Dual-layer Spectral Detector CT in Predicting Benign and Malignant Laryngeal Tumors

Dual-layer Spectral Detector CT in Predicting Benign and Malignant Laryngeal Tumors | 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 Article Dual-layer Spectral Detector CT in Predicting Benign and Malignant Laryngeal Tumors Fang Wang, Danping Zhang, Xiaodi Zhang, Guidong Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3834703/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 Objectives: To investigate the quantitative parameters of dual-layer spectral detector CT in the assessment of benign and malignant laryngeal tumors. Methods: Spectral images of 47 patients were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP), including 26 larynx squamous carcinoma (LSCC) lesions and 28 benign lesions. For the 54 lesions in the AP and VP, the slope of the spectral curve (λHUa and λHUv) from 40keV to 90keV, CT values of conventional images (CTa and CTv), iodine concentrations (ICa and ICv), normalized IC (NICa and NICv), effective atomic number (Zeff-a and Zeff-v), and normalized Zeff (nZeff-a and nZeff-v) were calculated and analyzed between LSCC and benign patients. ROC curve, independent sample t-test, and Spearman rank-sum test were used in statistical analysis. Results: λHUv 40-60keV had the highest diagnostic efficiency in all slope of the spectral curve. So λHU 40-60keV was the best slope. All parameters in AP didn’t have well identifying efficiency. But all parameters in VP had perfect identifying efficiency. Especially the ICv was the highest, which sensitivity and specificity were 88.5% and 85.7%. All measured quantitative parameters except CTa and nZeff-a of AP were statistically significant difference between LSCC and benign laryngeal tumors. In the AP, quantitative parameters were weakly positively correlated with two kinds of tumors, especially CTa. But five quantitative parameters ( λHUv 40-60keV ,CTv,ICv, NICv, Zeff-v) were strongly positively in the VP,which the r values are 0.734, 0.696, 0.748, 0.691, 0.730 respectively. Health sciences/Medical research Health sciences/Oncology Dual-layer Spectral Detector CT laryngeal tumor iodine density map effective atomic number spectral curve Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Laryngeal tumors most often refer to squamous cell carcinomas of the larynx. They can be divided into benign and malignant tumors. Benign tumors commonly include laryngeal papilloma, and vocal cord polyp. Laryngeal cancer is a common malignancy, accounting for about 2%-3% of all cancers. The patients who are mostly affected are between 50 and 70 years old, with more incidence in males than females [ 1 ] . The primary symptoms of laryngeal tumors include hoarseness, dyspnea, cough, and dysphagia. Smoking is the main risk factor. In addition, long-term inhalation of harmful substances, alcoholism, environmental pollution, laryngeal papilloma, and laryngeal leukoplakia may be related to the occurrence of laryngeal cancer. The incidence rate is escalating as the environmental pollution increases, especially in large cities. Determining benign or malignant condition is the key to making treatment plans. Cancer tissue biopsy is the gold standard for the diagnosis of laryngeal tumors, but the process is invasive and it has certain limitations. CT examination is ideal to identify the location and display the laryngeal tumors and the CT results help in making reasonable treatment plans [ 2 ] .In recent years, with the development of CT technology, spectral CT has been widely used in tumor identification, and detection and evaluation of radiotherapy and chemotherapy efficacies. At present, many studies explored spectral CT in identifying lung cancer, breast cancer, and laryngeal cancer staging [ 3 – 8 ] . Under the same X-ray source condition, the dual-layer spectral detector CT absorbs photons between high and low energy separation through the upper and lower detectors for imaging [ 9 – 10 ] . Compared with the single-layer detector of traditional CT and spectral CT for imaging through instantaneous switching of high and low energy in one rotation, the feature of spectral CT can improve tissue contrast from the initial data level, and provide more additional details. In addition, conventional images and spectral data can be reconstructed by one scan of spectral CT, such as virtual monoenergetic images, spectral curve, iodine density maps, and effective atomic number images, which can be used for qualitative and quantitative analysis and comparison of CT values and SD values at different energy levels. The slope of spectral curve, normalized iodine densities and normalized effective atomic numbers can differentiate the benign and malignant conditions, and provide reference basis for the clinical treatment plan and therapy efficacy evaluation. Spectral CT low keV virtual monoenergetic image can improve enhanced tissue display, increase tissue contrast, improve the detection rate of small lesions, reduce image noise, and improve signal-to-noise ratio [ 11 – 13 ] . Iodine density map has the potential to quantify iodine enhancement and improve the visualization of iodine in contrast-enhanced tissues [ 14 ] . The quantitative analysis of iodine in tissues and organs can directly reflect the blood supply of tissues and organs, such as blood supply to the benign and malignant vocal cord lesions, advanced gastric cancer [ 15 – 16 ] .The effective atomic number diagram can be used for material identification, material separation, and others. Different tissue structures and pathological types have different spectral curves [ 17 ] .Lin et al. compared the relationship between quantitative parameters of non-small cell lung cancer, Ki-67 expression level and EGFR mutation status by spectral CT [ 18 ] . Chen et al. found that there was a moderate positive correlation between spectral CT imaging parameters and Ki-67 markers in lung adenocarcinoma [ 19 ] . Cheng S M et al. found that the quantitative parameters of spectral CT imaging had a significant positive correlation with the Ki-67 grade in advanced gastric cancer and early gastric cancer [ 20 ] . Wang et al. found that spectral CT quantitative parameters can be used to evaluate the immunohistochemical biomarkers in invasive breast cancer [ 21 ] . However, there are few studies on laryngeal tumors with dual-layer spectral detector CT. Therefore, this study aimed to compare the differences and to determine the correlation of quantitative parameters between benign and malignant laryngeal tumors using spectral CT to evaluate the application