Nomogram for distinguishing glioblastoma from solitary brain metastasis involving the subependymal zone based on apparent diffusion coefficient and conventional MRI features

Nomogram for distinguishing glioblastoma from solitary brain metastasis involving the subependymal zone based on apparent diffusion coefficient and conventional MRI features | 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 Nomogram for distinguishing glioblastoma from solitary brain metastasis involving the subependymal zone based on apparent diffusion coefficient and conventional MRI features Ping Wang, Li Han, Shengdan Liu, Linling Wang, Dawei Liao, Xiaofei Lu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8543692/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Accurately distinguishing glioblastoma (GBM) from solitary brain metastasis (SBM) involving the subventricular zone (SVZ) preoperatively is highly important but challenging in actual clinical practice. This study investigated the value of the relative apparent diffusion coefficient (rADC) of the SVZ and peritumoral edema, in combination with conventional magnetic resonance imaging (MRI) features, for distinguishing between these two diseases. Methods A total of 119 patients with GBM and SBM showing SVZ (+) on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images were included. These patients were further categorized into two subgroups: subgroup 1 (T2-FLAIR and T1CE SVZ (+)) and subgroup 2 (T2-FLAIR SVZ (+) but T1CE SVZ (-)). The conventional MRI features and rADC values of the SVZ and peritumoral edema were compared. Diagnostic efficacy was evaluated by calculating the area under the receiver operating characteristic curves, whereas clinical applicability was analysed using nomograms. Results Combining rADC values from the SVZ and peritumoral edema with conventional MRI features demonstrated good predictive performance, with areas under the curve (AUC) of 0.879 for the entire group, 0.869 for subgroup 1, and 0.886 for subgroup 2. The C-indices for the nomograms were 0.859 for the entire group, 0.819 for subgroup 1, and 0.886 for subgroup 2. The calibration curves of the nomograms closely aligned with the ideal curves, and the decision curve analysis indicated a greater overall net benefit. Conclusion Combining rADC values from the SVZ and peritumoral edema with conventional MRI features enables noninvasive preoperative differentiation between GBM and SBM with SVZ (+). The nomograms are useful tools for predicting GBM and SBM with SVZ (+). Glioblastoma Solitary brain metastasis Subependymal zone Magnetic resonance imaging Apparent diffusion coefficient Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Glioblastoma (GBM) is a prevalent primary malignant tumor of the central nervous system that constitutes approximately 14% of all primary brain tumors [ 1 ]. Brain metastasis is a frequent secondary malignant tumor affecting the central nervous system in adults, with an estimated 20%-40% of all malignant tumors capable of spreading to the brain as they progress [ 2 ]. Both of these processes may involve the subependymal zone (SVZ) [ 1 – 5 ]. When they present as a solitary mass involving the SVZ during initial medical visits, the clinical symptoms and imaging features are similar, often leading to frequent misdiagnoses. However, there are significant differences in the clinical management of these two diseases. The treatment of GBM involves maximal safe surgical resection of the disease, followed by radiotherapy and chemotherapy [ 4 , 6 ]. For solitary brain metastasis (SBM), a systematic examination is necessary to search for primary lesions and to perform clinical staging before making further treatment decisions [ 7 ]. The current gold standard for diagnosing GBM and SBM with SVZ (+) is pathological examination of tissue samples. However, the invasiveness of this procedure restricts its clinical application. Therefore, it is important to noninvasively distinguish GBM from SBM with the SVZ (+) prior to surgery. Traditionally, SVZ (+) has been defined as an enhancement within a 5-mm area near the lateral ventricle on contrast-enhanced T1-weighted imaging (T1CE) [ 3 , 8 – 9 ]. However, there is an ongoing debate regarding the traditional definition of the SVZ (+). The enhancement on T1CE only indicates the disruption of the blood-brain barrier and the formation of tumor-associated vessels. Thus, it does not accurately delineate the actual boundary of GBM, especially for the nonenhanced portion of GBM [ 5 ]. A recent study proposed integrating T2-FLAIR and T1CE images to define the SVZ (+) of GBM, finding that this method can provide improved prognostic ability [ 5 ]. Consequently, this study integrates T2-FLAIR and T1CE images to classify the SVZ (+). Research has indicated that MRI features such as prominent vessels passing through the lesion, the edema ratio, and the ratio of peritumoral edema to the tumor area are useful for distinguishing between GBM and SBM [ 10 – 11 ]. However, the limitation is that these assessments are significantly influenced by the observer's subjective bias and lack quantifiable measures. Diffusion-weighted imaging is an MRI technique that enables the quantitative assessment of water molecule diffusion within biological tissues. Previous research has reported that the apparent diffusion coefficient (ADC) value of peritumoral edema can assist in differentiating between GBM and SBM [ 12 – 17 ]. However, there are some limitations. For instance, delineating the region of interest (ROI) in the peritumoral area at an equal distance from the tumor core poses a challenge in routine clinical practice. To simplify the measurement process, this study introduced the ADC values of the SVZ affected by tumors. To our knowledge, few studies have investigated the differential diagnostic value of rADC values from the SVZ and peritumoral edema, in combination with conventional MRI features, for distinguishing between GBM and SBM with SVZ (+). We aimed to investigate the value of the rADC values of the SVZ and peritumoral edema, in combination with conventional MRI features, for distinguishing GBM from SBM with SVZ (+) according to various definition criteria. Materials and methods Study design and patients Data from patients with GBM confirmed by surgical pathology and SBM confirmed by either surgical pathology or clinical follow-up were retrospectively collected at our hospital from January 2020 to February 2025. The exclusion criteria were as follows: (I) GBM with multiple intracranial lesions; (II) patients who had previously undergone surgery, radiotherapy, or other treatments before MRI examination; (III) patients with a low signal‒to‒noise ratio or significant artifacts affecting observation; (IV) patients without an SVZ (+); (V) patients with inconsistent assessment of the SVZ (+) by observers; (VI) patients without edema around the enhancing lesion; and (VII) patients with incomplete data. The detailed patient selection flowchart is depicted in Fig. 1 . The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (No. KY2023041). Informed consent was waived in the study for retrospective analysis. Image acquisition All patients were scanned via a 1.5T magnetic resonance scanner (Achieva, Philips Health care). The MRI sequences included the following: T1-weighted imaging (T1WI): repetition time (TR): 488.1 ms, echo time (TE): 15.0 ms, layer thickness: 5.5 mm, field of view (FOV): 23.0 cm × 23.0 cm, matrix: 256 × 256; T2-weighted imaging (T2WI): TR: 3750.5 ms, TE: 100 ms, layer thickness: 5.5 mm, FOV: 23.0 cm × 23.0 cm, matrix: 256 × 256; T2-FLAIR: TR: 8500 ms, TE: 100 ms, inversion time: 2400 ms, layer thickness 5.5 mm, FOV: 23.0 cm × 23.0 cm, matrix: 256 × 256; diffusion-weighted imaging: TR: 2680.5 ms, TE: 98.8 ms, layer thickness: 5.5 mm, FOV: 23.0 cm × 23.0 cm, matrix: 128 × 128, and b values of 0 and 1000 s/mm 2 . T1CE: TR: 156.2 ms, TE: 2.4 ms, layer thickness: 5.5 mm. Gadopentetate dimeglumine (Magenev, Bayer, Germany) was injected via the cubital vein at a dose of 0.1 mmol/kg and an injection flow rate of 1.0 mL/s. Image analysis Two senior radiologists, each with 8 and 10 years of experience in the field, performed a blinded evaluation of the SVZ (+) in patients with GBM and SBM. Referencing the previous study, the SVZ (+) was defined as having high signal intensity on T2-FLAIR or T1CE images within a 5 mm area adjacent to the lateral ventricular wall [ 5 ]. Additionally, since patients with SVZ (+) on T1CE images also exhibited SVZ (+) on T2-FLAIR images, patients with SVZ (+) on T2-FLAIR images were further subdivided into two subgroups on the basis of the combined evaluation of T1CE and T2-FLAIR images: subgroup 1: patients with SVZ (+) on T2-FLAIR and T1CE; subgroup 2: patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE. Patients with inconsistent assessments of the SVZ (+) by the two observers were excluded. Both observers also assessed the conventional MRI features of GBM and SBM with the SVZ (+) in a blinded manner. Any discrepancies were resolved through consultation. The conventional MRI features evaluated included the following: region of the SVZ (+) (frontal horn, body, occipital horn, temporal horn), side of the lesion center (right, central/bilateral, left), enhancement quality (mild/minimal, marked/avid), proportion of enhancement (≥ 50%, < 50%), proportion of necrosis (≥ 50%, < 50%), cysts (presence, absence), thickness of enhancing margin (thin, thick/solid, none), definition of enhancing margin (well-defined, poorly defined), prominent blood vessels passing through the lesion (presence, absence), edema across the midline (presence, absence), hemorrhage (presence, absence), diffusion restriction (presence, absence), pial invasion (presence, absence), cortical involvement (presence, absence), deep white matter (WM) invasion (presence, absence), enhancing tumor across the midline (presence, absence), major diameter, and edema ratio [ 10 , 18 , 19 – 22 ]. The region of the SVZ (+) was evaluated at the largest section of the SVZ (+) on T2-FLAIR images. The cysts were defined as round or oval-shaped regions within the tumor, exhibiting high T2WI and low T1WI signal intensities, similar to those of cerebrospinal fluid [ 19 ]. The thickness of the enhancing margin was classified as thin if it was a regular and thin rim. It was considered thick if most of the edges were nodular and/or thick. If there was only solid enhancement without an edge enhancement rim, it was defined as none [ 19 ]. Diffusion restriction was defined as the appearance of low signal intensity in the tumor on the ADC map. The major diameter of the tumor was measured as the longest diameter observed on T1CE images, whereas the edema ratio was calculated as the ratio of the longest diameter of the peritumoral edma on T2WI to the longest diameter of the tumor on T1CE. Using the automatically generated ADC map, regions of interest (ROIs) referring to the T1CE or T2-FLAIR images were manually drawn on the ADC map at the largest section of the SVZ (+), peritumoral edema, and contralateral normal white matter (cNAWM), avoiding areas of cysts, necrosis, calcification, and hemorrhage. No fewer than three ROIs were placed, each with an area of 15–25 mm². A schematic diagram of ROI placement is shown in Fig. 2 . To eliminate the effects of age on the ADC values and minimize measurement errors, the mean apparent diffusion coefficient (ADCmean), maximum apparent diffusion coefficient (ADCmax), and minimum apparent diffusion coefficient (ADCmin) of the SVZ and peritumoral edema were divided by their corresponding values in the cNAWM. This process