Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma

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This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation༎ Methods There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40keV-equivalent image (VMI 40keV ), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λ HU ) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups. Results There were significant differences in VMI 40keV , IC, Eff-Z, and λ HU in AP and VP among the three groups (all p 0.05). The VMI 40keV , IC, Eff-Z, and λ HU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p < 0.05). In the ROC analysis, IC performed the best in the identification of the poorly differentiated group and non-poorly differentiated group in VP (AUC = 0.729, Sensitivity = 0.829, and Specificity = 0.569 under the threshold of 21.08 mg/ml). Conclusions Quantitative parameters of spectral CT could offer supplemental information for the preoperative differential diagnosis of ESCC with different degrees of differentiation. Esophageal squamous cell carcinoma Spectral CT pathological grade Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to the global cancer statistics 2020, esophageal cancer ranks seventh in terms of incidence and sixth in mortality overall, the latter signifying that esophageal cancer is responsible for one in every 18 cancer deaths in 2020[ 1 ]. The treatment strategies and prognosis evaluation for esophageal cancer are mainly determined by the clinical TNM stage and pathological TNM (pTNM) stage, the latter remains relevant for early-stage cancers and as an important staging and survival reference point[ 2 ]. The overall 5-year survival of patients with esophageal cancer ranges from 15–25%. Diagnoses made at earlier stages are associated with better outcomes than those made at later stages[ 3 ], the latter is more prone to lymph node metastasis and local recurrence as well as shorter survival[ 4 ]. In China, esophageal squamous cell carcinoma (ESCC) is the predominant pathological type of esophageal cancer[ 1 ]. A definitive diagnosis of esophageal lesions is based on histological examination; however, an invasive method is not always readily available, and local samples might not fully reflect the overall heterogeneity of the tumor. Thus, an accurate and non-invasive method is urgently needed in evaluating the pathological grade of ESCC. It is generally accepted that CT is an important modality for evaluating esophageal tumors. Conventional CT is often used to identify the location of primary lesions and distal metastases, but its role in determining pTNM is limited. Spectral CT has been widely used for qualitative and quantitative imaging of different malignancies and could provide more accurate and complete information on cancers for detection and prognoses evaluation. With the use of material decomposition techniques, one can obtain Virtual monoenergetic images (VMI), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), or other material-specific information[ 5 ]. In recent years, spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies including glioma[ 6 ], pancreatic neuroendocrine neoplasms[ 7 ], gastric adenocarcinoma[ 8 ], ovarian tumours[ 9 ], non-small cell lung cancer[ 10 ], and clear cell renal cell carcinoma (ccRCC)[ 11 ]. However, to the best of our knowledge, spectral parameters have not been applied for the pathological grades of esophageal cancer so far. Consequently, this study was conducted to explore the significance of quantitative assessment with several parameters derived from spectral CT in differentiating ESCC with different degrees of differentiation. Materials And Methods Patient characteristics This retrospective study was approved by the Ethics Committee of our hospital and all patients signed the informed consent. A total of 191 patients with histologically proven ESCC were enrolled in this study from June 2018 to March 2020. The inclusion criteria were as follows: (1) No contraindications to contrast-enhanced CT examination; (2) No radiation therapy or chemotherapy before surgery; (3) Within 1 week after spectral CT scan the lesions were resected; (4) Postoperative pathologic confirmation of ESCC. The main exclusion criteria were as follows: (1) Patients who were found to be allergic to iodine contrast agent before enhanced CT examination; (2) Patients who had already received radiation therapy or chemotherapy; (3) Images with poor quality due to artifacts. Spectral CT image acquisition All inspections were conducted using a Revolution CT scanner (GE Healthcare, Milwaukie USA) with the spectral CT acquisition mode. The scan protocol included a 5 mm slice thickness, tube voltage of 70 and 140 kV with a fast kilovolt peak–switching technique, CT automatic exposure control (AEC) systems adjusting the tube current, and gantry speed of 0.5 seconds per rotation, and helical pitch of 0.992:1. The nonionic contrast agent Iohexol (China, Jiangsu Yangtze River Pharmaceutical Group) was used for the enhanced examination of patients, containing 300mgI/ml of iodine, the weight-dependent dose of 1.5 ml/kg, and an infusion rate of 3.0 ml/s. Scanning was done when the CT value of the aortic arch reached 100 HU, and the arterial phase (AP) and venous phase (VP) started 30 and 60 seconds, respectively, after the administration of contrast agents. Spectral CT image analysis All spectral CT images were reconstructed with a slice thickness of 1.25 mm, and then the images were transferred to the workstation. Two radiologists with 6 and 20 years of experience in esophagus CT diagnosis measured and analyzed the imaging in a blinded and randomized manner respectively. A round or oval region of interest (ROI) was selected according to the size and location of the lesions to measure the virtual monoenergetic 40keV-equivalent image (VMI 40keV ), IC, WC, and Eff-Z in both the AP and VP. To reduce measurement variation, ROIs were placed three times in the tumor area without distinguishable necrosis or hemorrhage, and the average of triplicate measurements was used as the final data value. The slope of the spectral curve (λ HU ) was defined as the difference between the CT value at 40 keV and that at 70 keV divided by the energy difference(30keV), and it was calculated as follows: λ HU = [(CT(40keV)-CT(70keV)]/(70-40)[12]. Pathological analysis All tissues were obtained from surgical operation. Based on the 8th edition of AJCC classification[4], these patients were divided into three groups according to postoperative pathological results: well differentiated ESCC, moderately differentiated ESCC and poorly differentiated ESCC. Statistical analysis Statistical analyses were conducted using SPSS26.0. Quantitative variables are expressed as mean ± standard deviation, and categorical variables are presented as frequencies (percentages). The differences in VMI 40keV , IC, WC, Eff-Z, and λ HU of the three groups were statistically analyzed using one-way ANOVA and pairwise comparison was performed with LSD. Receiver operating characteristic (ROC) analysis was conducted for each parameter to differentiate the poorly differentiated and non-poorly differentiated groups. Results Patient characteristics A total of 191 patients (mean age 68.49±6.78 years), 157 men (mean age 68.34 ±7.16 years), and 34 women (mean age 69.18±4.64 years) were included. Characteristics of our patients are summarized in Table 1. Quantitative Spectral Parameters Comparison There were significant differences in VMI 40keV , IC, Eff-Z, and λ HU in AP and VP among the three groups (all p 0.05). The VMI 40keV , IC, Eff-Z, and λ HU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p < 0.05; Table 2-3). Quantitative Diagnostic Value Evaluation ROC curves for