Comparison between electron density imaging with dual-energy computed tomography without contrast medium and magnetic resonance imaging for high-grade glioma

Comparison between electron density imaging with dual-energy computed tomography without contrast medium and magnetic resonance imaging for high-grade glioma | 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 Comparison between electron density imaging with dual-energy computed tomography without contrast medium and magnetic resonance imaging for high-grade glioma Takahiro Yamauchi, Tomokazu Ishida, Toshihide Itoh, Tadahiro Tsubota, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4708655/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Dual-energy computed tomography (DECT) has been used for the prediction of glioma grading and malignancy, in addition to magnetic resonance imaging ( MRI) findings. In DECT, electron density (ED) can be quantitatively measured and determined with high accuracy. However, no studies have demonstrated whether DECT alone can detect tumor-infiltrating areas or evaluate malignancy. Therefore, we evaluated the use of ED imaging with DECT showing high-density areas of high-grade glioma (HGG), compared it with MRI gadolinium-enhanced T1-weighted image (GdT1) enhancing area, and analyzed its effectiveness in evaluating malignancy. Methods Ten patients with enhanced masses on GdT1 MRI were enrolled in this retrospective study. Ten slices of ED and GdT1 images of 10 patients with HGG were analyzed by three raters. The relationships between the area of high ED on DECT and enhanced area on GdT1 and between the relative ED of the high ED area and contralateral white matter on DECT were determined. Results Linear regression analysis revealed a positive correlation between areas of high ED and Gd enhancement among all raters (rater A: R 2 = 0.910, P < 0.0001, B: R 2 = 0.857, P = 0.0001, C: R 2 = 0.717, P = 0.002), and the intraclass correlation coefficient was 0.75. A comparison of the relative ED between the tumor and contralateral white matter revealed that the mean and maximum relative ED of the tumor area was significantly higher than that of the contralateral white matter (mean: P = 0.049, maximum: P = 0.0002). Conclusions ED images of DECT show a high ED area similar to the Gd enhancement area in HGG, and the mean and maximum relative EDs of this area are significantly higher than those of the contralateral white matter. Dual-energy CT electron density imaging gadolinium-enhanced imaging high-grade glioma Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Glioma is an aggressive tumor, with the treatment of high-grade glioma (HGG) being difficult, especially glioblastoma, isocitrate dehydrogenase (IDH)-wildtype and astrocytoma, IDH-mutant, grade IV [ 3 , 24 ]. HGG treatment consists of surgical resection, followed by radiation and chemotherapy. Therefore, the surgical resection rate and grading of HGG are related to prognosis; further, preoperative diagnosis, including genetic information and prediction of infiltrating tumor cells, is important [ 9 , 17 , 19 ]. During glioma surgery, several imaging studies are required for safe and maximal resection. Magnetic resonance imaging (MRI) is the gold standard for diagnosing glioma, especially in HGG, and gadolinium-enhanced T1-weighted image (GdT1) is effective for determining diagnosis for subtypes, tumor-infiltrating area and malignancy [ 22 ]. Recent studies have reported that advanced MRI techniques, including diffusion-weighted imaging, amide proton transfer imaging, arterial spin labeling, and MR spectroscopy, are useful for grading gliomas, determining tumor resection area, and predicting recurrence [ 1 , 4 , 8 , 14 , 16 , 20 , 23 ]. Although MRI is essential for glioma treatment, it cannot be performed on some patients due to claustrophobia or because of the presence of metal or pacemaker in their body. Artifacts due to the presence of metal (coil, clip) and movement over a long examination time may decrease MRI accuracy. Furthermore, MRI is expensive and requires maintenance procedures. Additionally, several patients with HGG require an examination using a contrast medium [ 22 ]. Using a contrast medium is invasive due to insertion of needle and risk of allergy and anaphylactic shock, and it cannot be used in some patients because of renal dysfunction or allergy. In the MRI era, the use of X-ray computed tomography (CT) to detect brain tumors, including glioma, is relatively ineffective. Recently, dual-energy CT (DECT) has been reported to be useful for differentiating glioma grade and recurrence [ 2 , 13 ]. DECT is a CT technique that uses two different photon spectra. A major advantage of DECT is its material-differentiation ability, which is based on high- and low-peak X-ray tube voltage acquisition [ 21 ]. One aspect that can be measured using DECT is electron density (ED). ED indicates the probability of an electron being present at a specific point in space, which depends on the type of tissue [ 5 ]. Several studies have used DECT for the prediction of glioma grading and malignancy, in addition to MRI findings [ 6 , 13 ]. However, no studies have demonstrated that DECT alone can detect tumor-infiltrating areas or evaluate malignancy. In DECT, ED can be quantitatively measured and determined with high accuracy. Here, we aimed to study the ED image obtained using DECT to compare the enhancing area of HGG obtained on GdT1 MRI and analyze its effectiveness in evaluating malignancy. Material and Methods Patients This retrospective study was single-institution study. This study was conducted according to the principles of the Declaration of Helsinki, and approved by the research ethics committee of university of Fukui (IRB#20230130), and the requirement for informed consent was waived. Patients diagnosed with HGG based on the 2021 World Health Organization guidelines, histopathological findings, and genetic patterns who underwent DECT and MRI between January 2021 and October 2023 in our hospital were included in this study [ 11 ]. The time interval from DECT to MRI being performed for evaluation in this study was up to 2 weeks. Patients with noticeable enhanced masses on GdT1 MRI were included; those without enhanced masses or with only ring-enhanced masses were excluded because of the large heterogeneity due to necrosis and edema of cell density in the tumor. Finally, 10 patients—9 with glioblastomas and 1 with astrocytoma, grade IV—were enrolled in this study, comprising 4 males and 6 female patients (median age, 69.5 [range, 30–82] years, Table 1 ). Table 1 Characteristics of the study patients Characteristic Value Age, years (median, range) 69.5, 30-82 Sex (no, %) Male 4 (40) Female 6 (60) Diagnosis (no, %) Glioblastoma 9 (90) Astrocytoma, grade IV 1 (10) Minimum rED of the tumor area 1.026 ± 0.005 Minimum rED of the CLWM area 1.025 ± 0.004 Mean rED of the tumor area 1.031 ± 0.005 Mean rED of the CLWM area 1.028 ± 0.004 Maximum rED of the tumor area 1.036 ± 0.005 Maximum rED of the CLWM area 1.031 ± 0.004 ED = electron density, rED = relative electron density, CLWM = contralateral white matter Dual-energy computed tomography All CT examinations were performed using a dual-source DECT scanner (SOMATOM Force; Siemens Healthineers, Forchheim, Germany). Non-contrast DECT acquisition was performed in a dual-energy acquisition mode with the following scan parameters: pair of tube voltages, 80 kV and 150 kV with tin filtration (Sn150 kV); X-ray beam width, 64 × 0.6 mm; rotation time, 1.0 s; and pitch, 0.8. The effective tube current time values were set to 800 mAs at 80 kV and 533 mAs at Sn150 kV. The mean CT volume dose index was approximately 65.82 mGy. DE datasets (80 kV and Sn150 kV) were reconstructed using a quantitative convolution kernel of Qr40 and an iterative reconstruction algorithm ADMIRE (Siemens Healthineers, Forchheim Germany) of strength 3, with an increment of 0.6 mm and a slice thickness of 1 mm. All image data were transferred to a dedicated dual-energy application software on a workstation syngo.via (Siemens Healthineers, Forchheim, Germany) to generate the ED images. Magnetic resonance imaging All MRI scans were obtained using a 3.0-T clinical MRI unit (Discovery MR750, GE Healthcare, Milwaukee, WI, USA) with a 32-channel head receiver array and a body transmission coil system. Standard MRIs (plain