Accuracy of dual-energy computed tomography for bone marrow edema in the sacroiliac joint: a systematic review and meta-analysis

Accuracy of dual-energy computed tomography for bone marrow edema in the sacroiliac joint: a systematic review and meta-analysis | 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 Systematic Review Accuracy of dual-energy computed tomography for bone marrow edema in the sacroiliac joint: a systematic review and meta-analysis xin li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3193359/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The purpose of this study is to perform a systematic literature review and meta-analysis to assess the accuracy, sensitivity and specificity of dual-energy computed tomography (DECT) of the sacroiliac joint. Bone marrow edema of the sacroiliac joint is an early manifestation of some diseases, such as ankylosing spondylitis, usually examined by nuclear magnetic resonance (MRI), but some patients cannot tolerate MRI, so many studies have analyzed DECT examination. Methods PUBMED, CNKI, and EMBASE, 2023 for articles including (DECT) or (DE-CT) or (dual-energy CT) or "dual-energy CT" or (dual-energy computed tomography)) and ((sacroiliac joint) or (ankylosing spondylitis) or (sacroiliac arthritis)). A initial search identified 444 articles, and seven ultimately met the criteria. Data were extracted to calculate the sensitivity, specificity, and diagnostic odds for the analysis using the R software. Results 291 patients, 577 sacroiliac joints and 429 bone marrow edema (74.35%). All studies used magnetic resonance as a control group. For DECT, the overall sensitivity was 86.94%, specificity 87.24%, positive prediction rate 92.55% and negative prediction rate 83.73%. Conclusion DECT seems to be a promising diagnostic tool for detecting bone marrow edema in the sacroiliac joint and can be used as an alternative examination method for patients contraindicated to MRI examination. Nuclear Medicine & Medical Imaging double-energy computed tomography sacroiliac joint bone marrow edema Figures Figure 1 Figure 2 Figure 3 Introduction The term "bone marrow edema" was first used by Wilson in 1988. He noted high signal in the fluid sensitive sequence of MRI located in the subchondral bone [17] . Bone marrow oedema (BME) is an important marker of traumatic and non-traumatic bone lesions, which may cause pain and loss of joint function [1] . Bone marrow oedema in non-traumatic bone marrow lesions usually indicates early lesions, so early intervention may have positive clinical results [2] . Bone marrow oedema has become an important aspect to be considered in the diagnostic pathway of major musculoskeletal disorders. Bone marrow edema (BME) of the sacroiliac joint is an early radiographic manifestation of ankylosing spondylitis, and it is also the main basis for definite active sacroiliac arthritis. Delayed intervention produces negative clinical outcomes, so an early and accurate diagnosis of BME is crucial [2] . MRI is commonly used to detect bone marrow edema, but MRI is expensive and the examination time is long, which is not used as a routine examination for some patients [23] . During the MRI examination process, patients need to remain static, which is more difficult for some trauma patients and the elderly and children. There are also many other contraindications to MRI, such as some metal implants and claustrophobia [3] . Traditional CT cannot visualize bone marrow edema [24] . Dual-energy computed tomography (DE-CT), first described by Brooks in 1977, has gained popularity in recent years [18] [19] . It measures the electron density and the effective atomic number, which can be converted to the Hounsfield numbers by normalizing them to water [20] [21] . Dual energy CT (DECT) overcomes the limitations of conventional MRI, and we have studied dual energy CT (DECT) using virtual non-calcium (VNCa) technology and dual energy material decomposition imaging technology. In these years, many scholars have studied the value of DECT in bone marrow edema. Some studies have shown that the VNCa of DECT can accurately describe the BME at different anatomical locations (knee, ankle, hip, spine), but it has not been fully explored and summarized as an alternative imaging tool. Through this systematic review and meta-analysis to evaluate the overall diagnostic performance of the VNCA and mass decomposition imaging techniques of DECT [1–5] . Method Systematic review: A systematic review was conducted using the PRISMA guidelines in March 2023 [22] . System searches were conducted using PUBMED, CNKI, and EMBASE with search terms including (DECT) or (DE-CT) or (dual-energy CT) or "dual-energy CT" or (dual-energy computed tomography)) and ((sacroiliac joint) or (ankylosing spondylitis) or (sacroiliac arthritis)). Including all articles written in