all spectral CT parameters are shown in Fig. 1. Table 4-5 shows AUCs, thresholds, Sensitivities, and Specificities based on the ROC analysis. In the identification of poorly differentiated groups and non-poorly differentiated groups. VMI 40keV , IC, Eff-Z, and λ HU in VP showed an area under the ROC curve (AUC) of 0.725, 0.729, 0.729, and 0.723, respectively, while AP showed an AUC of 0.612, 0.616, 0.619, and 0.617, respectively. The highest sensitivity, specificity, and AUC were observed for IC in VP, the sensitivity of it in the identification between the poorly differentiated group and the non-poorly differentiated group was 82.9%, while the specificity was 56.9% under the threshold of 21.08 mg/ml. Discussion The pathological differentiation grade of ESCC influences the prognosis, as tumor grade increases, it is more likely to have a poorer prognosis and an elevated risk of death[10]. Our results showed that quantitative parameters derived from spectral CT, including VMI 40keV , IC, Eff-Z, and λ HU both in AP and VP, could be used to distinguish pathological grades of ESCC, as shown in Fig. 2-4. Spectral CT greatly reduces beam-hardening artifacts and generates VMIs with more accurate CT attenuation numbers at every energy level[13]. VMI 40keV reconstructions provided higher image quality due to a higher lesion to background attenuation ratio, i.e., higher contrast[14, 15]; Thus, we selected the 40 keV image for this study. In the present study, VMI 40keV in the AP and VP was significantly different among the three groups, which concurs with a previous study[13]. The distribution of iodine in the tissue is strongly correlated with local blood volume and vascular density[16]. Several studies have shown that IC correlates well with higher blood flow and vascularization[14, 17-22]. In our study, IC in the AP and VP was significantly different among the three groups, as tumor grade increased, it also increased. To be more precise, IC in the well differentiated group was the lowest while in the poorly differentiated ESCC group was the highest, signifying that the iodine uptake and vascularity of the low-grade ESCCs are lower than that of high-grade ESCCs. Additionally, Eff-Z, another quantitative index for different materials which represents the composite atom for a compound or mixture of various materials and is important to predict how x-rays interact with a substance[7], was analyzed in the current study. Previous studies have indicated that Eff-Z could depict lesion characterization and could be used to differentiate tumors[16, 23-25]. In our study, the same conclusions were reached. There were significant differences in Eff-Z among the three groups in both AP and VP. Eff-Z in high-grade ESCCs was higher than in low-grade ESCCs, indicative of the feasibility of Eff-Z as a differentiating factor for ESCCs with different degrees of differentiation. Regarding the λ HU of ESCC, λ HU and the tumor pathological grade showed a significant association during both phases, and tumors with a lower grade had lower λ HU . The spectral curve reflects different lesions or tissues that absorb X-rays at different rates[26]. Thus, our study indicated that with the increase in pathological grade of ESCC, the local enhancement and the iodine contrast agent of local lesions increased, which concurred with previous studies[9-11]. With regards to WC, it is not dependent on photon energy and is less affected by the beam hardening effect, unlike the CT attenuation number, and therefore it is a more reliable parameter in tumor characterization[27]. However, inconsistent with previous studies[9, 28], our study demonstrated that WC was not significantly different among the three groups, which was useless for differentiating pathological grades of ESCC. This may be related to the small sample size of this study, which needs further study and verification in the future. The prognosis of low-grade ESCC is better than high-grade ESCC, it will be of great value if we can distinguish them from each other on spectral CT. Thus, in our study ROC was generated to evaluate the diagnostic performance of spectral parameters to distinguish between the poorly differentiated group and the non-poorly differentiated group. The best diagnostic performance was found for IC in VP using a threshold value of 21.08 mg/ml, which resulted in a sensitivity, specificity, and AUC of 0.829, 0.569, and 0.729, respectively, also suggesting that tumor cells were metabolized vigorously and blood supply was abundant[28]. The diagnostic efficiency of spectral parameters was lower in the AP than in the VP, which is consistent with a previous study[12]. This may be because the contrast medium can fill the microvessels and penetrates the basement membrane into the intercellular space in the VP[24]. Therefore, the spectral parameters of the VP can better reflect the histological characteristics of the tumor. This study still has some limitations. Firstly, the study included a relatively small number of ESCC patients. Secondly, there is an inherent selection bias due to this study's retrospective nature. Thirdly, our study was based on the patients with ESCC, therefore, it can't be applied to other esophageal tumors, such as esophageal adenocarcinoma (EAC). Moreover, our study primarily focused on esophageal lesions without lymph node involvement. However, we encourage subsequent studies addressing these problems. Abbreviations ESCC, esophageal squamous cell carcinoma; AP, arterial phase; VP, venous phase; VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; λ HU , the slope of the spectral curve; ROI, region of interest; AUC, area under the ROC curve Conclusions In conclusion, parameters derived from spectral CT imaging could offer supplemental information to differentiate ESCC with different degrees of differentiation, which is particularly important in patients who can’t receive biopsy or surgery and might be a useful technology to help guide appropriate clinical diagnosis and prognosis. Declarations Acknowledgments The authors thank Dr. Jiayang Song for the statistical advice of this study. Authors’ contributions Yi Wang contributed to the conception and design of the study, data analysis, and writing of the manuscript. Ji Zhang and Jianguo Xia contributed to performing the experiments and writing and revising the manuscript. Weizhong Tian contributed to the interpretation of the data. Shuangfeng Tian and Liang He contributed to the data collection. All authors accept responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. Funding This study was supported by Jiangsu Province’s 333 High-Level Talent Project (grant number: BRA2020193), Jiangsu Provincial Medical Youth Talent (grant number: QNRC2016509), and Jiangsu Provincial High-Level Talent Project (grant number: LGY2018032). Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Conflict of interest The authors have no conflict of interest. Ethics approval and consent to participate This study was approved by Taizhou People's Hospital review board. Consent for publication All the authors have consented to the publication of this manuscript. 