and GdT1) were performed during the examination. The acquisition parameters of standard MRIs were as follows: GdT1, three-dimensional (3D) inversion-recovery prepared spoiled gradient recalled echo sequence; repetition time, 6.6 ms; echo time, 2.1 ms; field of view, 240 × 216 mm; slice thickness, 1.4 mm; inversion time, 700 ms; flip angle, 10°; matrix size, 420 × 192; parallel imaging factor, 2; and average, 1. Data processing and image evaluation First, an enhanced mass was assessed using GdT1 MRI (Fig. 1 a and b). All ED images were smoothed using 3D median and Gaussian blur filters using the ImageJ software (version 1.54g, National Institutes of Health, Bethesda, Maryland, USA) to remove significant image noise. A lookup table of the GEM was used, and the display window level/width was optimized to emphasize the tumor (Fig. 1 c and d). A radiological technician with 29 years of experience performed the data processing. Two board-certified neurosurgeons with 13 and 14 years of experience and a radiologist with 12 years of experience analyzed the area of each image. For measurement, a slice of the gadolinium-enhanced image with the maximum enhanced mass area was selected by a radiologist with 12 years of experience and neurosurgeon with 13 years of experience, and the same slice on the ED image was selected. In the ED image after processing, the area of high ED was surrounded freehand as a region of interest (ROI) on ImageJ (Fig. 2 a and b). The high ED area was defined as upper density than density of contralateral structure visually. This ROI was drawn based on the following rules for both DECT and MRI: continuous high ED area without cysts and ring enhancement with an evident necrotic core on MRI, excluding skip lesions with high ED on the same surrounding edema. As the ratios of length and width in each CT and MRI were different, the length and width of the brain on the same slice were measured to correct for the differences between CT and MRI measurements (Fig. 2 c). On GdT1 MRI, the enhanced area was surrounded and the length and width of the brain on the same slice were measured using the same method (Fig. 2 e and f). In the ED image, the Hounsfield unit (HU) on a round ROI of 200 mm 2 in the normal contralateral white matter (CLWM) based on FLAIR image was measured by a board-certified neurosurgeon with 13 years of experience to compare the relative ED (rED, relative to ED of water) between the tumor area (Fig. 2 b) and the CLWM (Fig. 2 d). rED was manually calculated using the following formula: ρ e (rED) = Δ HU/1000 + 1 Statistical analyses Linear regression analysis was performed between the areas after correction by each brain (brain: length × width) on CT and MRI for each rater. Inter-rater reliability (IRR) for the area ratio of high ED/brain, Gd enhancement/brain, ratio of area ratio of high ED/brain (HEDA), and Gd enhancement/brain (GDA) was evaluated using the intraclass correlation coefficient (ICC). The minimum, mean and maximum rED were examined using Student’s t-test. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of rED and CLWM were calculated. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) for rED were calculated. All analyses were performed using the JMP® 12.2 software (SAS Institute Inc., Cary, NC, USA). In all tests, a p value < 0.05 was considered statistically significant. Results Relationship between high ED and gadolinium enhancement area Ten slices of ED and GdT1 images from 10 patients with HGG were measured by three raters (two board-certified neurosurgeons with 13 and 14 years of experience and a radiologist with 12 years of experience). In regression analysis, the area ratios of high ED/brain and Gd enhancement/brain were linearly related. The graph showed a positive correlation between the area ratio of high ED/brain and Gd enhancement/brain in all raters (Fig. 3 a: R 2 = 0.910, P < .0001, board-certified neurosurgeon with 13 years of experience; B: R 2 = 0.857, P = 0.0001, board-certified neurosurgeon with 14 years of experience; C: R 2 = 0.717, P = 0.002, radiologist with 12 years of experience). Among the three raters, the ICCs of HEDA, GDA, and the HEDA/GDA ratio were 0.98, 0.97 and 0.75, respectively (Table 2 ). Table 2 Inter-rater reliability for the area ratio of high ED and Gd enhancement Parameter ICC Area ratio of high ED/brain 0.98 Area ratio of Gd enhancement/brain 0.97 Ratio of HEDA/GDA 0.75 ED = electron density, Gd = gadolinium, ICC = intraclass correlation coefficient, HEDA = area ratio of high ED/brain, GDA = area ratio of Gd enhancement/brain Comparison between relative ED of the tumor area and CLWM In rED measurement (Table 1 ), the minimum rEDs of the tumor area and CLWM were 1.026 ± 0.005 and 1.025 ± 0.004, and the difference was not significant (Fig. 4 a, P = 0.441). However, the mean and maximum rEDs of the tumor area and CLWM were 1.031 ± 0.005 and 1.028 ± 0.04, and 1.036 ± 0.005 and 1.031 ± 0.04, respectively. The mean and maximum rED of the tumor area were significantly higher than that of the CLWM (Fig. 4 c: mean rED, P = 0.049/4e: maximum rED, P = 0.0002). In a representative case, specimens from the high ED- and Gd-enhanced areas showed high cell density (Fig. 2 g) and 10% in Ki-67 (Fig. 2 h). Figure 4 [b (minimum rED), d (mean rED), and f (maximum rED)] shows the ROC curves. In the minimum rED, the AUC, cut-off value, sensitivity, specificity, PPV, and NPV were 0.654, 1.025, 55.6%, 77.8%, 75.0% and 70.0%, respectively. In the mean rED, the AUC, cut-off value, sensitivity, specificity, PPV, and NPV were 0.802, 1.028, 77.8%, 77.8%, 77.8% and 77.8%, respectively. In contrast, in the maximum rED, the AUC, cut-off value, sensitivity, specificity, PPV, and NPV were 0.962, 1.031, 100%, 77.8%, 81.8%, and 100%, respectively (Table 3 ). Table 3 Diagnostic accuracy of rED in differentiating HGG and CLWM Parameter AUC Cut-off value Sensitivity (%) Specificity (%) PPV (%) NPV (%) Minimum rED 0.654 1.025 55.6 77.8 75.0 70.0 Mean rED 0.802 1.028 77.8 77.8 77.8 77.8 Maximum rED 0.962 1.031 100 77.8 81.8 100 rED = relative electron density, HGG = high-grade glioma, CLWM = contralateral white matter, AUC = area under curve, PPV = positive predictive value, NPV = negative predictive value Discussion DECT and rED are primarily used in calculating radiotherapy dose and treatment planning [ 7 ]. DECT is effective for differentiating benign and malignant tumors in hepatocellular carcinoma, prostate carcinoma, and cervical cancer [ 10 , 12 , 18 , 25 , 26 ]. In brain tumors, ED may correlate with tumor histology, glioma grading, and recurrence [ 2 , 6 , 13 ]. However, several quantitative studies have been conducted on this aspect. When used only for cerebral infarctions, Noguchi et al. reported that X-map, a novel imaging technique in DECT, could visualize ischemic lesions [ 15 ]. In this study, we emphasized and visualized HGG lesions using ED images, such as Gd-enhanced MRI (Figs. 1 and 2 ). This is the first study to visualize and emphasize brain tumor-like enhanced images without contrast medium. Our data showed that the high ED area of the HGG correlated with the Gd enhancement area on the image. Although a difference existed in the range of correlation among the raters, including radiological technician, experienced neurosurgeons, all measurements from each of the three raters showed significant results. In various evaluations from different positions and experiences, the ICC for the HEDA/GDA ratio was 0.75 (value of over 0.7 means OK in IRR). Our results indicate that ED images obtained with DECT without a contrast medium may be capable of drawing images, such as GdT1 in HGG. MR images are effective for deciding tumor invasion area and using contrast medium emphasizes more. However, MRI cannot be performed for certain reasons, such as the presence of pacemaker, shunt system, and body metal and art, and conditions such as claustrophobia. Furthermore, the use of gadolinium in patients with allergy, asthma, and renal dysfunction remains a challenge. In this study, ED image without contrast medium resembled Gd-enhanced image on HGG. HGG patients in whom enhanced MRI cannot be conducted, due to the above-mentioned reasons, ED image may be useful. Furthermore, CT is inexpensive and requires a shorter examination time than does MRI. Contrast medium imaging emphasizes vessels, high blood volume, and disruption