Chinese, French, German and English to investigate the sensitivity, specificity and accuracy of DECT in bone marrow edema of the sacroiliac joints. Any repeated results, lack of full access to the original articles, and original studies that were not peer-reviewed (including abstracts, reviews, and case reports) were excluded. In total, seven compliant articles were found. These articles report the accuracy, specificity, and sensitivity of DECT for the detection of bone marrow oedema associated with the sacroiliac joint, using MRI as the reference standard. Data extraction: a systematic review of the data from the selected articles, including: study design. The number of joints, edema degree, anatomical location (sacrum / iliac bone), positive rate, negative rate, false positive rate, false negative rate, DECT technical parameters, diagnostic performance of DECT, and diagnostic physician level were included and used in meta-analysis. Screening and literature selection were conducted independently by two researchers, and any disagreement should be achieved by reaching a consensus between the two reviewers to avoid erroneous exclusion of eligible articles [6] . Statistical analysis: The data obtained were used to calculate sensitivity and specificity, as well as positive predictive value (PPV), negative predictive value (NPV), positive and negative likelihood ratios, and diagnostic odds. Raw data from individual studies illustrate all sensitivity, specificity, and diagnostic odds ratios. To pool sensitivity and specificity, a bivariate random effect model was applied [7] . To analyze the correlation between sensitivity and specificity, Spearman's correlation coefficient was calculated, where the coefficient of > 0.6 is considered to be considerable [9] . For the statistical analysis, the R software, version 5.4, was used. All continuous variables are presented as the mean and 95% confidence intervals. Categorical variables were expressed as percentages, and statistical significance was set to P < 0.05. MRI was used as a reference standard in all studies. In addition, Chen M et al and Chen et al not only distinguished the imaging modality, but also measured the sacrum and iliac bone separately to calculate the sensitivity and specificity of the sacrum and iliac bone, respectively [11] [14] . Song Feipeng et al. and Yang Liqin et al used the spectrum CT compared water-hydroxyapatite, water-calcium, lipid-calcium to analyzed the sacroiliac joint [13] [16] , including Song Feipeng et al. The bone marrow edema group sacroiliac joint iliac bone marrow water-HAP concentration and bone marrow edema group the same level sacral vertebral central sacral hole water-HAP concentration were compared. Chen Dandan et al. compared VNCa CT values with conventional CT values [14] . Results A total of 291 patients, 577 sacroiliac joints, and 429 bone marrow edema (74.35%) were included. All of the studies were conducted prospectively. Most of the studies overall evaluated the sacroiliac joints. In two studies, the binary sacroiliac joint [11] [15] , two studies divided the sacroiliac joint [10] [12] , and two studies divided the sacroiliac joint into sacral and iliac joint [11] [14] . Two studies used the mass decomposition imaging technique of the energy spectral CT to analyze the bone marrow edema in the sacroiliac joint [13] [16] . All studies performed double-blind diagnosis using two diagnostic physicians. Furthermore, different MRI sequences were used in different studies. One author used 1.5T [10] and for the remaining study 3.0T was used. The mean age of the patients was 24.8,53.3% male and 46.6% female, and only the overall specificity, sensitivity, and accuracy were described in one study. The sacroiliac joint was measured using four measures in two studies [10] [12] , two studies used dichotomies [11] [15] , and three studies used overall measures [13] [14] [16] . Table 1 Studies Year of Publication Age, y Number Joint Number BME MRI Classification System Experience Carotti M 2021 nm 40 80 36 1.5T Four-point nm Chen M 2020 37.1 40 80 16/40 3.0T Binary Radiologists 4y and 16y Wu H 2019 27 47 89 55/89 3.0T Four-point Radiologists 17y and 25y Song F 2020 17–22 32 64 nm 3.0T nm 2 Radiologists Chen D 2021 34 45 90 77/180 3.0T nm 3 Radiologists He X 2020 39 47 89 55/89 3.0T Binary 2 Radiologists Yang L 2022 M 29.76 F32.54 60 120 40/60 3.0T nm Radiologist both 5y The largest cohort included 60 patients, 120 sacroiliac joints, and 240 subdivisions [16]. The sensitivity of the raw data varies from 32.5% [14] and 53.3% [11] to 80.8 [11], and the specificity varies from 86.8% [14] and 96.3% [11] to 100%. The overall sensitivity was 86.94%, specificity 87.24%, positive prediction rate 92.55% and negative prediction rate 83.73%. All findings of DECT are presented in Table 2 . Table 2 Studies Year of Publication Interobserver