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Zhang B, Wu Q, Qiu X, Ding X, Wang J, Li J, Sun P, Hu X: Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma. BMC Cancer 2021, 21(1):874. Tables Table 1 Patient characteristics group n Sex age (y) ROI (mm 2 ) lymph node involvement F M AP VP Well differentiated ESCC 27 0 27 67.0±7.0 36~199 35~199 17 Moderately differentiated ESCC 82 20 62 66.4±7.2 26~184 27~181 27 Poorly differentiated ESCC 82 14 68 71.1±5.4 30~150 28~158 33 ESCC, esophageal squamous cell carcinoma; ROI, region of interest; AP, arterial phase; VP, venous phase. Table 2 Comparison of quantitative parameters among ESCC of different degrees of differentiation in VP group n VP VMI 40keV IC (mg/ml) WC (mg/ml) Eff-Z λ HU Well differentiated ESCC 27 192.66±29.44 19.85±3.59 1028.66±5.80 8.76±0.18 3.74±0.68 Moderately differentiated ESCC 82 210.98±80.27 21.31±4.58 1027.24±9.21 8.84±0.24 4.27±2.46 Poorly differentiated ESCC 82 234.88±44.03 24.88±5.15 1029.18±7.53 9.01±0.24 4.70±0.97 F Value 5.987 17.217 1.213 3.277 17.877 P Value 0.003 0.000 0.300 0.040 0.000 Except for the F and P-values, data are reported as mean ± standard deviation. ESCC, esophageal squamous cell carcinoma; VP, venous phase; VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; λ HU , the slope of the spectral curve. Table 3 Comparison of quantitative parameters among ESCC of different degrees of differentiation in AP group n AP VMI 40keV IC (mg/ml) WC (mg/ml) Eff-Z λ HU Well differentiated ESCC 27 137.85±25.18 13.66±3.13 1024.74±7.19 8.43±0.17 2.58±0.59 Moderately differentiated ESCC 82 162.81±45.75 16.49±5.29 1026.35±5.92 8.58±0.28 3.12±1.00 Poorly differentiated ESCC 82 172.18±44.41 17.65±5.13 1026.13±8.60 8.64±0.27 3.33±1.00 F Value 6.518 6.563 0.504 6.907 6.554 P Value 0.002 0.002 0.605 0.001 0.002 Except for the F and P-values, data are reported as mean ± standard deviation. ESCC, esophageal squamous cell carcinoma; AP, arterial phase; VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; λ HU , the slope of the spectral curve. Table 4 AUCs, Thresholds, Sensitivities, and Specificities for distinguishing the poorly differentiated group from the non-poorly differentiated group in VP Parameter AUC Threshold value Sensitivity Specificity VMI 40keV 0.725 204.36 81.7% 56.9% IC 0.729 21.08 82.9% 56.9% Eff-Z 0.729 8.84 79.3% 56.9% λ HU 0.723 3.97 82.9% 56.9% AUC, area under the ROC curve; VP, venous phase; VMI, virtual monoenergetic image; IC, iodine concentration; Eff-Z, effective atomic number; λ HU , the slope of the spectral curve. Table 5 AUCs, Thresholds, Sensitivities, and Specificities for distinguishing the poorly differentiated group from the non-poorly differentiated group in AP Parameter AUC Threshold value Sensitivity Specificity VMI 40keV 0.612 140.30 75.6% 47.7% IC 0.616 13.70 76.8% 45.9% Eff-Z 0.619 8.44 76.8% 45.9% λ HU 0.617 2.64 74.4% 49.5% AUC, area under the ROC curve; AP, arterial phase; VMI, virtual monoenergetic image; IC, iodine concentration; Eff-Z, effective atomic number; λ HU , the slope of the spectral curve. Supplementary Files datasets.xlsx Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2023 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Minor revision 26 May, 2023 Reviewers agreed at journal 01 Sep, 2022 Reviewer # 1 agreed at journal 31 Aug, 2022 Reviewers invited by journal 15 Jul, 2022 Editor assigned by journal 06 Jun, 2022 First submitted to journal 05 Jun, 2022 Submission checks completed at journal 05 Jun, 2022 Editor invited by journal 05 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1727634","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":120922841,"identity":"462460f3-5a62-4f42-9611-7aababdd1dbf","order_by":0,"name":"Yi Wang","email":"","orcid":"https://orcid.org/0000-0003-2654-4412","institution":"Jiangsu Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":120922842,"identity":"4d58c857-09d5-49cb-a296-764231d1ed51","order_by":1,"name":"Weizhong Tian","email":"","orcid":"","institution":"Jiangsu Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weizhong","middleName":"","lastName":"Tian","suffix":""},{"id":120922843,"identity":"164c9fc7-b67e-4b21-89db-8726b139fd76","order_by":2,"name":"Shuangfeng Tian","email":"","orcid":"","institution":"Jiangsu Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuangfeng","middleName":"","lastName":"Tian","suffix":""},{"id":120922844,"identity":"2d5c6b81-fa1a-400d-b1c3-e1799f5f9583","order_by":3,"name":"Liang He","email":"","orcid":"","institution":"Jiangsu Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"He","suffix":""},{"id":120922845,"identity":"052ed246-74af-46cc-9bed-4c2aa5a3fa13","order_by":4,"name":"Jianguo Xia","email":"","orcid":"","institution":"Jiangsu Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Xia","suffix":""},{"id":120922846,"identity":"1deb7f39-74cf-4cc0-a959-59f846940d0d","order_by":5,"name":"Ji Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACxmYGNhAtB+GykaDFGEIRowWmLLGBaC3M7ezPHvzcUZu+Xb7HgOFD2WEG/tkNBB2Wbth75njuzjYeA8YZ5w4zSNw5QFDLMQnetmO5G47xGDDzth1mMJBIIKSFsU3yb9uxdAOQlr/EaWFmk+Ztq0kAa2EkTgsbm7Rs2wHDDcfSCg72nEvnkbhBQIth//Fnkm/b6uQNDh/e+OBHmbUc/wxCWhrA1GEweQCIefCrBwJ5CFVHUOEoGAWjYBSMYAAAxRM/PzPH9M8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7503-5222","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ji","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-06-05 14:10:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1727634/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1727634/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-023-01068-5","type":"published","date":"2023-08-23T15:01:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":24209312,"identity":"4c3727f0-3b44-4b50-8c27-bbb6c61b3201","added_by":"auto","created_at":"2022-07-22 17:09:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77934,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of each parameter for the differential diagnosis of the poorly differentiated group (82 cases) from the non-poorly differentiated group (109 cases) in the arterial phase and venous phase, respectively. VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; λ\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1727634/v1/dae0b8ba496240677557aa0f.png"},{"id":24209314,"identity":"a5ab0c6a-2ebc-479f-a42a-8efd285dea5d","added_by":"auto","created_at":"2022-07-22 17:09:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":685850,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral images in venous phase and Photomicrograph (original magnification, ×100) example of well differentiated ESCC (male, 71 years old). VMI\u003csub\u003e40 keV\u003c/sub\u003e=185.88 HU, IC =18.88 mg/ml, WC = 1029.58 mg/ml, Eff-Z=8.71, λ\u003csub\u003eHU\u003c/sub\u003e=3.57. VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; λ\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve; Eff-Z, effective atomic number.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1727634/v1/de0efa78a6e195001ddd6942.png"},{"id":24209316,"identity":"87185391-0dcf-4853-b73f-0e7d19d5e7e8","added_by":"auto","created_at":"2022-07-22 17:09:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":579717,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral images in venous phase and Photomicrograph (original magnification, ×100) example of moderately differentiated ESCC (male, 68 years old). VMI\u003csub\u003e40 keV\u003c/sub\u003e=225.76 HU, IC =23.10 mg/ml, WC = 1034.81 mg/ml, Eff-Z=8.93, λ\u003csub\u003eHU\u003c/sub\u003e=4.36. VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; λ\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve; Eff-Z, effective atomic number.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1727634/v1/e753ed267122f3c752ab0f69.png"},{"id":24209320,"identity":"0243dd5b-846b-4d51-8373-ff1a6115cf89","added_by":"auto","created_at":"2022-07-22 17:09:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":687783,"visible":true,"origin":"","legend":"\u003cp\u003eSpectral images in venous phase and Photomicrograph (original magnification, ×100) example of poorly moderately differentiated ESCC (female, 64 years old). VMI\u003csub\u003e40 keV\u003c/sub\u003e=261.35 HU, IC =28.52 mg/ml, WC = 1026.40 mg/ml, Eff-Z=9.20, λ\u003csub\u003eHU\u003c/sub\u003e=5.36. VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; λ\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve; Eff-Z, effective atomic number.