of the blood–brain barrier, whereas ED imaging emphasizes high ED tissues. Our Gd-enhanced sample and high ED area showed high cell density and malignancy (Fig. 2 g and h), and the rED in the tumor area was higher than that in the CLWM in this study. Both contrast medium and ED images indirectly reveal the presence of tumor cells, and abnormal emphasis in the brain may predict the presence of high malignancy and density of tumor cells. Some studies have reported that ED and rED can predict glioma malignancy [ 2 , 6 ]. Based on our results, both area and quantitative analyses of the ED-emphasized area on ED images may show high-density areas in HGG with the same Gd enhancement on MRI as the gold standard for HGG. In the quantitative analysis of rEDs, the maximum rED showed the biggest difference (Fig. 4 a, c, and e). HGG includes strong tumor heterogeneity through a homogeneously enhanced mass, and ED may reflect tumor heterogeneity. By contrast, cell heterogeneity is rare in normal brains, and the presence of heterogeneity in ED images may indicate high malignancy. Our results demonstrated the usefulness of using the ED on CT in HGG with only an evident mass. A cut-off value of 1.031 of the maximum rED distinguishes high density of tumor cells from normal brain. Typical HGG and glioblastoma often exhibit ring enhancement, with several contents such as abnormal vessels, necrosis, inflammatory cells, and tumor cells are noted in this area. We presume that high ED implies high cell density; however, thin membrane of the ring enhancement indicates abnormal vessel and not high cell density. Furthermore, the inside of the ring enhancement is almost necrosis. We excluded the ring-enhanced shape due to risk of uncertain measurement of ED, and only 10 cases with noticeably enhanced mass were included. However, as several HGGs exhibit ring enhancement, ED images of all HGG shapes should be included in future studies. Furthermore, our study presents results from only one shape with noticeable mass, from among the several types of HGG shape. ED tends to be higher near the bone because of CT; however, the underlying mechanism of this type of occurrence in HGG remains unclear. Thus, elucidation of this aspect and the pathological evidence needs to be clarified. It is challenging to visualize all shapes of HGG, including cysts and ring enhancements. In future studies, additional cases may make our results more applicable to HGGs with various shapes. Conclusions To our knowledge, this is the first study to visualize brain tumor using ED image obtained with DECT, which showed a high ED area, similar to the Gd enhancement area in the HGG; furthermore, the rED was higher in this area than in the CLWM, especially remarkable in the maximum rED. This method may help diagnose HGG in patients who cannot undergo MRI because of various limitations. Declarations Acknowledgments We would like to express our appreciation to Yuki Matta and Koki Takahashi (Department of Radiology, University of Fukui Hospital) who helped with the data processing. Conflicts of interest All authors have no financial conflicts of interest to disclose concerning this article. Author contributions All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by Takahiro Yamauchi, Tomokazu Ishida, Toshihide Itoh, Tadahiro Tsubota, Kenji Takata, Yoshifumi Higashino, Tetsuya Tsujikawa and Ken-ichiro Kikuta. The first draft of the manuscript was written by Takahiro Yamauchi and all authors commented on the previous manuscript. All authors read and approved the final manuscript. S upplementary information NA Funding No funding was received for the research. Data availability NA Code availability NA Ethical approval This retrospective study was approved by the research ethics committee of university of Fukui (IRB#20230130). Consent to participate Opt-out method was received in this study. Consent for publication NA References Brendle C, Hempel JM, Schittenhelm J, Skardelly M, Tabatabai G, Bender B, Ernemann U, Klose U. Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol. 2018;28:421–428 Chakrabarti R, Gupta Vm Vyas S. Correlation of dual energy computed tomography electron density measurements with cerebral glioma grade. Neuroradiol J. 2022;35:352–362 Committee of Brain Tumor Registry of Japan, Report of Brain Tumor Registry of Japan (2005–2008) 14th Edition. Neurol Med Chir. 2017;57:1–102 Crain ID, Elias PS, Chapple K, Scheck AC, Karis JP, Preul MC. Improving the utility of 1h-mrs for the differentiation of glioma recurrence from radiation necrosis. J Neurooncol. 2017;133:97–105 Domingo LR. Molecular electron density theory: a modern view of reactivity in organic chemistry. Molecules. 2016;21:1319 Kaichi Y, Tatsugami F, Nakamura Y, Baba Y, Iida M, Higaki T, Kiguchi M, Tsushima S, Yamasaki F, Amatya VJ, Takeshima Y, Kurisu K, Awai K. Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT. Medicine (Baltimore). 2018;97:e11670 Kassaee A, Cheng C, Yin L, Zou W, Li T, Lin A, Swisher-McClure S, Lukens JN, Lustig RA, O'Reilly S, Dong L, Ms RH, Teo BK. Dual-energy computed tomography proton-dose calculation with scripting and modified Hounsfield units. Int J Part Ther. 2021;8:62–72 Kong L, Chen H, Yang Y, Chen L. A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clin Radiol. 2017;72:255–261 Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R; A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg. 2001;95:190–198 Li M, Zheng X, Li J, Yang Y, Lu C, Xu H, Yu B, Xiao L, Zhang G, Hua Y. Dual-energy computed tomography imaging of thyroid nodule specimens: comparison with pathologic findings. Invest Radiol. 2012;47:58–64 Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23:1231–1251 Lv P, Lin XZ, Li J, Li W, Chen K. Differentiation of small hepatic hemangioma from small hepatocellular carcinoma: recently introduced spectral CT method. Radiology. 2011;259:720–729 Lv Y, Zhou J, Lv X, Tian L, He H, Liu Z, Wu Y, Han L, Sun M, Yang Y, Guo C, Li C, Zhang R, Xie C, Chen Y, Chen Z. Dual-energy spectral CT quantitative parameters for the differentiation of glioma recurrence from treatment-related changes: a preliminary study. BMC Med Imaging. 2020;20:5 Ma H, Wang Z, Xu K, Shao Z, Yang C, Xu P, Liu X, Hu C, Lu X, Rong Y. Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery. Exp Ther Med. 2017;13:2691–698 Noguchi K, Itoh T, Naruto N, Takashima S, Tanaka K, Kuroda S. A novel imaging technique (X-Map) to identify acute ischemic lesions using noncontrast dual-energy computed tomography. J Stroke Cerebrovasc Dis. 2017;26:34–41 Nyberg E, Honce J, Kleinschmidt-DeMasters BK, Shukri B, Kreidler S, Nagae L. Arterial spin labelling: pathologically proven superiority over conventional MRI for detection of high-grade glioma progression after treatment. Neuroradiol J. 2016;29:377–383 Parsons DW, Jones S, Zhang X, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA Jr, Hartigan J, Smith DR, Strausberg RL, Marie SK, Shinjo SM, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW. An integrated genomic analysis of human glioblastoma multiforme. Science. 2008;321:1807–1812 Srinivasan A, Parker RA, Manjunathan A, Ibrahim M, Shah GV, Mukherji SK. Differentiation of benign and malignant neck pathologies: preliminary experience using spectral computed tomography. J Comput Assist Tomogr. 2013;37:666–672 Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO; European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med .2005;352:987–996 Togao O, Yoshiura T, Keupp J, Hiwatashi A, Yamashita K, Kikuchi K, Suzuki Y, Suzuki SO, Iwaki T, Hata N, Mizoguchi M, Yoshimoto K, Sagiyama K, Takahashi M, Honda H (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 2014;16:441 – 418 Van Abbema JK, Van der Schaaf A, Kristanto W, Groen JM, Greuter MJ. Feasibility and accuracy of tissue characterization with dual source computed tomography. Phys Med. 2012;28:25–32 Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28:1963–1972 Yamauchi T, Kitai R, Kodera T, Arishima H, Matsuda K, Isozaki M, Ishida S, Matta Y, Kanamoto M, Kimura H, Kikuta K. Comparison of amide proton transfer imaging with perfusion imaging of using arterial spin-labeling for evidence of tumor invasion in glioblastoma. Interdiscip Neurosurg Adv Tech Case Manag. 2022;28:101461 Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2013;360(8):765–773 Yu Y, Lin X, Chen K, Chai W, Hu S, Tang R, Zhang J, Cao L, Yan F. Hepatocellular carcinoma and focal nodular hyperplasia of the liver: differentiation with CT spectral imaging. Eur Radiol. 