yielded normalized ADC values: the relative apparent diffusion coefficients of the SVZ (rADCmean-s, rADCmax-s, and rADCmin-s) and the relative apparent diffusion coefficient of peritumoral edema (rADCmean-e, rADCmax-e, and rADCmin-e). The conventional MRI features and rADC values were measured and evaluated in the same manner in 50 patients by a radiologist (with 10 years of experience), with an interval of at least 1 month to evaluate intraobserver agreement. Pathology analysis Both surgical resection and craniotomy biopsy samples were subjected to pathological examination to determine whether the tumor was a glioma or brain metastasis. The isocitrate dehydrogenase (IDH) gene locus was detected in glioma patients via Sanger sequencing. When no mutation was identified, the patient was diagnosed with IDH-wild type [ 23 ]. Gliomas with a pathological classification of grade 4 and wild-type IDH were diagnosed as GBM [ 6 ]. Statistical analysis Statistical analysis was performed via R (version 4.2.1, http://www.R-project.org ) software. Kappa tests and intraclass correlation coefficient (ICC) analyses were performed on data collected by two observers. When the kappa coefficient and ICC value for inter- or intraobserver reliability exceeded 0.75, the data consistency was deemed good. The measurement data are presented as the means ± standard deviations. An independent samples t test was applied if the measurement data conformed to a normal distribution; otherwise, the Mann‒Whitney U test was utilized. Categorical data are presented as frequencies (percentages), with intergroup comparisons conducted via the chi-square test. Statistically significant parameters were included in the multifactorial logistic regression to create joint prediction models. Receiver operating characteristic (ROC) curves were plotted to distinguish between GBM and SBM with SVZ (+). The nomograms, calibration curves and decision curves for the pivotal parameters with an AUC greater than 0.6 were plotted. The performance of the nomogram was assessed in the entire cohort, subgroup 1 and subgroup 2. A P value < 0.05 was regarded as statistically significant. Results Clinical data of the participants The clinical data of the included GBM and SBM patients are summarized in Table 1 . The average age of the GBM group was younger than that of the SBM group (P = 0.011), whereas the difference in sex distribution was not statistically significant (P = 0.075). Table 1 Clinical data of participants Characteristics GBM (n = 79) SBM (n = 40) Z/χ 2 value P value Age (years) 57.05 ± 13.36 62.90 ± 9.00 -2.546 0.011 Sex, n (%) 3.162 0.075 Male 44 (55.7) 29 (72.5) Female 35 (44.3) 11 (27.5) Primary tumor, n (%) - - Lung cancer - 32 (80.0) Rectal cancer - 3 (7.5) Breast Cancer - 1 (2.5) Thyroid cancer - 1 (2.5) Hepatocarcinoma 1 (2.5) Uncertain source - 2 (5.0) Chi-square test and Mann‒Whitney U test were used; GBM, glioblastoma; SBM, solitary brain metastasis. Consistency analysis The kappa values for inter- and intraobserver reliability regarding the region of the SVZ (+), side of lesion center, enhancement quality, proportion of enhancement, proportion of necrosis, cysts, thickness of enhancing margin, definition of enhancing margin, prominent blood vessels passing through the lesion, edema across the midline, hemorrhage, diffusion restriction, pial invasion, cortical involvement, deep WM invasion, and enhancing tumor across the midline ranged from 0.796 to 0.983 and 0.796 to 0.992, respectively. The ICC values for the inter- and intraobserver reliability of the major diameter, edema ratio, rADCmean-s, rADCmax-s, rADCmin-s, rADCmean-e, rADCmax-e, and rADCmin-e ranged from 0.853 to 0.979 and 0.931 to 0.982, respectively. The interobserver and intraobserver reliability of the conventional MRI features and rADC values were good, as shown in Supplementary Table A.1 and A.2. Comparison of conventional MRI features between GBM and SBM with SVZ (+) The thickness of the enhancing margin, prominent blood vessels passing through the lesion, deep WM invasion, major diameter and edema ratio were significantly different between GBM and SBM with SVZ (+) on T2-FLAIR images (P < 0.05). When performing subgroup analysis, the differences in the thickness of the enhancing margin, prominent blood vessels passing through the lesion, major diameter, and edema ratio between GBM and SBM were statistically significant in subgroup 1, whereas the differences in prominent blood vessels passing through the lesion, deep WM invasion, major diameter, and edema ratio between GBM and SBM were statistically significant in subgroup 2 (P < 0.05). These findings are illustrated in Table 2 and Fig. 3 . Table 2 Conventional MRI features and rADC values comparison of the GBM and SBM with SVZ (+) Parameters T2-FLAIR SVZ (+) P value T2-FLAIR and T1CE SVZ (+) P value T2-FLAIR SVZ (+) but T1CE SVZ (-) P value GBM (n = 79) SBM (n = 40) GBM (n = 51) SBM (n = 15) GBM (n = 28) SBM (n = 25) Region of the SVZ (+) 0.947 0.708 0.932 Frontal horn 13(16.5) 8(20.0) 7(13.7) 2(13.3) 6(21.4) 6(24.0) Body 32(40.5) 16(40.0) 19(37.3) 4(26.7) 13(46.4) 12(48.0) Occipital horn 9(11.4) 5(12.5) 5(9.8) 3(20.0) 4(14.3) 2(8.0) Temporal horn 25(31.6) 11(27.5) 20(39.2) 6(40.0) 5(17.9) 5(20.0) Side of lesion center 0.557 0.173 0.685 Right 38(48.1) 24(60.0) 25(49.0) 11(73.3) 13(46.4) 13(52.0) Center/Bilateral 3(3.8) 1(2.5) 3(5.9) 1(6.7) 0(0.0) 0(0.0) Left 38(48.1) 15(37.5) 23(45.1) 3(20.0) 15(53.6) 12(48.0) Enhancement quality 1.000 0.406 1.000 Mild/Minimal 2(2.5) 1(2.5) 1(2.0) 1(6.7) 1(3.6) 0(0.0) Marked/Avid 77(97.5) 39(97.5) 50(98.0) 14(93.3) 27(96.4) 25(100.0) Proportion of enhancement 0.942 0.310 0.610 ≥50% 44(55.7) 22(55.0) 28(54.9) 6(40.0) 16(57.1) 16(64.0) <50% 35(44.3) 18(45.0) 23(45.1) 9(60.0) 12(42.9) 9(36.0) Proportion of necrosis 0.850 0.310 0.305 ≥50% 37(46.8) 18(45.0) 23(45.1) 9(60.0) 14(50.0) 9(36.0) <50% 42(53.2) 22(55.0) 28(54.9) 6(40.0) 14(50.0) 16(64.0) Cysts 0.855 0.727 0.384 Presence 29(36.7) 14(35.0) 23(45.1) 6(40.0) 6(21.4) 8(32.0) Absence 50(63.3) 26(65.0) 28(54.9) 9(60.0) 22(78.6) 17(68.0) Thickness of enhancing margin 0.015 0.047 0.385 Thin 1(1.2) 5(12.5) 0(0.0) 2(13.3) 1(3.5) 3(12.0) Thick/Solid 68(86.1) 27(67.5) 46(90.2) 11(73.4) 22(78.6) 16(64.0) None 10(12.7) 8(20.0) 5(9.8) 2(13.3) 5(17.9) 6(24.0) Definition of enhancing margin 0.402 1.000 0.098 Well-defined 76(96.2) 37(92.5) 48(94.1) 15(100.0) 28(100.0) 22(88.0) Poorly defined 3(3.8) 3(7.5) 3(5.9) 0(0.0) 0(0.0) 3(12.0) Prominent blood vessels passing through the lesion <0.001 0.010 <0.001 Presence 44(55.7) 6(15.0) 26(51.0) 2(13.3) 18(64.3) 4(16.0) Absence 35(44.3) 34(85.0) 25(49.0) 13(86.7) 10(35.7) 21(84.0) Edema across the midline 0.079 0.807 0.148 Presence 21(26.6) 5(12.5) 14(27.5) 3(20.0) 7(25.0) 2(8.0) Absence 58(73.4) 35(87.5) 37(72.5) 12(80.0) 21(75.0) 23(92.0) Hemorrhage 0.059 0.160 0.184 Presence 38(48.1) 12(30.0) 24(47.1) 4(26.7) 14(50.0) 8(32.0) Absence 41(51.9) 28(70.0) 27(52.9) 11(73.3) 14(50.0) 17(68.0) Diffusion restriction 0.261 - 0.597 Presence 78(98.7) 38(95.0) 51(100.0) 15(100.0) 27(96.4) 23(92.0) Absence 1(1.3) 2(5.0) 0(0.0) 0(0.0) 1(3.6) 2(8.0) Pial invasion 0.650 0.062 0.374 Presence 29(36.7) 13(32.5) 20(39.2) 2(13.3) 9(32.1) 11(44.0) Absence 50(63.3) 27(67.5) 31(60.8) 13(86.7) 19(67.9) 14(56.0) Cortical involvement 0.745 0.112 1.000 Presence 67(84.8) 33(82.5) 40(78.4) 8(53.3) 27(96.4) 25(100.0) Absence 12(15.2) 7(17.5) 11(21.6) 7(46.7) 1(3.6) 0(0.0) Deep WM invasion 0.005 0.331 0.023 Presence 45(57.0) 12(30.0) 31(60.8) 7(46.7) 14(50.0) 5(20.0) Absence 34(43.0) 28(70.0) 20(39.2) 8(53.3) 14(50.0) 20(80.0) Enhancing tumor across the midline 1.000 0.852 1.000 Presence 8(10.1) 4(10.0) 7(13.7) 3(20.0) 1(3.6) 1(4.0) Absence 71(89.9) 36(90.0) 44(86.3) 12(80.0) 27(96.4) 24(96.0) Major diameter (cm) 5.24 ± 1.42 3.99 ± 1.27 <0.001 5.70 ± 1.30 4.43 ± 1.22 0.001 4.42 ± 1.28 3.72 ± 1.24 0.047 Edema ratio 1.64 ± 0.70 2.39 ± 1.04 <0.001 1.44 ± 0.28 1.94 ± 0.63 0.009 2.00 ± 1.04 2.65 ± 1.16 0.008 rADCmean-s 1.46 ± 0.34 1.76 ± 0.38 <0.001 1.37 ± 0.34 1.53 ± 0.34 0.136 1.60 ± 0.31 1.90 ± 0.33 0.001 rADCmax-s 1.43 ± 0.33 1.65 ± 0.37 0.001 1.35 ± 0.32 1.46 ± 0.34 0.248 1.56 ± 0.30 1.76 ± 0.35 0.033 rADCmin-s 1.49 ± 0.36 1.94 ± 0.49 <0.001 1.41 ± 0.35 1.61 ± 0.43 0.091 1.64 ± 0.32 2.13 ± 0.42 <0.001 rADCmean-e 2.12 ± 0.38 2.13 ± 0.41 0.887 2.15 ± 0.38 1.97 ± 0.43 0.121 2.06 ± 0.38 2.22 ± 0.38 0.126 rADCmax-e 2.08 ± 0.38 2.05 ± 0.40 0.689 2.11 ± 0.38 1.91 ± 0.45 0.087 2.02 ± 0.37 2.14 ± 0.35 0.252 rADCmin-e 2.15 ± 0.39 2.26 ± 0.50 0.174 2.18 ± 0.40 2.06 ± 0.46 0.336 2.10 ± 0.39 2.39 ± 0.48 0.021 Chi-square test, independent samples t test and Mann‒Whitney U test were used. MRI, magnetic resonance imaging; SVZ, subventricular zone; GBM, glioblastoma; SBM, solitary brain metastasis; WM, white matter; .rADCmean-s, relative mean apparent diffusion coefficient of the subependymal zone; rADCmax-s, relative maximum apparent diffusion coefficient of the subependymal zone; rADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone; rADCmean-e, relative mean apparent diffusion coefficient of periotumoral edema; rADCmax-e, relative maximum apparent diffusion coefficient of periotumoral edema; rADCmin-e, relative minimum apparent diffusion coefficient of periotumoral edema. Comparison of the rADC values between GBM and SBM with SVZ (+) The rADCmean-s, rADCmax-s, and rADCmin-s were significantly different between GBM and SBM with SVZ (+) on T2-FLAIR images (P 0.05), whereas the rADCmean-s, rADCmax-s, rADCmin-s, and rADCmin-e were significantly different in subgroup 2 (P < 0.05) (Table 2 and Fig. 3 ) . Efficacy of conventional MRI features and rADC values for distinguishing GBM from SBM with SVZ (+) The ROC curves for distinguishing GBM from SBM with SVZ (+) are presented in Tables 3 and Fig. 4 . ROC curve analysis revealed that the rADCmin-s, major diameter and rADCmin-s exhibited the highest area under the curve (AUC) values of 0.766, 0.774, and 0.831, respectively, for distinguishing GBM from SBM in the T2-FLAIR SVZ (+) group, subgroup 1, and subgroup 2 during individual diagnosis, whereas the combined analysis achieved AUC values of 0.879, 0.869, and 0.886, respectively. Table 3 Diagnostic performance for differentiating GBM from SBM with SVZ (+) Parameters Cut off AUC 95% CI Sensitivity Specificity T2-FLAIR SVZ (+) group Thickness of enhancing margin - 0.487 0.397–0.577 0.200 0.873 Prominent blood vessels passing through the lesion - 0.703 0.625–0.782 0.850 0.557 Deep WM invasion - 0.635 0.544–0.725 0.700 0.570 Major diameter 4.880 0.760 0.667–0.854 0.825 0.658 Edema ratio 2.055 0.753 0.648–0.859 0.600 0.924 rADCmean-s 1.765 0.729 0.628–0.829 0.625 0.823 rADCmax-s 1.670 0.678 0.571–0.784 0.550 0.810 rADCmin-s 1.895 0.766 0.669–0.864 0.600 0.887 Combination 0.394 0.879 0.809–0.949 0.911 0.775 Subgroup 1 Thickness of enhancing margin - 0.458 0.324–0.591 0.133 0.902 Prominent blood vessels passing through the lesion - 0.688 0.575–0.801 0.867 0.510 Major diameter 4.880 0.774 0.626–0.922 0.733 0.765 Edema ratio 2.115 0.743 0.575–0.911 0.467 1.000 Combination 0.094 0.869 0.773–0.966 1.000 0.588 Subgroup 2 Prominent blood vessels passing through the lesion - 0.741 0.625–0.858 0.840 0.643 Deep WM invasion - 0.650 0.526–0.774 0.800 0.500 Major diameter 4.335 0.684 0.536–0.833 0.760 0.643 Edema ratio 2.055 0.711 0.558–0.864 0.680 0.821 rADCmean-s 1.765 0.748 0.611–0.885 0.760 0.714 rADCmax-s 1.795 0.667 0.518–0.816 0.480 0.821 rADCmin-s 1.865 0.831 0.716–0.945 0.800 0.786 rADCmin-e 1.985 0.699 0.555–0.843 0.880 0.500 Combination 0.595 0.886 0.776–0.995 0.800 0.964 T2-FLAIR, T2-weighted fluid attenuated inversion recovery; SVZ, subventricular