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1727634/v1/dd53e1a9798cf72528d145ee.png"},{"id":42781239,"identity":"3f7f4258-bd9f-47db-8b50-745cf2e1a719","added_by":"auto","created_at":"2023-09-07 15:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2199424,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1727634/v1/9e07301b-161f-4533-9e19-f6151d6ed671.pdf"},{"id":24209324,"identity":"456871b5-f146-491f-a2f5-62fadc270ba0","added_by":"auto","created_at":"2022-07-22 17:09:34","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51828,"visible":true,"origin":"","legend":"","description":"","filename":"datasets.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1727634/v1/9ac56a1d3f9a8540ff63450c.xlsx"}],"financialInterests":"","formattedTitle":"Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the global cancer statistics 2020, esophageal cancer ranks seventh in terms of incidence and sixth in mortality overall, the latter signifying that esophageal cancer is responsible for one in every 18 cancer deaths in 2020[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The treatment strategies and prognosis evaluation for esophageal cancer are mainly determined by the clinical TNM stage and pathological TNM (pTNM) stage, the latter remains relevant for early-stage cancers and as an important staging and survival reference point[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The overall 5-year survival of patients with esophageal cancer ranges from 15\u0026ndash;25%. Diagnoses made at earlier stages are associated with better outcomes than those made at later stages[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the latter is more prone to lymph node metastasis and local recurrence as well as shorter survival[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In China, esophageal squamous cell carcinoma (ESCC) is the predominant pathological type of esophageal cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A definitive diagnosis of esophageal lesions is based on histological examination; however, an invasive method is not always readily available, and local samples might not fully reflect the overall heterogeneity of the tumor. Thus, an accurate and non-invasive method is urgently needed in evaluating the pathological grade of ESCC.\u003c/p\u003e \u003cp\u003eIt is generally accepted that CT is an important modality for evaluating esophageal tumors. Conventional CT is often used to identify the location of primary lesions and distal metastases, but its role in determining pTNM is limited. Spectral CT has been widely used for qualitative and quantitative imaging of different malignancies and could provide more accurate and complete information on cancers for detection and prognoses evaluation. With the use of material decomposition techniques, one can obtain Virtual monoenergetic images (VMI), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), or other material-specific information[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In recent years, spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies including glioma[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], pancreatic neuroendocrine neoplasms[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], gastric adenocarcinoma[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], ovarian tumours[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], non-small cell lung cancer[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and clear cell renal cell carcinoma (ccRCC)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, to the best of our knowledge, spectral parameters have not been applied for the pathological grades of esophageal cancer so far.\u003c/p\u003e \u003cp\u003eConsequently, this study was conducted to explore the significance of quantitative assessment with several parameters derived from spectral CT in differentiating ESCC with different degrees of differentiation.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of our hospital and all patients signed the informed consent. A total of 191 patients with histologically proven ESCC were enrolled in this study from June 2018 to March 2020. The inclusion criteria were as follows: (1) No contraindications to contrast-enhanced CT examination; (2) No radiation therapy or chemotherapy before surgery; (3) Within 1 week after spectral CT scan the lesions were resected; (4) Postoperative pathologic confirmation of ESCC. The main exclusion criteria were as follows: (1) Patients who were found to be allergic to iodine contrast agent before enhanced CT examination; (2) Patients who had already received radiation therapy or chemotherapy; (3) Images with poor quality due to artifacts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpectral CT image acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll inspections were conducted using a Revolution CT scanner (GE Healthcare, Milwaukie USA) with the spectral CT acquisition mode. The scan protocol included a 5 mm slice thickness, tube voltage of 70 and 140 kV with a fast kilovolt peak\u0026ndash;switching technique, CT automatic exposure control (AEC) systems adjusting the tube current, and gantry speed of 0.5 seconds per rotation, and helical pitch of 0.992:1. The nonionic contrast agent Iohexol (China, Jiangsu Yangtze River Pharmaceutical Group) was used for the enhanced examination of patients, containing 300mgI/ml of iodine, the weight-dependent dose of 1.5 ml/kg, and an infusion rate of 3.0 ml/s. Scanning was done when the CT value of the aortic arch reached 100 HU, and the arterial phase (AP) and venous phase (VP) started 30 and 60 seconds, respectively, after the administration of contrast agents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpectral CT image analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll spectral CT images were reconstructed with a slice thickness of 1.25\u0026thinsp;mm, and then the images were transferred to the workstation. Two radiologists with 6 and 20 years of experience in esophagus CT diagnosis measured and analyzed the imaging in a blinded and randomized manner respectively. A round or oval region of interest (ROI) was selected according to the size and location of the lesions to measure the virtual monoenergetic 40keV-equivalent image (VMI\u003csub\u003e40keV\u003c/sub\u003e), IC, WC, and Eff-Z in both the AP and VP. To reduce measurement variation, ROIs were placed three times in the tumor area without distinguishable necrosis or hemorrhage, and the average of triplicate measurements was used as the final data value. The slope of the spectral curve (\u0026lambda;\u003csub\u003eHU\u003c/sub\u003e) was defined as the difference between the CT value at 40 keV and that at 70 keV divided by the energy difference(30keV), and it was calculated as follows: \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e = [(CT(40keV)-CT(70keV)]/(70-40)[12]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathological analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll tissues were obtained from surgical operation. Based on the 8th edition of AJCC classification[4], these patients were divided into three groups according to postoperative pathological results: well differentiated ESCC, moderately differentiated ESCC and poorly differentiated ESCC. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using SPSS26.0. Quantitative variables are expressed as mean \u0026plusmn; standard deviation, and categorical variables are presented as frequencies (percentages). The differences in VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, WC, Eff-Z, and \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e of the three groups were statistically analyzed using one-way ANOVA and pairwise comparison was performed with LSD. Receiver operating characteristic (ROC) analysis was conducted for each parameter to differentiate the poorly differentiated and non-poorly differentiated groups.