value of spectral CT in predicting benign and malignant laryngeal tumors. Methods Patient data This retrospective study was approved by the the Ethics Committee of the Affiliated Hospital of Southwest Medical University, the experimental protocols were performed in accordance with the approved guidelines, and the requirement for informed consent was waived because of the retrospective nature of the study. Retrospective analysis on 47 patients with hoarseness, dyspnea, cough, and dysphagia who did not receive radiotherapy, chemotherapy, and/or surgical treatment before the examination was performed. These patients were examined from October 2020 to July 2022.All subsequent surgical procedure information, histopathological results, and signed informed consent were obtained. All patients underwent dual-phase neck enhanced scan using dual-layer spectral detector CT. There were 46 males and 1 female, with an average age of 62.4 ± 10.7 years. The histopathological results showed that there were 26 larynx squamous carcinoma (LSCC) lesions and 28 other benign lesions, a total of 54 lesions. Scanning parameters Philips IQon spectral CT was used. The scanning range was from the base of the skull to the root of the neck. Scanning parameters included automatic current-time product (mAs), 120 kVp, detector collimation of 64 mm × 0.625 mm, frame rotation time of 0.5 s, and pitch of 0.953. The arterial phase and venous phase scans were completed at 28-30s and 55-60s after injecting Iopamidol(370mg I/mL), respectively. The Ulich high-pressure syringe was connected through the elbow vein, with a total amount of 55 mL at 3.0 mL/s. Subsequently, 30 mL normal saline was injected at the same rate. After the scan, conventional CT images and spectral based images (SBI) were obtained. The 40-90keV(interval of 10keV)virtual monoenergetic images, iodine density maps, and effective atomic number maps were generated on the Philips Intellispace Portal (ISP)workstation. Image analysis A radiologist delineated ROI (area of about 4–10 mm 2 ) at the most obvious enhancement area of the lesion and the center of the same layer of the internal carotid artery both in the arterial phase and venous phase. The iodine density and effective atomic number of the lesion and internal carotid artery were recorded. The CT values of lesions on conventional images, and between 40keV and 90keV(interval of 10keV)were recorded. The λHU between different energy levels were calculated using formulas ① NIC and nZ eff were calculated according to formulas ② and ③. CT 40keV and CT 90keV were the CT values of the lesions at 40keV and 90kev, respectively. The IC lesion , IC internal carotid artery , Zeff lesion, and Zeff internal carotid artery were the iodine concentration and effective atomic number of the lesion and internal carotid artery, respectively. Statistical analysis SPSS version 22.0 was used for statistical analysis. The data were expressed as mean ± standard deviation. ROC curve was used to evaluate the efficacy of different λHU to diagnose laryngeal tumors and to define the best slope. Then ROC curve was used to evaluate the diagnostic efficacy of best slope, CT value, IC, NIC, Zeff, and nZeff in both arterial phase and venous phase. The differences in quantitative parameters between LSCC and benign laryngeal tumors were analyzed by an independent sample t-test. The correlation between quantitative parameters and benign or malignant laryngeal tumors was analyzed by the Spearman rank-sum test. Results Diagnostic efficiency of quantitative parameters of spectral CT The differential diagnostic efficiency indexes of λHU are shown in Table 1 ,Fig. 1 and Fig. 2 . In the arterial phase, all AUC values were 0.7, therefore, all λHUv had a good diagnostic value. And λHUv 40 − 60keV and λHUv 60 − 70keV were the largest (0.927), and the diagnostic efficiencies were the highest. Using paired sample t-test, a significant difference was found between λHUv 40 − 60keV and λHUv 60 − 70keV (P < 0.001), λHUv 40 − 60keV was 3.457 ± 1.174, λHUv 60 − 70keV was 1.522 ± 0.518, and λHU 40 − 60keV had the best slope. Table 1 Differential efficacy of different slope λHUa in the arterial and venous phase slope critical value AUC sensitivity specificity λHUa 40 − 50keV 2.49 0.684 0.731 0.679 λHUa 40 − 60keV 1.975 0.679 0.731 0.679 λHUa 40 − 70keV 1.610 0.672 0.731 0.679 λHUa 40 − 80keV 0.975 0.670 0.923 0.429 λHUa 40 − 90keV 1.110 0.678 0.769 0.607 λHUa 50 − 60keV 1.450 0.652 0.654 0.679 λHUa 50 − 70keV 0.835 0.650 0.923 0.429 λHUa 50 − 80keV 0.690 0.656 0.923 0.429 λHUa 50 − 90keV 0.585 0.665 0.923 0.429 λHUa 60 − 70keV 0.700 0.655 0.846 0.5 λHUa 60 − 80keV 0.575 0.648 0.846 0.5 λHUa 60 − 90keV 0.430 0.669 0.923 0.429 λHUa 70 − 80keV 0.425 0.663 0.923 0.464 λHUa 70 − 90keV 0.335 0.681 0.962 0.429 λHUa 80 − 90keV 0.255 0.675 0.923 0.464 λHUv 40 − 50keV 3.985 0.925 0.885 0.857 λHUv 40 − 60keV 3.17 0.927 0.885 0.857 λHUv 40 − 70keV 2.575 0.926 0.885 0.857 λHUv 40 − 80keV 2.150 0.924 0.885 0.857 λHUv 40 − 90keV 1.840 0.924 0.885 0.857 λHUv 50 − 60keV 2.345 0.924 0.885 0.857 λHUv 50 − 70keV 1.875 0.924 0.885 0.857 λHUv 50 − 80keV 1.540 0.926 0.885 0.857 λHUv 50 − 90keV 1.305 0.924 0.885 0.857 λHUv 60 − 70keV 1.395 0.927 0.885 0.857 λHUv 60 − 80keV 1.140 0.926 0.885 0.857 λHUv 60 − 90keV 0.955 0.924 0.885 0.857 λHUv 70 − 80keV 0.990 0.926 0.808 0.929 λHUv 70 − 90keV 0.730 0.924 0.885 0.857 λHUv 80 − 90keV 0.600 0.922 0.846 0.857 The differential diagnostic efficiency indexes of CT value, IC, NIC, Zeff, and nZeff are shown in Table 2 , Fig. 3 and Fig. 4 . The area under the ROC curve in the ICv was the largest (0.932), the corresponding sensitivity was 88.5%, and the specificity was 85.7%. Table 2 Differential efficacy of conventional CT value, IC, NIC, Zeff, and nZeff in the arterial and venous phase parameters critical value AUC sensitivity specificity CTa 63.95 0.596 0.731 0.5 ICa 0.795 0.696 0.731 0.679 NICa 0.095 0.740 0.808 0.679 Zeff-a 7.745 0.688 0.769 0.607 nZeff-a 0.735 0.645 0.654 0.607 CTv 82.45 0.902 0.846 0.857 ICv 1.285 0.932 0.885 0.857 NICv 0.52 0.899 0.808 0.893 Zeff-v 8.05 0.922 0.885 0.857 nZeff-v 0.945 0.727 0.462 0.929 Comparison of quantitative parameters between LSCC and benign laryngeal tumors The quantitative parameters in the arterial and venous phase between LSCC and benign laryngeal tumors are shown in Table 3 .The images of spectral CT are shown in Fig. 5 . In the arterial phase, λHUa, ICa, NICa, and Zeff-a had significant differences between LSCC and benign laryngeal tumors (P < 0.05). But CTa and nZeff-a had no statistically significant