Diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging of the non-enhancing peritumoral region predict overall survival in glioblastoma

Diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging of the non-enhancing peritumoral region predict overall survival in glioblastoma | 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 Diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging of the non-enhancing peritumoral region predict overall survival in glioblastoma Meilian Xiong, Jie Kang, Feifei Yu, Xiaoye Lin, Feng Wang, Shujie Yu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7532681/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To investigate the predictive value of diffusion-weighted imaging (DWI) and dynamic sensitive contrast perfusion-weighted imaging (DSC-PWI) parameters based on contrast-enhancing regions (CER) and non-enhancing peritumoral regions (NEPR) for overall survival (OS) in patients with IDH wild-type glioblastoma (GBM) after gross-total resection (GTR). Methods Adult patients with IDH wild-type GBM who underwent GTR and were histologically confirmed at our institution were retrospectively collected and followed up. Patients were categorized into two groups, short-term survivors (STS; OS ≦ 16 months, n = 33) and long-term survivors (LTS; OS > 16 months, n = 28). The relative minimum apparent diffusion coefficient (rADCmin-t, rADCmin-p) and relative maximum cerebral blood volume (rCBVmax-t, rCBVmax-p) in the CER and NEPR were measured and analyzed via independent samples t-test, ROC curves, and Spearman correlation. Results 61 patients were included, with 33 in the STS group (mean age: 59.55 ± 10.24 years, 21 males) and 28 in the LTS group (mean age: 54.96 ± 11.60 years, 19 males). Compared with the LTS, STS demonstrated lower rADCmin-p (1.35 ± 0.27 vs. 1.80 ± 0.32, p < 0.001) and higher rCBVmax-p (4.46 ± 2.34 vs. 2.17 ± 0.85, p 0.05). In contrast, there was no significant difference in rADCmin-t and rCBVmax-t of CER between the two groups (both p > 0.05). Conclusion Preoperative rADCmin-p and rCBVmax-p in NEPR serve as crucial imaging markers for predicting OS in patients with IDH-wildtype GBM after GTR. glioblastoma diffusion-weighted imaging dynamic susceptibility contrast-enhanced perfusion-weighted imaging overall survival peritumoral region Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Glioblastoma (GBM) is the most common and invasive primary malignant brain tumor in adults [ 1 , 2 ]. GBM is characterized by an extremely poor prognosis even with multimodal treatment including maximal safe surgical resection, radiation therapy and temozolomide (TMZ) chemotherapy. Even with such aggressive treatments, patients' median overall survival (OS) remains dismally low at approximately 12–16 months [ 3 – 5 ]. Such treatment resistance and poor prognosis are largely attributed to the significant intertumoral and intratumoral heterogeneity of GBM, which leads to differences in tumor invasiveness, treatment responsiveness, and ultimately results in significant survival differences [ 6 , 7 ]. This heterogeneity extends beyond the primary target of traditional therapies, namely the tumor contrast-enhancing region (CER). Tumor cells in GBM characteristically infiltrate diffusely into the surrounding brain parenchyma, forming a non-enhancing peritumoral region (NEPR) containing edema and infiltrating tumor cells [ 8 , 9 ]. Critically, even after successful gross total resection (GTR) of the CER, the residual NEPR remains a major site of tumor recurrence [ 10 ]. Cells within the NEPR, especially those located at the resection margins, typically exhibit greater proliferative and invasive potential than those in the CER core [ 11 , 12 ]. Therefore, the biological characteristics of NEPR are increasingly recognized as a key determinant of patient prognosis. Factors known to influence OS in GBM include age, sex, isocitrate dehydrogenase 1 (IDH1) mutation, O6-methylguanine methyltransferase (MGMT) gene promoter methylation, and extent of resection (EOR) [ 13 , 14 ]. The pursuit of supramaximal resection (i.e., resections that extend beyond the CER boundaries to areas of FLAIR abnormality) in clinical practice highlights the widely recognized importance of NEPR [ 15 , 16 ]. However, defining the optimal EOR for supramaximal resection within the NEPR relies heavily on the subjective judgment of the surgeon and lacks objective criteria [ 17 ]. Conventional MRI (cMRI), which relies on macroscopic changes and a certain threshold of tumor cell density, has fundamental limitations in its ability to noninvasively detect infiltrating tumors and their biological activity within the NEPR. Advanced functional MRI techniques, such as dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) and diffusion-weighted imaging (DWI), are expected to overcome this limitation by probing the tumor microenvironment [ 18 – 21 ]. In the NEPR, tumor-infiltrated areas exhibit high relative cerebral blood volume (rCBV) and low apparent diffusion coefficient (ADC) due to abnormal neovascularization and restricted diffusion of water molecules, while the vasogenic edema shows the opposite pattern. Therefore, quantitative analysis of rCBV and ADC characteristics in the NEPR enables noninvasive differentiation between tumor infiltration and edema. Although the potential of combined DSC-PWI and DWI for GBM grading has been demonstrated, the prognostic value of CER and NEPR of GBM in the context of GTR remains insufficiently explored [ 22 ]. Therefore, our retrospective study aims to provide an objective imaging basis for clinical assessment of tumor invasiveness and optimization of the extent of surgical resection by evaluating the prognostic significance of preoperative DWI and DSC-PWI parameters. Methods This study was approved by the Institutional Review Board of our hospital, and written informed consent was waived due to the retrospective nature. Patient population From March 2015 to December 2021, a total of 88 consecutive patients with GBM pathologically diagnosed by surgical resection were retrospectively collected. The inclusion criteria included: (1) age ≥ 18 years; (2) histological diagnosis of IDH-wild GBM according to the 2021 WHO classification; (3) available preoperative DWI, DSC-PWI, and cMRI including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1WI (CE-T1WI); (4) achievement of GTR (defined as complete resection of the CER); (5) postoperative MRI obtained within 72 hours or 2 weeks after surgery; (6) presence of NEPR. The exclusion criteria included: (1) incomplete or lack of preoperative or postoperative MRI scan ; (2) poor perfusion quality; (3) lack of NEPR; (4) suboptimal images with severe artifacts. Finally, 61 patients (mean age, 57.44 ± 11.04 years, 41 males) were enrolled in present study. The patient enrollment process of this study is shown in Fig. 1 . The included patients all underwent standard adjuvant chemoradiotherapy after surgery. Overall survival (OS) of the patients was calculated as the duration (in months) from the date of surgery to death or last follow-up visit. The patients were divided into two groups: long‑term survivors (LTS; OS > 16 months, n = 28) and short‑term survivors (STS; OS ≤ 16 months, n = 33) using the median OS (16 months) of our cohort as the discriminator. Image acquisition All MR images were obtained from two 3.0T MR scanners (Skyra/Verio, Siemens, Germany) with a 20-channel head coil. A small preload dose of gadobenate dimeglumine (Gd-BOPTA) was injected to reduce the effects of leakage before DSC-PWI images acquisition. During the first three phases, the images were acquired before injection of the contrast material to establish an unenhanced baseline. When reaching the fourth phase, a bolus of Gd-BOPTA was administered intravenously for the scan at a rate of 5ml/s and a standard dose of 0.1 mmol/kg, followed by a 20mL saline injected at the same rate. The detailed MRI protocols used are depicted in Table 1 . Table 1 MRI scanning parameters Parameters T2WI T2-FLAIR T1WI/CE-T1WI DWI DSC-PWI TR (ms) 6000 9000/8500 250 8200/4600 1500/1600 TE (ms) 96/125 94/81 2.48/2.46 