Sensitivity(95%CI) Specificity(95%CI) ROC NPV(95%CI) PPV(95%CI) Carotti M 2021 nm 90.0% 92.8% 0.953 89.7% 93.1% Chen M 2020 0.7 81.3% (57.0–93.4) 91.7% (74.2–97.7) 88.0% (70.0–95.8) 86.7%(62.1–96.3) Ilium 80.8 (62.1–91.5) 95.3 (90.2–97.9) 0.9 96.1% (91.2–98.3) 77.8% (59.2–89.4) Sacrum 53.3 (36.1–69.8) 93.8 (88.2–96.8) 0.87 89.6% (83.2–93.7) 66.7 (46.7–83.0) Overall 66.1 (53.0–77.1) 94.6 (91.7–96.7) 92.7 (89.0–95.3) 72.5 (59.5–82.9) Wu H 2019 0.81 90.0% 83.0% Reader 1 87% (75, 94) 94%(79, 99) 0.93 82 .0%(66–92) 96 .0%(85–99) Reader 2 93% (82, 98) 91%(75, 98) 0.91 89 .0%(72–96) 94.0% (84–99) Chen D 2021 nm 78.4% 86.8% Ilium 78.4% 86.8% 0.72 85.2% 80.6% Sacrum 32.5% 100.0% 0.706 64.9% 100.0% He X 2020 0.81 90.0% 83.0% Reader 1 89.0%(77–96) 83.0%(65–93) 0.93 82.0%(66–92) 96.0%(85–99) Reader 2 95.0%(84–99) 79.0%(62–91) 0.91 89.0%(72–96) 94.0%(84–99) Yang L 2022 nm 91.94% 87.50% 0.925 nm nm Song F 2020 nm nm 85.9% nm nm nm Abbreviate: CI: confidence interval; NPV: negative predicted value, PPV: positive predicted value. Two studies analyzed the sensitivity and specificity of DECT by substance pairs and relative water, calcium and lipid content [13] [16] , and one of them studied the relationship between the concentration of water-HAP and ASDAS [13] . Discussion The systematic discussion included seven studies, which discussed the sensitivity and specificity of dual-energy CT through the virtual non-calcium technology and material decomposition imaging technology of DECT. Previous discussion of the literature on DECT has not focused on its use in the sacroiliac joint, and our analysis is the first such study of its kind to our knowledge. In other studies, multipotent CT was found to have a better noise reduction function [26] . The existing gold standard for bone marrow oedema is MRI [27] , and the existing analysis shows that DECT has 92% sensitivity and specificity for bone marrow oedema and 96% [28] . There have also been studies evaluating the accuracy of DE-CT for vertebral fractures. Sensitivity and specificity were 89% and 98%, respectively [28] . This is a great help to the radiologist's diagnosis [29] . DECT is using two ray sources as well as two corresponding detectors, in the fast kVp switch, the tube voltage follows the pulse curve, collected twice per projection at high and low tube voltage. Therefore, 2 scintillation layers can separate high energy and low energy spectra [30] . This study has some limitations. First, there are few existing studies on the sacroiliac joint, and the largest cohort included only 47 patients [15] . Second, only a few studies have reported the reliability between observations [11] [12] [15] . Furthermore, using different MRI as the gold standard for comparison and not using the exact same reconstruction algorithm and cutoff values, this makes comparisons difficult. Finally, only two studies have mentioned matter decomposition imaging methods. Conclusion DECT is a promising imaging method to detect bone marrow edema in the sacroiliac joint and provides a good substitute for patients who cannot undergo MRI. This facilitates the clinical diagnostic work. Declarations Declaration of Interest Statement We declare that we have no financial and personal relationships with other people organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. References Ren Q, Tang D, Xiong Z, Zhao H, Zhang S. Traumatic bone marrow lesions in dual-energy computed tomography. Insights Imaging. 2022 Oct 29;13(1):174. doi: 10.1186/s13244-022-01312-6. PMID: 36308637; PMCID: PMC9617981. Chen Z, Chen Y, Zhang H, Jia X, Zheng X, Zuo T. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3193359","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":220633947,"identity":"d6604538-73a7-4c8b-ba15-3719bcf9fcbb","order_by":0,"name":"xin li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACfvb+75//VNjw2B9vIFKLZM8BMwaeM2lyDGcOEKnF4IaDGQNv22FjhhsJxNoygyHtgQTb4cTGmY833mCosYkmqIVfuuG4gQFPemKzdFqxBcOxtNwGgrbMOdggkSBhndgmnWMmwdhwmLAWgxvJDBIHDJgTeyTPEK0ljU2yIcHZWEKCh0gtkj1nmI0ZDqTJGfAA/ZJAjF/42XsYHzP+s+ExYD+88caHGhvCWlAcKZFAinKIFlJ1jIJRMApGwcgAADLdP2zyV4OsAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"xin","middleName":"","lastName":"li","suffix":""}],"badges":[],"createdAt":"2023-07-22 02:19:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3193359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3193359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40560070,"identity":"a5c911cd-ff4b-4633-8dd9-d6505646d7d8","added_by":"auto","created_at":"2023-07-25 18:34:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":418853,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3193359/v1/7b94ecd9b38cb7130104f84f.png"},{"id":40560069,"identity":"900cc404-fc0d-496c-ae6e-8b4bf23e17c0","added_by":"auto","created_at":"2023-07-25 18:34:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7377,"visible":true,"origin":"","legend":"\u003cp\u003eA forest map of the sensitivity and specificity\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3193359/v1/36641f0a650352daa81f23fb.png"},{"id":40560192,"identity":"fc3459e8-32b2-4d32-939e-c0cd9ff9e2cb","added_by":"auto","created_at":"2023-07-25 18:42:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8907,"visible":true,"origin":"","legend":"\u003cp\u003eSROC curves between the sensitivity and specificity of dual-energy computed tomography scans.