2013;23:1660–1668 Zhang XF, Lu Q, Wu LM, Zou AH, Hua XL, Xu JR. Quantitative iodine-based material decomposition images with spectral CT imaging for differentiating prostatic carcinoma from benign prostatic hyperplasia. Acad Radiol. 2013;20:947–956 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Nov, 2024 Reviews received at journal 04 Nov, 2024 Reviews received at journal 25 Sep, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers invited by journal 28 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 22 Aug, 2024 First submitted to journal 08 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4708655","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357429717,"identity":"d4b5f81f-5d52-4391-99eb-1d58c8ca5ea4","order_by":0,"name":"Takahiro Yamauchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACNgkGZoYEBgY5BgbGB4wNDBJgUQNitBgzMDAbEKeFAaQFCBIbIFqIAHzSzYcNHu6oTd9wgJlNcmabBQN/+wGG4gJ8DpM5lpyQeOZ4LljLxjYJBokzCQzGM/D6Jcf4QGLbsdwN998fk3wI1MJwA+gzHiK0pBuAbAFpkSdGS0JiW02CAcxhBgS1AP1ikNh2wHDmAWZmyxnnJHgMzyQ24PWL/Ozmw5I/2+rk+Q4wM97sKauTkzt++JgxvhCDgsNwFtBJjG3GhHUw1KHwmB8ToWUUjIJRMApGDgAAKsFF6PGhSDUAAAAASUVORK5CYII=","orcid":"","institution":"University of Fukui","correspondingAuthor":true,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"Yamauchi","suffix":""},{"id":357429718,"identity":"ba8ba1eb-bb77-4856-9c4c-2d4e3d072a81","order_by":1,"name":"Tomokazu Ishida","email":"","orcid":"","institution":"Fujita Medical Innovation Center Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Tomokazu","middleName":"","lastName":"Ishida","suffix":""},{"id":357429722,"identity":"06c208ce-53d3-472a-913e-1de4e484e401","order_by":2,"name":"Toshihide Itoh","email":"","orcid":"","institution":"Siemens Healthineers","correspondingAuthor":false,"prefix":"","firstName":"Toshihide","middleName":"","lastName":"Itoh","suffix":""},{"id":357429723,"identity":"9c832119-3a93-476f-a0cf-ba9c343857e8","order_by":3,"name":"Tadahiro Tsubota","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Tadahiro","middleName":"","lastName":"Tsubota","suffix":""},{"id":357429725,"identity":"1f5cd3c0-cdbd-45ee-a8fe-c5677de41503","order_by":4,"name":"Kenji Takata","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Takata","suffix":""},{"id":357429726,"identity":"477dddf8-51f3-4872-a74d-9e23649bc560","order_by":5,"name":"Yoshifumi Higashino","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Yoshifumi","middleName":"","lastName":"Higashino","suffix":""},{"id":357429730,"identity":"afa60e36-f8d6-4892-b4a1-1b38bccd8e19","order_by":6,"name":"Tetsuya Tsujikawa","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Tetsuya","middleName":"","lastName":"Tsujikawa","suffix":""},{"id":357429731,"identity":"32a3e8e3-352f-4279-bca7-0159d16141fc","order_by":7,"name":"Ken-ichiro Kikuta","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Ken-ichiro","middleName":"","lastName":"Kikuta","suffix":""}],"badges":[],"createdAt":"2024-07-09 02:47:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4708655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4708655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66676061,"identity":"9c77f29b-7a81-4fdd-82cc-30998b9096e1","added_by":"auto","created_at":"2024-10-15 11:12:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":662398,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative MRI and CT images of a 30-year-old woman with astrocytoma, isocitrate dehydrogenase-mutant, grade IV. (a) T1WI image shows a large low-intensity area in the right frontal and temporal lobes and basal ganglia. (b) After gadolinium enhancement, the main large enhanced mass and several enhanced skip lesions are emphasized in the same low-intensity area. (c) Mixed CT image of the same slice on GdT1 shows an abnormal area with heterogeneous density in the right frontal and temporal lobes and basal ganglia. (d) Electron density image obtained from dual-energy CT after processing and using the lookup table of GEM. A large high-electron-density mass is emphasized in almost the same lesion as large enhanced mass on gadolinium-enhanced T1-weighted image\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4708655/v1/c12bcc008c9a9cbb6a5d11fe.png"},{"id":66675125,"identity":"d1cdf550-4fad-46d1-a735-74d9afdc4188","added_by":"auto","created_at":"2024-10-15 11:04:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":792567,"visible":true,"origin":"","legend":"\u003cp\u003eBiopsy case of a 76-year-old woman with glioblastoma, isocitrate dehydrogenase-wild type. (a) Mixed CT image shows an abnormality in the left parietal lobe. After optimizing the electron density (ED) image, high ED area of the left parietal lobe is surrounded by free hand as a region of interest (ROI) on ImageJ (b, free line). For correction difference between CT and MRI, the length and width of the brain on the same slice are measured (c, both arrow lines). Moreover, the Hounsfield unit on round ROI of 200 mm\u003csup\u003e2\u003c/sup\u003e in the normal contralateral white matter is measured to compare relative ED between the tumor and this area (d, round). In gadolinium-enhanced T1-weighted image MRI, enhanced area is surrounded (e, free line) and the length and width of the brain on the same slice (f, both arrow lines) are measured using the same method used to ED image. Specimens from high ED and Gd-enhanced area show high cell density (g, hematoxylin and eosin stain; original magnification, ×200) and 10% in Ki-67 (h, original magnification, ×400)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4708655/v1/7067ae086c6a65684e80cf77.png"},{"id":66674172,"identity":"a1c913df-ff3f-4d71-8c1d-7d848ef37980","added_by":"auto","created_at":"2024-10-15 10:56:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84475,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis between the area ratio of high electron density (ED)/brain and gadolinium (Gd) enhancement/brain. All graphs show positive correlation between the area ratio of high ED/brain and Gd enhancement/brain (A: R\u003csup\u003e2\u003c/sup\u003e = .910, P \u0026lt; .0001, board-certified neurosurgeon with 13 years of experience; B: R\u003csup\u003e2\u003c/sup\u003e = .857, P = .0001, board-certified neurosurgeon with 14 years of experience; C: R\u003csup\u003e2\u003c/sup\u003e = .717, P = .002, radiologist with 12 year of experience)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4708655/v1/9e102f57fd0f8f91f185dc95.png"},{"id":66674175,"identity":"1841154b-3d80-44a8-ba04-a3a012b74948","added_by":"auto","created_at":"2024-10-15 10:56:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222694,"visible":true,"origin":"","legend":"\u003cp\u003ea and b present the correlation between contralateral white matter (CLWM) and high-electron-density (ED) area in minimum relative ED. The box and whisker plot depicts the distribution of mean relative ED values for CLWM and high ED area (a). No significant difference is found between the two groups (P = .441). (b) Graph shows area under the receiver operating characteristic (ROC) curve for mean relative ED. The area under the curve (AUC) value is 0.654. c and d present the correlation between CLWM and high ED area in mean relative ED. The box and whisker plot depicts the distribution of the mean relative ED values for the CLWM and high ED areas (c). Significant difference is found between the two groups (P = .049). (d) The graph shows the area under ROC curve for the mean relative ED. The AUC value is 0.802. e and f present the correlation between CLWM and high ED area in maximum relative ED. The box and whisker plot depicts the distribution of the maximum relative ED values for the CLWM and high ED areas (e). Significant difference is found between the two groups (P = .0002). (f) The graph shows the area under ROC curve for the maximum relative ED. The AUC value is 0.962\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4708655/v1/3d30c77a008add910547efcf.png"},{"id":66676082,"identity":"d990a278-c34b-425a-aa48-cd8288bce185","added_by":"auto","created_at":"2024-10-15 11:12:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2713306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4708655/v1/8cf9b46d-21bb-4fed-b9fd-03ce9b161a1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison between electron density imaging with dual-energy computed tomography without contrast medium and magnetic resonance imaging for high-grade glioma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma is an aggressive tumor, with the treatment of high-grade glioma (HGG) being difficult, especially glioblastoma, isocitrate dehydrogenase (IDH)-wildtype and astrocytoma, IDH-mutant, grade IV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. HGG treatment consists of surgical resection, followed by radiation and chemotherapy. Therefore, the surgical resection rate and grading of HGG are related to prognosis; further, preoperative diagnosis, including genetic information and prediction of infiltrating tumor cells, is important [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. During glioma surgery, several imaging studies are required for safe and maximal resection. Magnetic resonance imaging (MRI) is the gold standard for diagnosing glioma, especially in HGG, and gadolinium-enhanced T1-weighted image (GdT1) is effective for determining diagnosis for subtypes, tumor-infiltrating area and malignancy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have reported that advanced MRI techniques, including diffusion-weighted imaging, amide proton transfer imaging, arterial spin labeling, and MR spectroscopy, are useful for grading gliomas, determining tumor resection area, and predicting recurrence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although MRI is essential for glioma treatment, it cannot be performed on some patients due to claustrophobia or because of the presence of metal or pacemaker in their body. Artifacts due to the presence of metal (coil, clip) and movement over a long examination time may decrease MRI accuracy. Furthermore, MRI is expensive and requires maintenance procedures. Additionally, several patients with HGG require an examination using a contrast medium [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Using a contrast medium is invasive due to insertion of needle and risk of allergy and anaphylactic shock, and it cannot be used in some patients because of renal dysfunction or allergy. In the MRI era, the use of X-ray computed tomography (CT) to detect brain tumors, including glioma, is relatively ineffective. Recently, dual-energy CT (DECT) has been reported to be useful for differentiating glioma grade and recurrence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDECT is a CT technique that uses two different photon spectra. A major advantage of DECT is its material-differentiation ability, which is based on high- and low-peak X-ray tube voltage acquisition [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. One aspect that can be measured using DECT is electron density (ED). ED indicates the probability of an electron being present at a specific point in space, which depends on the type of tissue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several studies have used DECT for the prediction of glioma grading and malignancy, in addition to MRI findings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, no studies have demonstrated that DECT alone can detect tumor-infiltrating areas or evaluate malignancy. In DECT, ED can be quantitatively measured and determined with high accuracy.\u003c/p\u003e \u003cp\u003eHere, we aimed to study the ED image obtained using DECT to compare the enhancing area of HGG obtained on GdT1 MRI and analyze its effectiveness in evaluating malignancy.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis retrospective study was single-institution study. This study was conducted according to the principles of the Declaration of Helsinki, and approved by the research ethics committee of university of Fukui (IRB#20230130), and the requirement for informed consent was waived.\u003c/p\u003e \u003cp\u003ePatients diagnosed with HGG based on the 2021 World Health Organization guidelines, histopathological findings, and genetic patterns who underwent DECT and MRI between January 2021 and October 2023 in our hospital were included in this study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The time interval from DECT to MRI being performed for evaluation in this study was up to 2 weeks. Patients with noticeable enhanced masses on GdT1 MRI were included; those without enhanced masses or with only ring-enhanced masses were excluded because of the large heterogeneity due to necrosis and edema of cell density in the tumor. Finally, 10 patients\u0026mdash;9 with glioblastomas and 1 with astrocytoma, grade IV\u0026mdash;were enrolled in this study, comprising 4 males and 6 female patients (median age, 69.5 [range, 30\u0026ndash;82] years, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge, years (median, range)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.5, 30-82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex (no, %)\u003c/p\u003e \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 \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\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\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 \u003cp\u003e4 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\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\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 \u003cp\u003e6 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiagnosis (no, %)\u003c/p\u003e \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 \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlioblastoma\u003c/p\u003e \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 \u003cp\u003e9 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAstrocytoma, grade IV\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMinimum rED of the tumor area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.026\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMinimum rED of the CLWM area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMean rED of the tumor area\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMean rED of the CLWM area\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMaximum rED of the tumor area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.036\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMaximum rED of the CLWM area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eED\u0026thinsp;=\u0026thinsp;electron density, rED\u0026thinsp;=\u0026thinsp;relative electron density, CLWM\u0026thinsp;=\u0026thinsp;contralateral white matter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDual-energy computed tomography\u003c/h2\u003e \u003cp\u003eAll CT examinations were performed using a dual-source DECT scanner (SOMATOM Force; Siemens Healthineers, Forchheim, Germany). Non-contrast DECT acquisition was performed in a dual-energy acquisition mode with the following scan parameters: pair of tube voltages, 80 kV and 150 kV with tin filtration (Sn150 kV); X-ray beam width, 64 \u0026times; 0.6 mm; rotation time, 1.0 s; and pitch, 0.8. The effective tube current time values were set to 800 mAs at 80 kV and 533 mAs at Sn150 kV. The mean CT volume dose index was approximately 65.82 mGy.\u003c/p\u003e \u003cp\u003eDE datasets (80 kV and Sn150 kV) were reconstructed using a quantitative convolution kernel of Qr40 and an iterative reconstruction algorithm ADMIRE (Siemens Healthineers, Forchheim Germany) of strength 3, with an increment of 0.6 mm and a slice thickness of 1 mm. All image data were transferred to a dedicated dual-energy application software on a workstation syngo.via (Siemens Healthineers, Forchheim, Germany) to generate the ED images.