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 191 patients (mean age 68.49\u0026plusmn;6.78 years), 157 men (mean age 68.34 \u0026plusmn;7.16 years), and 34 women (mean age 69.18\u0026plusmn;4.64 years) were included. Characteristics of our patients are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Spectral Parameters Comparison \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were significant differences in VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, Eff-Z, and \u0026lambda;\u003csub\u003eHU \u003c/sub\u003ein AP and VP among the three groups (all p \u0026lt; 0.05) except for WC (p \u0026gt; 0.05). The VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, Eff-Z, and \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p \u0026lt; 0.05; Table 2-3). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Diagnostic Value Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves for all spectral CT parameters are shown in Fig. 1. Table 4-5 shows AUCs, thresholds, Sensitivities, and Specificities based on the ROC analysis.\u003c/p\u003e\n\u003cp\u003eIn the identification of poorly differentiated groups and non-poorly differentiated groups. VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, Eff-Z, and \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e in VP showed an area under the ROC curve (AUC) of 0.725, 0.729, 0.729, and 0.723, respectively, while AP showed an AUC of 0.612, 0.616, 0.619, and 0.617, respectively. The highest sensitivity, specificity, and AUC were observed for IC in VP, the sensitivity of it in the identification between the poorly differentiated group and the non-poorly differentiated group was 82.9%, while the specificity was 56.9% under the threshold of 21.08 mg/ml.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe pathological differentiation grade of ESCC influences the prognosis, as tumor grade increases, it is more likely to have a poorer prognosis and an elevated risk of death[10]. Our results showed that quantitative parameters derived from spectral CT, including VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, Eff-Z, and \u0026lambda;\u003csub\u003eHU\u0026nbsp;\u003c/sub\u003eboth in AP and VP, could be used to distinguish pathological grades of ESCC, as shown in\u0026nbsp;Fig. 2-4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpectral CT greatly reduces beam-hardening artifacts and generates VMIs with more accurate CT attenuation numbers at every energy level[13]. VMI\u003csub\u003e40keV\u003c/sub\u003e reconstructions provided higher image quality due to a higher lesion to background attenuation ratio, i.e., higher contrast[14, 15]; Thus, we selected the 40 keV image for this study. In the present study, VMI\u003csub\u003e40keV\u003c/sub\u003e in the AP and VP was significantly different among the three groups, which concurs with a previous study[13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of iodine in the tissue is strongly correlated with local blood volume and vascular density[16].\u0026nbsp;Several studies have shown that IC correlates well with higher blood flow and vascularization[14, 17-22]. In our study, IC in the AP and VP was significantly different among the three groups, as tumor grade increased, it also increased. To be more precise, IC in the well differentiated group was the lowest while in the poorly differentiated ESCC group was the highest, signifying that the iodine uptake and vascularity of the low-grade ESCCs are lower than that of high-grade ESCCs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, Eff-Z, another quantitative index for different materials which represents the composite atom for a compound or mixture of various materials and is important to predict how x-rays interact with a substance[7], was analyzed in the current study.\u0026nbsp;Previous studies have indicated that Eff-Z could depict lesion characterization and could be used to differentiate tumors[16, 23-25]. In our study, the same conclusions were reached. There were significant differences in Eff-Z among the three groups in both AP and VP.\u0026nbsp;Eff-Z\u0026nbsp;in high-grade ESCCs was higher than in low-grade ESCCs, indicative of the feasibility of\u0026nbsp;Eff-Z\u0026nbsp;as a differentiating factor for ESCCs\u0026nbsp;with different degrees of differentiation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e of ESCC,\u0026nbsp;\u0026lambda;\u003csub\u003eHU\u003c/sub\u003e and the tumor pathological grade showed a significant association during both phases, and tumors with a lower grade had lower \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e. The spectral curve reflects different lesions or tissues that absorb X-rays at different rates[26]. Thus, our study indicated that with the increase in pathological grade of ESCC, the local enhancement and the iodine contrast agent of local lesions increased, which concurred with previous studies[9-11].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith regards to WC, it is not dependent on photon energy and is less affected by the beam hardening effect, unlike the CT attenuation number, and therefore it is a more reliable parameter in tumor characterization[27]. However, inconsistent with previous studies[9, 28], our study demonstrated that WC was not significantly different among the three groups, which was useless for differentiating pathological grades of ESCC. This may be related to the small sample size of this study, which needs further study and verification in the future.\u003c/p\u003e\n\u003cp\u003eThe prognosis of low-grade ESCC is better than high-grade ESCC,\u0026nbsp;it will be of great value if we can distinguish them from each other on spectral CT. Thus, in our study ROC was generated to evaluate the diagnostic performance of spectral parameters to distinguish between the\u0026nbsp;poorly differentiated group and the non-poorly differentiated group. The best diagnostic performance was found for IC in VP using a threshold value of 21.08 mg/ml, which resulted in a sensitivity, specificity, and AUC of 0.829, 0.569, and 0.729, respectively, also suggesting that tumor cells were metabolized vigorously and blood supply was abundant[28]. The diagnostic efficiency of spectral parameters was lower in the AP than in the VP, which is consistent with a previous study[12]. This may be because the contrast medium can fill the microvessels and penetrates the basement membrane into the intercellular space in the VP[24]. Therefore, the spectral parameters of the VP can better reflect the histological characteristics of the tumor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study still has some limitations. Firstly, the study included a relatively small number of ESCC patients. Secondly, there is an inherent selection bias due to this study\u0026apos;s retrospective nature. Thirdly, our study was based on the patients with ESCC, therefore, it can\u0026apos;t be applied to other esophageal tumors, such as esophageal adenocarcinoma (EAC). Moreover, our study primarily focused on esophageal lesions without lymph node involvement. However, we encourage subsequent studies addressing these problems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eESCC, esophageal squamous cell carcinoma; AP, arterial phase; VP, venous phase; VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve; ROI, region of interest; AUC, area under the ROC curve\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, parameters derived from spectral CT imaging could offer supplemental information to