difference. In the venous phase, all quantitative parameters of the two groups were statistically significant. Table 3 Comparison of quantitative parameters between LSCC and benign laryngeal tumor in the arterial and venous phase parameters LSCC benign tumor t P λHUa 40 − 60keV 1.36 ± 0.53 1.07 ± 0.51 2.022 0.048 CTa 69.89 ± 12.61 66.12 ± 14.68 1.010 0.317 ICa 0.96 ± 0.37 0.75 ± 0.37 2.119 0.039 NICa 0.13 ± 0.07 0.09 ± 0.05 2.448 0.018 Zeff-a 7.85 ± 0.2 7.73 ± 0.22 2.215 0.031 nZeff-a 0.75 ± 0.05 0.73 ± 0.05 1.887 0.065 λHUv 40 − 60keV 2.49 ± 0.62 1.56 ± 0.35 6.9 < 0.001 CTv 96.79 ± 14.18 72.96 ± 11.81 6.725 < 0.001 ICv 1.76 ± 0.42 1.09 ± 0.26 7.159 < 0.001 NICv 0.63 ± 0.19 0.42 ± 0.1 5.006 < 0.001 Zeff-v 8.27 ± 0.22 7.94 ± 0.14 6.767 < 0.001 nZeff-v 0.95 ± 0.03 0.92 ± 0.03 3.352 0.001 Figure 5 . The spectral images of a 51 years old male patient with LSCC on the right vocal cord and polyp on the left vocal cord. A1-A6 are routine image, 40keV image, 90keV image, iodine density diagram, effective atomic number diagram, and spectral curve, respectively in the arterial phase. B1-B6 are images from the venous phase. In the arterial phase, λHUa 40 − 60keV , CTa, ICa, NICa, Zeff-a, and nZeff-a of LSCC were 2.44, 71.9HU, 0.89 mg/mL, 0.11, 7.82, and 0.74, respectively. They were 1.83, 0.50 mg/mL, 0.06, 7.58, and 0.72, respectively for the vocal cord polyp. In the venous phase, λHUv 40 − 60keV , CTv, ICv, NICv, Zeff-v, and nZeff-v of LSCC were 4.32, 94.3HU, 1.76 mg/mL, 0.68, 8.27, and 0.96, respectively. But they were 3.10, 1.15 mg/mL, 0.45, 7.97, and 0.92, respectively on the vocal cord polyp. The enhancement of lesions in the venous phase was more obvious, and the quantitative parameters were higher. All the quantitative parameters of LSCC were higher than vocal cord polyps. Correlation between quantitative parameters of spectral CT and benign or malignant laryngeal tumors The results of correlation analysis between quantitative parameters of laryngeal tumors are shown in Table 4 and Table 5 . In the arterial phase, λHUa 40 − 60keV , ICa, NICa, Zeff-a, and nZeff-a were weakly positively correlated with benign and malignant laryngeal tumors, except CTa. But, all quantitative parameters were strongly positively correlated with benign and malignant laryngeal tumors in the venous phase, especially ICv was the highest. Table 4 Correlation of quantitative parameters and benign or malignant laryngeal tumors in the arterial phase parameters λHUa 40 − 60keV CTa ICa NICa Zeff-a nZeff-a r 0.308 0.166 0.339 0.418 0.325 0.252 p 0.023 0.229 0.012 0.002 0.017 0.066 Table 5 Correlation of quantitative parameters and benign or malignant laryngeal tumors in the venous phase parameters λHUv 40 − 60keV CTv ICv NICv Zeff-v nZeff-v r 0.734 0.696 0.748 0.691 0.730 0.398 p < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.003 Discussion Laryngeal tumors can be divided into malignant tumors and benign lesions. Their symptoms can be manifested as hoarseness, dyspnea, cough, and dysphagia. Many examination methods, such as laryngoscopy, CT, and MRI are used for diagnosing laryngeal tumors. A laryngoscope can directly display and observe the surface and surrounding tissue of the lesion to carry out biopsy and operation, but the surrounding tissue invasion cannot be evaluated. MRI has high soft-tissue resolution and has certain advantages in the observation of laryngeal cartilage and the characterization of lesions. But MRI has a low spatial resolution, and therefore, it is difficult to show the details of lesions clearly. And the scanning time is long, swallowing artifacts are easily produced. CT has a good spatial resolution, density resolution, and fast scanning. It is the first choice of diagnostic means for patients with laryngeal tumors. CT can accurately detect the location, and size and scope of lesions, which is of great significance for clinical treatment. It is difficult to distinguish laryngeal cancer lesions from other benign laryngeal lesions on conventional CT images [ 22 ] . Spectral CT with multi-parameter quantitative analysis technology has been widely used in the qualitative diagnosis of tumor homology, tumor pathological types, differentiation of benign and malignant tumors, and others. Conclusions In this study, most of the differences in spectral CT parameters between LSCC and benign laryngeal tumors were more statistically significant than conventional images CT values. The quantitative parameters of LSCC were basically higher than benign lesions, which could distinguish between LSCC and benign lesions. The ROC curve was further used to evaluate the differentiation ability of quantitative parameters. It was found that the efficiency of identifying benign and malignant laryngeal tumors in the venous phase was higher than that in the arterial phase, suggesting that quantitative parameters in the venous phase are helpful to distinguish LSCC and benign laryngeal tumors. The authors speculate that LSCC may have been a malignant tumor, which may have grown faster than benign laryngeal tumors, so the blood supply of LSCC was more abundant. Especially in the venous phase, the difference of iodine absorption was greater, so the IC difference in the venous phase was significant, and its diagnostic efficiency of benign and malignant was the highest. In conclusion, the quantitative parameters of spectral CT have more values in evaluating the benign and malignant of laryngeal tumors than conventional images CT parameters. The efficiency of identifying benign and LSCC by venous iodine density was the highest. However, this study was a retrospective analysis with small sample size. There may have been some deviations in the data obtained. Larger sample size will be used in future research. The research on the application value of spectral CT in differentiating benign and malignant laryngeal tumors is in the ascendent phase, and it still needs to be further explored in detail. Abbreviations AP Arterial phase VP Venous phase LSCC Larynx squamous carcinoma λHUa slope of the spectral curve in arterial phase λHUv slope of the spectral curve in venous phase CTa CT values of conventional images in arterial phase CTv CT values of conventional images in venous phase ICa Iodine concentrations in arterial phase ICv Iodine concentrations in venous phase NICa Normalized IC in arterial phase NICv Normalized IC in venous phase Zeff-a Effective atomic number in arterial phase Zeff-v Effective atomic number in venous phase nZeff-a Normalized Zeff in arterial phase nZeff-v Normalized Zeff in venous phase ROC Receiver operating curve SD Standard