102/65 30 FA (degree) 150/90 150 70 180 90 FOV (mm 2 ) 220×220 220×220 220×220 220×220 220×220 ST (mm) 5 5 5 5 5 b -value (s/mm 2 ) / / / 0,1000 / Matrix 384×384/ 320×320 256×256/ 320×320 256×256/ 320×320 192×192 128×128 TR, time of repeatation; TE, time of echo; FA, flip angle; FOV, field of view; ST, section thickness; T2WI, T2 weighted imaging; T2-FLAIR, T2-fluid attenuated inversion recovery; T1WI, T1 weighted imaging; CE-T1WI, contrast-enhanced T1 weighted imaging; DWI, diffusion-weighted imaging; DSC-PWI dynamic susceptibility contrast-enhanced perfusion-weighted imaging Image processing and analysis All cMRI features were assessed separately by two radiologists (with 10 and 3 years of experience in neuroradiology, respectively), who were unaware of the histopathological findings. When they disagreed, the images were re-evaluated by a senior neuroradiologist (with 33 years of experience), who made the final decisions about the classification of the image features. Imaging features evaluated included: (1) tumor location (frontal, parietal, temporal, occipital, and other sites); (2) proportion of necrosis (none, ≤ 33% or > 34%); (3) degree of edema; (4) enhancement patterns (ring-like, nodular or mixed enhancement); (5) definition of enhanced margin (well or poorly defined). Edema was defined as a non-enhancing region on CE-T1WI and high signal intensity outside the CER of tumor on T2WI and T2-FLAIR. Edema was scored based on its maximum distance from the margin of tumor on T2WI or T2-FLAIR: 0 indicating not apparent (≤ 1cm), 1 indicating mild to moderate (> 1cm and 2cm) edema [ 23 ]. The cMRI features above were defined in terms of the Visually Accessible Rembrandt Images (VASARI) imaging criteria.( https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project ) DSC-PWI were post-processed on a clinical workstation (Syngo.Via software, Siemens Healthcare). The CBV pseudo color maps were automatically reconstructed using the “single-compartment model” and the deconvolution algorithm of exogenous perfusion. ADC maps were calculated automatically by the MRI system. The regions of interest (ROIs) were independently drawn on the ADC and CBV maps by two neuroradiologists on the Siemens workstation. Five non-overlapping ROIs (0.2-0.4cm 2 ) were manually placed on the ADC maps in the CER (excluding any areas of hemorrhagic, cystic, necrosis, and vessels) and NEPR (defined as the hyperintense region in T2WI and FLAIR beyond the CET) with the visually lowest ADC values, respectively. The ROIs with the lowest ADC values were chosen as the minimum ADC values (ADCmin-t and ADCmin-p). To minimize individual variability, five ROIs of the same size were manually placed on the contralateral normal-appearing white matter (CNWM) on the ADC maps to obtain the mean values. The relative ADCmin value (rADCmin) was calculated by dividing the ADCmin by the mean ADC value of the corresponding CNWM. The maximum relative CBV values (rCBVmax) were measured on the CBV maps using the same way as the measurements of ADC values. The ROIs corresponding to the ADC and CBV were different. Statistical analysis All statistical analyses were conducted using SPSS software (version 27.0, SPSS), MedCalc statistical software (version 19.0.4, MedCalc), Graphpad Prism (version 8.0, GraphPad Software) and R software (version 4.1.0, http://www.Rproject.org ). The chi-square test and Fisher’s exact test were performed for comparisons in sex and cMRI features between the two groups. All continuous variables were tested for normality using quantile-quantile plot (Q-Q plot). The normally distributed continuous variables were represented by the mean ± standard deviation (SD). Spearman correlation analysis was made to reveal the correlations between OS and rADCmin and rCBVmax parameters, respectively. The independent samples t-test was conducted to compare the differences in age, rADCmin and rCBVmax between the groups. The receiver operating characteristic (ROC) curve and the binary logistic regression analysis were used to assess the prognostic performance of rADCmin-p and rCBVmax-p values. The area under the curve (AUC), specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and Youden index (YI) were further calculated. A pairwise comparisons of AUCs were conducted using the DeLong test with a Bonferroni correction to compare the superiority of MR parameters. Kaplan-Meier survival curves were drawn to compare the two groups in terms of overall survival. Interobserver agreements for cMRI characteristics and quantitative parameters was evaluated using the intraclass correlation coefficient (ICC) the Cohen's Kappa. p 0.75). Demographic and cMRI characteristics Table 2 summarizes the demographic and cMRI characteristics included in this study. None of the demographic and cMRI characteristics, such as age, sex, location, necrosis, edema, enhancement pattern, and definition of enhanced margin, reached statistically significant differences between the STS and LTS groups (all p > 0.05). Table 2 Comparison of demographic and cMRI characteristics between the STS and LTS groups STS(n = 33) LTS(n = 28) p- value Demography Sex (M/F) 21 / 12 19 / 9 0.730 Age (years) 59.55 ± 10.24 54.96 ± 11.60 0.107 Location 0.820 Frontal lobe 16 10 Parietal lobe 6 7 Temporal lobe 7 7 Occipital lobe 2 1 Others 2 3 Necrosis 0.160 Yes 26 26 None 7 2 Edema 0.196 ≤ 1 cm 0 0 > 1 cm and ≤ 2 cm 16 9 > 2 cm 17 19 Enhancement pattern 0.286 Ring-like 21 23 Nodular 4 2 Mixed 8 3 Definition of enhanced margin 1.000 Well defined 4 4 Poorly defined 29 24 Binary data are displayed by count numbers, and continuous data are presented as means ± standard deviation. cMRI, conventional MR imaging; STS, short‑term survivors; LTS, long‑term survivors Comparison of DWI and DSCPWI quantitative parameters between the STS and LTS groups We observed that the rADCmin-p values were positively correlated with OS (Spearman: r = 0.59, p < 0.001) and rCBVmax-p values were negatively correlated with OS (Spearman: r = − 0.68, p < 0.001) as shown in Table 3 . The results of the comparison in rADCmin and rCBVmax of the CER and NEPR are demonstrated in Fig. 2 and Table 4 . The rADCmin-p values were significantly lower in patients in the STS group than those in the LTS group ( p < 0.001). The rCBVmax-p values were significantly higher in STS group than in LTS group ( p 0.05). Representative cases are shown in Fig. 3 and Fig. 4 . Table 3 Spearman correlation between OS and rADCmin and rCBVmax parameters r p- value rADCmin-t vs OS 0.04 0.778 rADCmin-p vs OS 0.59 < 0.001* rCBVmax-t vs OS 0.04 0.787 rCBVmax-p vs OS −0.68 < 0.001* rADCmin-p, relative minimum apparent diffusion coefficient in the non-enhancing peritumoral region; rCBVmax-p, relative maximum cerebral blood volume in the non-enhancing peritumoral region; OS, overall survival; r, correlation coefficient *Significant difference ( p < 0.05) Table 4 Comparison of DWI and DSC PWI quantitative parameters between the STS and LTS groups STS(n = 33) LTS(n = 28) p- value rADCmin-t 1.05 ± 0.20 1.04 ± 0.23 0.801 rADCmin-p 1.35 ± 0.27 1.80 ± 0.32 < 0.001* rCBVmax-t 23.45 ± 11.16 25.49 ± 9.34 0.447 rCBVmax-p 4.46 ± 2.34 2.17 ± 0.85 < 0.001* Continuous data are presented as means ± standard deviation. DWI, diffusion-weighted imaging; DSC-PWI, dynamic susceptibility contrast-enhanced perfusion-weighted imaging; STS, short‑term survivors; LTS, long‑term survivors; rADCmin, relative minimum apparent diffusion coefficient; rCBVmax, relative maximum cerebral blood volume; rADCmin-t and rCBVmax-t, rADCmin and rCBVmax in the contrast-enhancing region of tumor, respectively; rADCmin-p and rCBVmax-p, rADCmin and rCBVmax in the non-enhancing peritumoral region, respectively *Significant difference ( p < 0.05) ROC Curve Analysis of the rADCmin-p and rCBVmax-p values to differentiate the STS and LTS groups ROC curve analysis was conducted to evaluate the prognostic performance of the DWI and DSC-PWI parameters that significantly differed between the two groups (rADCmin-p and rCBVmax-p). As shown in Fig. 5 and Table 5 , the rADCmin-p values for predicting OS were found to have an AUC of 0.856, sensitivity