM surgery and its clinical value. Fourth, the study was conducted at a single center, and the heterogeneity in data collection and streamline-count biases require further external validation. We did not find a suitable external data set to valid our results, because there were few DTI examinations performed after GBM surgery (before radiotherapy). Conclusions In conclusion, it remained widespread disruption to the structural connectivity after GBM surgery. The disruption of connectivity integrity was correlated with patient’s progression time, especially in the tumor area around the surgical cavity. Therefore, studying fiber connectivity may provide a new and valuable tool for patient stratification and precise treatment after GBM surgery. Abbreviations AAL = Automatic Anatomical Labelling, AUC = area under the curve, DTI = diffusion tensor imaging, FLAIR = fluid attenuated inversion recovery, GBM = glioblastoma, KPS = Karnofsky performance status, ROC = Receiver operating characteristic, T2WI = T2-weighted imaging. Declarations Acknowledgments: Not applicable. Consent for publication: Not applicable. Funding: This study was funded by the Natural Scientific Foundation of China (82171916), Tianjin Medical Talents Project (TJSJMYXYC-D2-059), Tianjin Education Committee Research Project (2023KJ064, 2023KJ065), Tianjin Science and Technology Major Projects and Projects (24ZXGQSY00070), Tianjin Municipal Education Commission Scientific Research Program (2024ZD060), Joint Funds of the Natural Science Foundation of Tianjin (25JCLMJC00070), and Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-002A). Data Availability: Data generated or analyzed during the study are available from the corresponding author by request. Authors' contributions: QZF, SX, TH designed the study; QZF, XJS, XYJ collected the all data; QZF, XJS, XYJ, PW analyzed the data; PW, SX, TH supervised the study; QZF wrote the manuscript. All authors read and approved the final manuscript. Competing interests: The authors declare that they have no competing interests. Ethics Approval and Consent to Participate: This study was approved by the Institutional Review Board of Tianjin huanhu Hospital (approval no. 2025-026) and conducted in accordance with the principles of the Declaration of Helsinki. The requirement for patient consent was waived owing to the retrospective study design. References Horbinski Craig NL, Burt P, Jana, et al. NCCN Guidelines® Insights: Central Nervous System Cancers, Version 2.2022. J Natl Compr Canc Netw. 2023;21:12–20. doi.org/10.6004/jnccn.2023.0002 . Yang J, Zhang X, Gao X et al. Fiber Density and Structural Brain Connectome in Glioblastoma Are Correlated With Glioma Cell Infiltration. Neurosurgery,2023,92(6):1234–42. 10.1227/neu.0000000000002356 Zhao C, Liang B, Li X, et al. Anatomical distribution and prognostic heterogeneity in glioma: unique clinical features of occipital glioblastoma. J Neurooncol. 2025. 10.1007/s11060-025-05144-4 . Jin Y, Randall JW, Elhalawani H et al. Detection of Glioblastoma Subclinical Recurrence Using Serial Diffusion Tensor Imaging. Cancers (Basel),2020,12(3):568. doi.org/10.3390/cancers12030568 Metz M-C, Molina-Romero M, Lipkova J et al. Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression. Cancers (Basel),2020,12(3): 728. doi.org/10.3390/cancers12030728 Francesca M, Cozzi RC, Mayrand Y, Wan et al. Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review. [J] J Neuroimaging,2025,35(1):e13251. doi.org/10.1111/jon.13251 Wei Y, Li C, Cui Z, et al. Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients. Brain. 2023;146(4):1714–27. doi.org/10.1093/brain/awac360 . Gordian Prasse H-J, Meyer C, Scherlach, et al. Preoperative language tract integrity is a limiting factor in recovery from aphasia after glioma surgery. Neuroimage Clin. 2023;37:103310. doi.org/10.1016/j.nicl.2022.103310 . Fagerholm ED, Hellyer PJ, Scott G, et al. Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain. 2015;138:1696–709. doi.org/10.1093/brain/awv075 . Squarcina L, Bertoldo A, Ham TE, et al. A robust method for investigating thalamic white matter tracts after traumatic brain injury. NeuroImage. 2012;63:779–88. doi.org/10.1016/j.neuroimage.2012.07.016 . Louis David N, Perry Arie W, Pieter, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23:1231–51. doi.org/10.1093/neuonc/noab106 . Wang Hanwei Z, Linlan W, Hao, et al. Preoperative vascular heterogeneity based on dynamic susceptibility contrast MRI in predicting spatial pattern of locally recurrent high-grade gliomas. Eur Radiol. 2024;34:1982–93. doi.org/10.1007/s00330-023-10149-6 . Ellingson BM, Wen PY, Cloughesy TF. Modified Criteria for Radiographic Response Assessment in Glioblastoma Clinical Trials. Neurotherapeutics. 2017;14:307–20. 10.1007/s13311-016-0507-6 . Chaichana K, Parker S, Olivi A, Quiñones-Hinojosa A. A proposed classification system that projects outcomes based on preoperative variables for adult patients with glioblastoma multiforme. J Neurosurg. 2010;112:997–1004. doi.org/10.3171/2009.9.JNS09805 . Daniel AG, Park KY, Roland JL, et al. Functional connectivity within glioblastoma impacts overall survival. Neuro Oncol. 2020;23:412–21. doi.org/10.1093/neuonc/noaa189 . Stoecklein VM, Stoecklein S, Galiè F, et al. Resting-state fMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients. Neuro Oncol. 2020;22:1388–98. doi.org/10.1093/neuonc/noaa044 . Alafandi A, van Garderen KA, Klein S, et al. Association of pre-radiotherapy tumour burden and overall survival in newly diagnosed glioblastoma adjusted for MGMT promoter methylation status. Eur J Cancer. 2023;188:122–30. doi.org/10.1016/j.ejca.2023.04.021 . Bette S, Huber T, Gempt J, et al. Local fractional anisotropy is reduced in Areas with Tumor recurrence in Glioblastoma. Radiology. 2017;283:499–507. doi.org/10.1148/radiol.2016152832 . Varun Venkataramani Y, Yang MC, Schubert, et al. Glioblastoma hijacks neuronal mechanisms for brain invasion. Cell. 2022;185(16):2899–e291731. 10.1016/j.cell.2022.06.054 . Yannik Streibel MO, Breckwoldt J, Hunger, et al. Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model. Sci Rep. 2024;14(1):15613. doi.org/10.1038/s41598-024-66519-7 . Ronghua MXQ,Wei, Zheng et al. Progressive brain structural abnormality in cerebral small vessel disease assessed with MR imaging by using causal network analysis. Neuroimage Clin. 2024; 44:103672. doi.org/10.1016/j.nicl.2024.103672 Sihvonen AJ, Soinila S, Särkämö T. Post-stroke enriched auditory environment induces structural connectome plasticity: secondary analysis from a randomized controlled trial. Brain Imaging Behav. 2022;16(4):1813–22. doi.org/10.1007/s11682-022-00661-6 . Duffau H. Introducing the concept of brain metaplasticity in glioma: how to reorient the pattern of neural reconfiguration to optimize the therapeutic strategy. J Neurosurg. 2021;136(2):613–7. doi.org/10.3171/2021.5.JNS211214 . Lizarazu M, Gil-Robles S, Pomposo I, et al. Spatiotemporal dynamics of postoperative functional plasticity in patients with brain tumors in language areas. Brain Lang. 2020;202:104741. doi.org/10.1016/j.bandl.2019.104741 . Zhong LM, Li TF, Shu H, et al. (TS) 2WM: tumor segmentation and tract statistics for assessing white matter integrity with applications to glioblastoma patients. NeuroImage. 2020;223:117368. doi.org/10.1016/j.neuroimage.2020.117368 . Mandal AS, Romero-Garcia R, Hart MG, et al. Genetic, cellular, and connectomic characterization of the brain regions commonly plagued by glioma. Brain. 2020;143:3294–307. doi.org/10.1093/brain/awaa277 . outhwell DG, Hervey-Jumper SL, Perry DW, et al. Intraoperative mapping during repeat awake craniotomy reveals the functional plasticity of adult cortex. J Neurosurg. 2016;124(5):1460–9. doi.org/10.3171/2015.5.JNS142833 . Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 Apr, 2026 Reviews received at journal 05 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor invited by journal 19 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 15 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8614285\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":592838548,\"identity\":\"7967b04d-71d6-494b-951e-51f34c2972df\",\"order_by\":0,\"name\":\"Quanzhi Feng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin huanhu hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Quanzhi\",\"middleName\":\"\",\"lastName\":\"Feng\",\"suffix\":\"\"},{\"id\":592838562,\"identity\":\"d45aaa60-44a8-49b5-b092-b54266bd4b6a\",\"order_by\":1,\"name\":\"Xinjun Suo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin huanhu hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinjun\",\"middleName\":\"\",\"lastName\":\"Suo\",\"suffix\":\"\"},{\"id\":592838570,\"identity\":\"7b0d1cb6-19b4-4656-9c27-50efe9ed11c9\",\"order_by\":2,\"name\":\"Xiyue Jing\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin huanhu hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiyue\",\"middleName\":\"\",\"lastName\":\"Jing\",\"suffix\":\"\"},{\"id\":592838576,\"identity\":\"f9b970b1-7b7b-45ad-b47a-47d62d9eeb8f\",\"order_by\":3,\"name\":\"Pan Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin huanhu hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pan\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":592838579,\"identity\":\"5a4c7847-25f7-49a5-b689-dc69c981f168\",\"order_by\":4,\"name\":\"Shuang