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3193359/v1/76d68a816f8f234fac9f4ae4.png"},{"id":40560193,"identity":"6c157c7a-4edf-4d86-b90f-0f41ab3e75c7","added_by":"auto","created_at":"2023-07-25 18:42:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":865684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3193359/v1/660ae563-486a-4ab1-96d4-bc9c08e8e580.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAccuracy of dual-energy computed tomography for bone marrow edema in the sacroiliac joint: a systematic review and meta-analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe term \"bone marrow edema\" was first used by Wilson in 1988. He noted high signal in the fluid sensitive sequence of MRI located in the subchondral bone \u003csup\u003e[17]\u003c/sup\u003e. Bone marrow oedema (BME) is an important marker of traumatic and non-traumatic bone lesions, which may cause pain and loss of joint function \u003csup\u003e[1]\u003c/sup\u003e. Bone marrow oedema in non-traumatic bone marrow lesions usually indicates early lesions, so early intervention may have positive clinical results \u003csup\u003e[2]\u003c/sup\u003e. Bone marrow oedema has become an important aspect to be considered in the diagnostic pathway of major musculoskeletal disorders. Bone marrow edema (BME) of the sacroiliac joint is an early radiographic manifestation of ankylosing spondylitis, and it is also the main basis for definite active sacroiliac arthritis. Delayed intervention produces negative clinical outcomes, so an early and accurate diagnosis of BME is crucial \u003csup\u003e[2]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMRI is commonly used to detect bone marrow edema, but MRI is expensive and the examination time is long, which is not used as a routine examination for some patients \u003csup\u003e[23]\u003c/sup\u003e. During the MRI examination process, patients need to remain static, which is more difficult for some trauma patients and the elderly and children. There are also many other contraindications to MRI, such as some metal implants and claustrophobia \u003csup\u003e[3]\u003c/sup\u003e. Traditional CT cannot visualize bone marrow edema \u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDual-energy computed tomography (DE-CT), first described by Brooks in 1977, has gained popularity in recent years \u003csup\u003e[18] [19]\u003c/sup\u003e. It measures the electron density and the effective atomic number, which can be converted to the Hounsfield numbers by normalizing them to water \u003csup\u003e[20] [21]\u003c/sup\u003e. Dual energy CT (DECT) overcomes the limitations of conventional MRI, and we have studied dual energy CT (DECT) using virtual non-calcium (VNCa) technology and dual energy material decomposition imaging technology. In these years, many scholars have studied the value of DECT in bone marrow edema. Some studies have shown that the VNCa of DECT can accurately describe the BME at different anatomical locations (knee, ankle, hip, spine), but it has not been fully explored and summarized as an alternative imaging tool. Through this systematic review and meta-analysis to evaluate the overall diagnostic performance of the VNCA and mass decomposition imaging techniques of DECT \u003csup\u003e[1\u0026ndash;5]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eSystematic review: A systematic review was conducted using the PRISMA guidelines in March 2023 \u003csup\u003e[22]\u003c/sup\u003e. System searches were conducted using PUBMED, CNKI, and EMBASE with search terms including (DECT) or (DE-CT) or (dual-energy CT) or \"dual-energy CT\" or (dual-energy computed tomography)) and ((sacroiliac joint) or (ankylosing spondylitis) or (sacroiliac arthritis)). Including all articles written in Chinese, French, German and English to investigate the sensitivity, specificity and accuracy of DECT in bone marrow edema of the sacroiliac joints. Any repeated results, lack of full access to the original articles, and original studies that were not peer-reviewed (including abstracts, reviews, and case reports) were excluded. In total, seven compliant articles were found. These articles report the accuracy, specificity, and sensitivity of DECT for the detection of bone marrow oedema associated with the sacroiliac joint, using MRI as the reference standard.