differentiate ESCC with different degrees of differentiation, which is particularly important in patients who can\u0026rsquo;t receive biopsy or surgery and might be a useful technology to help guide appropriate clinical diagnosis and prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors thank Dr. Jiayang Song for the statistical advice of this study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eYi Wang\u003c/strong\u003e contributed to the conception and design of the study, data analysis, and writing of the manuscript. \u003cstrong\u003eJi Zhang\u003c/strong\u003e and \u003cstrong\u003eJianguo Xia\u003c/strong\u003e contributed to performing the experiments and writing and revising the manuscript. \u003cstrong\u003eWeizhong Tian\u003c/strong\u003e contributed to the interpretation of the data. \u003cstrong\u003eShuangfeng Tian\u003c/strong\u003e and \u003cstrong\u003eLiang He\u003c/strong\u003e contributed to the data collection. All authors accept responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study was supported by Jiangsu Province\u0026rsquo;s 333 High-Level Talent Project (grant number: BRA2020193), Jiangsu Provincial Medical Youth Talent (grant number: QNRC2016509), and Jiangsu Provincial High-Level Talent Project (grant number: LGY2018032).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study was approved by Taizhou People\u0026apos;s Hospital review board.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAll the authors have consented to the publication of this manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71(3):209-249.\u003c/li\u003e\n\u003cli\u003eRice TW, Ishwaran H, Ferguson MK, Blackstone EH, Goldstraw P: Cancer of the Esophagus and Esophagogastric Junction: An Eighth Edition Staging Primer. J Thorac Oncol 2017, 12(1):36-42.\u003c/li\u003e\n\u003cli\u003ePennathur A, Gibson MK, Jobe BA, Luketich JD: Oesophageal carcinoma. The Lancet 2013, 381(9864):400-412.\u003c/li\u003e\n\u003cli\u003eJiang D, Wang H, Song Q, Wang H, Wang Q, Tan L, Hou Y: Comparison of the prognostic difference between ypTNM and equivalent pTNM stages in esophageal squamous cell carcinoma based on the 8th edition of AJCC classification. J Cancer 2020, 11(7):1808-1815.\u003c/li\u003e\n\u003cli\u003eMcCollough CH, Leng S, Yu L, Fletcher JG: Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. Radiology 2015, 276(3):637-653.\u003c/li\u003e\n\u003cli\u003eYingying L, Zhe Z, Xiaochen W, Xiaomei L, Nan J, Shengjun S: Dual-layer detector spectral CT-a new supplementary method for preoperative evaluation of glioma. Eur J Radiol 2021, 138:109649.\u003c/li\u003e\n\u003cli\u003eLi WX, Miao F, Xu XQ, Zhang J, Wu ZY, Chen KM, Yan FH, Lin XZ: Pancreatic Neuroendocrine Neoplasms: CT Spectral Imaging in Grading. Acad Radiol 2021, 28(2):208-216.\u003c/li\u003e\n\u003cli\u003eLu Z, Wu S, Yan C, Chen J, Li Y: Clinical value of energy spectrum curves of dual-energy computer tomography may help to predict pathological grading of gastric adenocarcinoma. Transl Cancer Res 2021, 10(1):1-9.\u003c/li\u003e\n\u003cli\u003eElsherif SB, Zheng S, Ganeshan D, Iyer R, Wei W, Bhosale PR: Does dual-energy CT differentiate benign and malignant ovarian tumours? Clin Radiol 2020, 75(8):606-614.\u003c/li\u003e\n\u003cli\u003eLin LY, Zhang Y, Suo ST, Zhang F, Cheng JJ, Wu HW: Correlation between dual-energy spectral CT imaging parameters and pathological grades of non-small cell lung cancer. Clin Radiol 2018, 73(4):412 e411-412 e417.\u003c/li\u003e\n\u003cli\u003eWei J, Zhao J, Zhang X, Wang D, Zhang W, Wang Z, Zhou J: Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC). PLoS One 2018, 13(5):e0195699.\u003c/li\u003e\n\u003cli\u003eDeng L, Zhang G, Lin X, Han T, Zhang B, Jing M, Zhou J: Comparison of Spectral and Perfusion Computed Tomography Imaging in the Differential Diagnosis of Peripheral Lung Cancer and Focal Organizing Pneumonia. Front Oncol 2021, 11:690254.\u003c/li\u003e\n\u003cli\u003eHan D, Yu Y, He T, Yu N, Dang S, Wu H, Ren J, Duan X: Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO/ISUP classification of clear cell renal cell carcinoma. Clin Radiol 2021, 76(8):627 e623-627 e629.\u003c/li\u003e\n\u003cli\u003eLohofer FK, Kaissis GA, Koster FL, Ziegelmayer S, Einspieler I, Gerngross C, Rasper M, Noel PB, Koerdt S, Fichter A et al: Improved detection rates and treatment planning of head and neck cancer using dual-layer spectral CT. Eur Radiol 2018, 28(12):4925-4931.\u003c/li\u003e\n\u003cli\u003eNagayama Y, Tanoue S, Inoue T, Oda S, Nakaura T, Utsunomiya D, Yamashita Y: Dual-layer spectral CT improves image quality of multiphasic pancreas CT in patients with pancreatic ductal adenocarcinoma. Eur Radiol 2020, 30(1):394-403.\u003c/li\u003e\n\u003cli\u003eWang X, Liu D, Zeng X, Jiang S, Li L, Yu T, Zhang J: Dual-energy CT quantitative parameters for evaluating Immunohistochemical biomarkers of invasive breast cancer. Cancer Imaging 2021, 21(1):4.\u003c/li\u003e\n\u003cli\u003eBoning G, Adelt S, Feldhaus F, Fehrenbach U, Kahn J, Hamm B, Streitparth F: Spectral CT in clinical routine imaging of neuroendocrine neoplasms. Clin Radiol 2021, 76(5):348-357.\u003c/li\u003e\n\u003cli\u003eAdam SZ, Rabinowich A, Kessner R, Blachar A: Spectral CT of the abdomen: Where are we now? Insights Imaging 2021, 12(1):138.\u003c/li\u003e\n\u003cli\u003eLennartz S, Tager P, Zopfs D, Iuga AI, Reimer RP, Zaske C, Grosse Hokamp N, Maintz D, Heidenreich A, Drzezga A et al: Lymph Node Assessment in Prostate Cancer: Evaluation of Iodine Quantification With Spectral Detector CT in Correlation to PSMA PET/CT. Clin Nucl Med 2021, 46(4):303-309.\u003c/li\u003e\n\u003cli\u003eSun X, Niwa T, Ozawa S, Endo J, Hashimoto J: Detecting lymph node metastasis of esophageal cancer on dual-energy computed tomography. Acta Radiol 2022, 63(1):3-10.\u003c/li\u003e\n\u003cli\u003eGe X, Yu J, Wang Z, Xu Y, Pan C, Jiang L, Yang Y, Yuan K, Liu W: Comparative study of dual energy CT iodine imaging and standardized concentrations before and after chemoradiotherapy for esophageal cancer. BMC Cancer 2018, 18(1):1120.\u003c/li\u003e\n\u003cli\u003eYang CB, Zhang S, Jia YJ, Yu Y, Duan HF, Zhang XR, Ma GM, Ren C, Yu N: Dual energy spectral CT imaging for the evaluation of small hepatocellular carcinoma microvascular invasion. Eur J Radiol 2017, 95:222-227.\u003c/li\u003e\n\u003cli\u003eBuus TW, Sandahl M, Thorup KS, Rasmussen F, Redsted S, Christiansen P, Jensen AB, Pedersen EM: Breast cancer: comparison of quantitative dual-layer spectral CT and axillary ultrasonography for preoperative diagnosis of metastatic axillary lymph nodes. Eur Radiol Exp 2021, 5(1):16.\u003c/li\u003e\n\u003cli\u003eZhang Z, Zou H, Yuan A, Jiang F, Zhao B, Liu Y, Chen J, Zuo M, Gong L: A Single Enhanced Dual-Energy CT scan May Distinguish Lung Squamous Cell Carcinoma From Adenocarcinoma During the Venous phase. Acad Radiol 2020, 27(5):624-629.\u003c/li\u003e\n\u003cli\u003eZhou Y, Hou P, Zha K, Liu D, Wang F, Zhou K, Gao J: Spectral Computed Tomography for the Quantitative Assessment of Patients With Carcinoma of the Gastroesophageal Junction: Initial Differentiation Between a Diagnosis of Squamous Cell Carcinoma and Adenocarcinoma. J Comput Assist Tomogr 2019, 43(2):187-193.\u003c/li\u003e\n\u003cli\u003eBrooks RA: A quantitative theory of the Hounsfield unit and its application to dual energy scanning. J Comput Assist Tomogr 1977, 1(4):487-493.\u003c/li\u003e\n\u003cli\u003eLiu G, Li M, Li G, Li Z, Liu A, Pu R, Cao H, Liu Y: Assessing the Blood Supply Status of the Focal Ground-Glass Opacity in Lungs Using Spectral Computed Tomography. Korean J Radiol 2018, 19(1):130-138.\u003c/li\u003e\n\u003cli\u003eZhang B, Wu Q, Qiu X, Ding X, Wang J, Li J, Sun P, Hu X: Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma. BMC Cancer 2021, 21(1):874.