Xia\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACg8MMDMxglvz7jw8SKmpI0cKQYGzw4MwxwlokGxBazCQftjAT1sLPzvz4c2GbXZ68w4G0isQGNgb+9u4EvFrYmNnMpGe2JRcbHmw4diNxhwyDxJmzGwhoYTBj5m1jTtzYzNh2I/EMG4OBRC5+LfzM7J8/87bVJ25sY2YrSGxjJqxFspnHQJq37XDifB42NgaitBgc5imT5jl3PHGDBA+zRMKZYzwE/WJw/vjmzzxl1YnzZ/AwfvxRUSPH396LXwtC7wEIzUOcchCQbyBe7SgYBaNgFIwwAABcokXWbiq46QAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Shuang\",\"middleName\":\"\",\"lastName\":\"Xia\",\"suffix\":\"\"},{\"id\":592838583,\"identity\":\"05be5b93-d0b9-4ac2-9afb-55e685e48250\",\"order_by\":5,\"name\":\"Tong Han\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin huanhu hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tong\",\"middleName\":\"\",\"lastName\":\"Han\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-16 01:38:14\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8614285/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8614285/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":102939635,\"identity\":\"f6e11354-0d91-477d-976d-8f2843126560\",\"added_by\":\"auto\",\"created_at\":\"2026-02-18 16:56:55\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":386470,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlow chart of patients with GBM and healthy controls inclusion and exclusion\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/5b40b35fe0bb95e2d544c40a.jpeg\"},{\"id\":102939631,\"identity\":\"115af332-aa42-4f5b-a2bd-c0937cbd378c\",\"added_by\":\"auto\",\"created_at\":\"2026-02-18 16:56:54\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1459866,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe flowchart of this study. \\u003c/strong\\u003eStep 1: obtain individual AAL by converting the AAL template from MNI 152 standard space to the individual space of patients with GBM and healthy controls. Step 2: obtain individual fiber connections and disrupted fibers. Tracked the whole brain fibers of all GBM patients and healthy controls, and then used the mean and standard deviation of the fiber connection matrix of the healthy controls as reference, the disrupted fiber in every patient is identified as the WM connection with a strength of over 2SD (95% confidence interval) lower than the mean strength of the controls. Step 3: classification of disrupted fibers and cerebral regions. We imported the disrupted fibers and individual AAL templates back into DSI Studio software, and classify them based on the positional relationship between the disrupted fibers, disrupted cerebral brain regions, and FLAIR abnormal signal areas. Divided the disrupted fibers into direct and indirect disruption based on whether they pass through the lesion. According to the positional relationship between the lesion and the cerebral regions connected by disrupted fibers, they are divided into tumor disrupted cerebral regions, distant disrupted cerebral regions, and indirect disrupted cerebral regions. Step 4: explored the characteristics and clinical significance of disrupted fibers.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/4e41cc56e5cb45e931028f84.jpeg\"},{\"id\":102939632,\"identity\":\"28134c0d-26ef-4de7-9087-2c31236bc280\",\"added_by\":\"auto\",\"created_at\":\"2026-02-18 16:56:54\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":639077,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eClassification of the disrupted fibers and cerebral brain regions.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePostoperative lesion (A), disrupted fibers (B), tumor disrupted cerebral regions (C), distant disrupted cerebrum regions (D), and indirect disrupted cerebral regions (E).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/8c186221d29c6f0b0a200402.jpeg\"},{\"id\":102939629,\"identity\":\"474adc70-9235-49fc-b9f3-11d8d972f644\",\"added_by\":\"auto\",\"created_at\":\"2026-02-18 16:56:54\",\"extension\":\"jpeg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":866419,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe results of disrupted fibers. \\u003c/strong\\u003eThe differences in the number of disrupted fibers in the three types of cerebral regions (A), comparison of the number of fibers in the tumor disrupted cerebral regions during the 6-12 months period with other progression time points (B), and the top 30 pairs of disrupted fiber connections in three types of cerebral regions (C).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/378fb7423617d8a62ef22d6d.jpeg\"},{\"id\":102939630,\"identity\":\"9657574b-b48f-44c8-9e3b-2d90e6ab6d04\",\"added_by\":\"auto\",\"created_at\":\"2026-02-18 16:56:54\",\"extension\":\"jpeg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":650886,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eClinical significance of the disrupted fibers. \\u003c/strong\\u003eRelationship between the KPS score before radiotherapy and disrupted fibers in three types of cerebral regions (A and B), and relationship between the enhanced lesion volume before radiotherapy and disrupted fibers (C). The ability to predict short-term progression based on the number of disrupted fibers in three types of cerebral regions (D). The survival curve of the low risk and high risk groups (E), the dotted line represented the median progression time, and the blue and red areas represent the 95% CI. Confusion matrices in the stratified patients (F), deep red and pink red: number cases of incorrectly classified; pale purple and light blue: number cases of in correctly classified.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/3c7e2767256239a5105431a0.jpeg\"},{\"id\":102964081,\"identity\":\"766f7cd6-5e9b-402b-84c5-af0f15160126\",\"added_by\":\"auto\",\"created_at\":\"2026-02-19 04:21:25\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":5265120,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/5745e692-4607-4494-bdde-e9b43435c4c9.pdf\"},{\"id\":102939633,\"identity\":\"9c445101-57f2-41d0-a426-fe9ec6366c09\",\"added_by\":\"auto\",\"created_at\":\"2026-02-18 16:56:54\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2017547,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryfiles.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8614285/v1/15bbe5f64e7677e250c2d2ac.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"\\u003cp\\u003eShort-term progression risk stratification in glioblastoma using post-resection structural connectivity biomarkers\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"‌Background\",\"content\":\"\\u003cp\\u003eThe high recurrence rate and poor response to treatment of glioblastoma (GBM) have been attributed to its heterogeneity, which has therefore garnered considerable research attention \\u003csup\\u003e[\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. Despite achieving \\u0026zwnj;gross total resection\\u0026zwnj; (GTR) and administering \\u0026zwnj;concurrent radiochemotherapy\\u0026zwnj;, GBM recurrence\\u0026zwnj; remains the predominant clinical challenge in neuro-oncological practice. GBM exhibits widespread infiltration into white matter tracts, which remains undetectable on conventional MRI \\u003csup\\u003e[2\\u0026thinsp;\\u0026minus;\\u0026thinsp;3]\\u003c/sup\\u003e. And postoperative recurrence correlates closely with the spatial locations of fiber tract disruption \\u003csup\\u003e[4\\u0026thinsp;\\u0026minus;\\u0026thinsp;6]\\u003c/sup\\u003e. Therefore, it is crucial to accurately identify disrupted fiber tracts.\\u003c/p\\u003e\\n\\u003cp\\u003eRecently, the structural connectivity method based on preoperative diffusion magnetic resonance imaging can reveal and quantify GBM invasion that is not visible on conventional MRI \\u003csup\\u003e[\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. This approach models brain regions as nodes while the white matter connections among brain regions as edges, forming a complex network. Therefore, studying fiber connectivity may provide valuable tools for stratification and precision treatment of GBM patients. However, for brain tumors, objectively assessing white matter integrity through diffusion tensor imaging (DTI) faces some obstacles in clinical settings. Mass effect and peritumoral edema could complicate the assessment of white matter integrity \\u003csup\\u003e[\\u003cspan class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. Postoperative research utilizing \\u0026zwnj;healthy control-derived templates\\u0026zwnj; for tract localization, combined with the \\u0026zwnj;voxel projection step of tract-based spatial statistics (TBSS)\\u003csup\\u003e[9\\u0026minus;\\u003cspan class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e\\u0026zwnj;, may provide more reliable progression prediction models in GBM patients. So, further study is needed on the relationship between postoperative DTI assessment and GBM recurrence.\\u003c/p\\u003e\\n\\u003cp\\u003eTherefore, we hypothesized that focal and global disturbance to the structural connectome after GBM surgery correlated with Karnofsky performance status (KPS) score and impact patient outcomes. First, based on constructed structural networks using DTI data from healthy controls, regional connectome disruption was quantitatively analyzed for each patient, and to characterize patterns of disrupted structural connectivity among three compartments. Subsequently, the differences in disrupted fibers among three compartments at various progression time points and its relationship with the patient\\u0026apos;s performance before radiotherapy were explored. Finally, investigating the predictive value of disrupted structural connectivity in patient progression time using connectivity disruption characteristics, survival curves for risk stratification were constructed and validating this stratification\\u0026apos;s efficacy.