\u003c/p\u003e\u003cp\u003eData extraction: a systematic review of the data from the selected articles, including: study design. The number of joints, edema degree, anatomical location (sacrum / iliac bone), positive rate, negative rate, false positive rate, false negative rate, DECT technical parameters, diagnostic performance of DECT, and diagnostic physician level were included and used in meta-analysis. Screening and literature selection were conducted independently by two researchers, and any disagreement should be achieved by reaching a consensus between the two reviewers to avoid erroneous exclusion of eligible articles \u003csup\u003e[6]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStatistical analysis: The data obtained were used to calculate sensitivity and specificity, as well as positive predictive value (PPV), negative predictive value (NPV), positive and negative likelihood ratios, and diagnostic odds. Raw data from individual studies illustrate all sensitivity, specificity, and diagnostic odds ratios. To pool sensitivity and specificity, a bivariate random effect model was applied \u003csup\u003e[7]\u003c/sup\u003e. To analyze the correlation between sensitivity and specificity, Spearman's correlation coefficient was calculated, where the coefficient of \u0026gt;\u0026thinsp;0.6 is considered to be considerable \u003csup\u003e[9]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor the statistical analysis, the R software, version 5.4, was used. All continuous variables are presented as the mean and 95% confidence intervals. Categorical variables were expressed as percentages, and statistical significance was set to P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eMRI was used as a reference standard in all studies. In addition, Chen M et al and Chen et al not only distinguished the imaging modality, but also measured the sacrum and iliac bone separately to calculate the sensitivity and specificity of the sacrum and iliac bone, respectively \u003csup\u003e[11] [14]\u003c/sup\u003e. Song Feipeng et al. and Yang Liqin et al used the spectrum CT compared water-hydroxyapatite, water-calcium, lipid-calcium to analyzed the sacroiliac joint \u003csup\u003e[13] [16]\u003c/sup\u003e, including Song Feipeng et al. The bone marrow edema group sacroiliac joint iliac bone marrow water-HAP concentration and bone marrow edema group the same level sacral vertebral central sacral hole water-HAP concentration were compared. Chen Dandan et al. compared VNCa CT values with conventional CT values \u003csup\u003e[14]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 291 patients, 577 sacroiliac joints, and 429 bone marrow edema (74.35%) were included. All of the studies were conducted prospectively. Most of the studies overall evaluated the sacroiliac joints. In two studies, the binary sacroiliac joint \u003csup\u003e[11] [15]\u003c/sup\u003e, two studies divided the sacroiliac joint \u003csup\u003e[10] [12]\u003c/sup\u003e, and two studies divided the sacroiliac joint into sacral and iliac joint \u003csup\u003e[11] [14]\u003c/sup\u003e. Two studies used the mass decomposition imaging technique of the energy spectral CT to analyze the bone marrow edema in the sacroiliac joint \u003csup\u003e[13] [16]\u003c/sup\u003e. All studies performed double-blind diagnosis using two diagnostic physicians. Furthermore, different MRI sequences were used in different studies. One author used 1.5T \u003csup\u003e[10]\u003c/sup\u003e and for the remaining study 3.0T was used.\u003c/p\u003e \u003cp\u003eThe mean age of the patients was 24.8,53.3% male and 46.6% female, and only the overall specificity, sensitivity, and accuracy were described in one study. The sacroiliac joint was measured using four measures in two studies \u003csup\u003e[10] [12]\u003c/sup\u003e, two studies used dichotomies \u003csup\u003e[11] [15]\u003c/sup\u003e, and three studies used overall measures \u003csup\u003e[13] [14] [16]\u003c/sup\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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of\u003c/p\u003e \u003cp\u003ePublication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJoint \u003c/p\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClassification \u003c/p\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExperience\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotti M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFour-point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRadiologists \u003c/p\u003e \u003cp\u003e4y and 16y\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55/89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFour-point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRadiologists \u003c/p\u003e \u003cp\u003e17y and 25y\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSong F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 Radiologists\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77/180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3 Radiologists\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHe X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55/89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 Radiologists\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM 29.76 \u003c/p\u003e \u003cp\u003eF32.