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003ePatient characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"27.086614173228348%\"\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.4015748031496065%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"11.968503937007874%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"10.393700787401574%\"\u003e\n \u003cp\u003eage (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"20.787401574803148%\"\u003e\n \u003cp\u003eROI (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"22.362204724409448%\"\u003e\n \u003cp\u003elymph node involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.359375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.84375%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.84375%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.6875%\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.265625%\"\u003e\n \u003cp\u003eVP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.044025157232703%\"\u003e\n \u003cp\u003eWell differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.389937106918239%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.9748427672955975%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.9748427672955975%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.377358490566039%\"\u003e\n \u003cp\u003e67.0\u0026plusmn;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.949685534591195%\"\u003e\n \u003cp\u003e36~199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.962264150943396%\"\u003e\n \u003cp\u003e35~199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.32704402515723%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.044025157232703%\"\u003e\n \u003cp\u003eModerately differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.389937106918239%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.9748427672955975%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.9748427672955975%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.377358490566039%\"\u003e\n \u003cp\u003e66.4\u0026plusmn;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.949685534591195%\"\u003e\n \u003cp\u003e26~184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.962264150943396%\"\u003e\n \u003cp\u003e27~181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.32704402515723%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.044025157232703%\"\u003e\n \u003cp\u003ePoorly differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.389937106918239%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.9748427672955975%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.9748427672955975%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.377358490566039%\"\u003e\n \u003cp\u003e71.1\u0026plusmn;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.949685534591195%\"\u003e\n \u003cp\u003e30~150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.962264150943396%\"\u003e\n \u003cp\u003e28~158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.32704402515723%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eESCC, esophageal squamous cell carcinoma; ROI, region of interest;\u0026nbsp;AP, arterial phase; VP, venous phase.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable align=\"left\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eComparison of quantitative parameters among ESCC of different degrees of differentiation in VP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"6.58307210031348%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"65.2037617554859%\"\u003e\n \u003cp\u003eVP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.951923076923077%\"\u003e\n \u003cp\u003eVMI\u003csub\u003e40keV\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003e\n \u003cp\u003eIC (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.432692307692307%\"\u003e\n \u003cp\u003eWC (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.432692307692307%\"\u003e\n \u003cp\u003eEff-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.432692307692307%\"\u003e\n \u003cp\u003e\u0026lambda;\u003csub\u003eHU\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eWell differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.58307210031348%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.009404388714733%\"\u003e\n \u003cp\u003e192.66\u0026plusmn;29.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.225705329153605%\"\u003e\n \u003cp\u003e19.85\u0026plusmn;3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e1028.66\u0026plusmn;5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e8.76\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e3.74\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eModerately differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.58307210031348%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.009404388714733%\"\u003e\n \u003cp\u003e210.98\u0026plusmn;80.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.225705329153605%\"\u003e\n \u003cp\u003e21.31\u0026plusmn;4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e1027.24\u0026plusmn;9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e8.84\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e4.27\u0026plusmn;2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003ePoorly differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.58307210031348%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.009404388714733%\"\u003e\n \u003cp\u003e234.88\u0026plusmn;44.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.225705329153605%\"\u003e\n \u003cp\u003e24.88\u0026plusmn;5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e1029.18\u0026plusmn;7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e9.01\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e4.70\u0026plusmn;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eF Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.58307210031348%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.009404388714733%\"\u003e\n \u003cp\u003e5.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.225705329153605%\"\u003e\n \u003cp\u003e17.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e3.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e17.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.58307210031348%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.009404388714733%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.225705329153605%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.322884012539184%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eExcept for the F and P-values, data are reported as mean \u0026plusmn; standard deviation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eESCC, esophageal squamous cell carcinoma;\u0026nbsp;VP, venous phase; VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eComparison of quantitative parameters among ESCC of different degrees of differentiation in AP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"5.956112852664577%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"65.8307210031348%\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eVMI\u003csub\u003e40keV\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eIC (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eWC (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eEff-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u0026lambda;\u003csub\u003eHU\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eWell differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.956112852664577%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e137.85\u0026plusmn;25.