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA retrospective study was conducted on 876 patients with pathologically confirmed GBM and postoperative DTI examination from November 2016 to April 2024. Recruiting age and gender matched healthy control groups as reference for DTI data analysis, a total of 72 volunteers were recruited by the end of this study. The detailed process diagram for inclusion and exclusion is shown in \\u003cstrong\\u003eFigure 1\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eFor GBM patients, the inclusion criteria were as follows: (I) pathological diagnostic criteria meeting the 2021 World Health Organization fifth edition classification of glioma \\u003csup\\u003e[11]\\u003c/sup\\u003e; (II) no history of preoperative treatment; (III) the enhanced lesion was resected at least 90% (the aim is to effectively remove the effect of the mass on the compression of the peritumoral fibers), the assessment method is that postoperative contrast-enhanced MRI within 72 hours shows\\u0026nbsp;\\u0026zwnj;no abnormal enhancement\\u0026zwnj;\\u0026nbsp;or only\\u0026nbsp;\\u0026zwnj;linear enhancement at the surgical cavity margin. While no literature has confirmed that a tumor resection rate of at least 90% can completely eliminate the mass effect on surrounding tissues, our case observations revealed that a resection rate of at least 90% demonstrated no mass effect signs (e.g., midline shift, brain tissue compression) on one-month postoperative MRI. (IV) DTI was performed within 3 days before radiotherapy. The reason is that our hospital routinely performs MRI navigation within 3 days before radiotherapy for precise treatment planning. To ensure data consistency, cases exceeding this 3-day window (11 cases) were excluded from the dataset. (V) regular follow-up; and (VI) pathology or radiographic evidence supporting the diagnosis of true progression. Meanwhile, the exclusion criteria were as follows: (I) the extent of resection is less than 90% and biopsy; (II) pseudoprogression or radiation necrosis; (III) cerebrospinal fluid dissemination; (IV) assessment criteria with which true progression could not be assessed; (V) poor imaging quality and incomplete images; (VI) loss to follow-up; (VII) registration error during data preprocessing; (VIII) DTI was performed more than 3 days before radiotherapy; and (Ⅺ) the MRI performed within 3 days before radiotherapy revealed significant tumor progression with mass effect (e.g., midline shift, brain tissue compression).\\u003c/p\\u003e\\n\\u003cp\\u003eFor the healthy control group, the inclusion criteria were as follows: (I) Age \\u0026gt; 18 years; (II) there is no history of intracranial tumor, inflammation, cerebrovascular events. The exclusion criteria were as follows: (I) Failure to complete MRI examination and poor image quality; (II) claustrophobia.\\u003c/p\\u003e\\n\\u003cp\\u003eThe clinical and imaging characteristics of the patients with GBM were collected, including age, gender, extent of resection, interval time from postoperative to radiotherapy, postoperative adjuvant therapy, time to recurrence, recurrence pattern, histological grade, the status of IDH and MGMT promoter methylation, tumor location, KPS score before radiotherapy, and volume of enhanced lesions before radiotherapy. The design flow chart of this study is shown in \\u003cstrong\\u003eFigure 2\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMRI Examinations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll images were acquired from 3.0T MR scanner with 20 channel coil (Skyra and Prisma, Siemens Healthineers, Erlangen, Germany). For GBM patients, the scanned sequence included T2-weighted imaging (T\\u003csub\\u003e2\\u003c/sub\\u003eWI), diffusion weighted imaging (DWI), and T\\u003csub\\u003e1\\u003c/sub\\u003eWI enhancement, the detailed parameters were shown \\u003cstrong\\u003eSupplementary Table S1\\u003c/strong\\u003e. Functional sequences were used to assess the true progression after GBM treatment and included perfusion-weighted imaging (PWI) and magnetic resonance spectroscopy (MRS). PWI included arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), and dynamic contrast enhancement (DCE), the parameters of functional sequences are provided in Supplementary materials Methods. In addition, GBM patients underwent navigation examination within 3 days before radiotherapy, with scanned sequences including fluid attenuated inversion recovery (FLAIR), T\\u003csub\\u003e2\\u003c/sub\\u003eWI, T\\u003csub\\u003e1\\u003c/sub\\u003eWI enhancement, and diffusion tensor imaging (DTI). The scanned sequences of the healthy controls included T\\u003csub\\u003e2\\u003c/sub\\u003eWI, 3D-T1 MPRAGE, and DTI, please refer to \\u003cstrong\\u003eSupplementary Table S2\\u003c/strong\\u003e for specific parameters.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssessment of true progression for GBM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll enrolled patients underwent maximal safe resection (the extent of enhanced tumor resection \\u0026ge; 90%) via the intraoperative navigation system. Postoperative radiotherapy combined with temozolomide-based chemotherapy and sequential temozolomide-based chemotherapy were performed. If O6-methylguanine-DNA methyltransferase (MGMT) methylation was negative on molecular pathology, platinumbased sensitizers were added. All patients underwent MRI enhancement within 3 days after surgery, 3 days before radiotherapy, and 2-4 weeks after radiotherapy, and followup MRI enhancement was performed every 2-4 months. All patients were followed up to true progression, including local progression and distant recurrence.\\u003c/p\\u003e\\n\\u003cp\\u003eLocal progression included the progression of residual lesion within 2 cm of the surgical cavity and the appearance of new abnormal enhanced lesions, distant recurrence is defined as new lesion more than 2cm from the surgical cavity, excluding cerebrospinal fluid dissemination \\u003csup\\u003e[12]\\u003c/sup\\u003e. All cases of true progression were confirmed by the following: (I) gold standard, the second operation was confirmed by pathology, 18% of all patients; (II) follow-up criteria [Response Assessment in Modified NeuroOncology (RANO) criteria] \\u003csup\\u003e[13]\\u003c/sup\\u003e, continuous follow-up with enhanced MRI, 75% of all patients; and (III) when appearance of a new enhancing lesion appeared and no subsequent follow-up MRI, with assessment of functional MRI (PWI and MRS), 7% of all patients. Lesion progression was confirmed as true progression only when concordant abnormalities were demonstrated concurrently on both PWI and MRS. The specific manifestation is enhanced lesions showed hyperperfusion, increased choline (Cho) level, decreased N-acetyl aspartate (NAA) level, and a Cho:NAA ratio \\u0026gt;2.5 were considered indicative of tumor progression.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDTI data analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eThe initial stage involves DTI data preprocessing, specifically designed to spatially normalize the\\u0026nbsp;\\u003c/em\\u003eAutomatic Anatomical Labelling (AAL) atlas\\u003cem\\u003e\\u0026nbsp;from standard space to individual subject space\\u003c/em\\u003e, as shown in \\u003cstrong\\u003eSupplementary Figure S1\\u003c/strong\\u003e. The names of cerebral regions in the detailed AAL template are shown in \\u003cstrong\\u003eSupplementary Table S3\\u003c/strong\\u003e. \\u003cem\\u003eSubsequently, DSI Studio was utilized to conduct whole-brain fiber tractography, with all successfully tracked fibers saved in .trk format for further analysis\\u003c/em\\u003e. Finally, \\u003cem\\u003ethese\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003e.trk fiber tractography data were loaded into MATLAB for analysis, enabling the quantification of inter-regional connectivity across 90 predefined\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003ecerebral\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;regions in both patient and healthy control cohorts.\\u003c/em\\u003e The detailed methodology and parameter configurations for DTI data analysis are provided in \\u003cstrong\\u003ethe Supplementary Materials\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClassification of the disrupted fibers and cerebral brain regions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFirstly, we calculated the mean and standard deviation (SD) of the fiber connectivity strength between cerebral brain regions in all healthy controls. Then, we compared the fiber connection strength of individual patients with the healthy controls. Finally, we defined the significantly decreased fiber connection in patients as those with a strength of over 2SD (95% confidence) lower than the mean strength of the healthy controls, and the two cerebral brain regions with fiber connections as disrupted brain regions. Next, we imported the disrupted fibers and individual AAL templates back into DSI Studio software, and classify them based on the positional relationship between the disrupted fibers, disrupted cerebral brain regions, and FLAIR abnormal signal areas (\\u003cstrong\\u003eFigure 3\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eThe disrupted fibers were divided into two categories (\\u003cstrong\\u003eFigure 3B\\u003c/strong\\u003e). 1) Direct disruption: the disrupted fiber passed through the FLAIR abnormal signal area. 2) Indirect disruption: the disrupted fiber did not pass through the FLAIR abnormal signal area. The disrupted cerebral regions were divided into three categories. 