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRadiologist \u003c/p\u003e \u003cp\u003eboth 5y\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe largest cohort included 60 patients, 120 sacroiliac joints, and 240 subdivisions [16]. The sensitivity of the raw data varies from 32.5% [14] and 53.3% [11] to 80.8 [11], and the specificity varies from 86.8% [14] and 96.3% [11] to 100%. The overall sensitivity was 86.94%, specificity 87.24%, positive prediction rate 92.55% and negative prediction rate 83.73%. All findings of DECT are presented in 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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear of\u003c/p\u003e \u003cp\u003ePublication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterobserver\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eROC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotti M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.3% (57.0\u0026ndash;93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.7% (74.2\u0026ndash;97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.0% (70.0\u0026ndash;95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.7%(62.1\u0026ndash;96.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.8 (62.1\u0026ndash;91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.3 (90.2\u0026ndash;97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.1% (91.2\u0026ndash;98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.8% (59.2\u0026ndash;89.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSacrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.3 (36.1\u0026ndash;69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.8 (88.2\u0026ndash;96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.6% (83.2\u0026ndash;93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7 (46.7\u0026ndash;83.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.1 (53.0\u0026ndash;77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.6 (91.7\u0026ndash;96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.7 (89.0\u0026ndash;95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.5 (59.5\u0026ndash;82.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReader 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87% (75, 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94%(79, 99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82 .0%(66\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96 .0%(85\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReader 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93% (82, 98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91%(75, 98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89 .0%(72\u0026ndash;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.0% (84\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSacrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHe X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReader 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.0%(77\u0026ndash;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.0%(65\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.0%(66\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96.0%(85\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReader 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.0%(84\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.0%(62\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.0%(72\u0026ndash;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.0%(84\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSong F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAbbreviate: CI: confidence interval; NPV: negative predicted value, PPV: positive predicted value.\u003c/p\u003e \u003cp\u003eTwo studies analyzed the sensitivity and specificity of DECT by substance pairs and relative water, calcium and lipid content \u003csup\u003e[13] [16]\u003c/sup\u003e, and one of them studied the relationship between the concentration of water-HAP and ASDAS \u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe systematic discussion included seven studies, which discussed the sensitivity and specificity of dual-energy CT through the virtual non-calcium technology and material decomposition imaging technology of DECT. Previous discussion of the literature on DECT has not focused on its use in the sacroiliac joint, and our analysis is the first such study of its kind to our knowledge.