zone; GBM, glioblastoma; SBM, solitary brain metastasis; WM, white matter; AUC, area under the curve; CI, confidence interval; rADCmean-s, relative mean apparent diffusion coefficient of the subependymal zone; rADCmax-s, relative maximum apparent diffusion coefficient of the subependymal zone; rADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone; rADCmin-e, relative minimum apparent diffusion coefficient of periotumoral edema. Development and performance of nomograms for differentiating GBM from SBM The nomograms, calibration curves and decision curves based on the parameters with AUCs greater than 0.6 are shown in Fig. 5 . The C indices for the nomograms were 0.859 for the entire group, 0.819 for subgroup 1, and 0.886 for subgroup 2. The calibration curve of the nomograms closely aligned with the ideal curve, and the decision curve analysis indicated a greater overall net benefit. The optimal cut-off values were determined to be 128 points for the entire group, 63 points for subgroup 1, and 63 points for subgroup 2, corresponding to a hazard level of 0.5. All patients were subsequently categorized into either the GBM or SBM group based on the risk stratification. Discrimination between the two groups was significant across the entire group, subgroup 1, and subgroup 2 (P < 0.001), as shown in Table 4 . The risk classifier indicated that the nomogram had an accuracy of 88.2%, a sensitivity of 96.2%, and a specificity of 72.5% for the entire group. For subgroup 1, these values were 87.9%, 98.0%, and 53.3%, respectively. In subgroup 2, the accuracy was 86.8%, the sensitivity was 89.3%, and the specificity was 84.0%. Table 4 Conventional MRI features and rADC values comparison of the GBM and SBM with SVZ (+) T2-FLAIR SVZ (+) (n = 119) P value T2-FLAIR and T1CE SVZ (+) (n = 66) P value T2-FLAIR SVZ (+) but T1CE SVZ (-)(n = 53) P value GBM SBM GBM SBM GBM SBM Risk of GBM <0.001 <0.001 <0.001 High risk 76 (96.2) 11 (27.5) 50 (98.0) 7 (46.7) 25 (89.3) 4 (16.0) Low risk 3 (3.8) 29 (72.5) 1 (2.0) 8 (53.3) 3 (10.7) 21 (84.0) GBM, glioblastoma; SBM, solitary brain metastasis; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging; T2-FLAIR, T2-weighted fluid attenuated inversion recovery; T1CE, contrast-enhanced T1-weighted imaging; SVZ, subventricular zone. Discussion This study focused on the diagnostic value of the rADC values from SVZ and peritumoral edema, combined with conventional MRI features, for distinguishing GBM from SBM with SVZ (+). Considering the controversy in the definition of the SVZ (+), we integrated T2-FLAIR and T1CE images to classify the SVZ (+). Then, patients with SVZ (+) on T2-FLAIR images were further categorized into two subgroups: subgroup 1 (T2-FLAIR and T1CE SVZ (+)) and subgroup 2 (T2-FLAIR SVZ (+) but T1CE SVZ (-)). This study revealed that conventional MRI features, including prominent blood vessels passing through the lesion, major diameter and the edema ratio, were significantly different between GBM and SBM with SVZ (+), as defined by the three definition criteria. The rADC values, including the rADCmean-s, rADCmax-s, and rADCmin-s, differed between GBM and SBM in the T2-FLAIR SVZ (+) group and subgroup 2, whereas none of the rADC values were significantly different between GBM and SBM in subgroup 1. Furthermore, the nomograms demonstrated strong predictive value, with C-indices ranging from 0.819 to 0.886, based on the three defined criteria. In this study, GBM patients with SVZ (+) were younger than those who were SBM, which was consistent with the findings of Sirén et al. [ 24 ]. The lesions in the GBM patient with SVZ (+) tended to exhibit prominent blood vessels passing through the lesion, longer major diameters and smaller peritumoral edema ratios, which was largely consistent with the findings of Voicu et al. [ 10 ] and Maurer et al. [ 21 ]. The reasons may be as follows: the rapid growth of GBM necessitates a rich blood supply of new capillaries, resulting in vessels that are coarser than those of SBM [ 25 – 27 ]. Therefore, vessels in the GBM are easier to detect. The new capillaries in GBM closely resemble the vasculature of normal brain tissue, maintaining vascular continuity and preserving the function of the blood‒brain barrier [ 27 – 28 ]. In contrast, the new capillaries in SBM reflect the vasculature of their tissue of origin, often displaying disrupted structures and interrupted continuity, resulting in a lack of blood‒brain barrier function [ 27 ]. This results in significantly greater permeability of new capillaries in SBM than in GBM, enhancing the risk of extensive peritumoral edema. Furthermore, this study revealed that the major diameter of lesions in SBM was shorter than that in GBM, resulting in a higher peritumoral edema ratio for SBM than for GBM. Zhang et al. [ 18 ] reported that SBM often affects the subcortical WM, manifesting as a uniformly smooth-enhancing ring on T1CE, whereas GBM tends to affect the deep WM, distinguished by an irregularly thickened, unevenly enhanced ring on T1CE. This study revealed similar results, which may be related to the aggressive growth patterns of GBM. The study found that the rADC values in the SVZ were significantly different between the two tumor types in the T2-FLAIR SVZ (+) group and subgroup 2, whereas no statistically significant difference in rADC values was observed in subgroup 1. We speculated that the reasons may be as follows: the T2-FLAIR SVZ (+) group and subgroup 2 included patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE images. In GBM patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE images, the SVZ contains glioma stem cells that migrate from the GBM mass and tumor cells that infiltrate along white matter fibre bundles, leading to increased cell density and decreased ADC values in the SVZ [ 4 , 16 ]. However, in SBM patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE images, the SVZ exhibits only vasogenic edema without an increase in tumor cells; thus, the cell density does not increase, and the ADC values do not decrease [ 17 , 29 ]. Consequently, the relative rADC values of the SVZ in GBM patients are lower than those in SBM patients in the T2-FLAIR SVZ (+) group and subgroup 2. Subgroup 1 consisted of patients who were SVZ (+) on T2-FLAIR and T1CE images. When the SVZ is affected on T1CE images, it is speculated that the diffusion of water molecules in the SVZs of both GBM and SBM resembles that in the tumor parenchyma, characterized by numerous malignant cells, high cell density, and narrow intercellular space. This results in restricted water molecule diffusion and decreased ADC values in both GBM and SBM patients [ 12 – 16 ]. Consequently, the rADC values of the SVZ are not effective in differentiating GBM from SBM in subgroup 1. Additionally, in contrast to the rADC values of the SVZ, the majority of the peritumoral rADC values were incapable of distinguishing GBM from SBM with SVZ (+) in this study, which was inconsistent with the findings of Papageorgiou et al. [ 30 ]. The following speculations have been made: for the convenience of routine clinical practice, this study defined the peritumoral region as a random area of peritumoral edema, whereas Papageorgiou et al. [ 30 ] defined it as the area within 1 cm of the enhanced tumor edge. Previous studies reported that the degree of peritumoral infiltration of GBM decreases with increasing distance from the tumor core [ 16 ]. This may impact the differential diagnostic value of the rADC value of peritumoral edema in this study. In the next step, we plan to group peritumoral edema on the basis of the distance from the enhanced tumor to further validate the results of this study. In both the T2-FLAIR SVZ (+) group and subgroup 2, the optimal parameter for distinguishing GBM from SBM was rADCmin-s, which was similar to the findings of Ke et al. [ 31 ], suggesting that rADCmin may be more sensitive for the detection of water molecule diffusion within tissue structures. The reason could be that the ADCmin values within the ROIs represent the most densely packed tumor cells within the measurement range, thereby minimizing the interference of tumor heterogeneity [ 31 ]. In our study, when the rADCmean-s, rADCmax-s, and rADCmin-s were less than 1.765, 1.670, and 1.895, respectively, there was a greater likelihood of GBM in the T2-FLAIR SVZ (+) group. Additionally, when rADCmean-s, rADCmax-s, rADCmin-s, and rADCmin-e were less than 1.765, 1.795, 1.865, and 1.985, respectively, there was a greater likelihood of GBM in subgroup 2. When multiple parameters were used for joint prediction, the diagnostic efficacy was greater than that of a single parameter, suggesting that combined diagnosis is beneficial for reducing diagnostic bias and improving diagnostic efficacy. When delineating the SVZ (+) using T2-FLAIR images, the combined diagnosis performance was superior to that of the SVZ (+) typically defined by T1CE. After classifying the SVZ (+) by integrating T2-FLAIR and T1CE images, the combined diagnosis exhibited the highest diagnostic performance in subgroup 2 (AUC = 0.886). Additionally, easy-to-use diagnostic nomograms were developed to differentiate between GBM and SBM patients with SVZ (+), as defined by various criteria in our study. The diagnostic nomograms demonstrated a favourable predictive value. Through the nomogram, clinicians can noninvasively distinguish between GBM and SBM patients with SVZ (+) prior to surgery in routine clinical practice, thereby aiding in the development of treatment strategies and the evaluation of patient prognosis. Given that this was a single-center, retrospective study and that the sample size of SBM patients with SVZ (+) was relatively small, especially within subgroup 1, this could introduce bias into the results. The findings of this study necessitate further validation through a multicenter prospective cohort study with a larger sample size, which will be the focus of our future research. The limitations of this study were as follows: (I) This was a single-center retrospective study, and the sample size of SBM patients with SVZ (+) was relatively small, especially within subgroup 1; (II) the peritumoral area was not grouped according to the distance from the tumor core, which might impact the differential diagnostic value of rADC values in peritumoral edema; and (III) the use of a 1.5T MRI scanner and manual ROI placement may affect image resolution and the accuracy of the ADC value. For future research, we plan to utilize high-field-strength MRI scanners in conjunction with a semiautomatic ROI segmentation technique to validate our findings in a larger cohort of patients. Conclusion In conclusion, the combination of rADC values from the SVZ and peritumoral edema with conventional MRI features aids in distinguishing GBM from SBM with SVZ(+), as defined by different criteria. Our nomograms are useful tools for predicting GBM and SBM with SVZ(+). Abbreviations MRI, magnetic resonance imaging GBM, glioblastoma SBM, solitary brain metastasis SVZ, subependymal zone rADC, relative apparent diffusion coefficient rADCmean-s, relative mean apparent diffusion coefficient of the subependymal zone rADCmax-s, relative maximum apparent diffusion coefficient of the subependymal zone rADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone rADCmean-e, relative mean apparent diffusion coefficient of peritumoral edema rADCmax-e, relative maximum apparent diffusion coefficient of peritumoral edema rADCmin-e, relative minimum apparent diffusion coefficient of peritumoral edema WM, white matter cNAWM, contralateral normal white matter T1WI, T1-weighted imaging T2WI, T2-weighted imaging T2-FLAIR, T2-weighted fluid-attenuated inversion recovery T1CE, contrast-enhanced T1-weighted imaging TR, repetition time TE, echo time FOV, field of view ROI, region of interest ROC, receiver operating characteristic AUC, area under the curve CI, confidence interval IDH, isocitrate dehydrogenase ICC, intraclass correlation coefficient Declarations Consent to participate This study is a retrospective study. All data used in this study was anonymized Consent for publication Not applicable. Declaration of Competing Interest The authors declare that they have no competing interests. Ethical approval The study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (No. KY2023041). Written informed consent from patients was waived due to the study's retrospective design. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Appendices Supplementary data was shown in Table A.1 and Table A.2. Funding This work was supported by the Sichuan Science and Technology Program of China (Grant No. 2022YFS0616), the Luzhou Science and Technology Program (Grant No. 2025MYF031), and the Project for Doctors of Affiliated Hospital, Southwest Medical University (Grant No. 2018–17129). Author Contribution The conception and design of the study: PW, LH, GXC. Administrative support: GXC, LY. Acquisition of data: PW, LH, DWL, XFL. Analysis and interpretation of data: PW, LH, SDL, LLW, DWL. Drafting the article: All authors. Revising it critically for important intellectual content: PW, GXC. Final approval of the version to be submitted: All authors. Acknowledgements None. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Price M, Ryan K, Shoaf ML, et al. Childhood, adolescent, and adult primary brain and central nervous system tumor statistics for practicing healthcare providers in neuro-oncology, CBTRUS 2015–2019. Neurooncol Pract. 2023;11(1):5–25. Martínez-Espinosa I, Serrato JA, Ortiz-Quintero B. MicroRNAs in Lung Cancer Brain Metastasis. Int J Mol Sci. 2024;25(19):10325. Pan S, Chen Y, Zhao S, et al. MRI features and prognostic evaluation in patients with subventricular zone-contacting IDH-wild-type glioblastoma. Radiol Oncol. 2025;59(2):329–36. Li S, Dong L, Pan Z, et al. 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Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade. Neurosurg Rev. 2023;46(1):83. Additional Declarations No competing interests reported. Supplementary Files Appendices.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Editor invited by journal 16 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 16 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8543692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589088360,"identity":"571de33b-2093-43f3-a377-9315f7be2095","order_by":0,"name":"Ping Wang","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Wang","suffix":""},{"id":589088361,"identity":"d944bcab-e325-4567-93eb-3a9db51efe67","order_by":1,"name":"Li Han","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Han","suffix":""},{"id":589088362,"identity":"2257913a-51cb-476b-9634-481864db19c9","order_by":2,"name":"Shengdan Liu","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shengdan","middleName":"","lastName":"Liu","suffix":""},{"id":589088363,"identity":"6bddf8bc-b0b9-4cc5-becf-26fef25dcf9e","order_by":3,"name":"Linling Wang","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linling","middleName":"","lastName":"Wang","suffix":""},{"id":589088365,"identity":"f751e417-e396-4e1d-8f3a-f2dfb67ba2f5","order_by":4,"name":"Dawei Liao","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Liao","suffix":""},{"id":589088367,"identity":"1fbbb0e6-5324-4a4a-a66f-632e5aef83a9","order_by":5,"name":"Xiaofei Lu","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Lu","suffix":""},{"id":589088370,"identity":"c11e77f2-7410-4f01-a961-de956212507e","order_by":6,"name":"Lu Yang","email":"","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yang","suffix":""},{"id":589088371,"identity":"3271e414-37c6-40f8-bea7-8c478625d3e5","order_by":7,"name":"Guangxiang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie2QsYoCQQyGI4GxGZg2gvgMAUELX2YGQSthyy3kbsFjLU6x9h0sLC13Gdhq7KfUxlq7u+7GK67c3VK4+YpAwv8REoBI5DXpXO8JDARiedHpspWCQ2IYqm4+5YurWimCJIPZb9yod/3A5rzaHZiIbefo9Sg1mQC1/tS1Cvmb5oQtstczb059IHc+1ipMttBhiwhK5Y0TYbJoUsqskGwle5MnJscWilqVWVCot7ECWinkxRSJ5xyejKRdJRtvUTs3/r6nk/cc1ePxlS4Har2tV4D0s67+elkf/11TPOtbczASiUT+Lz8ulUwm7bwL4QAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Radiology, the Affiliated Hospital, Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guangxiang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-07 16:23:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8543692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8543692/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102462959,"identity":"21453bcd-3d7e-4d28-93d3-c645b9a21de6","added_by":"auto","created_at":"2026-02-12 01:12:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":771400,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient recruitment; GBM, glioblastoma; SBM, solitary brain metastasis; SVZ, subventricular zone; T2-FLAIR, T2-weighted fluid attenuated inversion recovery; T1CE, contrast-enhanced T1-weighted imaging; rADC, relative apparent diffusion coefficient.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/e43450bd884263ab89472933.jpeg"},{"id":102462955,"identity":"f84fa146-ccc9-4938-a529-f9157ed3dff8","added_by":"auto","created_at":"2026-02-12 01:12:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":366836,"visible":true,"origin":"","legend":"\u003cp\u003eExample of ROI placement. With respect to the T2-FLAIR (A) and T1CE (B) images, three ROIs were placed on the ADC map (C) in the following areas: the SVZ (+) (yellow circle), the peritumoral edema (black circle), and the contralateral normal-appearing white matter (blue circle and white circle). ROI, region of interest; T1CE, contrast-enhanced T1-weighted imaging; T2-FLAIR, T2-weighted fluid attenuated inversion recovery; SVZ, subventricular zone; ADC, apparent diffusion coefficient.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/08e695ae05ff16dc7e4812ea.jpeg"},{"id":102462956,"identity":"819bdd0b-3c2e-4845-8734-162e8882d0de","added_by":"auto","created_at":"2026-02-12 01:12:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1130115,"visible":true,"origin":"","legend":"\u003cp\u003eA-E: A GBM patient with SVZ (+) on T2-FLAIR and T1CE images. Axial T1WI (A), T2WI (B), and T2-FLAIR (C) images show a mass in the left frontal lobe, with a prominent blood vessel (white arrow) and cyst. The tumor's solid component shows low signal intensity on the ADC map (D) and thick enhancing margin on T1CE (E). F-J: A SBM patient with SVZ (+) on T2-FLAIR and T1CE. Axial T1WI (F), T2WI (G), and T2-FLAIR (H) images show a mass with a cyst in the right temporal‒occipital lobe. The tumor's solid component shows low signal intensity on the ADC map (I) and thick enhancing margin on T1CE (J). K-O: A GBM patient with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE. Axial T1WI (K), T2WI (L), and T2-FLAIR (M) images show a mass in the left temporal-insular lobe, with a prominent blood vessel (yellow arrow). The tumor's solid component displays low signal intensity on the ADC map (N) and thick enhancing margin on T1CE (O). P-T: A SBM patient with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE. Axial T1WI (P), T2WI (Q), and T2-FLAIR (R) images show a mass with cysts in the right parietooccipital lobe. The tumor's solid component exhibits low signal intensity on the ADC map (S) and thin enhancing margin on T1CE (T). GBM, glioblastoma; SBM, solitary brain metastasis; SVZ, subventricular zone; T1CE, contrast-enhanced T1-weighted imaging; T2-FLAIR, T2-weighted fluid attenuated inversion recovery.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/5b0d00e3ba9262b6ce19fafa.jpeg"},{"id":102746043,"identity":"16420cf7-8603-4e80-a907-c6a436299ddc","added_by":"auto","created_at":"2026-02-16 08:55:23","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":248061,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for distinguishing GBM from SBM with SVZ (+). A: ROC curves of the T2-FLAIR SVZ (+) group; B: ROC curves of subgroup 1; C: ROC curves of subgroup 2. ROC, receiver operating characteristic; GBM, glioblastoma; SBM, solitary brain metastasis; SVZ, subventricular zone.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/48d4745f9cfef0c33f6def87.jpeg"},{"id":102745534,"identity":"ede9406e-d318-408e-a855-e2215bb28af2","added_by":"auto","created_at":"2026-02-16 08:51:31","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":634666,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram, calibration curves, and decision curves for distinguishing GBM from SBM with SVZ (+). A-C: Nomogram, calibration curves, and decision curves of the T2-FLAIR SVZ (+) group; D-F: Nomogram, calibration curves, and decision curves of subgroup 1; G-I: Nomogram, calibration curves, and decision curves of subgroup 2. GBM, glioblastoma; SBM, solitary brain metastasis; SVZ, subventricular zone; WM, white matter, rADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/62e62cddc13e5d5a5d6fe173.jpeg"},{"id":102750584,"identity":"45824775-d0a4-4af0-8ca6-8dee0decb19d","added_by":"auto","created_at":"2026-02-16 09:20:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4617503,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/8828a40a-4605-47a0-abf0-85532c827fcf.pdf"},{"id":102462954,"identity":"7c29a739-b6ae-4647-a3f9-c066db38616c","added_by":"auto","created_at":"2026-02-12 01:12:40","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":64000,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.doc","url":"https://assets-eu.researchsquare.com/files/rs-8543692/v1/79982c208253c668240dd907.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram for distinguishing glioblastoma from solitary brain metastasis involving the subependymal zone based on apparent diffusion coefficient and conventional MRI features","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma (GBM) is a prevalent primary malignant tumor of the central nervous system that constitutes approximately 14% of all primary brain tumors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Brain metastasis is a frequent secondary malignant tumor affecting the central nervous system in adults, with an estimated 20%-40% of all malignant tumors capable of spreading to the brain as they progress [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Both of these processes may involve the subependymal zone (SVZ) [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. When they present as a solitary mass involving the SVZ during initial medical visits, the clinical symptoms and imaging features are similar, often leading to frequent misdiagnoses. However, there are significant differences in the clinical management of these two diseases. The treatment of GBM involves maximal safe surgical resection of the disease, followed by radiotherapy and chemotherapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For solitary brain metastasis (SBM), a systematic examination is necessary to search for primary lesions and to perform clinical staging before making further treatment decisions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The current gold standard for diagnosing GBM and SBM with SVZ (+) is pathological examination of tissue samples. However, the invasiveness of this procedure restricts its clinical application. Therefore, it is important to noninvasively distinguish GBM from SBM with the SVZ (+) prior to surgery.