1) Tumor disrupted cerebral regions (\\u003cstrong\\u003eFigure 3C\\u003c/strong\\u003e): the disrupted cerebral regions were located in the FLAIR abnormal signal area, and the disrupted fibers were direct disruption. 2) Distant disrupted cerebral regions (\\u003cstrong\\u003eFigure 3D\\u003c/strong\\u003e): the disrupted cerebral regions were not located in the FLAIR abnormal signal area, and the disrupted fibers were direct disruption. 3) Indirect disrupted cerebral region (\\u003cstrong\\u003eFigure 3E\\u003c/strong\\u003e): the disrupted cerebral regions were not located in the FLAIR abnormal signal area, and the disrupted fibers were indirect disruption.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAt last, within the files documenting inter-cerebral region fiber connection counts for all patients, we screened for significantly decreased fiber connections and their corresponding disrupted brain regions in each patient. Based on the classification of disrupted cerebral regions, we quantified the number of disrupted fiber connections within three distinct brain regions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data were analyzed by SPSS 26.0, GraphPad Prism 8.0 and R4.4.2 software, and the significance level was set at \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; .05. Continuous variables were expressed as mean and SD, or quartiles (P\\u003csub\\u003e25\\u003c/sub\\u003e, P\\u003csub\\u003e50\\u003c/sub\\u003e and P\\u003csub\\u003e75\\u003c/sub\\u003e), and categorical variables were presented as number. The normality test and variance homogeneity test of data were conducted. Two-sample t-test and\\u0026nbsp;Mann-Whitney U test\\u0026nbsp;were used to compare continuous data, and chi-square test was used to compare classified data. The number of disrupted fibers in three types of cerebral regions did not conform to the normal distribution, and nonparametric test (Kruskal-Wallis) was used, the Dunn multiple comparison test was used for the post hoc analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eThe relationship between the number of disrupted fibers in three types of cerebral regions and clinical variables such as KPS score before radiotherapy and volume of enhanced lesions before radiotherapy was analyzed using Pearson correlation analysis. Receiver operating characteristic (ROC) curve was used to analyse the ability to predict progression time based on the number of disrupted fibers in three types of cerebral regions. Calculating the Youden index (Youden index=sensitivity+species-1), and define patients with values below the index as the low-risk group, and those above the index as the high-risk group. Kaplan Meier method and log rank test were used to construct progression time curves for GBM patients.\\u003c/p\\u003e\\n\\u003cp\\u003eThe number of fibers in three disrupted cerebral regions were used to build univariate and multivariate Cox proportional hazard models of the progression time for GBM patients. The model was adjusted for potential clinical confounders (age, sex, KPS score before radiotherapy), imaging confounder (volume of enhanced lesions before radiotherapy), and molecular confounder (MGMT promoter methylation status). The ability to predict progression time based on the disrupted fibers was assessed by calculating the concordance probability (\\u003cem\\u003eC\\u003c/em\\u003e index) of the different models and tested for significance.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e1-Demographic Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study retrospectively analyzed the clinical and imaging data of 876 patients with GBM from a single center between November 2016 and April 2024. A total of 113 patients and 65 healthy controls who met the inclusion and exclusion criteria were included in the final analysis. The clinical and imaging characteristics of the patients with GBM and healthy controls were listed in \\u003cstrong\\u003eTable 1\\u003c/strong\\u003e. The whole-brain fiber count was significantly higher in healthy controls compared to GBM patients (t=5.24, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001)\\u0026zwnj;. The average progression time of patients was 13.8\\u0026plusmn;8.1 months. The number of patients at 6, \\u0026le;12, \\u0026le;18, \\u0026le;24, and \\u0026gt;24 months were respectively 16, 33, 37, 16, and 11.\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"\\\" width=\\\"709\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 709px;\\\"\\u003e\\n \\u003cp\\u003eTable\\u0026nbsp;1\\u0026nbsp;Clinical and imaging characteristics of the patients with GBM\\u0026nbsp;and healthy controls\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003eGBM\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e(n\\u0026nbsp;=\\u0026nbsp;113)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003eHealthy controls\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(n = 65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003eStatistics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eAge\\u0026nbsp;(Year)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e57.4\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;10.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e58.9\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;10.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e0.86\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.39\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eGender\\u0026nbsp;(M/F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e76/37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e36/29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e2.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e0.11\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eExtent of resection (total/\\u0026nbsp;\\u0026ge;\\u0026nbsp;90%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e86/27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eInterval time from postoperative to\\u0026nbsp;radiotherapy\\u0026nbsp;(Day)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e30.4\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;12.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003ePostoperative adjuvant therapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eradiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eradiotherapy and chemotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e106\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eTime to\\u0026nbsp;recurrence\\u0026nbsp;(\\u0026le;12\\u0026nbsp;months\\u0026nbsp;/ \\u0026gt;\\u0026nbsp;12\\u0026nbsp;months)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e49/64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eRecurrent\\u0026nbsp;pattern\\u0026nbsp;(Local recurrence/distant recurrence)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e97/33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eHistological grade\\u0026nbsp;(IV grade)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e113\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eIDH\\u0026nbsp;mutation\\u0026nbsp;(-)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e113\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eMGMT promoter\\u0026nbsp;methylation (+/-)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e37/76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eTumor location\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eFrontal\\u0026nbsp;lobe\\u0026nbsp;(L/R)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e38\\u0026nbsp;(11/27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eTemporal\\u0026nbsp;lobe\\u0026nbsp;(L/R)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e33\\u0026nbsp;(15/18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eParietal\\u0026nbsp;lobe\\u0026nbsp;(L/R)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e23\\u0026nbsp;(14/9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eOccipital\\u0026nbsp;lobe\\u0026nbsp;(L/R)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e19\\u0026nbsp;(13/6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eInvolved corpus callosum\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eInvolved SVZ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eCortical contact\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eThe number of fibers in whole cerebral (ten thousand strips)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e13.5\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;1.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e14.8 \\u0026plusmn;\\u0026nbsp;1.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e5.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eThe number of fibers in the\\u0026nbsp;distant disrupted cerebral\\u0026nbsp;region\\u0026nbsp;(strip)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e37.3 (28.1, 51.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 61px;\\\"\\u003e\\n \\u003cp\\u003e117\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 59px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eThe number of fibers in the\\u0026nbsp;indirect disrupted cerebral\\u0026nbsp;region\\u0026nbsp;(strip)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e8 (0, 26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 386px;\\\"\\u003e\\n \\u003cp\\u003eThe number of fibers in the\\u0026nbsp;tumor disrupted cerebral\\u0026nbsp;region\\u0026nbsp;(strip)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 106px;\\\"\\u003e\\n \\u003cp\\u003e46.1 (35.6, 58.