deviation SBI Spectral based images ISP Philips Intellispace Portal ROI Region of Interest AUC Area under the curve Declarations Data availability The datasets generated and analyzed during the current study are available from the corresponding authors upon reasonable request. Competing interests (mandatory) The authors declare no competing interests Author Contributions 1 guarantor of integrity of the entire study: Fang Wang, Guidong Dai; 2 study concepts and design:Fang Wang, Guidong Dai; 3 literature research: Fang Wang, Guidong Dai,Danping Zhang; 4 clinical studies: Fang Wang, Danping Zhang; 5 experimental studies / data analysis :Fang Wang, Danping Zhang,Xiaodi Zhang; 6 statistical analysis: All authors; 7 manuscript preparation: All authors; 8 manuscript editing:Fang Wang, Danping Zhang,Xiaodi Zhang; References Huang HN. Modern otolaryngology head and neck surgery . Fudan University Press,241–244(2003).. Wang D, Zhang WS,et al. Evaluation of CT in observing laryngeal and hypopharyngeal carcinoma after surgery.Journal of Clinical Radilolgy, 20,821–824(2011). Wang, X., Fu, X., & Zhang, T. Non-small cell lung cancer: Spectral computed tomography quantitative parameters for preoperative diagnosis of metastatic lymph nodes. 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Qu Jiao.Clinical Application of DSCT Dual Energy Technology in Qualitative Analysis of Laryngeal Lesions and Preoperative Staging of Laryngeal Carcinoma[D].Kunming medical university,8–9(2019). Additional Declarations No competing interests reported. Supplementary Files researchdata.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3834703","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266847394,"identity":"c277ba6e-a82d-4065-9628-496592c45963","order_by":0,"name":"Fang Wang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""},{"id":266847395,"identity":"f260e5a1-5b62-4bd9-9d65-330d943260b0","order_by":1,"name":"Danping Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Danping","middleName":"","lastName":"Zhang","suffix":""},{"id":266847396,"identity":"88be3006-e20e-49d4-935f-645d593f61f0","order_by":2,"name":"Xiaodi Zhang","email":"","orcid":"","institution":"Philips (China)","correspondingAuthor":false,"prefix":"","firstName":"Xiaodi","middleName":"","lastName":"Zhang","suffix":""},{"id":266847397,"identity":"f04c10ca-c24f-47c9-8070-81c115d71ea3","order_by":3,"name":"Guidong Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACNmbmAwc+VNjU9xOthY+dLfHgjDNpjDMbiNUix89jfJiz7RDjhgPEO4zH4DBj2wFm4+PJGxh+VGwjRgtbweGCc3fYzM48K2DsOXObGC3MGw7PKHvGY3Yjx4CZsY0oLQwGh3nYDksYzyBeCwtQS9thAwMJ4rWwJYACOUEC6JeDRPlFvv/w4Q/AqEzgb0/e+OBHBRFakECCwQGS1IO1kKpjFIyCUTAKRggAAKolPzp+ZmSdAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guidong","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2024-01-04 13:46:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3834703/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3834703/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49714081,"identity":"88e85efd-be8c-4569-8992-b4ab1e9fbf76","added_by":"auto","created_at":"2024-01-16 20:45:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210688,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of different slope λHUa in the arterial phase\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/030dc8fbcd15d201fa0fab34.jpg"},{"id":49713201,"identity":"631eb968-aabe-4ac9-a7c2-9997c45086ef","added_by":"auto","created_at":"2024-01-16 20:37:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184647,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of different slope λHUv in the venous phase\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/4a926c2ce3334b208a60aa08.jpg"},{"id":49713199,"identity":"19da9dbd-aa7c-4bd1-9c4b-1336ab2175fb","added_by":"auto","created_at":"2024-01-16 20:37:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169883,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of conventional CT value， IC, NIC, Zeff, and nZeff in the arterial phase\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/a44deb6a9edf1748c616ff7a.jpg"},{"id":49713194,"identity":"8bb93d8e-5f74-4cdc-83ee-d5f80cc694b4","added_by":"auto","created_at":"2024-01-16 20:37:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163300,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of conventional CT value, IC, NIC, Zeff, and nZeff in the venous phase\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/57cd6add7cae2584e79e21a2.jpg"},{"id":49713198,"identity":"231f674c-8937-4ea8-be10-5d1c56eaa53d","added_by":"auto","created_at":"2024-01-16 20:37:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147889,"visible":true,"origin":"","legend":"\u003cp\u003eThe spectral images of a 51 years old male patient with LSCC on the right vocal cord and polyp on the left vocal cord. A1-A6 are routine image, 40keV image, 90keV image, iodine density diagram, effective atomic number diagram, and spectral curve, respectively in the arterial phase. B1-B6 are images from the venous phase. In the arterial phase, λHUa\u003csub\u003e40-60keV\u003c/sub\u003e, CTa, ICa, NICa, Zeff-a, and nZeff-a of LSCC were 2.44, 71.9HU, 0.89 mg/mL, 0.11, 7.82, and 0.74, respectively. They were 1.83, 0.50 mg/mL, 0.06, 7.58, and 0.72, respectively for the vocal cord polyp. In the venous phase, λHUv\u003csub\u003e40-60keV\u003c/sub\u003e, CTv, ICv, NICv, Zeff-v, and nZeff-v of LSCC were 4.32, 94.3HU, 1.76 mg/mL, 0.68, 8.27, and 0.96, respectively. But they were 3.10, 1.15 mg/mL, 0.45, 7.97, and 0.92, respectively on the vocal cord polyp. The enhancement of lesions in the venous phase was more obvious, and the quantitative parameters were higher. All the quantitative parameters of LSCC were higher than vocal cord polyps.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/646726e8e096333ea17b9332.jpg"},{"id":52068056,"identity":"06da41ed-8c18-4e7a-8af2-dc1337cd3139","added_by":"auto","created_at":"2024-03-06 07:21:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":667322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/f9711ed7-f71b-4b5a-b6d5-b4b147864573.pdf"},{"id":49713196,"identity":"76c2a42a-ff4d-418f-8325-9e8a85f647aa","added_by":"auto","created_at":"2024-01-16 20:37:36","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39829,"visible":true,"origin":"","legend":"","description":"","filename":"researchdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3834703/v1/261044a9a382643439a773ea.