of 85.71%, specificity of 78.79%, and cut-off value of 1.47, respectively. The rCBVmax-p cut-off value was set at 2.60 with an AUC, sensitivity, and specificity of 0.832, 82.14%, and 78.79%, respectively. A combination model of rADCmin-p and rCBVmax-p showed the highest AUC, sensitivity, and specificity (0.909, 92.86%, and 81.82%, respectively). However, the pairwise comparisons of the above three AUCs did not reach significance ( p * AUC rADCmin−p VS AUC rCBVmax−p = 0.66, p * AUC rADCmin−p VS AUC rADCmin−p + rCBVmax−p = 0.10, p * AUC rCBVmax−p VS AUC rADCmin−p + rCBVmax−p = 0.08). Table 5 ROC curve analysis of the rADCmin-p and rCBVmax-p for differentiating the STS group and LTS groups Logistic regression models Cut-off value Sen(%) Spe(%) PPV(%) NPV(%) YI AUC rADCmin-p 1.47 85.71 78.79 77.40 86.70 0.65 0.856 rCBVmax-p 2.60 82.14 78.79 76.70 83.90 0.61 0.832 rADCmin-p + CBVmax-p 92.86 81.82 81.20 93.10 0.75 0.909 DWI, diffusion-weighted imaging; DSC-PWI, dynamic susceptibility contrast-enhanced perfusion-weighted imaging; STS, short‑term survivors; LTS, long‑term survivors; rADCmin, relative minimum apparent diffusion coefficient; rCBVmax, relative maximum cerebral blood volume; rADCmin-t and rCBVmax-t, rADCmin and rCBVmax in the contrast-enhancing region of tumor, respectively; rADCmin-p and rCBVmax-p, rADCmin and rCBVmax in the non-enhancing peritumoral region, respectively; YI, Youden index; Sen, sensitivity; Spe, specificity; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curves Kaplan-Meier Survival Analysis The Kaplan-Meier survival curves of the cohort dichotomized according to the median OS (n = 16 months) are illustrated in Fig. 6 . There was significant difference between the STS and LTS group (p < 0.001). As shown in Table 6 , the median OS of the STS and LTS groups were 9.00 months [95% CI, 6.29–11.71 months] and 30.00 months [95% CI, 20.60–39.40 months], respectively. Table 6 Kaplan-Meier survival analysis of the cohort dichotomized according to the median OS STS (n = 33) LTS(n = 28) p- value Mean OS(M) 8.91(7.07, 10.75) 41.97(29.75, 54.18) < 0.001* Median OS(M) 9.00(6.29, 11.71) 30.00(20.60, 39.40) Data in parentheses are the 95% confidence interval. OS, overall survival; STS, short‑term survivors; LTS, long‑term survivors *Significant difference ( p < 0.05) Discussion This study systematically evaluated the prognostic value of preoperative DWI and DSC-PWI parameters in the CER and NEPR of GBM. The results showed that rADCmin-p and rCBVmax-p of NEPR are key predictors of OS. The rADCmin-p was significantly lower in STS than that in LTS, while the rCBVmax-p was significantly higher (both p 0.05). This finding suggests that the functional imaging characteristics of NEPR are critical markers for evaluating the prognosis of GBM patients after GTR and provide a critical basis for imaging-guided extent of surgical resection. In this study, neither demographic characteristics nor cMRI characteristics (e.g., age, gender, tumor location, necrosis percentage, etc.) showed significant associations with OS, which differed from some previous studies [ 24 – 27 ]. This discrepancy may stem from the fact that this study strictly included patients with IDH wild-type GBM as defined by the 2021 WHO classification and excluded IDH mutant tumors with better prognosis and lower age [ 28 ]; also the small sample size and the high heterogeneity of GBM weakened the strength of association of demographic and morphological characteristics with prognosis. This finding further corroborates the unique value of advanced functional MRI techniques in overcoming morphological limitations and revealing tumor heterogeneity. ADC derived from DWI reflects the degree of diffusion restriction of water molecules, and its decrease has been shown to correlate strongly with increased tumor cell density[ 29 , 27 ]. In this study, we found that rADCmin-p was significantly lower and strongly positively correlated with OS in the NEPR of the STS group (r = 0.59, p < 0.001), which strongly demonstrated that the low ADC region mapped the foci of high-density tumor cell infiltration within the NEPR. This spatial heterogeneity stems from the invasive nature of GBM, i.e., tumor cells can infiltrate beyond the CER into the NEPR, mixing with vasogenic edema to form low ADC regions [ 30 ]. Chang et al. also noted that the amount of signal intensity reduction in the ADC map was proportional to the likelihood of tumor recurrence [ 31 ]. Notably, our results showed that rADCmin-t within the CER in the context of GTR was not associated with prognosis ( p > 0.05), which is inconsistent with the results of some previous studies. Choi et al. found that the 10th percentile of ADC in CER was negatively correlated with OS, but the above study included patients who did not undergo GTR, and the residual tumor burden in CER directly affected the prognosis [ 32 ]; while our study was strictly limited to the GTR population, and the parameters of CER lost their predictive significance after resection. Therefore, our study indicates that preoperative rADCmin-p can provide an objective basis for identifying regions with high tumor cell infiltration in NEPR. The rCBV obtained from DSC-PWI quantitatively reflects abnormal perfusion [ 29 ]. In the present study, rCBVmax-p of NEPR was significantly higher and strongly negatively correlated with OS in the STS (r = -0.68, p < 0.001). Histologically, infiltration of residual tumor cells in NEPR disrupts the vascular endothelium and leads to contrast agent leakage; in addition, the hyperpermeable and structurally disordered new blood vessels drive tumor progression through pathways such as vascular endothelial growth factor (VEGF), resulting in an increase in rCBV in NEPR [ 33 , 34 ]. This is consistent with the finding by Xing et al. that the rCBV in the NEPR of tumor recurrence areas is significantly higher than that in non-recurrence areas [ 35 ]. In addition, rCBVmax-t in CER also lost its prognostic value after GTR (p > 0.05). This result is consistent with the finding by Mills et al. that CBV in CER has no predictive significance, suggesting that there may be limitations in relying solely on functional imaging parameters of CER [ 36 ]. This differentiation between the prognostic value of NEPR and CER stems from the differences in their vascular microenvironment: the vasculature of CER is mostly of the mature type, whereas NEPR is dominated by neovascularization that promotes tumor invasion, and rADCmin-p reflects the difference in heterogeneity between the two. In addition, the combined model of rCBVmax-p and rADCmin-p demonstrated excellent predictive efficacy (AUC = 0.909). The critical values (rADCmin-p ≤ 1.47, rCBVmax-p ≥ 2.60) can be used as objective thresholds for preoperative identification of high-risk patients. Currently, there is a lack of objective criteria for the EOR for supramaximal resection of GBM, and this study suggests that rADCmin-p and rCBVmax-p can localize the "high-risk subregion" (low ADC, high CBV) of NEPR, providing a basis for expanding the EOR and thus reducing the risk of local recurrence. Our study has several limitations. Firstly, the study was based on retrospective data and had a small sample size. A larger sample size and prospective studies are needed to determine the reproducibility of the preliminary findings of this study. Secondly, the study's use of manually sketched ROIs for image analysis may be subjective. Finally, the MRI images in the study were from two 3.0 T MRI scanners, but we optimized the MRI sequences to minimize differences and calculated relative values to reduce potential bias due to individual differences. In conclusion, this study clarifies the prognostic predictive value of DWI and DSC-PWI in IDH wild-type GBM patients after GTR. Specifically, rADCmin-p and rCBVmax-p of NEPR are key imaging markers for predicting OS. Declarations All authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was funded by the National Natural Science Foundation of China (No. 82371905), Fujian Provincial Finance Project (No. 22SCZZX023), and Fujian Provincial Health Technology Project (No.2024CXA027). Author Contribution M. Xiong, J. Kang, F. Yu, and Z. Xing contributed to the study conception and design. Material preparation, data collection, and analysis were performed by X. Lin, F. Wang, S. Yu, and Y. Zhu. M. Xiong and D. Cao were responsible for data extraction and analysis and interpretation of the results. The first draft of the manuscript was written by J. Kang and F. Yu, and all authors read and approved the final manuscript. Acknowledgements Not applicable Human Ethics and Consent to Participate declarations : Not applicable Ethics Approval declaration : This study was approved by the relevant Institutional Review Board, and written informed consent was waived due to the retrospective nature. 