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 709px;\\\"\\u003e\\n \\u003cp\\u003eData are presented as means \\u0026plusmn; standard deviation, number or quartiles. All significant values are bold. GBM, glioblasstoma; M, male; F, female; IDH, isocitrate dehydrogenase; MGMT, O6-methylguanine-DNA methyltransferase; L, left; R, right; SVZ, subventricularzone. \\u003csup\\u003ea\\u003c/sup\\u003e represents two sample t-test, \\u003csup\\u003eb\\u003c/sup\\u003e represents chi-square test, \\u003csup\\u003ec\\u0026nbsp;\\u003c/sup\\u003erepresents Mann-Whitney U test.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2-Comparison of Disrupted fibers results among the cerebral regions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWhole-brain analysis revealed a gradient of fiber disruption severity: highest in tumor disrupted cerebral regions, intermediate in distant disrupted cerebral regions, and lowest in indirect disrupted cerebral regions (F = 117, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001), refer to \\u003cstrong\\u003eFigure 4A\\u003c/strong\\u003e. Moreover, we further compared the number of disrupted fibers at different progression time points, the results showed that the number of disrupted fibers in the tumor cerebral regions during the 6-12months were markedly larger than that of other progression time points (F=32.5, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001) , see the \\u003cstrong\\u003eTable 2\\u003c/strong\\u003e, and the post hoc analysis was shown in \\u003cstrong\\u003eFigure 4B\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eAccording to the number of disrupted fibers, we extracted the top 30 pairs of fiber connections in every type of cerebral regions (\\u003cstrong\\u003eFigure 4C\\u003c/strong\\u003e). Next, we divided the number of disrupted fibers into seven levels, namely \\u0026lt;50, 50~100, 101~200, 201~300, 301~600, 601~1000, and \\u0026gt;1000strips, and extracted the top 30 pairs of fiber connections in every level for the three types of disrupted cerebral regions, as shown in \\u003cstrong\\u003eSupplementary Figure S2\\u003c/strong\\u003e. These results suggest that despite the recovery period of approximately one month after GBM surgery (the median interval from postoperative to radiotherapy of this study was 30.4 days), the disruption of the fiber connection between the two cerebral hemispheres was still prominent.\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition, because of the heterogeneity of GBM occurrence sites, we analyzed the differences in the disrupted fibers in three types of cerebral regions at the different cerebral lobes. As shown in \\u003cstrong\\u003eSupplementary Table S4\\u003c/strong\\u003e, there was no significant statistical difference between the number of disrupted fibers in the three types of cerebral regions at the frontal lobe and occipital lobe (\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u0026gt; 0.05), and a statistically significant difference was found at the temporal lobe and parietal lobe (\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.001). The post hoc analysis was shown in \\u003cstrong\\u003eSupplementary Figure S3\\u003c/strong\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eTable 2 Comparison of disrupted fibers at different progression time points\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eCharacteristic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eDisrupted\\u003c/p\\u003e\\n \\u003cp\\u003ecerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 65px;\\\"\\u003e\\n \\u003cp\\u003eProgression time point\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 6months\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 12months\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 18months\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; 24months\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026nbsp;24months\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eNumber of cases\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eNumber of fibers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eTumor disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e38.5（35.1, 73.8）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e49 (42.5, 72.9) \\u003csup\\u003e*#\\u0026amp;\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e32.2 (22.4, 48.8) \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e36 (12.5, 37.8) \\u003csup\\u003e#\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e22 (19.7, 36.3) \\u003csup\\u003e\\u0026amp;\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eIndirect disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e49.1 (46.3, 76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e44.1 (39.2, 50.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e51 (31.8, 58.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e51.2 (31.2, 67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e39.8 (24.1, 55.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eDistant disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e8.59 (0.75, 36.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e12.4 (7, 26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e7 (0, 22.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e0 (0, 38.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e0 (0, 10.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eMann-Whitney U test. All significant values are bold. * , \\u003csup\\u003e#\\u003c/sup\\u003e, and \\u003csup\\u003e\\u0026amp;\\u003c/sup\\u003e indicate statistical differences among different progression time points.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3-Clinical significance of the disrupted fibers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe compared the disrupted fibers of patient subgroups stratified by the KPS score of 80 as reported \\u003csup\\u003e[14]\\u003c/sup\\u003e. We found that a worse KPS score before radiotherapy was associated with higher disruptions of among three types of cerebral regions (all \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05; \\u003cstrong\\u003eFigure 5A\\u003c/strong\\u003e). We also found significant negative correlations between the KPS score before radiotherapy and the number of fibers in tumor disrupted cerebral regions and distant disrupted cerebral regions (r\\u003csub\\u003etumor\\u003c/sub\\u003e = -0.349, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001; r\\u003csub\\u003edistant\\u003c/sub\\u003e = -0.236, \\u003cem\\u003eP\\u003c/em\\u003e = 0.01), the number of fibers in indirect disrupted cerebral regions was not correlated with KPS score before radiotherapy (r = -0.063, \\u003cem\\u003eP\\u003c/em\\u003e = 0.51), see \\u003cstrong\\u003eFigure 5B\\u003c/strong\\u003e. However, the correlation between the number of fibers in three types of disrupted cerebral regions and the enhanced lesion volume before radiotherapy was not exist (r\\u003csub\\u003etumor\\u003c/sub\\u003e = 0.118, \\u003cem\\u003eP\\u003c/em\\u003e = 0.21; r\\u003csub\\u003edistant\\u003c/sub\\u003e = 0.068, \\u003cem\\u003eP\\u003c/em\\u003e = 0.47; r\\u003csub\\u003eindirect\\u003c/sub\\u003e = 0.032, \\u003cem\\u003eP\\u003c/em\\u003e = 0.74), see \\u003cstrong\\u003eFigure 5C\\u003c/strong\\u003e. The possible reason is that the enhanced lesions were totally resected in 76.1% included cases.\\u003c/p\\u003e\\n\\u003cp\\u003eNext, we analyzed the ability of the disrupted fibers to predict progression time and the results showed that\\u0026nbsp;the number of fibers in tumor disrupted cerebral regions had the highest AUC value (\\u003cstrong\\u003eFigure 5D\\u003c/strong\\u003e), for tumor disrupted cerebral regions, AUC = 0.803, 95% CI (0.727, 0.833), \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001; for distant disrupted cerebral regions, AUC\\u003csub\\u003e\\u0026nbsp;\\u003c/sub\\u003e= 0.585, 95% CI (0.477, 0.693), \\u003cem\\u003eP\\u003c/em\\u003e = 0.12; for indirect disrupted cerebral regions, AUC = 0.567, 95% CI (0.446, 0.671), \\u003cem\\u003eP\\u003c/em\\u003e = 0.29.\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, according to the Youden index of fiber count of tumor disrupted cerebral regions, stratified patients showed that there was a significant statistical difference in median progression time (10 months (95% CI, 9-12) vs 15 months (95%CI, 14-19.5), \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001) (\\u003cstrong\\u003eFigure 5E\\u003c/strong\\u003e). In the low risk group, 81% of patients progressed beyond 1 year, while in the high risk group, 71% of patients progressed within 1 year, with a statistically significant difference (\\u0026chi;\\u0026sup2; = 30.29, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001), see \\u003cstrong\\u003eFigure 5F\\u003c/strong\\u003e. Compared to the low risk group, the high risk group had a higher proportion of female patients (\\u0026chi;\\u0026sup2; = 10.3, \\u003cem\\u003eP\\u003c/em\\u003e = 0.001), lower KPS scores before radiotherapy (t = 2.66, \\u003cem\\u003eP\\u003c/em\\u003e = 0.009), while no significant differences were observed in age, MGMT methylation status, enhanced lesion volume before radiotherapy, or interval time from postoperative to radiotherapy (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"left\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eTable\\u0026nbsp;3\\u0026nbsp;Clinical\\u0026nbsp;characteristics\\u0026nbsp;comparison between High\\u0026nbsp;risk and Low\\u0026nbsp;risk\\u0026nbsp;populations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eHigh risk\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e(n\\u0026nbsp;=\\u0026nbsp;52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eLow risk\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(n = 61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eStatistics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eAge\\u0026nbsp;(Year)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e56.4\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;10.