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-layer Spectral Detector CT in Predicting Benign and Malignant Laryngeal Tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLaryngeal tumors most often refer to squamous cell carcinomas of the larynx. They can be divided into benign and malignant tumors. Benign tumors commonly include laryngeal papilloma, and vocal cord polyp. Laryngeal cancer is a common malignancy, accounting for about 2%-3% of all cancers. The patients who are mostly affected are between 50 and 70 years old, with more incidence in males than females\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The primary symptoms of laryngeal tumors include hoarseness, dyspnea, cough, and dysphagia. Smoking is the main risk factor. In addition, long-term inhalation of harmful substances, alcoholism, environmental pollution, laryngeal papilloma, and laryngeal leukoplakia may be related to the occurrence of laryngeal cancer. The incidence rate is escalating as the environmental pollution increases, especially in large cities. Determining benign or malignant condition is the key to making treatment plans. Cancer tissue biopsy is the gold standard for the diagnosis of laryngeal tumors, but the process is invasive and it has certain limitations. CT examination is ideal to identify the location and display the laryngeal tumors and the CT results help in making reasonable treatment plans\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.In recent years, with the development of CT technology, spectral CT has been widely used in tumor identification, and detection and evaluation of radiotherapy and chemotherapy efficacies. At present, many studies explored spectral CT in identifying lung cancer, breast cancer, and laryngeal cancer staging\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Under the same X-ray source condition, the dual-layer spectral detector CT absorbs photons between high and low energy separation through the upper and lower detectors for imaging\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Compared with the single-layer detector of traditional CT and spectral CT for imaging through instantaneous switching of high and low energy in one rotation, the feature of spectral CT can improve tissue contrast from the initial data level, and provide more additional details. In addition, conventional images and spectral data can be reconstructed by one scan of spectral CT, such as virtual monoenergetic images, spectral curve, iodine density maps, and effective atomic number images, which can be used for qualitative and quantitative analysis and comparison of CT values and SD values at different energy levels. The slope of spectral curve, normalized iodine densities and normalized effective atomic numbers can differentiate the benign and malignant conditions, and provide reference basis for the clinical treatment plan and therapy efficacy evaluation. Spectral CT low keV virtual monoenergetic image can improve enhanced tissue display, increase tissue contrast, improve the detection rate of small lesions, reduce image noise, and improve signal-to-noise ratio\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Iodine density map has the potential to quantify iodine enhancement and improve the visualization of iodine in contrast-enhanced tissues\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The quantitative analysis of iodine in tissues and organs can directly reflect the blood supply of tissues and organs, such as blood supply to the benign and malignant vocal cord lesions, advanced gastric cancer\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.The effective atomic number diagram can be used for material identification, material separation, and others. Different tissue structures and pathological types have different spectral curves\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.Lin et al. compared the relationship between quantitative parameters of non-small cell lung cancer, Ki-67 expression level and EGFR mutation status by spectral CT\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Chen et al. found that there was a moderate positive correlation between spectral CT imaging parameters and Ki-67 markers in lung adenocarcinoma\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Cheng S M et al. found that the quantitative parameters of spectral CT imaging had a significant positive correlation with the Ki-67 grade in advanced gastric cancer and early gastric cancer\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Wang et al. found that spectral CT quantitative parameters can be used to evaluate the immunohistochemical biomarkers in invasive breast cancer\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, there are few studies on laryngeal tumors with dual-layer spectral detector CT. Therefore, this study aimed to compare the differences and to determine the correlation of quantitative parameters between benign and malignant laryngeal tumors using spectral CT to evaluate the application value of spectral CT in predicting benign and malignant laryngeal tumors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient data\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the the Ethics Committee of the Affiliated Hospital of Southwest Medical University, the experimental protocols were performed in accordance with the approved guidelines, and the requirement for informed consent was waived because of the retrospective nature of the study. Retrospective analysis on 47 patients with hoarseness, dyspnea, cough, and dysphagia who did not receive radiotherapy, chemotherapy, and/or surgical treatment before the examination was performed. These patients were examined from October 2020 to July 2022.All subsequent surgical procedure information, histopathological results, and signed informed consent were obtained. All patients underwent dual-phase neck enhanced scan using dual-layer spectral detector CT. There were 46 males and 1 female, with an average age of 62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 years. The histopathological results showed that there were 26 larynx squamous carcinoma (LSCC) lesions and 28 other benign lesions, a total of 54 lesions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScanning parameters\u003c/h2\u003e \u003cp\u003ePhilips IQon spectral CT was used. The scanning range was from the base of the skull to the root of the neck. Scanning parameters included automatic current-time product (mAs), 120 kVp, detector collimation of 64 mm \u0026times; 0.625 mm, frame rotation time of 0.5 s, and pitch of 0.953. The arterial phase and venous phase scans were completed at 28-30s and 55-60s after injecting Iopamidol(370mg I/mL), respectively. The Ulich high-pressure syringe was connected