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMagnetic resonance imaging\u003c/h2\u003e \u003cp\u003eAll MRI scans were obtained using a 3.0-T clinical MRI unit (Discovery MR750, GE Healthcare, Milwaukee, WI, USA) with a 32-channel head receiver array and a body transmission coil system. Standard MRIs (plain and GdT1) were performed during the examination. The acquisition parameters of standard MRIs were as follows: GdT1, three-dimensional (3D) inversion-recovery prepared spoiled gradient recalled echo sequence; repetition time, 6.6 ms; echo time, 2.1 ms; field of view, 240 \u0026times; 216 mm; slice thickness, 1.4 mm; inversion time, 700 ms; flip angle, 10\u0026deg;; matrix size, 420 \u0026times; 192; parallel imaging factor, 2; and average, 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData processing and image evaluation\u003c/h2\u003e \u003cp\u003eFirst, an enhanced mass was assessed using GdT1 MRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and b). All ED images were smoothed using 3D median and Gaussian blur filters using the ImageJ software (version 1.54g, National Institutes of Health, Bethesda, Maryland, USA) to remove significant image noise. A lookup table of the GEM was used, and the display window level/width was optimized to emphasize the tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA radiological technician with 29 years of experience performed the data processing. Two board-certified neurosurgeons with 13 and 14 years of experience and a radiologist with 12 years of experience analyzed the area of each image. For measurement, a slice of the gadolinium-enhanced image with the maximum enhanced mass area was selected by a radiologist with 12 years of experience and neurosurgeon with 13 years of experience, and the same slice on the ED image was selected. In the ED image after processing, the area of high ED was surrounded freehand as a region of interest (ROI) on ImageJ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and b). The high ED area was defined as upper density than density of contralateral structure visually. This ROI was drawn based on the following rules for both DECT and MRI: continuous high ED area without cysts and ring enhancement with an evident necrotic core on MRI, excluding skip lesions with high ED on the same surrounding edema. As the ratios of length and width in each CT and MRI were different, the length and width of the brain on the same slice were measured to correct for the differences between CT and MRI measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). On GdT1 MRI, the enhanced area was surrounded and the length and width of the brain on the same slice were measured using the same method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the ED image, the Hounsfield unit (HU) on a round ROI of 200 mm\u003csup\u003e2\u003c/sup\u003e in the normal contralateral white matter (CLWM) based on FLAIR image was measured by a board-certified neurosurgeon with 13 years of experience to compare the relative ED (rED, relative to ED of water) between the tumor area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) and the CLWM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). rED was manually calculated using the following formula:\u003c/p\u003e \u003cp\u003e \u003cb\u003eρ\u003c/b\u003e \u003csub\u003ee\u003c/sub\u003e (rED) = \u003cb\u003eΔ\u003c/b\u003eHU/1000\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eLinear regression analysis was performed between the areas after correction by each brain (brain: length \u0026times; width) on CT and MRI for each rater. Inter-rater reliability (IRR) for the area ratio of high ED/brain, Gd enhancement/brain, ratio of area ratio of high ED/brain (HEDA), and Gd enhancement/brain (GDA) was evaluated using the intraclass correlation coefficient (ICC). The minimum, mean and maximum rED were examined using Student\u0026rsquo;s t-test. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of rED and CLWM were calculated. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) for rED were calculated. All analyses were performed using the JMP\u0026reg; 12.2 software (SAS Institute Inc., Cary, NC, USA). In all tests, a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between high ED and gadolinium enhancement area\u003c/h2\u003e \u003cp\u003eTen slices of ED and GdT1 images from 10 patients with HGG were measured by three raters (two board-certified neurosurgeons with 13 and 14 years of experience and a radiologist with 12 years of experience). In regression analysis, the area ratios of high ED/brain and Gd enhancement/brain were linearly related. The graph showed a positive correlation between the area ratio of high ED/brain and Gd enhancement/brain in all raters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.910, P\u0026thinsp;\u0026lt;\u0026thinsp;.0001, board-certified neurosurgeon with 13 years of experience; B: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.857, P\u0026thinsp;=\u0026thinsp;0.0001, board-certified neurosurgeon with 14 years of experience; C: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.717, P\u0026thinsp;=\u0026thinsp;0.002, radiologist with 12 years of experience).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the three raters, the ICCs of HEDA, GDA, and the HEDA/GDA ratio were 0.98, 0.97 and 0.75, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eInter-rater reliability for the area ratio of high ED and Gd enhancement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea ratio of high ED/brain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea ratio of Gd enhancement/brain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of HEDA/GDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eED\u0026thinsp;=\u0026thinsp;electron density, Gd\u0026thinsp;=\u0026thinsp;gadolinium, ICC\u0026thinsp;=\u0026thinsp;intraclass correlation coefficient, HEDA\u0026thinsp;=\u0026thinsp;area ratio of high ED/brain, GDA\u0026thinsp;=\u0026thinsp;area ratio of Gd enhancement/brain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eComparison between relative ED of the tumor area and CLWM\u003c/h2\u003e \u003cp\u003eIn rED measurement (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the minimum rEDs of the tumor area and CLWM were 1.026\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 and 1.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004, and the difference was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, P\u0026thinsp;=\u0026thinsp;0.441). However, the mean and maximum rEDs of the tumor area and CLWM were 1.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 and 1.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, and 1.036\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 and 1.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, respectively. The mean and maximum rED of the tumor area were significantly higher than that of the CLWM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec: mean rED, P\u0026thinsp;=\u0026thinsp;0.049/4e: maximum rED, P\u0026thinsp;=\u0026thinsp;0.0002). In a representative case, specimens from the high ED- and Gd-enhanced areas showed high cell density (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg) and 10% in Ki-67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e [b (minimum rED), d (mean rED), and f (maximum rED)] shows the ROC curves. In the minimum rED, the AUC, cut-off value, sensitivity, specificity, PPV, and NPV were 0.654, 1.025, 55.6%, 77.8%, 75.0% and 70.0%, respectively. In the mean rED, the AUC, cut-off value, sensitivity, specificity, PPV, and NPV were 0.802, 1.028, 77.8%, 77.8%, 77.8% and 77.8%, respectively. In contrast, in the maximum rED, the AUC, cut-off value, sensitivity, specificity, PPV, and NPV were 0.962, 1.031, 100%, 77.8%, 81.8%, and 100%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic accuracy of rED in differentiating HGG and CLWM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum rED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean rED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum rED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003erED\u0026thinsp;=\u0026thinsp;relative electron density, HGG\u0026thinsp;=\u0026thinsp;high-grade glioma, CLWM\u0026thinsp;=\u0026thinsp;contralateral white matter, AUC\u0026thinsp;=\u0026thinsp;area under curve, PPV\u0026thinsp;=\u0026thinsp;positive predictive value, NPV\u0026thinsp;=\u0026thinsp;negative predictive value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\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\u003eDECT and rED are primarily used in calculating radiotherapy dose and treatment planning [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. DECT is effective for differentiating benign and malignant tumors in hepatocellular carcinoma, prostate carcinoma, and cervical cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In brain tumors, ED may correlate with tumor histology, glioma grading, and recurrence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, several quantitative studies have been conducted on this aspect. When used only for cerebral infarctions, Noguchi et al. reported that X-map, a novel imaging technique in DECT, could visualize ischemic lesions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, we emphasized and visualized HGG lesions using ED images, such as Gd-enhanced MRI (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is the first study to visualize and emphasize brain tumor-like enhanced images without contrast medium. Our data showed that the high ED area of the HGG correlated with the Gd enhancement area on the image. Although a difference existed in the range of correlation among the raters, including radiological technician, experienced neurosurgeons, all measurements from each of the three raters showed significant results. In various evaluations from different positions and experiences, the ICC for the HEDA/GDA ratio was 0.75 (value of over 0.7 means OK in IRR). Our results indicate that ED images obtained with DECT without a contrast medium may be capable of drawing images, such as GdT1 in HGG.