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e58.3\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;10.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.35\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eGender\\u0026nbsp;(M/F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e27/25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e49/12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e10.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.001\\u003c/strong\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eInterval time from postoperative to\\u0026nbsp;radiotherapy\\u0026nbsp;(Day)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e29.2\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;12.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e31.6\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;12.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.32 \\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eMGMT promoter\\u0026nbsp;methylation (+/-)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e39/13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e37/24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.11 \\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eKPS scores before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e77.1\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;17.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e84.3\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;10.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e2.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.009\\u003c/strong\\u003e \\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003eEnhanced lesion volume before radiotherapy\\u0026nbsp;(cm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0 (0, 16.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0 (0.38, 13.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e1519\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd\\u003e\\n \\u003cp\\u003e0.68\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003eData are presented as means \\u0026plusmn; standard deviation, number or quartiles. All significant values are bold. GBM, glioblasstoma; M, male; F, female; MGMT, O6-methylguanine-DNA methyltransferase. \\u003csup\\u003ea\\u003c/sup\\u003e represents two sample t-test, \\u003csup\\u003eb\\u003c/sup\\u003e represents chi-square test, \\u003csup\\u003ec\\u0026nbsp;\\u003c/sup\\u003erepresents Mann-Whitney U test.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4-\\u003c/strong\\u003e\\u003cstrong\\u003eCox proportional hazard models of the short-term progression\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, we further constructed Cox proportional hazard models to explore the impact of the disrupted fibers on short-term progression. Univariate Cox regression analysis revealed that only the number of fibers in the tumor disrupted cerebral regions was associated with short-term progression (hazard ratio [HR] = 2.101 [95% CI, 1.43-3.087], \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001, \\u003cem\\u003eC\\u003c/em\\u003e index = 0.617) . In a Multivariate Cox regression analysis addition of the disrupted fibers revealed a performance increase from a \\u003cem\\u003eC\\u003c/em\\u003e index of 0.607-0.647 compared to a model with clinical data (age, sex, and KPS score before radiotherapy) alone (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). Also, with the addition of MGMT status, the model with information on the disrupted fibers (\\u003cem\\u003eC\\u003c/em\\u003e index 0.661) performed better than the model without (\\u003cem\\u003eC\\u003c/em\\u003e index 0.619, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). By incorporating the enhanced lesion volume before radiotherapy, the model with the disrupted fibers (\\u003cem\\u003eC\\u003c/em\\u003e index 0.662) outperformed the same model without this information (\\u003cem\\u003eC\\u003c/em\\u003e index 0.619, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). All results are summarized in \\u003cstrong\\u003eTable 4\\u003c/strong\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"102%\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" valign=\\\"top\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eTable 4 Performance measures of multivariate cox proportional hazard models used to predict the short-term progression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModels\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eParameter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003eHR，95%CI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eC\\u003c/em\\u003e index\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eModel a vs .b\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 1:\\u0026nbsp;number of fibers in the disrupted cerebral regions (univariate)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003etumor disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e2.101，1.43-3.087\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.617\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eindirect disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.307，0.842-2.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.233\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.567\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003edistant disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.419，0.969-2.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.070\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.559\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 2a:\\u003c/p\\u003e\\n \\u003cp\\u003esex + age + KPS score before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003esex (F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.594，1.052-2.415\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.028\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.607\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.018，0.997-1.041\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.096\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eKPS score before radiotherapy (\\u0026le;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.86，0.58-1.273\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.452\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"4\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 2b:\\u003c/p\\u003e\\n \\u003cp\\u003enumber of fibers in tumor disrupted cerebral regions\\u0026nbsp;+ sex + age + KPS score before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003enumber of fibers in tumor disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e2.428,1.614-3.654\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.647\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003esex (F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.60,1.05-2.439\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.029\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.022,0.999-1.046\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.052\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eKPS score before radiotherapy (\\u0026le;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.706,0.47-1.061\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.094\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"4\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 3a:\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003esex + age + KPS score before radiotherapy + MGMT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003esex (F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.741,1.135-2.671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.011\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.619\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.014,0.993-1.035\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.184\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eKPS score before radiotherapy (\\u0026le;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.913,0.615-1.355\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.650\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eMGMT unmethylated\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.607,1.05-2.458\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.029\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"5\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 3b:\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003enumber of fibers in tumor disrupted cerebral regions\\u0026nbsp;+ sex + age + KPS score before radiotherapy + MGMT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003enumber of fibers in tumor disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e2.477,1.637-3.748\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.661\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003esex (F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.657,1.079-2.543\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.021\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.016,0.995-1.039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.136\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eKPS score before radiotherapy (\\u0026le;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.763,0.507-1.148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.194\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eMGMT unmethylated\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.645,1.067-2.534\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.024\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"5\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 4a:\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003esex + age + KPS score before radiotherapy + MGMT + enhanced lesion volume before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003esex (F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.742,1.135-2.672\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.011\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.619\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.014,0.993-1.035\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.189\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eKPS score before radiotherapy (\\u0026le;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.916,0.61-1.376\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.674\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eMGMT unmethylated\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.615,1.041-2.504\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.032\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eenhanced lesion volume before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.999,0.993-1.007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.931\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"6\\\" valign=\\\"top\\\" style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eModel 4b:\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003enumber of fibers in tumor disrupted cerebral regions\\u0026nbsp;+ sex + age + KPS score before radiotherapy + MGMT + enhanced lesion volume before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003enumber of fibers in tumor disrupted cerebral regions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e2.49,1.643-3.774\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e0.662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt;0.001\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003esex (F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.655,1.078-2.541\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.021\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.016,0.994-1.039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.145\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eKPS score before radiotherapy (\\u0026le;80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.773,0.509-1.172\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.225\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eMGMT unmethylated\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e1.676,1.073-2.618\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.023\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 37px;\\\"\\u003e\\n \\u003cp\\u003eenhanced lesion volume before radiotherapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 14px;\\\"\\u003e\\n \\u003cp\\u003e0.998,0.992-1.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 6px;\\\"\\u003e\\n \\u003cp\\u003e0.737\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\" valign=\\\"top\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eAll significant values are bold. KPS, karnofsky performance status. F, female.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present study employed a connectome approach to investigate the disruption of structural connectivity after GBM surgery. Although GBM involves resection of at least 90% of the enhancing core with a recovery period of approximately one month, there remains extensive disruption of neural connectivity within the focal lesion (abnormal signal area around the surgical cavity), while disruption to neural fibers outside the focal lesion is minimal. The fiber integrity loss in the tumor disrupted cerebral regions was associated with worse patient performance, and patients who progress within one year have significantly more disrupted fibers in this area compared to those who progress after one year. And the number of fibers in the tumor disrupted cerebral regions could stratify patients with short-time progression.\\u003c/p\\u003e \\u003cp\\u003eResting-state fMRI has reported that gliomas can cause extensive functional damage and affect patient survival time \\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e, and the disruption of structural connectivity and topological changes in GBM are associated with patient survival \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e, indicating the importance of characterizing global neural connectivity. However, there is a problem with these studies, which is the impact of the mass effect of preoperative GBM, particularly in assessing the displacement or disruption of peritumoral fibers. Therefore, we screened GBM patients with the extent of resection at least 90% and a recovery period of about one month (the impact of surgery has basically disappeared, eg cerebral edema, mass effect caused by intra-surgical hemorrhage and inflammation.), explored the characteristics and clinical significance of brain structural connectivity. The results demonstrated widespread disruption of fiber connectivity around the surgical cavity, \\u0026zwnj;with patients progressing within one year exhibiting more severe fiber destruction\\u0026zwnj; compared to those progressing after one year. \\u0026zwnj;This suggests that the severity of fiber disruption may have the capability to stratify progression\\u0026zwnj; time of GBM .\\u003c/p\\u003e \\u003cp\\u003eOur study has important clinical implications. We found that the number of fibers in tumor and distant disrupted cerebral regions were negatively correlated with the KPS score before radiotherapy, indicating that the physical condition and quality of life of postoperative patients were related to the fiber connectivity status of their local and overall brain structure. However, the correlation between the fiber connectivity status of local and overall brain structures and the volume of enhanced lesions before radiotherapy no exist, which is opposite to the findings of Yiran Wei et al. that tumor volume leads to changes in brain tissue topology \\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. This may be related to our inclusion criteria, as effectively removed the mass effect of GBM may eliminate the topological changes of brain tissue. In addition, postoperative pre-radiotherapy higher enhancing tumour volume were significantly associated with shorter overall survival \\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e, but this relationship may no longer exist when extent to resection of the enhanced lesion\\u0026thinsp;\\u0026gt;\\u0026thinsp;90%.\\u003c/p\\u003e \\u003cp\\u003eThe peritumoral region is the key focus area postoperation of GBM \\u0026zwnj;due to its frequent association with progression \\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e\\u0026zwnj;. Compared to distant disruption and indirect disruption, \\u0026zwnj;the quantity of fibers in tumor disrupted cerebral regions surrounding the surgical cavity demonstrated a significant correlation with patients' time to progression. In addition, compared with traditional clinical and molecular factors such as sex, age, tumor volume, and MGMT methylation status, fiber connectivity measurement can provide better biomarkers for GBM stratification. We found that the number of fibers in tumor disrupted cerebral regions can predict short-term progression in patients after GBM surgery. Due to the remarkable heterogeneity of GBM, the development of quantitative prognostic markers is crucial for precise treatment. The structural connectivity confers a novel approach to investigate the systematic changes of neural connectivity in GBM \\u003csup\\u003e[2\\u003cspan additionalcitationids=\\\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. It could enable us to understand the interaction between tumour invasion and neural connectivity, which promises to stratify patients more precisely. Moreover, DTI imaging and analysis are relatively easy to implement in clinical practice.\\u003c/p\\u003e \\u003cp\\u003eOur study has some limitations. Firstly, old cerebrovascular lesions or demyelinating lesions can also lead to the disruption of fiber. Although our data does not include large lesions with a maximum diameter\\u0026thinsp;\\u0026ge;\\u0026thinsp;1cm, small lesions may also affect the statistics. Secondly, preoperative GBM can cause local and global changes in brain tissue topology \\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e. However, we did not conduct further graph theory analysis because the significance of analyzing only the topological changes in brain tissue after removing tumor effect may be small, and comparative analysis before and after surgery may have greater clinical significance. Thirdly, studies have reported the presence of functional areas around gliomas during direct intraoperative electrical stimulation at the first resection, but, at the second surgery these areas no longer had function, indicating neural plasticity during the growth of the lesion \\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. Due to the lack of DTI data for preoperative GBM, further research is needed on the neural plasticity changes before and after GBM surgery and its clinical value. Fourth, the study was conducted at a single center, and the heterogeneity in data collection and streamline-count biases require further external validation. We did not find a suitable external data set to valid our results, because there were few DTI examinations performed after GBM surgery (before radiotherapy).