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Clin Radiol 68:e689–e697 Chang PD, Chow DS, Yang PH, Filippi CG, Lignelli A (2017) Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images. AJR Am J Roentgenol 208:57–65 Choi YS, Ahn SS, Kim DW, Chang JH, Kang SG, Kim EH, Kim SH, Rim TH, Lee SK (2016) Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma. Radiology 281:175–184 Chryplewicz A, Scotton J, Tichet M, Zomer A, Shchors K, Joyce JA, Homicsko K, Hanahan D (2022) Cancer cell autophagy, reprogrammed macrophages, and remodeled vasculature in glioblastoma triggers tumor immunity. Cancer Cell 40:1111-27.e9 Watkins S, Robel S, Kimbrough IF, Robert SM, Ellis-Davies G, Sontheimer H (2014) Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells. Nat Commun 5:4196 Xing Z, Wang C, Yang W, She D, Yang X, Cao D (2024) Predicting glioblastoma recurrence using multiparametric MR imaging of non-enhancing peritumoral regions at baseline. Heliyon 10:e30411 Mills SJ, Patankar TA, Haroon HA, Balériaux D, Swindell R, Jackson A (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 27:853–858 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7532681","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517767562,"identity":"95801cb0-13a2-488b-bcd7-dffef625cec7","order_by":0,"name":"Meilian Xiong","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meilian","middleName":"","lastName":"Xiong","suffix":""},{"id":517767563,"identity":"f2add4c2-a764-4f4c-8f6d-5e45b42f5662","order_by":1,"name":"Jie Kang","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Kang","suffix":""},{"id":517767564,"identity":"93ee5ffb-3da8-4bd0-8f7c-8f973e273320","order_by":2,"name":"Feifei Yu","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Yu","suffix":""},{"id":517767567,"identity":"4a81467b-9958-4b1e-8b5f-e9050c407334","order_by":3,"name":"Xiaoye Lin","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoye","middleName":"","lastName":"Lin","suffix":""},{"id":517767569,"identity":"b51a738d-acac-46c6-a54b-57c7b7a77f0f","order_by":4,"name":"Feng Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical 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07:17:34","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113811,"visible":true,"origin":"","legend":"","description":"","filename":"cccb8a0fb8e646e28544243f6a0d07bc1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/d234ea28b6d6307f66b1f285.xml"},{"id":91955470,"identity":"778ce360-fe03-4e01-8f4a-a340e5371b50","added_by":"auto","created_at":"2025-09-23 07:09:34","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123425,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/b8b3f4102029050077394aaa.html"},{"id":91956595,"identity":"d9b9eca5-2a62-4a59-8558-539a799fb5a7","added_by":"auto","created_at":"2025-09-23 07:17:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":444277,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of patient enrollment\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/9fc6aaf49e8ae872dbbf25fa.png"},{"id":91955453,"identity":"755ca486-597c-41f6-92a6-63ae8f4ce869","added_by":"auto","created_at":"2025-09-23 07:09:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":359519,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots show rADCmin and rCBVmax for the two groups. Boundaries of boxes indicate 25th and 75th percentiles, and lines in boxes indicate medians. *ns \u0026gt; 0.05; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/7affbd3b6fa350ee0e8ca027.png"},{"id":91955454,"identity":"755d5df0-84cc-40a9-bca1-56bd07bd582d","added_by":"auto","created_at":"2025-09-23 07:09:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1164425,"visible":true,"origin":"","legend":"\u003cp\u003eA 41-year-old female patient diagnosed with GBM achieved an overall survival of 26 months after GTR. A and E Axial T2WI demonstrates a heterogeneous lesion with an ill-defined margin on the left frontal lobe, accompanied by obvious edema. B and F Contrast-enhanced axial T1WI reveals a ring-like enhancement pattern of the lesion, surrounded by a non-enhancing region. C-D The corresponding ADC map and color CBV image illustrate the CER of the tumor with a rADCmin-t value of 0.95 and a rCBVmax-t of 35.88. G-H The corresponding ADC map and color CBV image illustrate a rADCmin-p value of 1.94 and a rCBVmax-p value of 2.27 in the NEPR\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/366d4176026cf9fee14f4395.png"},{"id":91956596,"identity":"bcec4552-07ea-4a16-9f1f-cfd98c028523","added_by":"auto","created_at":"2025-09-23 07:17:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1140597,"visible":true,"origin":"","legend":"\u003cp\u003eA 61-year-old woman with GBM who only survived for 6 months after GTR. A and E Axial T2WI demonstrates heterogeneous high signal intensity on the right frontal-parietal lobe. B and F Contrast-enhanced axial T1WI reveals a ring-like pattern of enhancement. C-D The corresponding ADC map and color CBV image show a rADCmin-t value of 1.00 and a rCBVmax-t value of 16.30 in the CER. G-H The corresponding ADC map and correlative color CBV image show the NEPR with a rADCmin-p value of 0.82 and a rCBVmax-p value of 7.55\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/426473b7bcb2dd162577d6f8.png"},{"id":91955473,"identity":"e42136f4-f1cd-446c-9a9e-dd94661c86ab","added_by":"auto","created_at":"2025-09-23 07:09:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2478342,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves of the single and combined logistic regression models for differentiating the STS group and LTS groups\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/866ae680ebfee08a80995979.png"},{"id":91956597,"identity":"99878c97-f1ce-4f28-93c4-cd60541d6d74","added_by":"auto","created_at":"2025-09-23 07:17:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":422597,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves of the cohort dichotomized according to the median OS\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/1299b53a820fa71d2e4f97af.png"},{"id":93293997,"identity":"2a7a065b-fd77-447d-8d24-4bbe3782978c","added_by":"auto","created_at":"2025-10-11 09:53:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6869625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7532681/v1/bc3135d0-4ba8-48fb-9e6d-3a1682cccccf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging of the non-enhancing peritumoral region predict overall survival in glioblastoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GBM) is the most common and invasive primary malignant brain tumor in adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. GBM is characterized by an extremely poor prognosis even with multimodal treatment including maximal safe surgical resection, radiation therapy and temozolomide (TMZ) chemotherapy. Even with such aggressive treatments, patients' median overall survival (OS) remains dismally low at approximately 12\u0026ndash;16 months [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such treatment resistance and poor prognosis are largely attributed to the significant intertumoral and intratumoral heterogeneity of GBM, which leads to differences in tumor invasiveness, treatment responsiveness, and ultimately results in significant survival differences [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis heterogeneity extends beyond the primary target of traditional therapies, namely the tumor contrast-enhancing region (CER). Tumor cells in GBM characteristically infiltrate diffusely into the surrounding brain parenchyma, forming a non-enhancing peritumoral region (NEPR) containing edema and infiltrating tumor cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Critically, even after successful gross total resection (GTR) of the CER, the residual NEPR remains a major site of tumor recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Cells within the NEPR, especially those located at the resection margins, typically exhibit greater proliferative and invasive potential than those in the CER core [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, the biological characteristics of NEPR are increasingly recognized as a key determinant of patient prognosis.