\u003c/p\u003e \u003cp\u003eTraditionally, SVZ (+) has been defined as an enhancement within a 5-mm area near the lateral ventricle on contrast-enhanced T1-weighted imaging (T1CE) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, there is an ongoing debate regarding the traditional definition of the SVZ (+). The enhancement on T1CE only indicates the disruption of the blood-brain barrier and the formation of tumor-associated vessels. Thus, it does not accurately delineate the actual boundary of GBM, especially for the nonenhanced portion of GBM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A recent study proposed integrating T2-FLAIR and T1CE images to define the SVZ (+) of GBM, finding that this method can provide improved prognostic ability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, this study integrates T2-FLAIR and T1CE images to classify the SVZ (+).\u003c/p\u003e \u003cp\u003eResearch has indicated that MRI features such as prominent vessels passing through the lesion, the edema ratio, and the ratio of peritumoral edema to the tumor area are useful for distinguishing between GBM and SBM [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the limitation is that these assessments are significantly influenced by the observer's subjective bias and lack quantifiable measures. Diffusion-weighted imaging is an MRI technique that enables the quantitative assessment of water molecule diffusion within biological tissues. Previous research has reported that the apparent diffusion coefficient (ADC) value of peritumoral edema can assist in differentiating between GBM and SBM [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there are some limitations. For instance, delineating the region of interest (ROI) in the peritumoral area at an equal distance from the tumor core poses a challenge in routine clinical practice. To simplify the measurement process, this study introduced the ADC values of the SVZ affected by tumors. To our knowledge, few studies have investigated the differential diagnostic value of rADC values from the SVZ and peritumoral edema, in combination with conventional MRI features, for distinguishing between GBM and SBM with SVZ (+).\u003c/p\u003e \u003cp\u003eWe aimed to investigate the value of the rADC values of the SVZ and peritumoral edema, in combination with conventional MRI features, for distinguishing GBM from SBM with SVZ (+) according to various definition criteria.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003eData from patients with GBM confirmed by surgical pathology and SBM confirmed by either surgical pathology or clinical follow-up were retrospectively collected at our hospital from January 2020 to February 2025. The exclusion criteria were as follows: (I) GBM with multiple intracranial lesions; (II) patients who had previously undergone surgery, radiotherapy, or other treatments before MRI examination; (III) patients with a low signal‒to‒noise ratio or significant artifacts affecting observation; (IV) patients without an SVZ (+); (V) patients with inconsistent assessment of the SVZ (+) by observers; (VI) patients without edema around the enhancing lesion; and (VII) patients with incomplete data. The detailed patient selection flowchart is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (No. KY2023041). Informed consent was waived in the study for retrospective analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImage acquisition\u003c/h3\u003e\n\u003cp\u003eAll patients were scanned via a 1.5T magnetic resonance scanner (Achieva, Philips Health care). The MRI sequences included the following: T1-weighted imaging (T1WI): repetition time (TR): 488.1 ms, echo time (TE): 15.0 ms, layer thickness: 5.5 mm, field of view (FOV): 23.0 cm \u0026times; 23.0 cm, matrix: 256 \u0026times; 256; T2-weighted imaging (T2WI): TR: 3750.5 ms, TE: 100 ms, layer thickness: 5.5 mm, FOV: 23.0 cm \u0026times; 23.0 cm, matrix: 256 \u0026times; 256; T2-FLAIR: TR: 8500 ms, TE: 100 ms, inversion time: 2400 ms, layer thickness 5.5 mm, FOV: 23.0 cm \u0026times; 23.0 cm, matrix: 256 \u0026times; 256; diffusion-weighted imaging: TR: 2680.5 ms, TE: 98.8 ms, layer thickness: 5.5 mm, FOV: 23.0 cm \u0026times; 23.0 cm, matrix: 128 \u0026times; 128, and b values of 0 and 1000 s/mm\u003csup\u003e2\u003c/sup\u003e. T1CE: TR: 156.2 ms, TE: 2.4 ms, layer thickness: 5.5 mm. Gadopentetate dimeglumine (Magenev, Bayer, Germany) was injected via the cubital vein at a dose of 0.1 mmol/kg and an injection flow rate of 1.0 mL/s.\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eTwo senior radiologists, each with 8 and 10 years of experience in the field, performed a blinded evaluation of the SVZ (+) in patients with GBM and SBM. Referencing the previous study, the SVZ (+) was defined as having high signal intensity on T2-FLAIR or T1CE images within a 5 mm area adjacent to the lateral ventricular wall [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, since patients with SVZ (+) on T1CE images also exhibited SVZ (+) on T2-FLAIR images, patients with SVZ (+) on T2-FLAIR images were further subdivided into two subgroups on the basis of the combined evaluation of T1CE and T2-FLAIR images: subgroup 1: patients with SVZ (+) on T2-FLAIR and T1CE; subgroup 2: patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE. Patients with inconsistent assessments of the SVZ (+) by the two observers were excluded. Both observers also assessed the conventional MRI features of GBM and SBM with the SVZ (+) in a blinded manner. Any discrepancies were resolved through consultation. The conventional MRI features evaluated included the following: region of the SVZ (+) (frontal horn, body, occipital horn, temporal horn), side of the lesion center (right, central/bilateral, left), enhancement quality (mild/minimal, marked/avid), proportion of enhancement (\u0026ge;\u0026thinsp;50%, \u0026lt; 50%), proportion of necrosis (\u0026ge;\u0026thinsp;50%, \u0026lt; 50%), cysts (presence, absence), thickness of enhancing margin (thin, thick/solid, none), definition of enhancing margin (well-defined, poorly defined), prominent blood vessels passing through the lesion (presence, absence), edema across the midline (presence, absence), hemorrhage (presence, absence), diffusion restriction (presence, absence), pial invasion (presence, absence), cortical involvement (presence, absence), deep white matter (WM) invasion (presence, absence), enhancing tumor across the midline (presence, absence), major diameter, and edema ratio [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The region of the SVZ (+) was evaluated at the largest section of the SVZ (+) on T2-FLAIR images. The cysts were defined as round or oval-shaped regions within the tumor, exhibiting high T2WI and low T1WI signal intensities, similar to those of cerebrospinal fluid [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The thickness of the enhancing margin was classified as thin if it was a regular and thin rim. It was considered thick if most of the edges were nodular and/or thick. If there was only solid enhancement without an edge enhancement rim, it was defined as none [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Diffusion restriction was defined as the appearance of low signal intensity in the tumor on the ADC map. The major diameter of the tumor was measured as the longest diameter observed on T1CE images, whereas the edema ratio was calculated as the ratio of the longest diameter of the peritumoral edma on T2WI to the longest diameter of the tumor on T1CE. Using the automatically generated ADC map, regions of interest (ROIs) referring to the T1CE or T2-FLAIR images were manually drawn on the ADC map at the largest section of the SVZ (+), peritumoral edema, and contralateral normal white matter (cNAWM), avoiding areas of cysts, necrosis, calcification, and hemorrhage. No fewer than three ROIs were placed, each with an area of 15\u0026ndash;25 mm\u0026sup2;. A schematic diagram of ROI placement is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To eliminate the effects of age on the ADC values and minimize measurement errors, the mean apparent diffusion coefficient (ADCmean), maximum apparent diffusion coefficient (ADCmax), and minimum apparent diffusion coefficient (ADCmin) of the SVZ and peritumoral edema were divided by their corresponding values in the cNAWM. This process yielded normalized ADC values: the relative apparent diffusion coefficients of the SVZ (rADCmean-s, rADCmax-s, and rADCmin-s) and the relative apparent diffusion coefficient of peritumoral edema (rADCmean-e, rADCmax-e, and rADCmin-e). The conventional MRI features and rADC values were measured and evaluated in the same manner in 50 patients by a radiologist (with 10 years of experience), with an interval of at least 1 month to evaluate intraobserver agreement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePathology analysis\u003c/h3\u003e\n\u003cp\u003eBoth surgical resection and craniotomy biopsy samples were subjected to pathological examination to determine whether the tumor was a glioma or brain metastasis. The isocitrate dehydrogenase (IDH) gene locus was detected in glioma patients via Sanger sequencing. When no mutation was identified, the patient was diagnosed with IDH-wild type [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Gliomas with a pathological classification of grade 4 and wild-type IDH were diagnosed as GBM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed via R (version 4.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software. Kappa tests and intraclass correlation coefficient (ICC) analyses were performed on data collected by two observers. When the kappa coefficient and ICC value for inter- or intraobserver reliability exceeded 0.75, the data consistency was deemed good. The measurement data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. An independent samples t test was applied if the measurement data conformed to a normal distribution; otherwise, the Mann‒Whitney U test was utilized. Categorical data are presented as frequencies (percentages), with intergroup comparisons conducted via the chi-square test. Statistically significant parameters were included in the multifactorial logistic regression to create joint prediction models. Receiver operating characteristic (ROC) curves were plotted to distinguish between GBM and SBM with SVZ (+). The nomograms, calibration curves and decision curves for the pivotal parameters with an AUC greater than 0.6 were plotted. The performance of the nomogram was assessed in the entire cohort, subgroup 1 and subgroup 2. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical data of the participants\u003c/h2\u003e \u003cp\u003eThe clinical data of the included GBM and SBM patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of the GBM group was younger than that of the SBM group (P\u0026thinsp;=\u0026thinsp;0.011), whereas the difference in sex distribution was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.075).\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\u003eClinical data of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBM (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ/χ\u003csup\u003e2\u003c/sup\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e57.05\u0026thinsp;\u0026plusmn;\u0026thinsp;13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.90\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\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 \u003cp\u003e3.