through the elbow vein, with a total amount of 55 mL at 3.0 mL/s. Subsequently, 30 mL normal saline was injected at the same rate. After the scan, conventional CT images and spectral based images (SBI) were obtained. The 40-90keV(interval of 10keV)virtual monoenergetic images, iodine density maps, and effective atomic number maps were generated on the Philips Intellispace Portal (ISP)workstation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImage analysis\u003c/h2\u003e \u003cp\u003eA radiologist delineated ROI (area of about 4\u0026ndash;10 mm\u003csup\u003e2\u003c/sup\u003e) at the most obvious enhancement area of the lesion and the center of the same layer of the internal carotid artery both in the arterial phase and venous phase. The iodine density and effective atomic number of the lesion and internal carotid artery were recorded. The CT values of lesions on conventional images, and between 40keV and 90keV(interval of 10keV)were recorded. The λHU between different energy levels were calculated using formulas ① NIC and nZ\u003csub\u003eeff\u003c/sub\u003e were calculated according to formulas ② and ③. \u003cem\u003eCT\u003c/em\u003e\u003csub\u003e40keV\u003c/sub\u003e and \u003cem\u003eCT\u003c/em\u003e\u003csub\u003e90keV\u003c/sub\u003e were the CT values of the lesions at 40keV and 90kev, respectively. The \u003cem\u003eIC\u003c/em\u003e\u003csub\u003elesion\u003c/sub\u003e, \u003cem\u003eIC\u003c/em\u003e\u003csub\u003einternal carotid artery\u003c/sub\u003e, \u003cem\u003eZeff\u003c/em\u003e\u003csub\u003elesion,\u003c/sub\u003e and \u003cem\u003eZeff\u003c/em\u003e\u003csub\u003einternal carotid artery\u003c/sub\u003e were the iodine concentration and effective atomic number of the lesion and internal carotid artery, respectively.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS version 22.0 was used for statistical analysis. The data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. ROC curve was used to evaluate the efficacy of different λHU to diagnose laryngeal tumors and to define the best slope. Then ROC curve was used to evaluate the diagnostic efficacy of best slope, CT value, IC, NIC, Zeff, and nZeff in both arterial phase and venous phase. The differences in quantitative parameters between LSCC and benign laryngeal tumors were analyzed by an independent sample t-test. The correlation between quantitative parameters and benign or malignant laryngeal tumors was analyzed by the Spearman rank-sum test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic efficiency of quantitative parameters of spectral CT\u003c/h2\u003e \u003cp\u003eThe differential diagnostic efficiency indexes of λHU are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ,Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the arterial phase, all AUC values were \u0026lt;\u0026thinsp;0.7, therefore, all λHUa had no diagnostic value. In the venous phase, all AUC values were \u0026gt;\u0026thinsp;0.7, therefore, all λHUv had a good diagnostic value. And λHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e and λHUv\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e were the largest (0.927), and the diagnostic efficiencies were the highest. Using paired sample t-test, a significant difference was found between λHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e and λHUv\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), λHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e was 3.457\u0026thinsp;\u0026plusmn;\u0026thinsp;1.174, λHUv\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e was 1.522\u0026thinsp;\u0026plusmn;\u0026thinsp;0.518, and λHU\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e had the best slope.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential efficacy of different slope λHUa in the arterial and venous phase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecritical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003esensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003especificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;50keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e70\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e70\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e80\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;50keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e50\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;70keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e60\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e70\u0026thinsp;\u0026minus;\u0026thinsp;80keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e70\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e80\u0026thinsp;\u0026minus;\u0026thinsp;90keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe differential diagnostic efficiency indexes of CT value, IC, NIC, Zeff, and nZeff are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The area under the ROC curve in the ICv was the largest (0.932), the corresponding sensitivity was 88.5%, and the specificity was 85.7%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential efficacy of conventional CT value, IC, NIC, Zeff, and nZeff in the arterial and venous phase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecritical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003esensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003especificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeff-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enZeff-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeff-v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enZeff-v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of quantitative parameters between LSCC and benign laryngeal tumors\u003c/h2\u003e \u003cp\u003eThe quantitative parameters in the arterial and venous phase between LSCC and benign laryngeal tumors are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.The images of spectral CT are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In the arterial phase, λHUa, ICa, NICa, and Zeff-a had significant differences between LSCC and benign laryngeal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). But CTa and nZeff-a had no statistically significant difference. In the venous phase, all quantitative parameters of the two groups were statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of quantitative parameters between LSCC and benign laryngeal tumor in the arterial and venous phase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebenign tumor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.89\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.12\u0026thinsp;\u0026plusmn;\u0026thinsp;14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeff-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enZeff-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e96.79\u0026thinsp;\u0026plusmn;\u0026thinsp;14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.96\u0026thinsp;\u0026plusmn;\u0026thinsp;11.