\u003c/p\u003e\u003cp\u003eFactors known to influence OS in GBM include age, sex, isocitrate dehydrogenase 1 (IDH1) mutation, O6-methylguanine methyltransferase (MGMT) gene promoter methylation, and extent of resection (EOR) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The pursuit of supramaximal resection (i.e., resections that extend beyond the CER boundaries to areas of FLAIR abnormality) in clinical practice highlights the widely recognized importance of NEPR [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, defining the optimal EOR for supramaximal resection within the NEPR relies heavily on the subjective judgment of the surgeon and lacks objective criteria [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Conventional MRI (cMRI), which relies on macroscopic changes and a certain threshold of tumor cell density, has fundamental limitations in its ability to noninvasively detect infiltrating tumors and their biological activity within the NEPR.\u003c/p\u003e\u003cp\u003eAdvanced functional MRI techniques, such as dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) and diffusion-weighted imaging (DWI), are expected to overcome this limitation by probing the tumor microenvironment [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the NEPR, tumor-infiltrated areas exhibit high relative cerebral blood volume (rCBV) and low apparent diffusion coefficient (ADC) due to abnormal neovascularization and restricted diffusion of water molecules, while the vasogenic edema shows the opposite pattern. Therefore, quantitative analysis of rCBV and ADC characteristics in the NEPR enables noninvasive differentiation between tumor infiltration and edema. Although the potential of combined DSC-PWI and DWI for GBM grading has been demonstrated, the prognostic value of CER and NEPR of GBM in the context of GTR remains insufficiently explored [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, our retrospective study aims to provide an objective imaging basis for clinical assessment of tumor invasiveness and optimization of the extent of surgical resection by evaluating the prognostic significance of preoperative DWI and DSC-PWI parameters.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This study was approved by the Institutional Review Board of our hospital, and written informed consent was waived due to the retrospective nature.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient population\u003c/h2\u003e\u003cp\u003eFrom March 2015 to December 2021, a total of 88 consecutive patients with GBM pathologically diagnosed by surgical resection were retrospectively collected. The inclusion criteria included: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) histological diagnosis of IDH-wild GBM according to the 2021 WHO classification; (3) available preoperative DWI, DSC-PWI, and cMRI including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1WI (CE-T1WI); (4) achievement of GTR (defined as complete resection of the CER); (5) postoperative MRI obtained within 72 hours or 2 weeks after surgery; (6) presence of NEPR. The exclusion criteria included: (1) incomplete or lack of preoperative or postoperative MRI scan ; (2) poor perfusion quality; (3) lack of NEPR; (4) suboptimal images with severe artifacts. Finally, 61 patients (mean age, 57.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.04 years, 41 males) were enrolled in present study. The patient enrollment process of this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe included patients all underwent standard adjuvant chemoradiotherapy after surgery. Overall survival (OS) of the patients was calculated as the duration (in months) from the date of surgery to death or last follow-up visit. The patients were divided into two groups: long‑term survivors (LTS; OS\u0026thinsp;\u0026gt;\u0026thinsp;16 months, n\u0026thinsp;=\u0026thinsp;28) and short‑term survivors (STS; OS\u0026thinsp;\u0026le;\u0026thinsp;16 months, n\u0026thinsp;=\u0026thinsp;33) using the median OS (16 months) of our cohort as the discriminator.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImage acquisition\u003c/h3\u003e\n\u003cp\u003eAll MR images were obtained from two 3.0T MR scanners (Skyra/Verio, Siemens, Germany) with a 20-channel head coil. A small preload dose of gadobenate dimeglumine (Gd-BOPTA) was injected to reduce the effects of leakage before DSC-PWI images acquisition. During the first three phases, the images were acquired before injection of the contrast material to establish an unenhanced baseline. When reaching the fourth phase, a bolus of Gd-BOPTA was administered intravenously for the scan at a rate of 5ml/s and a standard dose of 0.1 mmol/kg, followed by a 20mL saline injected at the same rate. The detailed MRI protocols used are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMRI scanning parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2WI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT2-FLAIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT1WI/CE-T1WI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDWI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDSC-PWI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9000/8500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8200/4600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1500/1600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTE (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96/125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94/81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.48/2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102/65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFA (degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150/90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFOV (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220\u0026times;220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220\u0026times;220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220\u0026times;220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e220\u0026times;220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e220\u0026times;220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e-value (s/mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMatrix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384\u0026times;384/\u003c/p\u003e\u003cp\u003e320\u0026times;320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256\u0026times;256/\u003c/p\u003e\u003cp\u003e320\u0026times;320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256\u0026times;256/\u003c/p\u003e\u003cp\u003e320\u0026times;320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e192\u0026times;192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128\u0026times;128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTR, time of repeatation; TE, time of echo; FA, flip angle; FOV, field of view; ST, section thickness; T2WI, T2 weighted imaging; T2-FLAIR, T2-fluid attenuated inversion recovery; T1WI, T1 weighted imaging; CE-T1WI, contrast-enhanced T1 weighted imaging; DWI, diffusion-weighted imaging; DSC-PWI dynamic susceptibility contrast-enhanced perfusion-weighted imaging\u003c/p\u003e\n\u003ch3\u003eImage processing and analysis\u003c/h3\u003e\n\u003cp\u003eAll cMRI features were assessed separately by two radiologists (with 10 and 3 years of experience in neuroradiology, respectively), who were unaware of the histopathological findings. When they disagreed, the images were re-evaluated by a senior neuroradiologist (with 33 years of experience), who made the final decisions about the classification of the image features. Imaging features evaluated included: (1) tumor location (frontal, parietal, temporal, occipital, and other sites); (2) proportion of necrosis (none, \u0026le; 33% or \u0026gt;\u0026thinsp;34%); (3) degree of edema; (4) enhancement patterns (ring-like, nodular or mixed enhancement); (5) definition of enhanced margin (well or poorly defined). Edema was defined as a non-enhancing region on CE-T1WI and high signal intensity outside the CER of tumor on T2WI and T2-FLAIR. Edema was scored based on its maximum distance from the margin of tumor on T2WI or T2-FLAIR: 0 indicating not apparent (\u0026le;\u0026thinsp;1cm), 1 indicating mild to moderate (\u0026gt;\u0026thinsp;1cm and \u0026lt;\u0026thinsp;2cm), and 2 indicating severe (\u0026gt;\u0026thinsp;2cm) edema [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The cMRI features above were defined in terms of the Visually Accessible Rembrandt Images (VASARI) imaging criteria.