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e13.66\u0026plusmn;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e1024.74\u0026plusmn;7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e8.43\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e2.58\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eModerately differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.956112852664577%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e162.81\u0026plusmn;45.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e16.49\u0026plusmn;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e1026.35\u0026plusmn;5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e8.58\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e3.12\u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003ePoorly differentiated ESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.956112852664577%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e172.18\u0026plusmn;44.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e17.65\u0026plusmn;5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e1026.13\u0026plusmn;8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e8.64\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e3.33\u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eF Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.956112852664577%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e6.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e6.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e6.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e6.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.213166144200628%\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.956112852664577%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.16614420062696%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eExcept for the F and P-values, data are reported as mean \u0026plusmn; standard deviation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eESCC, esophageal squamous cell carcinoma;\u0026nbsp;AP, arterial phase; VMI, virtual monoenergetic image; IC, iodine concentration; WC, water concentration; Eff-Z, effective atomic number; \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eAUCs, Thresholds, Sensitivities, and Specificities for distinguishing the poorly differentiated group from the non-poorly differentiated group in VP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eThreshold value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eVMI\u003csub\u003e40keV\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e204.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e81.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e56.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e21.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e82.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e56.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eEff-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e79.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e56.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u0026lambda;\u003csub\u003eHU\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e82.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e56.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eAUC, area under the ROC curve; VP, venous phase; VMI, virtual monoenergetic image; IC, iodine concentration; Eff-Z, effective atomic number; \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eAUCs, Thresholds, Sensitivities, and Specificities for distinguishing the poorly differentiated group from the non-poorly differentiated group in AP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.777403035413155%\"\u003e\n \u003cp\u003eThreshold value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eVMI\u003csub\u003e40keV\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.777403035413155%\"\u003e\n \u003cp\u003e140.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e75.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e47.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.777403035413155%\"\u003e\n \u003cp\u003e13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003eEff-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.777403035413155%\"\u003e\n \u003cp\u003e8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e\u0026lambda;\u003csub\u003eHU\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.777403035413155%\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e74.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.05564924114671%\"\u003e\n \u003cp\u003e49.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eAUC, area under the ROC curve; AP, arterial phase; VMI, virtual monoenergetic image; IC, iodine concentration; Eff-Z, effective atomic number; \u0026lambda;\u003csub\u003eHU\u003c/sub\u003e, the slope of the spectral curve.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Esophageal squamous cell carcinoma, Spectral CT, pathological grade","lastPublishedDoi":"10.21203/rs.3.rs-1727634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1727634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSpectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation༎\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThere were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40keV-equivalent image (VMI\u003csub\u003e40keV\u003c/sub\u003e), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λ\u003csub\u003eHU\u003c/sub\u003e) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were significant differences in VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, Eff-Z, and λ\u003csub\u003eHU\u003c/sub\u003e in AP and VP among the three groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) except for WC (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The VMI\u003csub\u003e40keV\u003c/sub\u003e, IC, Eff-Z, and λ\u003csub\u003eHU\u003c/sub\u003e in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the ROC analysis, IC performed the best in the identification of the poorly differentiated group and non-poorly differentiated group in VP (AUC\u0026thinsp;=\u0026thinsp;0.729, Sensitivity\u0026thinsp;=\u0026thinsp;0.829, and Specificity\u0026thinsp;=\u0026thinsp;0.569 under the threshold of 21.08 mg/ml).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eQuantitative parameters of spectral CT could offer supplemental information for the preoperative differential diagnosis of ESCC with different degrees of differentiation.\u003c/p\u003e","manuscriptTitle":"Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-22 17:09:32","doi":"10.21203/rs.3.rs-1727634/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2023-05-26T09:22:33+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-09-01T17:59:33+00:00","index":0,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-09-01T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-15T09:46:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-06-06T12:56:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2022-06-06T00:21:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-06-05T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-06-05T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0b1d18d-825d-405b-a4e5-4fd792f3bfca","owner":[],"postedDate":"July 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-07T15:06:43+00:00","versionOfRecord":{"articleIdentity":"rs-1727634","link":"https://doi.org/10.1186/s12880-023-01068-5","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2023-08-23 15:01:59","publishedOnDateReadable":"August 23rd, 2023"},"versionCreatedAt":"2022-07-22 17:09:32","video":"","vorDoi":"10.1186/s12880-023-01068-5","vorDoiUrl":"https://doi.org/10.1186/s12880-023-01068-5","workflowStages":[]},"version":"v1","identity":"rs-1727634","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1727634","identity":"rs-1727634","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}