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary tumor, n (%)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\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\u003e32 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectal cancer\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\u003e3 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast Cancer\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\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid cancer\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\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain source\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\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eChi-square test and Mann‒Whitney U test were used; GBM, glioblastoma; SBM, solitary brain metastasis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConsistency analysis\u003c/h3\u003e\n\u003cp\u003eThe kappa values for inter- and intraobserver reliability regarding the region of the SVZ (+), side of lesion center, enhancement quality, proportion of enhancement, proportion of necrosis, cysts, thickness of enhancing margin, definition of enhancing margin, prominent blood vessels passing through the lesion, edema across the midline, hemorrhage, diffusion restriction, pial invasion, cortical involvement, deep WM invasion, and enhancing tumor across the midline ranged from 0.796 to 0.983 and 0.796 to 0.992, respectively. The ICC values for the inter- and intraobserver reliability of the major diameter, edema ratio, rADCmean-s, rADCmax-s, rADCmin-s, rADCmean-e, rADCmax-e, and rADCmin-e ranged from 0.853 to 0.979 and 0.931 to 0.982, respectively. The interobserver and intraobserver reliability of the conventional MRI features and rADC values were good, as shown in Supplementary Table A.1 and A.2.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of conventional MRI features between GBM and SBM with SVZ (+)\u003c/h2\u003e \u003cp\u003eThe thickness of the enhancing margin, prominent blood vessels passing through the lesion, deep WM invasion, major diameter and edema ratio were significantly different between GBM and SBM with SVZ (+) on T2-FLAIR images (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When performing subgroup analysis, the differences in the thickness of the enhancing margin, prominent blood vessels passing through the lesion, major diameter, and edema ratio between GBM and SBM were statistically significant in subgroup 1, whereas the differences in prominent blood vessels passing through the lesion, deep WM invasion, major diameter, and edema ratio between GBM and SBM were statistically significant in subgroup 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eConventional MRI features and rADC values comparison of the GBM and SBM with SVZ (+)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eT2-FLAIR SVZ (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eT2-FLAIR and T1CE SVZ (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eT2-FLAIR SVZ (+) but T1CE SVZ (-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBM (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSBM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSBM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion of the SVZ (+)\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 \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal horn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccipital horn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal horn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSide of lesion center\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 \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCenter/Bilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15(53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhancement quality\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 \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild/Minimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarked/Avid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27(96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of enhancement\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 \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16(57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16(64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9(36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of necrosis\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 \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9(36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16(64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCysts\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 \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22(78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17(68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThickness of enhancing margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThick/Solid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22(78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16(64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e10(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDefinition of enhancing margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.098\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\u003e76(96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22(88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e3(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProminent blood vessels passing through the lesion\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18(64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4(16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21(84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdema across the midline\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 \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23(92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhage\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 \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17(68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffusion restriction\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 \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27(96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23(92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePial invasion\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 \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9(32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11(44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19(67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14(56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortical involvement\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 \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27(96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep WM invasion\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 \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhancing tumor across the midline\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 \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27(96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24(96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdema ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmean-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmax-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmin-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmean-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmax-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmin-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eChi-square test, independent samples t test and Mann‒Whitney U test were used. MRI, magnetic resonance imaging; SVZ, subventricular zone; GBM, glioblastoma; SBM, solitary brain metastasis; WM, white matter; .rADCmean-s, relative mean apparent diffusion coefficient of the subependymal zone; rADCmax-s, relative maximum apparent diffusion coefficient of the subependymal zone; rADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone; rADCmean-e, relative mean apparent diffusion coefficient of periotumoral edema; rADCmax-e, relative maximum apparent diffusion coefficient of periotumoral edema; rADCmin-e, relative minimum apparent diffusion coefficient of periotumoral edema.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the rADC values between GBM and SBM with SVZ (+)\u003c/h2\u003e \u003cp\u003eThe rADCmean-s, rADCmax-s, and rADCmin-s were significantly different between GBM and SBM with SVZ (+) on T2-FLAIR images (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subgroup analysis revealed no statistically significant differences in the rADC values between GBM and SBM in subgroup 1 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), whereas the rADCmean-s, rADCmax-s, rADCmin-s, and rADCmin-e were significantly different in subgroup 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003e \u003cb\u003eEfficacy of conventional MRI features and rADC values for distinguishing GBM from SBM with SVZ (+)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ROC curves for distinguishing GBM from SBM with SVZ (+) are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. ROC curve analysis revealed that the rADCmin-s, major diameter and rADCmin-s exhibited the highest area under the curve (AUC) values of 0.766, 0.774, and 0.831, respectively, for distinguishing GBM from SBM in the T2-FLAIR SVZ (+) group, subgroup 1, and subgroup 2 during individual diagnosis, whereas the combined analysis achieved AUC values of 0.879, 0.869, and 0.886, respectively.\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\u003eDiagnostic performance for differentiating GBM from SBM with SVZ (+)\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\u003eCut off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2-FLAIR SVZ (+) group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of enhancing margin\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\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.397\u0026ndash;0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProminent blood vessels passing through the lesion\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\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.625\u0026ndash;0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep WM invasion\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\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.544\u0026ndash;0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.667\u0026ndash;0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdema ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.648\u0026ndash;0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmean-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.628\u0026ndash;0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmax-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.571\u0026ndash;0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmin-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.669\u0026ndash;0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.809\u0026ndash;0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubgroup 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness of enhancing margin\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\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.324\u0026ndash;0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProminent blood vessels passing through the lesion\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\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.575\u0026ndash;0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.626\u0026ndash;0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdema ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.575\u0026ndash;0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.773\u0026ndash;0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubgroup 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProminent blood vessels passing through the lesion\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\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.625\u0026ndash;0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep WM invasion\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\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.526\u0026ndash;0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u0026ndash;0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdema ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.558\u0026ndash;0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmean-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.611\u0026ndash;0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmax-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.518\u0026ndash;0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmin-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.716\u0026ndash;0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erADCmin-e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.555\u0026ndash;0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776\u0026ndash;0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eT2-FLAIR, T2-weighted fluid attenuated inversion recovery; SVZ, subventricular zone; GBM, glioblastoma; SBM, solitary brain metastasis; WM, white matter; AUC, area under the curve; CI, confidence interval; rADCmean-s, relative mean apparent diffusion coefficient of the subependymal zone; rADCmax-s, relative maximum apparent diffusion coefficient of the subependymal zone; rADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone; rADCmin-e, relative minimum apparent diffusion coefficient of periotumoral edema.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and performance of nomograms for differentiating GBM from SBM\u003c/h2\u003e \u003cp\u003eThe nomograms, calibration curves and decision curves based on the parameters with AUCs greater than 0.6 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The C indices for the nomograms were 0.859 for the entire group, 0.819 for subgroup 1, and 0.886 for subgroup 2. The calibration curve of the nomograms closely aligned with the ideal curve, and the decision curve analysis indicated a greater overall net benefit. The optimal cut-off values were determined to be 128 points for the entire group, 63 points for subgroup 1, and 63 points for subgroup 2, corresponding to a hazard level of 0.5. All patients were subsequently categorized into either the GBM or SBM group based on the risk stratification. Discrimination between the two groups was significant across the entire group, subgroup 1, and subgroup 2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The risk classifier indicated that the nomogram had an accuracy of 88.2%, a sensitivity of 96.2%, and a specificity of 72.5% for the entire group. For subgroup 1, these values were 87.9%, 98.0%, and 53.3%, respectively. In subgroup 2, the accuracy was 86.8%, the sensitivity was 89.3%, and the specificity was 84.0%.\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\u003eConventional MRI features and rADC values comparison of the GBM and SBM with SVZ (+)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eT2-FLAIR SVZ (+)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eT2-FLAIR and T1CE SVZ (+) (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eT2-FLAIR SVZ (+) but T1CE SVZ (-)(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSBM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk of GBM\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 (89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21 (84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eGBM, glioblastoma; SBM, solitary brain metastasis; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging; T2-FLAIR, T2-weighted fluid attenuated inversion recovery; T1CE, contrast-enhanced T1-weighted imaging; SVZ, subventricular zone.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focused on the diagnostic value of the rADC values from SVZ and peritumoral edema, combined with conventional MRI features, for distinguishing GBM from SBM with SVZ (+). Considering the controversy in the definition of the SVZ (+), we integrated T2-FLAIR and T1CE images to classify the SVZ (+). Then, patients with SVZ (+) on T2-FLAIR images were further categorized into two subgroups: subgroup 1 (T2-FLAIR and T1CE SVZ (+)) and subgroup 2 (T2-FLAIR SVZ (+) but T1CE SVZ (-)). This study revealed that conventional MRI features, including prominent blood vessels passing through the lesion, major diameter and the edema ratio, were significantly different between GBM and SBM with SVZ (+), as defined by the three definition criteria. The rADC values, including the rADCmean-s, rADCmax-s, and rADCmin-s, differed between GBM and SBM in the T2-FLAIR SVZ (+) group and subgroup 2, whereas none of the rADC values were significantly different between GBM and SBM in subgroup 1. Furthermore, the nomograms demonstrated strong predictive value, with C-indices ranging from 0.819 to 0.886, based on the three defined criteria.\u003c/p\u003e \u003cp\u003eIn this study, GBM patients with SVZ (+) were younger than those who were SBM, which was consistent with the findings of Sir\u0026eacute;n et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The lesions in the GBM patient with SVZ (+) tended to exhibit prominent blood vessels passing through the lesion, longer major diameters and smaller peritumoral edema ratios, which was largely consistent with the findings of Voicu et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and Maurer et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The reasons may be as follows: the rapid growth of GBM necessitates a rich blood supply of new capillaries, resulting in vessels that are coarser than those of SBM [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, vessels in the GBM are easier to detect. The new capillaries in GBM closely resemble the vasculature of normal brain tissue, maintaining vascular continuity and preserving the function of the blood‒brain barrier [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, the new capillaries in SBM reflect the vasculature of their tissue of origin, often displaying disrupted structures and interrupted continuity, resulting in a lack of blood‒brain barrier function [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This results in significantly greater permeability of new capillaries in SBM than in GBM, enhancing the risk of extensive peritumoral edema. Furthermore, this study revealed that the major diameter of lesions in SBM was shorter than that in GBM, resulting in a higher peritumoral edema ratio for SBM than for GBM. Zhang et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that SBM often affects the subcortical WM, manifesting as a uniformly smooth-enhancing ring on T1CE, whereas GBM tends to affect the deep WM, distinguished by an irregularly thickened, unevenly enhanced ring on T1CE. This study revealed similar results, which may be related to the aggressive growth patterns of GBM.\u003c/p\u003e \u003cp\u003eThe study found that the rADC values in the SVZ were significantly different between the two tumor types in the T2-FLAIR SVZ (+) group and subgroup 2, whereas no statistically significant difference in rADC values was observed in subgroup 1. We speculated that the reasons may be as follows: the T2-FLAIR SVZ (+) group and subgroup 2 included patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE images. In GBM patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE images, the SVZ contains glioma stem cells that migrate from the GBM mass and tumor cells that infiltrate along white matter fibre bundles, leading to increased cell density and decreased ADC values in the SVZ [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, in SBM patients with SVZ (+) on T2-FLAIR but SVZ (-) on T1CE images, the SVZ exhibits only vasogenic edema without an increase in tumor cells; thus, the cell density does not increase, and the ADC values do not decrease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consequently, the relative rADC values of the SVZ in GBM patients are lower than those in SBM patients in the T2-FLAIR SVZ (+) group and subgroup 2. Subgroup 1 consisted of patients who were SVZ (+) on T2-FLAIR and T1CE images. When the SVZ is affected on T1CE images, it is speculated that the diffusion of water molecules in the SVZs of both GBM and SBM resembles that in the tumor parenchyma, characterized by numerous malignant cells, high cell density, and narrow intercellular space. This results in restricted water molecule diffusion and decreased ADC values in both GBM and SBM patients [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, the rADC values of the SVZ are not effective in differentiating GBM from SBM in subgroup 1. Additionally, in contrast to the rADC values of the SVZ, the majority of the peritumoral rADC values were incapable of distinguishing GBM from SBM with SVZ (+) in this study, which was inconsistent with the findings of Papageorgiou et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The following speculations have been made: for the convenience of routine clinical practice, this study defined the peritumoral region as a random area of peritumoral edema, whereas Papageorgiou et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] defined it as the area within 1 cm of the enhanced tumor edge. Previous studies reported that the degree of peritumoral infiltration of GBM decreases with increasing distance from the tumor core [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This may impact the differential diagnostic value of the rADC value of peritumoral edema in this study. In the next step, we plan to group peritumoral edema on the basis of the distance from the enhanced tumor to further validate the results of this study.\u003c/p\u003e \u003cp\u003eIn both the T2-FLAIR SVZ (+) group and subgroup 2, the optimal parameter for distinguishing GBM from SBM was rADCmin-s, which was similar to the findings of Ke et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggesting that rADCmin may be more sensitive for the detection of water molecule diffusion within tissue structures. The reason could be that the ADCmin values within the ROIs represent the most densely packed tumor cells within the measurement range, thereby minimizing the interference of tumor heterogeneity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our study, when the rADCmean-s, rADCmax-s, and rADCmin-s were less than 1.765, 1.670, and 1.895, respectively, there was a greater likelihood of GBM in the T2-FLAIR SVZ (+) group. Additionally, when rADCmean-s, rADCmax-s, rADCmin-s, and rADCmin-e were less than 1.765, 1.795, 1.865, and 1.985, respectively, there was a greater likelihood of GBM in subgroup 2. When multiple parameters were used for joint prediction, the diagnostic efficacy was greater than that of a single parameter, suggesting that combined diagnosis is beneficial for reducing diagnostic bias and improving diagnostic efficacy. When delineating the SVZ (+) using T2-FLAIR images, the combined diagnosis performance was superior to that of the SVZ (+) typically defined by T1CE. After classifying the SVZ (+) by integrating T2-FLAIR and T1CE images, the combined diagnosis exhibited the highest diagnostic performance in subgroup 2 (AUC\u0026thinsp;=\u0026thinsp;0.886). Additionally, easy-to-use diagnostic nomograms were developed to differentiate between GBM and SBM patients with SVZ (+), as defined by various criteria in our study. The diagnostic nomograms demonstrated a favourable predictive value. Through the nomogram, clinicians can noninvasively distinguish between GBM and SBM patients with SVZ (+) prior to surgery in routine clinical practice, thereby aiding in the development of treatment strategies and the evaluation of patient prognosis. Given that this was a single-center, retrospective study and that the sample size of SBM patients with SVZ (+) was relatively small, especially within subgroup 1, this could introduce bias into the results. The findings of this study necessitate further validation through a multicenter prospective cohort study with a larger sample size, which will be the focus of our future research.