(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project\u003c/span\u003e\u003cspan address=\"https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDSC-PWI were post-processed on a clinical workstation (Syngo.Via software, Siemens Healthcare). The CBV pseudo color maps were automatically reconstructed using the \u0026ldquo;single-compartment model\u0026rdquo; and the deconvolution algorithm of exogenous perfusion. ADC maps were calculated automatically by the MRI system.\u003c/p\u003e\u003cp\u003eThe regions of interest (ROIs) were independently drawn on the ADC and CBV maps by two neuroradiologists on the Siemens workstation. Five non-overlapping ROIs (0.2-0.4cm\u003csup\u003e2\u003c/sup\u003e) were manually placed on the ADC maps in the CER (excluding any areas of hemorrhagic, cystic, necrosis, and vessels) and NEPR (defined as the hyperintense region in T2WI and FLAIR beyond the CET) with the visually lowest ADC values, respectively. The ROIs with the lowest ADC values were chosen as the minimum ADC values (ADCmin-t and ADCmin-p). To minimize individual variability, five ROIs of the same size were manually placed on the contralateral normal-appearing white matter (CNWM) on the ADC maps to obtain the mean values. The relative ADCmin value (rADCmin) was calculated by dividing the ADCmin by the mean ADC value of the corresponding CNWM. The maximum relative CBV values (rCBVmax) were measured on the CBV maps using the same way as the measurements of ADC values. The ROIs corresponding to the ADC and CBV were different.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using SPSS software (version 27.0, SPSS), MedCalc statistical software (version 19.0.4, MedCalc), Graphpad Prism (version 8.0, GraphPad Software) and R software (version 4.1.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The chi-square test and Fisher\u0026rsquo;s exact test were performed for comparisons in sex and cMRI features between the two groups. All continuous variables were tested for normality using quantile-quantile plot (Q-Q plot). The normally distributed continuous variables were represented by the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Spearman correlation analysis was made to reveal the correlations between OS and rADCmin and rCBVmax parameters, respectively. The independent samples t-test was conducted to compare the differences in age, rADCmin and rCBVmax between the groups. The receiver operating characteristic (ROC) curve and the binary logistic regression analysis were used to assess the prognostic performance of rADCmin-p and rCBVmax-p values. The area under the curve (AUC), specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and Youden index (YI) were further calculated. A pairwise comparisons of AUCs were conducted using the DeLong test with a Bonferroni correction to compare the superiority of MR parameters. Kaplan-Meier survival curves were drawn to compare the two groups in terms of overall survival. Interobserver agreements for cMRI characteristics and quantitative parameters was evaluated using the intraclass correlation coefficient (ICC) the Cohen's Kappa. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eInterobserver measurements for cMRI characteristics and quantitative parameters showed a good agreement (all, ICC / Kappa\u0026thinsp;\u0026gt;\u0026thinsp;0.75).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and cMRI characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the demographic and cMRI characteristics included in this study. None of the demographic and cMRI characteristics, such as age, sex, location, necrosis, edema, enhancement pattern, and definition of enhanced margin, reached statistically significant differences between the STS and LTS groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of demographic and cMRI characteristics between the STS and LTS groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTS(n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLTS(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (M/F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 / 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 / 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrontal lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParietal lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccipital lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNecrosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1 cm and \u0026le;\u0026thinsp;2 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnhancement pattern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRing-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDefinition of enhanced margin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell defined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorly defined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBinary data are displayed by count numbers, and continuous data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. cMRI, conventional MR imaging; STS, short‑term survivors; LTS, long‑term survivors\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComparison of DWI and DSCPWI quantitative parameters between the STS and LTS groups\u003c/h3\u003e\n\u003cp\u003eWe observed that the rADCmin-p values were positively correlated with OS (Spearman: r\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and rCBVmax-p values were negatively correlated with OS (Spearman: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of the comparison in rADCmin and rCBVmax of the CER and NEPR are demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The rADCmin-p values were significantly lower in patients in the STS group than those in the LTS group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The rCBVmax-p values were significantly higher in STS group than in LTS group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, no significant difference was shown in rCBVmax-t and rADCmin-t between the two groups (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Representative cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman correlation between OS and rADCmin and rCBVmax parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erADCmin-t vs OS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erADCmin-p vs OS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erCBVmax-t vs OS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erCBVmax-p vs OS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003erADCmin-p, relative minimum apparent diffusion coefficient in the non-enhancing peritumoral region; rCBVmax-p, relative maximum cerebral blood volume in the non-enhancing peritumoral region; OS, overall survival; r, correlation coefficient\u003c/p\u003e\u003cp\u003e*Significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of DWI and DSC PWI quantitative parameters between the STS and LTS groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTS(n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLTS(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erADCmin-t\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erADCmin-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erCBVmax-t\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e23.45\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e25.