\u003c/p\u003e \u003cp\u003eMR images are effective for deciding tumor invasion area and using contrast medium emphasizes more. However, MRI cannot be performed for certain reasons, such as the presence of pacemaker, shunt system, and body metal and art, and conditions such as claustrophobia. Furthermore, the use of gadolinium in patients with allergy, asthma, and renal dysfunction remains a challenge. In this study, ED image without contrast medium resembled Gd-enhanced image on HGG. HGG patients in whom enhanced MRI cannot be conducted, due to the above-mentioned reasons, ED image may be useful. Furthermore, CT is inexpensive and requires a shorter examination time than does MRI. Contrast medium imaging emphasizes vessels, high blood volume, and disruption of the blood\u0026ndash;brain barrier, whereas ED imaging emphasizes high ED tissues. Our Gd-enhanced sample and high ED area showed high cell density and malignancy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg and h), and the rED in the tumor area was higher than that in the CLWM in this study. Both contrast medium and ED images indirectly reveal the presence of tumor cells, and abnormal emphasis in the brain may predict the presence of high malignancy and density of tumor cells. Some studies have reported that ED and rED can predict glioma malignancy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Based on our results, both area and quantitative analyses of the ED-emphasized area on ED images may show high-density areas in HGG with the same Gd enhancement on MRI as the gold standard for HGG.\u003c/p\u003e \u003cp\u003eIn the quantitative analysis of rEDs, the maximum rED showed the biggest difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, c, and e). HGG includes strong tumor heterogeneity through a homogeneously enhanced mass, and ED may reflect tumor heterogeneity. By contrast, cell heterogeneity is rare in normal brains, and the presence of heterogeneity in ED images may indicate high malignancy. Our results demonstrated the usefulness of using the ED on CT in HGG with only an evident mass. A cut-off value of 1.031 of the maximum rED distinguishes high density of tumor cells from normal brain.\u003c/p\u003e \u003cp\u003eTypical HGG and glioblastoma often exhibit ring enhancement, with several contents such as abnormal vessels, necrosis, inflammatory cells, and tumor cells are noted in this area. We presume that high ED implies high cell density; however, thin membrane of the ring enhancement indicates abnormal vessel and not high cell density. Furthermore, the inside of the ring enhancement is almost necrosis. We excluded the ring-enhanced shape due to risk of uncertain measurement of ED, and only 10 cases with noticeably enhanced mass were included. However, as several HGGs exhibit ring enhancement, ED images of all HGG shapes should be included in future studies. Furthermore, our study presents results from only one shape with noticeable mass, from among the several types of HGG shape. ED tends to be higher near the bone because of CT; however, the underlying mechanism of this type of occurrence in HGG remains unclear. Thus, elucidation of this aspect and the pathological evidence needs to be clarified. It is challenging to visualize all shapes of HGG, including cysts and ring enhancements. In future studies, additional cases may make our results more applicable to HGGs with various shapes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e To our knowledge, this is the first study to visualize brain tumor using ED image obtained with DECT, which showed a high ED area, similar to the Gd enhancement area in the HGG; furthermore, the rED was higher in this area than in the CLWM, especially remarkable in the maximum rED. This method may help diagnose HGG in patients who cannot undergo MRI because of various limitations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our appreciation to Yuki Matta and Koki Takahashi (Department of Radiology, University of Fukui Hospital) who helped with the data processing.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflicts of interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no financial conflicts of interest to disclose concerning this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the\u0026nbsp;study\u0026rsquo;s\u0026nbsp;conception and design. Material preparation, data collection and analysis were performed by Takahiro Yamauchi, Tomokazu Ishida, Toshihide Itoh, Tadahiro Tsubota, Kenji Takata, Yoshifumi Higashino, Tetsuya Tsujikawa and Ken-ichiro Kikuta. The first draft of the manuscript was written by Takahiro Yamauchi and all authors commented on\u0026nbsp;the\u0026nbsp;previous manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eS\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eupplementary information\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCode availability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical approval\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;retrospective study was approved by the research ethics committee of university of Fukui\u0026nbsp;(IRB#20230130).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpt-out method was received in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrendle C, Hempel JM, Schittenhelm J, Skardelly M, Tabatabai G, Bender B, Ernemann U, Klose U. Glioma grading and determination of IDH mutation status and ATRX loss by DCE and ASL perfusion. Clin Neuroradiol. 2018;28:421\u0026ndash;428\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakrabarti R, Gupta Vm Vyas S. Correlation of dual energy computed tomography electron density measurements with cerebral glioma grade. Neuroradiol J. 2022;35:352\u0026ndash;362\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommittee of Brain Tumor Registry of Japan, Report of Brain Tumor Registry of Japan (2005\u0026ndash;2008) 14th Edition. Neurol Med Chir. 2017;57:1\u0026ndash;102\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrain ID, Elias PS, Chapple K, Scheck AC, Karis JP, Preul MC. Improving the utility of 1h-mrs for the differentiation of glioma recurrence from radiation necrosis. J Neurooncol. 2017;133:97\u0026ndash;105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomingo LR. Molecular electron density theory: a modern view of reactivity in organic chemistry. Molecules. 2016;21:1319\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaichi Y, Tatsugami F, Nakamura Y, Baba Y, Iida M, Higaki T, Kiguchi M, Tsushima S, Yamasaki F, Amatya VJ, Takeshima Y, Kurisu K, Awai K. Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT. Medicine (Baltimore). 2018;97:e11670\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassaee A, Cheng C, Yin L, Zou W, Li T, Lin A, Swisher-McClure S, Lukens JN, Lustig RA, O'Reilly S, Dong L, Ms RH, Teo BK. Dual-energy computed tomography proton-dose calculation with scripting and modified Hounsfield units. Int J Part Ther. 2021;8:62\u0026ndash;72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong L, Chen H, Yang Y, Chen L. A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clin Radiol. 2017;72:255\u0026ndash;261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R; A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg. 2001;95:190\u0026ndash;198\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Zheng X, Li J, Yang Y, Lu C, Xu H, Yu B, Xiao L, Zhang G, Hua Y. Dual-energy computed tomography imaging of thyroid nodule specimens: comparison with pathologic findings. Invest Radiol. 