\u003c/p\u003e \u003cp\u003eThe limitations of this study were as follows: (I) This was a single-center retrospective study, and the sample size of SBM patients with SVZ (+) was relatively small, especially within subgroup 1; (II) the peritumoral area was not grouped according to the distance from the tumor core, which might impact the differential diagnostic value of rADC values in peritumoral edema; and (III) the use of a 1.5T MRI scanner and manual ROI placement may affect image resolution and the accuracy of the ADC value. For future research, we plan to utilize high-field-strength MRI scanners in conjunction with a semiautomatic ROI segmentation technique to validate our findings in a larger cohort of patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the combination of rADC values from the SVZ and peritumoral edema with conventional MRI features aids in distinguishing GBM from SBM with SVZ(+), as defined by different criteria. Our nomograms are useful tools for predicting GBM and SBM with SVZ(+).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMRI, magnetic resonance imaging\u003c/p\u003e\u003cp\u003eGBM, glioblastoma\u003c/p\u003e\u003cp\u003eSBM, solitary brain metastasis\u003c/p\u003e\u003cp\u003eSVZ, subependymal zone\u003c/p\u003e\u003cp\u003erADC, relative apparent diffusion coefficient\u003c/p\u003e\u003cp\u003erADCmean-s, relative mean apparent diffusion coefficient of the subependymal zone\u003c/p\u003e\u003cp\u003erADCmax-s, relative maximum apparent diffusion coefficient of the subependymal zone\u003c/p\u003e\u003cp\u003erADCmin-s, relative minimum apparent diffusion coefficient of the subependymal zone\u003c/p\u003e\u003cp\u003erADCmean-e, relative mean apparent diffusion coefficient of peritumoral edema\u003c/p\u003e\u003cp\u003erADCmax-e, relative maximum apparent diffusion coefficient of peritumoral edema\u003c/p\u003e\u003cp\u003erADCmin-e, relative minimum apparent diffusion coefficient of peritumoral edema\u003c/p\u003e\u003cp\u003eWM, white matter\u003c/p\u003e\u003cp\u003ecNAWM, contralateral normal white matter\u003c/p\u003e\u003cp\u003eT1WI, T1-weighted imaging\u003c/p\u003e\u003cp\u003eT2WI, T2-weighted imaging\u003c/p\u003e\u003cp\u003eT2-FLAIR, T2-weighted fluid-attenuated inversion recovery\u003c/p\u003e\u003cp\u003eT1CE, contrast-enhanced T1-weighted imaging\u003c/p\u003e\u003cp\u003eTR, repetition time\u003c/p\u003e\u003cp\u003eTE, echo time\u003c/p\u003e\u003cp\u003eFOV, field of view\u003c/p\u003e\u003cp\u003eROI, region of interest\u003c/p\u003e\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\u003cp\u003eAUC, area under the curve\u003c/p\u003e\u003cp\u003eCI, confidence interval\u003c/p\u003e\u003cp\u003eIDH, isocitrate dehydrogenase\u003c/p\u003e\u003cp\u003eICC, intraclass correlation coefficient\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003eThis study is a retrospective study. All data used in this study was anonymized\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eThe study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (No. KY2023041). Written informed consent from patients was waived due to the study's retrospective design. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eAppendices\u003c/h2\u003e \u003cp\u003eSupplementary data was shown in Table A.1 and Table A.2.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Sichuan Science and Technology Program of China (Grant No. 2022YFS0616), the Luzhou Science and Technology Program (Grant No. 2025MYF031), and the Project for Doctors of Affiliated Hospital, Southwest Medical University (Grant No. 2018–17129).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe conception and design of the study: PW, LH, GXC. Administrative support: GXC, LY. Acquisition of data: PW, LH, DWL, XFL. Analysis and interpretation of data: PW, LH, SDL, LLW, DWL. Drafting the article: All authors. Revising it critically for important intellectual content: PW, GXC. Final approval of the version to be submitted: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrice M, Ryan K, Shoaf ML, et al. Childhood, adolescent, and adult primary brain and central nervous system tumor statistics for practicing healthcare providers in neuro-oncology, CBTRUS 2015\u0026ndash;2019. Neurooncol Pract. 2023;11(1):5\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Espinosa I, Serrato JA, Ortiz-Quintero B. MicroRNAs in Lung Cancer Brain Metastasis. Int J Mol Sci. 2024;25(19):10325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan S, Chen Y, Zhao S, et al. 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J Neurooncol. 2024;167(1):89\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahng JY, Kang BH, Lee ST, et al. Clinicogenetic characteristics and the effect of radiation on the neural stem cell niche in subventricular zone-contacting glioblastoma. Radiother Oncol. 2023;186:109800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoicu IP, Pravat\u0026agrave; E, Panara V, et al. Differentiating solitary brain metastases from high-grade gliomas with MR: comparing qualitative versus quantitative diagnostic strategies. Radiol Med. 2022;127(8):891\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHakyemez B, Erdogan C, Gokalp G, et al. Solitary metastases and high-grade gliomas: radiological differentiation by morphometric analysis and perfusion-weighted MRI. Clin Radiol. 2010;65(1):15\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurmo F, L\u0026auml;tt J, Rydelius A, et al. 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Acad Radiol. 2024;31(3):1082\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenna G, Marinno S, Valeri F, et al. Diffusion tensor imaging in detecting gliomas sub-regions of infiltration, local and remote recurrences: a systematic review. Neurosurg Rev. 2024;47(1):301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParvaze PS, Bhattacharjee R, Verma YK, et al. Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis. NMR Biomed. 2023;36(5):e4884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhang H, Zhang H, et al. Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures. J Magn Reson Imaging. 2024;60(3):909\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCancer Imaging Archive. VASARI research project. \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. Accessed 6 May 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang K, Dai Y, Liu Y, et al. Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices. Eur Radiol. 2022;32(8):5659\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaurer MH, Synowitz M, Badakshi H, et al. 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Radiol Med. 2012;117(3):445\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed MH, Canney M, Carpentier A, et al. Unveiling the enigma of the blood\u0026ndash;brain barrier in glioblastoma: current advances from preclinical and clinical studies. Curr Opin Oncol. 2023;35(6):522\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaltirik Bilgin E, Unal O, Ciledag N. Vasogenic Edema Pattern in Brain Metastasis. J Coll Physicians Surg Pak. 2022;32(8):1020\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapageorgiou TS, Chourmouzi D, Drevelengas A, et al. Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases. Phys Med. 2015;31(7):767\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKe X, Zhao J, Liu X, et al. Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade. Neurosurg Rev. 2023;46(1):83.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma, Solitary brain metastasis, Subependymal zone, Magnetic resonance imaging, Apparent diffusion coefficient","lastPublishedDoi":"10.21203/rs.3.rs-8543692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8543692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurately distinguishing glioblastoma (GBM) from solitary brain metastasis (SBM) involving the subventricular zone (SVZ) preoperatively is highly important but challenging in actual clinical practice. This study investigated the value of the relative apparent diffusion coefficient (rADC) of the SVZ and peritumoral edema, in combination with conventional magnetic resonance imaging (MRI) features, for distinguishing between these two diseases.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 119 patients with GBM and SBM showing SVZ (+) on T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images were included. These patients were further categorized into two subgroups: subgroup 1 (T2-FLAIR and T1CE SVZ (+)) and subgroup 2 (T2-FLAIR SVZ (+) but T1CE SVZ (-)). The conventional MRI features and rADC values of the SVZ and peritumoral edema were compared. Diagnostic efficacy was evaluated by calculating the area under the receiver operating characteristic curves, whereas clinical applicability was analysed using nomograms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCombining rADC values from the SVZ and peritumoral edema with conventional MRI features demonstrated good predictive performance, with areas under the curve (AUC) of 0.879 for the entire group, 0.869 for subgroup 1, and 0.886 for subgroup 2. The C-indices for the nomograms were 0.859 for the entire group, 0.819 for subgroup 1, and 0.886 for subgroup 2. The calibration curves of the nomograms closely aligned with the ideal curves, and the decision curve analysis indicated a greater overall net benefit.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCombining rADC values from the SVZ and peritumoral edema with conventional MRI features enables noninvasive preoperative differentiation between GBM and SBM with SVZ (+). The nomograms are useful tools for predicting GBM and SBM with SVZ (+).\u003c/p\u003e","manuscriptTitle":"Nomogram for distinguishing glioblastoma from solitary brain metastasis involving the subependymal zone based on apparent diffusion coefficient and conventional MRI features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 01:12:14","doi":"10.21203/rs.3.rs-8543692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-17T09:11:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314978198120039861050532186975997554747","date":"2026-02-09T08:57:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T08:32:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T07:45:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T07:02:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T06:19:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-01-16T06:11:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"890ab1b0-954f-4e39-a3e7-36f8ee90079b","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T01:12:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 01:12:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8543692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8543692","identity":"rs-8543692","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}