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erCBVmax-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eContinuous data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. DWI, diffusion-weighted imaging; DSC-PWI, dynamic susceptibility contrast-enhanced perfusion-weighted imaging; STS, short‑term survivors; LTS, long‑term survivors; rADCmin, relative minimum apparent diffusion coefficient; rCBVmax, relative maximum cerebral blood volume; rADCmin-t and rCBVmax-t, rADCmin and rCBVmax in the contrast-enhancing region of tumor, respectively; rADCmin-p and rCBVmax-p, rADCmin and rCBVmax in the non-enhancing peritumoral region, respectively\u003c/p\u003e\u003cp\u003e*Significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eROC Curve Analysis of the rADCmin-p and rCBVmax-p values to differentiate the STS and LTS groups\u003c/em\u003e\u003c/p\u003e\u003cp\u003eROC curve analysis was conducted to evaluate the prognostic performance of the DWI and DSC-PWI parameters that significantly differed between the two groups (rADCmin-p and rCBVmax-p). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the rADCmin-p values for predicting OS were found to have an AUC of 0.856, sensitivity of 85.71%, specificity of 78.79%, and cut-off value of 1.47, respectively. The rCBVmax-p cut-off value was set at 2.60 with an AUC, sensitivity, and specificity of 0.832, 82.14%, and 78.79%, respectively. A combination model of rADCmin-p and rCBVmax-p showed the highest AUC, sensitivity, and specificity (0.909, 92.86%, and 81.82%, respectively). However, the pairwise comparisons of the above three AUCs did not reach significance (\u003cem\u003ep\u003c/em\u003e* AUC\u003csub\u003erADCmin\u0026minus;p\u003c/sub\u003e VS AUC\u003csub\u003erCBVmax\u0026minus;p\u003c/sub\u003e = 0.66, \u003cem\u003ep\u003c/em\u003e* AUC\u003csub\u003erADCmin\u0026minus;p\u003c/sub\u003e VS AUC\u003csub\u003erADCmin\u0026minus;p + rCBVmax\u0026minus;p\u003c/sub\u003e = 0.10, \u003cem\u003ep\u003c/em\u003e* AUC\u003csub\u003erCBVmax\u0026minus;p\u003c/sub\u003e VS AUC\u003csub\u003erADCmin\u0026minus;p + rCBVmax\u0026minus;p\u003c/sub\u003e = 0.08).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC curve analysis of the rADCmin-p and rCBVmax-p for differentiating the STS group and LTS groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLogistic regression models\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSen(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpe(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePPV(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNPV(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erADCmin-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erCBVmax-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e76.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erADCmin-p\u0026thinsp;+\u0026thinsp;CBVmax-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDWI, diffusion-weighted imaging; DSC-PWI, dynamic susceptibility contrast-enhanced perfusion-weighted imaging; STS, short‑term survivors; LTS, long‑term survivors; rADCmin, relative minimum apparent diffusion coefficient; rCBVmax, relative maximum cerebral blood volume; rADCmin-t and rCBVmax-t, rADCmin and rCBVmax in the contrast-enhancing region of tumor, respectively; rADCmin-p and rCBVmax-p, rADCmin and rCBVmax in the non-enhancing peritumoral region, respectively; YI, Youden index; Sen, sensitivity; Spe, specificity; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the curves\u003c/p\u003e\n\u003ch3\u003eKaplan-Meier Survival Analysis\u003c/h3\u003e\n\u003cp\u003eThe Kaplan-Meier survival curves of the cohort dichotomized according to the median OS (n\u0026thinsp;=\u0026thinsp;16 months) are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. There was significant difference between the STS and LTS group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the median OS of the STS and LTS groups were 9.00 months [95% CI, 6.29\u0026ndash;11.71 months] and 30.00 months [95% CI, 20.60\u0026ndash;39.40 months], respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKaplan-Meier survival analysis of the cohort dichotomized according to the median OS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTS (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLTS(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean OS(M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.91(7.07, 10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.97(29.75, 54.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian OS(M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.00(6.29, 11.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.00(20.60, 39.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData in parentheses are the 95% confidence interval. OS, overall survival; STS, short‑term survivors; LTS, long‑term survivors\u003c/p\u003e\u003cp\u003e*Significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the prognostic value of preoperative DWI and DSC-PWI parameters in the CER and NEPR of GBM. The results showed that rADCmin-p and rCBVmax-p of NEPR are key predictors of OS. The rADCmin-p was significantly lower in STS than that in LTS, while the rCBVmax-p was significantly higher (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, there was no significant difference in rADCmin-t and rCBVmax-t of CER between the two groups (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This finding suggests that the functional imaging characteristics of NEPR are critical markers for evaluating the prognosis of GBM patients after GTR and provide a critical basis for imaging-guided extent of surgical resection.\u003c/p\u003e\u003cp\u003eIn this study, neither demographic characteristics nor cMRI characteristics (e.g., age, gender, tumor location, necrosis percentage, etc.) showed significant associations with OS, which differed from some previous studies [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This discrepancy may stem from the fact that this study strictly included patients with IDH wild-type GBM as defined by the 2021 WHO classification and excluded IDH mutant tumors with better prognosis and lower age [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; also the small sample size and the high heterogeneity of GBM weakened the strength of association of demographic and morphological characteristics with prognosis. This finding further corroborates the unique value of advanced functional MRI techniques in overcoming morphological limitations and revealing tumor heterogeneity.\u003c/p\u003e\u003cp\u003eADC derived from DWI reflects the degree of diffusion restriction of water molecules, and its decrease has been shown to correlate strongly with increased tumor cell density[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, we found that rADCmin-p was significantly lower and strongly positively correlated with OS in the NEPR of the STS group (r\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which strongly demonstrated that the low ADC region mapped the foci of high-density tumor cell infiltration within the NEPR. This spatial heterogeneity stems from the invasive nature of GBM, i.e., tumor cells can infiltrate beyond the CER into the NEPR, mixing with vasogenic edema to form low ADC regions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Chang et al. also noted that the amount of signal intensity reduction in the ADC map was proportional to the likelihood of tumor recurrence [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, our results showed that rADCmin-t within the CER in the context of GTR was not associated with prognosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which is inconsistent with the results of some previous studies. Choi et al. found that the 10th percentile of ADC in CER was negatively correlated with OS, but the above study included patients who did not undergo GTR, and the residual tumor burden in CER directly affected the prognosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; while our study was strictly limited to the GTR population, and the parameters of CER lost their predictive significance after resection. Therefore, our study indicates that preoperative rADCmin-p can provide an objective basis for identifying regions with high tumor cell infiltration in NEPR.