2012;47:58\u0026ndash;64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23:1231\u0026ndash;1251\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv P, Lin XZ, Li J, Li W, Chen K. Differentiation of small hepatic hemangioma from small hepatocellular carcinoma: recently introduced spectral CT method. Radiology. 2011;259:720\u0026ndash;729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv Y, Zhou J, Lv X, Tian L, He H, Liu Z, Wu Y, Han L, Sun M, Yang Y, Guo C, Li C, Zhang R, Xie C, Chen Y, Chen Z. Dual-energy spectral CT quantitative parameters for the differentiation of glioma recurrence from treatment-related changes: a preliminary study. BMC Med Imaging. 2020;20:5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa H, Wang Z, Xu K, Shao Z, Yang C, Xu P, Liu X, Hu C, Lu X, Rong Y. Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery. Exp Ther Med. 2017;13:2691\u0026ndash;698\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoguchi K, Itoh T, Naruto N, Takashima S, Tanaka K, Kuroda S. A novel imaging technique (X-Map) to identify acute ischemic lesions using noncontrast dual-energy computed tomography. J Stroke Cerebrovasc Dis. 2017;26:34\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyberg E, Honce J, Kleinschmidt-DeMasters BK, Shukri B, Kreidler S, Nagae L. Arterial spin labelling: pathologically proven superiority over conventional MRI for detection of high-grade glioma progression after treatment. Neuroradiol J. 2016;29:377\u0026ndash;383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParsons DW, Jones S, Zhang X, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA Jr, Hartigan J, Smith DR, Strausberg RL, Marie SK, Shinjo SM, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW. An integrated genomic analysis of human glioblastoma multiforme. Science. 2008;321:1807\u0026ndash;1812\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrinivasan A, Parker RA, Manjunathan A, Ibrahim M, Shah GV, Mukherji SK. Differentiation of benign and malignant neck pathologies: preliminary experience using spectral computed tomography. J Comput Assist Tomogr. 2013;37:666\u0026ndash;672\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO; European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med .2005;352:987\u0026ndash;996\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTogao O, Yoshiura T, Keupp J, Hiwatashi A, Yamashita K, Kikuchi K, Suzuki Y, Suzuki SO, Iwaki T, Hata N, Mizoguchi M, Yoshimoto K, Sagiyama K, Takahashi M, Honda H (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 2014;16:441\u0026thinsp;\u0026ndash;\u0026thinsp;418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Abbema JK, Van der Schaaf A, Kristanto W, Groen JM, Greuter MJ. Feasibility and accuracy of tissue characterization with dual source computed tomography. Phys Med. 2012;28:25\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28:1963\u0026ndash;1972\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamauchi T, Kitai R, Kodera T, Arishima H, Matsuda K, Isozaki M, Ishida S, Matta Y, Kanamoto M, Kimura H, Kikuta K. Comparison of amide proton transfer imaging with perfusion imaging of using arterial spin-labeling for evidence of tumor invasion in glioblastoma. Interdiscip Neurosurg Adv Tech Case Manag. 2022;28:101461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2013;360(8):765\u0026ndash;773\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Y, Lin X, Chen K, Chai W, Hu S, Tang R, Zhang J, Cao L, Yan F. Hepatocellular carcinoma and focal nodular hyperplasia of the liver: differentiation with CT spectral imaging. Eur Radiol. 2013;23:1660\u0026ndash;1668\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang XF, Lu Q, Wu LM, Zou AH, Hua XL, Xu JR. Quantitative iodine-based material decomposition images with spectral CT imaging for differentiating prostatic carcinoma from benign prostatic hyperplasia. Acad Radiol. 2013;20:947\u0026ndash;956\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Imaging](https://link.springer.com/journal/44352)","snPcode":"44352","submissionUrl":"https://submission.springernature.com/new-submission/44352/3","title":"Discover Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dual-energy CT, electron density imaging, gadolinium-enhanced imaging, high-grade glioma","lastPublishedDoi":"10.21203/rs.3.rs-4708655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4708655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDual-energy computed tomography (DECT) has been used for the prediction of glioma grading and malignancy, in addition to magnetic resonance imaging \u003cb\u003e(\u003c/b\u003eMRI) findings. In DECT, electron density (ED) can be quantitatively measured and determined with high accuracy. However, no studies have demonstrated whether DECT alone can detect tumor-infiltrating areas or evaluate malignancy. Therefore, we evaluated the use of ED imaging with DECT showing high-density areas of high-grade glioma (HGG), compared it with MRI gadolinium-enhanced T1-weighted image (GdT1) enhancing area, and analyzed its effectiveness in evaluating malignancy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTen patients with enhanced masses on GdT1 MRI were enrolled in this retrospective study. Ten slices of ED and GdT1 images of 10 patients with HGG were analyzed by three raters. The relationships between the area of high ED on DECT and enhanced area on GdT1 and between the relative ED of the high ED area and contralateral white matter on DECT were determined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLinear regression analysis revealed a positive correlation between areas of high ED and Gd enhancement among all raters (rater A: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.910, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, B: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.857, P\u0026thinsp;=\u0026thinsp;0.0001, C: R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.717, P\u0026thinsp;=\u0026thinsp;0.002), and the intraclass correlation coefficient was 0.75. A comparison of the relative ED between the tumor and contralateral white matter revealed that the mean and maximum relative ED of the tumor area was significantly higher than that of the contralateral white matter (mean: P\u0026thinsp;=\u0026thinsp;0.049, maximum: P\u0026thinsp;=\u0026thinsp;0.0002).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eED images of DECT show a high ED area similar to the Gd enhancement area in HGG, and the mean and maximum relative EDs of this area are significantly higher than those of the contralateral white matter.\u003c/p\u003e","manuscriptTitle":"Comparison between electron density imaging with dual-energy computed tomography without contrast medium and magnetic resonance imaging for high-grade glioma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 10:56:40","doi":"10.21203/rs.3.rs-4708655/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-04T10:21:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T10:18:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-25T17:27:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54220054931682191151192523931567445070","date":"2024-09-25T14:24:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T12:32:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318548095117716590914448327426600130576","date":"2024-08-30T22:08:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-28T12:52:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T10:38:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-22T10:30:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Imaging","date":"2024-07-09T02:46:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Imaging](https://link.springer.com/journal/44352)","snPcode":"44352","submissionUrl":"https://submission.springernature.com/new-submission/44352/3","title":"Discover Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce500655-d3a6-4994-a771-02934e17aa46","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-15T04:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 10:56:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4708655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4708655","identity":"rs-4708655","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}