\u003c/p\u003e \u003cp\u003eIn other studies, multipotent CT was found to have a better noise reduction function \u003csup\u003e[26]\u003c/sup\u003e. The existing gold standard for bone marrow oedema is MRI \u003csup\u003e[27]\u003c/sup\u003e, and the existing analysis shows that DECT has 92% sensitivity and specificity for bone marrow oedema and 96% \u003csup\u003e[28]\u003c/sup\u003e. There have also been studies evaluating the accuracy of DE-CT for vertebral fractures. Sensitivity and specificity were 89% and 98%, respectively \u003csup\u003e[28]\u003c/sup\u003e. This is a great help to the radiologist's diagnosis \u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDECT is using two ray sources as well as two corresponding detectors, in the fast kVp switch, the tube voltage follows the pulse curve, collected twice per projection at high and low tube voltage. Therefore, 2 scintillation layers can separate high energy and low energy spectra \u003csup\u003e[30]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, there are few existing studies on the sacroiliac joint, and the largest cohort included only 47 patients \u003csup\u003e[15]\u003c/sup\u003e. Second, only a few studies have reported the reliability between observations \u003csup\u003e[11] [12] [15]\u003c/sup\u003e. Furthermore, using different MRI as the gold standard for comparison and not using the exact same reconstruction algorithm and cutoff values, this makes comparisons difficult. Finally, only two studies have mentioned matter decomposition imaging methods.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDECT is a promising imaging method to detect bone marrow edema in the sacroiliac joint and provides a good substitute for patients who cannot undergo MRI. This facilitates the clinical diagnostic work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that we have no financial and personal relationships with other people organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRen Q, Tang D, Xiong Z, Zhao H, Zhang S. 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Multiparametric evaluation of head and neck squamous cell carcinoma using a single-source dual-energy CT with fast kVp switching: state of the art. \u003cem\u003eCancers (Basel)\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e2015;7(4):2201\u0026ndash;2216.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"double-energy computed tomography, sacroiliac joint, bone marrow edema","lastPublishedDoi":"10.21203/rs.3.rs-3193359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3193359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe purpose of this study is to perform a systematic literature review and meta-analysis to assess the accuracy, sensitivity and specificity of dual-energy computed tomography (DECT) of the sacroiliac joint. Bone marrow edema of the sacroiliac joint is an early manifestation of some diseases, such as ankylosing spondylitis, usually examined by nuclear magnetic resonance (MRI), but some patients cannot tolerate MRI, so many studies have analyzed DECT examination.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePUBMED, CNKI, and EMBASE, 2023 for articles including (DECT) or (DE-CT) or (dual-energy CT) or \"dual-energy CT\" or (dual-energy computed tomography)) and ((sacroiliac joint) or (ankylosing spondylitis) or (sacroiliac arthritis)). A initial search identified 444 articles, and seven ultimately met the criteria. Data were extracted to calculate the sensitivity, specificity, and diagnostic odds for the analysis using the R software.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e291 patients, 577 sacroiliac joints and 429 bone marrow edema (74.35%). All studies used magnetic resonance as a control group. For DECT, the overall sensitivity was 86.94%, specificity 87.24%, positive prediction rate 92.55% and negative prediction rate 83.73%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDECT seems to be a promising diagnostic tool for detecting bone marrow edema in the sacroiliac joint and can be used as an alternative examination method for patients contraindicated to MRI examination.\u003c/p\u003e","manuscriptTitle":"Accuracy of dual-energy computed tomography for bone marrow edema in the sacroiliac joint: a systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-25 18:34:23","doi":"10.21203/rs.3.rs-3193359/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cba965a-30f6-4e4d-be13-bbba0826646c","owner":[],"postedDate":"July 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23468378,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2023-07-25T18:34:23+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-25 18:34:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3193359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3193359","identity":"rs-3193359","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}