\u003c/p\u003e\u003cp\u003eThe rCBV obtained from DSC-PWI quantitatively reflects abnormal perfusion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the present study, rCBVmax-p of NEPR was significantly higher and strongly negatively correlated with OS in the STS (r = -0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Histologically, infiltration of residual tumor cells in NEPR disrupts the vascular endothelium and leads to contrast agent leakage; in addition, the hyperpermeable and structurally disordered new blood vessels drive tumor progression through pathways such as vascular endothelial growth factor (VEGF), resulting in an increase in rCBV in NEPR [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This is consistent with the finding by Xing et al. that the rCBV in the NEPR of tumor recurrence areas is significantly higher than that in non-recurrence areas [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, rCBVmax-t in CER also lost its prognostic value after GTR (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This result is consistent with the finding by Mills et al. that CBV in CER has no predictive significance, suggesting that there may be limitations in relying solely on functional imaging parameters of CER [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This differentiation between the prognostic value of NEPR and CER stems from the differences in their vascular microenvironment: the vasculature of CER is mostly of the mature type, whereas NEPR is dominated by neovascularization that promotes tumor invasion, and rADCmin-p reflects the difference in heterogeneity between the two.\u003c/p\u003e\u003cp\u003eIn addition, the combined model of rCBVmax-p and rADCmin-p demonstrated excellent predictive efficacy (AUC\u0026thinsp;=\u0026thinsp;0.909). The critical values (rADCmin-p\u0026thinsp;\u0026le;\u0026thinsp;1.47, rCBVmax-p\u0026thinsp;\u0026ge;\u0026thinsp;2.60) can be used as objective thresholds for preoperative identification of high-risk patients. Currently, there is a lack of objective criteria for the EOR for supramaximal resection of GBM, and this study suggests that rADCmin-p and rCBVmax-p can localize the \"high-risk subregion\" (low ADC, high CBV) of NEPR, providing a basis for expanding the EOR and thus reducing the risk of local recurrence.\u003c/p\u003e\u003cp\u003eOur study has several limitations. Firstly, the study was based on retrospective data and had a small sample size. A larger sample size and prospective studies are needed to determine the reproducibility of the preliminary findings of this study. Secondly, the study's use of manually sketched ROIs for image analysis may be subjective. Finally, the MRI images in the study were from two 3.0 T MRI scanners, but we optimized the MRI sequences to minimize differences and calculated relative values to reduce potential bias due to individual differences.\u003c/p\u003e\u003cp\u003eIn conclusion, this study clarifies the prognostic predictive value of DWI and DSC-PWI in IDH wild-type GBM patients after GTR. Specifically, rADCmin-p and rCBVmax-p of NEPR are key imaging markers for predicting OS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (No. 82371905), Fujian Provincial Finance Project (No. 22SCZZX023), and Fujian Provincial Health Technology Project (No.2024CXA027).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM. Xiong, J. Kang, F. Yu, and Z. Xing contributed to the study conception and design. Material preparation, data collection, and analysis were performed by X. Lin, F. Wang, S. Yu, and Y. Zhu. M. Xiong and D. Cao were responsible for data extraction and analysis and interpretation of the results. The first draft of the manuscript was written by J. Kang and F. Yu, and all authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cb\u003eHuman Ethics and Consent to Participate declarations\u003c/b\u003e: Not applicable\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthics Approval declaration\u003c/b\u003e: This study was approved by the relevant Institutional Review Board, and written informed consent was waived due to the retrospective nature.\u003c/p\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Price M, Neff C, Cioffi G, Waite KA, Kruchko C, Barnholtz-Sloan JS (2022) CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015\u0026ndash;2019. 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AJNR Am J Neuroradiol 27:853\u0026ndash;858\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"glioblastoma, diffusion-weighted imaging, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, overall survival, peritumoral region","lastPublishedDoi":"10.21203/rs.3.rs-7532681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7532681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo investigate the predictive value of diffusion-weighted imaging (DWI) and dynamic sensitive contrast perfusion-weighted imaging (DSC-PWI) parameters based on contrast-enhancing regions (CER) and non-enhancing peritumoral regions (NEPR) for overall survival (OS) in patients with IDH wild-type glioblastoma (GBM) after gross-total resection (GTR).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAdult patients with IDH wild-type GBM who underwent GTR and were histologically confirmed at our institution were retrospectively collected and followed up. Patients were categorized into two groups, short-term survivors (STS; OS\u0026thinsp;≦\u0026thinsp;16 months, n\u0026thinsp;=\u0026thinsp;33) and long-term survivors (LTS; OS\u0026thinsp;\u0026gt;\u0026thinsp;16 months, n\u0026thinsp;=\u0026thinsp;28). The relative minimum apparent diffusion coefficient (rADCmin-t, rADCmin-p) and relative maximum cerebral blood volume (rCBVmax-t, rCBVmax-p) in the CER and NEPR were measured and analyzed via independent samples t-test, ROC curves, and Spearman correlation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e61 patients were included, with 33 in the STS group (mean age: 59.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24 years, 21 males) and 28 in the LTS group (mean age: 54.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.60 years, 19 males). Compared with the LTS, STS demonstrated lower rADCmin-p (1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 vs. 1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher rCBVmax-p (4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34 vs. 2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUCs of rADCmin-p and rCBVmax-p for OS prediction were 0.856 and 0.832, respectively, with the combined models achieving the highest AUC (0.909, \u003cem\u003ep\u003c/em\u003e \u0026gt;0.05). In contrast, there was no significant difference in rADCmin-t and rCBVmax-t of CER between the two groups (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePreoperative rADCmin-p and rCBVmax-p in NEPR serve as crucial imaging markers for predicting OS in patients with IDH-wildtype GBM after GTR.\u003c/p\u003e","manuscriptTitle":"Diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging of the non-enhancing peritumoral region predict overall survival in glioblastoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:09:29","doi":"10.21203/rs.3.rs-7532681/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13092c38-d917-472b-abda-136d6bfcea99","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-11T09:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:09:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7532681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7532681","identity":"rs-7532681","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}