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeff-v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enZeff-v\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The spectral images of a 51 years old male patient with LSCC on the right vocal cord and polyp on the left vocal cord. A1-A6 are routine image, 40keV image, 90keV image, iodine density diagram, effective atomic number diagram, and spectral curve, respectively in the arterial phase. B1-B6 are images from the venous phase. In the arterial phase, λHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e, CTa, ICa, NICa, Zeff-a, and nZeff-a of LSCC were 2.44, 71.9HU, 0.89 mg/mL, 0.11, 7.82, and 0.74, respectively. They were 1.83, 0.50 mg/mL, 0.06, 7.58, and 0.72, respectively for the vocal cord polyp. In the venous phase, λHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e, CTv, ICv, NICv, Zeff-v, and nZeff-v of LSCC were 4.32, 94.3HU, 1.76 mg/mL, 0.68, 8.27, and 0.96, respectively. But they were 3.10, 1.15 mg/mL, 0.45, 7.97, and 0.92, respectively on the vocal cord polyp. The enhancement of lesions in the venous phase was more obvious, and the quantitative parameters were higher. All the quantitative parameters of LSCC were higher than vocal cord polyps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between quantitative parameters of spectral CT and benign or malignant laryngeal tumors\u003c/h2\u003e \u003cp\u003eThe results of correlation analysis between quantitative parameters of laryngeal tumors are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In the arterial phase, λHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e, ICa, NICa, Zeff-a, and nZeff-a were weakly positively correlated with benign and malignant laryngeal tumors, except CTa. But, all quantitative parameters were strongly positively correlated with benign and malignant laryngeal tumors in the venous phase, especially ICv was the highest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of quantitative parameters and benign or malignant laryngeal tumors in the arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλHUa\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNICa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZeff-a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003enZeff-a\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of quantitative parameters and benign or malignant laryngeal tumors in the venous phase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλHUv\u003csub\u003e40\u0026thinsp;\u0026minus;\u0026thinsp;60keV\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNICv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZeff-v\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003enZeff-v\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLaryngeal tumors can be divided into malignant tumors and benign lesions. Their symptoms can be manifested as hoarseness, dyspnea, cough, and dysphagia. Many examination methods, such as laryngoscopy, CT, and MRI are used for diagnosing laryngeal tumors. A laryngoscope can directly display and observe the surface and surrounding tissue of the lesion to carry out biopsy and operation, but the surrounding tissue invasion cannot be evaluated. MRI has high soft-tissue resolution and has certain advantages in the observation of laryngeal cartilage and the characterization of lesions. But MRI has a low spatial resolution, and therefore, it is difficult to show the details of lesions clearly. And the scanning time is long, swallowing artifacts are easily produced. CT has a good spatial resolution, density resolution, and fast scanning. It is the first choice of diagnostic means for patients with laryngeal tumors. CT can accurately detect the location, and size and scope of lesions, which is of great significance for clinical treatment. It is difficult to distinguish laryngeal cancer lesions from other benign laryngeal lesions on conventional CT images\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Spectral CT with multi-parameter quantitative analysis technology has been widely used in the qualitative diagnosis of tumor homology, tumor pathological types, differentiation of benign and malignant tumors, and others.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, most of the differences in spectral CT parameters between LSCC and benign laryngeal tumors were more statistically significant than conventional images CT values. The quantitative parameters of LSCC were basically higher than benign lesions, which could distinguish between LSCC and benign lesions. The ROC curve was further used to evaluate the differentiation ability of quantitative parameters. It was found that the efficiency of identifying benign and malignant laryngeal tumors in the venous phase was higher than that in the arterial phase, suggesting that quantitative parameters in the venous phase are helpful to distinguish LSCC and benign laryngeal tumors. The authors speculate that LSCC may have been a malignant tumor, which may have grown faster than benign laryngeal tumors, so the blood supply of LSCC was more abundant. Especially in the venous phase, the difference of iodine absorption was greater, so the IC difference in the venous phase was significant, and its diagnostic efficiency of benign and malignant was the highest.\u003c/p\u003e \u003cp\u003eIn conclusion, the quantitative parameters of spectral CT have more values in evaluating the benign and malignant of laryngeal tumors than conventional images CT parameters. The efficiency of identifying benign and LSCC by venous iodine density was the highest. However, this study was a retrospective analysis with small sample size. There may have been some deviations in the data obtained. Larger sample size will be used in future research. The research on the application value of spectral CT in differentiating benign and malignant laryngeal tumors is in the ascendent phase, and it still needs to be further explored in detail.