\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn conclusion, it remained widespread disruption to the structural connectivity after GBM surgery. The disruption of connectivity integrity was correlated with patient\\u0026rsquo;s progression time, especially in the tumor area around the surgical cavity. Therefore, studying fiber connectivity may provide a new and valuable tool for patient stratification and precise treatment after GBM surgery.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eAAL = Automatic Anatomical Labelling, AUC = area under the curve, DTI = diffusion tensor imaging, FLAIR = fluid attenuated inversion recovery, GBM = glioblastoma, KPS = Karnofsky performance status, ROC = Receiver operating characteristic, T2WI = T2-weighted imaging.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments:\\u0026nbsp;\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u0026nbsp;\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eThis study was funded by the Natural Scientific Foundation of China (82171916), \\u0026nbsp;Tianjin Medical Talents Project (TJSJMYXYC-D2-059), Tianjin Education Committee Research Project (2023KJ064, 2023KJ065), Tianjin Science and Technology Major Projects and Projects (24ZXGQSY00070), Tianjin Municipal Education Commission Scientific Research Program (2024ZD060), Joint Funds of the Natural Science Foundation of Tianjin (25JCLMJC00070), and Tianjin Key Medical Discipline Construction Project (TJYXZDXK-3-002A).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability:\\u0026nbsp;\\u003c/strong\\u003eData generated or analyzed during the study are available from the corresponding author by request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions:\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eQZF, SX, TH designed the study;\\u0026nbsp;\\u003c/strong\\u003eQZF, \\u003cstrong\\u003eXJS, XYJ collected the all data; QZF, XJS, XYJ, PW analyzed the data; PW, SX, TH supervised the study; QZF wrote the manuscript. All authors read and approved the final manuscript.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u0026nbsp;\\u003c/strong\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval and Consent to Participate:\\u0026nbsp;\\u003c/strong\\u003eThis study was approved by the Institutional Review Board of Tianjin huanhu Hospital (approval no. 2025-026) and conducted in accordance with the principles of the Declaration of Helsinki. The requirement for patient consent was waived owing to the retrospective study design.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHorbinski Craig NL, Burt P, Jana, et al. NCCN Guidelines\\u0026reg; Insights: Central Nervous System Cancers, Version 2.2022. 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J Neurosurg. 2016;124(5):1460\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003edoi.org/10.3171/2015.5.JNS142833\\u003c/span\\u003e\\u003cspan address=\\\"10.3171/2015.5.JNS142833\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medical-imaging\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmim\",\"sideBox\":\"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bmim/default.aspx\",\"title\":\"BMC Medical Imaging\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"glioblastoma, short-term progression, structural connectivity, DTI\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8614285/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8614285/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u0026zwnj;\\u003cb\\u003eBackground\\u003c/b\\u003e\\u0026zwnj;: While structural connectome analysis enables preoperative mapping of glioblastoma (GBM) infiltration, mass effect-induced distortion compromises the accuracy of peritumoral tract assessment. We aimed to investigate fiber disruption characteristics and predict short-term progression based on structural connectivity features after eliminating mass effect.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMethods\\u003c/b\\u003e: We retrospectively analyzed 113 GBM patients with \\u0026ge;\\u0026thinsp;90% resection and 65 healthy controls. Diffusion tensor imaging (DTI) data were processed to construct structural connectomes, which were segmented into three compartments relative to the resection cavity: Tumor disrupted cerebral regions, anatomically confined to FLAIR hyperintense areas and direct fiber disruption; Distant disrupted cerebral regions, outside FLAIR hyperintense areas but exhibiting direct fiber disruption; Indirect disrupted cerebral regions, remote from FLAIR lesions with indirect fiber disruption. The patterns of differential disruption across compartments and progression timelines were quantified, along with their correlations to the Karnofsky performance status (KPS). The Area Under the Curve (AUC) evaluated how well disrupted fibers predict progression time. Patients with fiber disruption counts exceeding the Youden index were classified as high-risk versus low-risk for progression, validated by Kaplan-Meier analysis and Chi-square test. Structural connectivity disruption were used to predict short-term progression via Cox regression.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eResults\\u003c/b\\u003e: After eliminating mass effects, widespread structural connectome disruption was observed. Among 49 within 1-year progressers, tumor-disrupted regions showed more severe fiber disruption than later-progressing patients (F\\u0026thinsp;=\\u0026thinsp;32.5, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Fiber disruption in tumor-disrupted compartment negatively correlated with pre-radiotherapy KPS score (r=-0.349, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and best predicted progression time (AUC\\u0026thinsp;=\\u0026thinsp;0.803, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). High-risk patients progressed faster (10 months) than low-risk patients (15 months) (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). 81% of low-risk and 71% of high-risk patients were correctly identified (χ\\u0026sup2;=30.29, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Incorporating structural connectivity disruption significantly improved multivariable Cox regression performance over clinical/imaging variables alone (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eConclusions\\u003c/b\\u003e: Structural connectivity quantitatively maps postoperative regional cerebral disruption in GBM. Fiber disruption within the tumor-disrupted compartment may identify patients for short-term progression.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Short-term progression risk stratification in glioblastoma using post-resection structural connectivity biomarkers\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-18 16:56:49\",\"doi\":\"10.21203/rs.3.rs-8614285/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-04-14T07:43:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-05T21:44:06+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-03T09:14:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-27T17:36:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-26T13:40:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"246571620281731140392669688214019950216\",\"date\":\"2026-02-23T20:45:25+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-23T18:58:32+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"124211431860950241532164962386424221334\",\"date\":\"2026-02-20T08:18:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-19T14:51:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"22608435121700950287882331865044135035\",\"date\":\"2026-02-19T11:01:40+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"227187358862309013890526576515381215837\",\"date\":\"2026-02-19T06:24:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-18T09:47:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"83271185682341380560060882956577386063\",\"date\":\"2026-02-16T10:15:16+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"68343575276087036950047070971929513886\",\"date\":\"2026-02-15T12:54:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"28491461270973763927347470818313027842\",\"date\":\"2026-02-13T21:04:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"311296281425479420881056086602396013736\",\"date\":\"2026-02-12T17:10:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-02-12T11:19:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-01-19T18:29:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-01-16T13:05:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-01-16T13:04:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Medical Imaging\",\"date\":\"2026-01-16T01:24:42+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-medical-imaging\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmim\",\"sideBox\":\"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bmim/default.aspx\",\"title\":\"BMC Medical Imaging\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"af388ab6-d53a-42e2-9ba7-3dce7e21f7da\",\"owner\":[],\"postedDate\":\"February 18th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-14T07:57:39+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-18 16:56:49\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8614285\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8614285\",\"identity\":\"rs-8614285\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}