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVenous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLSCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLarynx squamous carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eλHUa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eslope of the spectral curve in arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eλHUv\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eslope of the spectral curve in venous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCT values of conventional images in arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTv\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCT values of conventional images in venous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIodine concentrations in arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICv\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIodine concentrations in venous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNICa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized IC in arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNICv\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized IC in venous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZeff-a\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffective atomic number in arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZeff-v\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffective atomic number in venous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003enZeff-a\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized Zeff in arterial phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003enZeff-v\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized Zeff in venous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpectral based images\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhilips Intellispace Portal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of Interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests (mandatory)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 guarantor of integrity of the entire study:\u0026nbsp;Fang Wang, Guidong Dai;\u003c/p\u003e\n\u003cp\u003e2 study concepts and design:Fang Wang, Guidong Dai;\u003c/p\u003e\n\u003cp\u003e3 literature research:\u0026nbsp;Fang Wang, Guidong Dai,Danping Zhang;\u003c/p\u003e\n\u003cp\u003e4 clinical studies:\u0026nbsp;Fang Wang, Danping Zhang;\u003c/p\u003e\n\u003cp\u003e5 experimental studies / data analysis :Fang Wang, Danping Zhang,Xiaodi Zhang;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6 statistical analysis:\u0026nbsp;All authors;\u003c/p\u003e\n\u003cp\u003e7 manuscript preparation:\u0026nbsp;All authors;\u003c/p\u003e\n\u003cp\u003e8 manuscript editing:Fang Wang, Danping Zhang,Xiaodi Zhang;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang HN. \u003cem\u003eModern otolaryngology head and neck surgery\u003c/em\u003e. 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Spectral CT imaging parameters and Ki-67 labeling index in lung adenocarcinoma.Chinese Journal of Cancer Research,32,96\u0026ndash;104(2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, S.-M., Ling, W., Zhu, J., Xu, J.-R., Wu, L.-M., \u0026amp; Gong, H.-X. Dual Energy Spectral CT Imaging in the assessment of Gastric Cancer and cell proliferation: A Preliminary Study. Scientific Reports, 8,17619;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-35712-w(2018\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-35712-w(2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Liu, D., Zeng, X., Jiang, S., Li, L., Yu, T., \u0026amp; Zhang, J. Dual-energy CT quantitative parameters for evaluating Immunohistochemical biomarkers of invasive breast cancer. 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(2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu Jiao.Clinical Application of DSCT Dual Energy Technology in Qualitative Analysis of Laryngeal Lesions and Preoperative Staging of Laryngeal Carcinoma[D].Kunming medical university,8\u0026ndash;9(2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dual-layer Spectral Detector CT, laryngeal tumor, iodine density map, effective atomic number, spectral curve","lastPublishedDoi":"10.21203/rs.3.rs-3834703/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3834703/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjectives: To investigate the quantitative parameters of dual-layer spectral detector CT in the assessment of benign and malignant laryngeal tumors.\u003c/p\u003e\n\u003cp\u003eMethods: Spectral images of 47 patients were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP), including 26 larynx squamous carcinoma (LSCC) lesions and 28 benign lesions. For the 54 lesions in the AP and VP, the slope of the spectral curve (λHUa and λHUv) from 40keV to 90keV, CT values of conventional images (CTa and CTv), iodine concentrations (ICa and ICv), normalized IC (NICa and NICv), effective atomic number (Zeff-a and Zeff-v), and normalized Zeff (nZeff-a and nZeff-v) were calculated and analyzed between LSCC and benign patients. ROC curve, independent sample t-test, and Spearman rank-sum test were used in statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eλHUv\u003csub\u003e40-60keV\u003c/sub\u003e had the highest diagnostic efficiency in all slope of the spectral curve. So λHU\u003csub\u003e40-60keV\u003c/sub\u003e was the best slope. All parameters in AP didn’t have well identifying efficiency. But all parameters in VP had perfect identifying efficiency. Especially the ICv was the highest, which sensitivity and specificity were 88.5% and 85.7%. All measured quantitative parameters except CTa and nZeff-a of AP were statistically significant difference between LSCC and benign laryngeal tumors. In the AP, quantitative parameters were weakly positively correlated with two kinds of tumors, especially CTa. But five quantitative parameters ( λHUv\u003csub\u003e40-60keV\u003c/sub\u003e,CTv,ICv, NICv, Zeff-v) were strongly positively in the VP,which the r values are 0.734, 0.696, 0.748, 0.691, 0.730 respectively.\u003c/p\u003e","manuscriptTitle":"Dual-layer Spectral Detector CT in Predicting Benign and Malignant Laryngeal Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 20:37:31","doi":"10.21203/rs.3.rs-3834703/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":"035e9829-3b80-4faa-9a64-c01f03102f6b","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28122144,"name":"Health sciences/Medical research"},{"id":28122145,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2024-04-11T12:05:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 20:37:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3834703","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3834703","identity":"rs-3834703","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}