The application value of dual-energy computed tomography (DECT) multi- parameter imaging in lung adenocarcinoma and squamous cell carcinoma

The application value of dual-energy computed tomography (DECT) multi- parameter imaging in lung adenocarcinoma and squamous cell carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The application value of dual-energy computed tomography (DECT) multi- parameter imaging in lung adenocarcinoma and squamous cell carcinoma Xingxing Zheng, Hongzhe Tian, Wei Li, Jun Li, Kai Xu, Chenwang Jin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4589013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2024 Read the published version in BMC Pulmonary Medicine → Version 1 posted 22 You are reading this latest preprint version Abstract Background: Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung adenocarcinoma (AC), and lung squamous cell carcinoma (SC) are the two primary types of NSCLC. Determining the pathological type of NSCLC is important in establishing the most effective treatment method. Dual-energy computed tomography (DECT) multi-parameter imaging is an imaging technology that provides accurate and reliable disease diagnosis, and its uses are utilized for the combined diagnostic efficacy of AC and SC. Methods: We analyzed 71 lung cancer patients (36 squamous cell carcinomas; 35 adenocarcinomas) who had undergone enhanced DECT scans, including arterial and venous phases in this single-center retrospective study. The tumor diameter, water concentration (WC), iodine concentration (IC), normalized iodine concentration (NIC), Z effective (Zeff), and slope of the curve ( K ) in lesions were evaluated during two scanning phases in the two separate pathological types of lung cancers. Statistical analysis was used to determine the diagnostic efficacy of morphological parameters alone, and the combined efficacy of spectral parameters and morphological parameters. Results: In a univariate analysis involving 71 lung cancer patients, the results from Zeff, IC, NIC, and K from the AC's arterial and venous phase images were more elevated than those from the SC ( P <0.05). In contrast, the WC results were lower than those from SC ( P <0.05). The area under the ROC curve (AUC) for multi-parameter joint prediction typing was 0.831, with a corresponding sensitivity of 63.9% and specificity of 94.3%. Conclusion: It is possible to distinguish between central SC and AC using the spectrum characteristics of DECT-enhanced scanning (Zeff, IC, NIC, K, WC, and tumor diameter). Diagnostic effectiveness can be greatly improved when multiple variables are included, and practical treatment plans can be formulated, as well as predicting prognosis in clinical settings. Energy spectrum CT Lung adenocarcinoma Lung squamous cell carcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Lung cancer is one of the world’s most prevalent and a major contributor to cancer-related mortality[ 1 , 2 ]. The most pervasive pathological forms of lung cancer are lung adenocarcinoma (AC) and squamous cell carcinoma (SC), and each has a unique therapeutic treatment strategy, with notable variations in chemotherapy regimens[ 3 – 5 ]. The identification of the pathological type of lung cancer before beginning therapy might therefore aid in the development of precise treatment strategies and prognostication. The majority of pathological diagnoses of lung cancer currently depend on cytological analysis, fiberoptic bronchoscopy, or fine needle aspiration biopsy[ 6 ]. Tumor tissue may be difficult to collect due to the occurrence of some tumors. Some tumors are situated deep within the lungs or close to large blood vessels and bones. Additionally, some patients have physical limitations that prevent them from undergoing invasive procedures, and some struggle to get tissue specimens and incur high testing costs, thus rendering the above methods unsuitable[ 7 ]. A non-invasive, safe, and economical technique is an essential tool to assist in identifying the histological type of malignancies. At present, conventional Computed tomography (CT) is a commonly used method for diagnosing lung cancer, but due to the beam hardening effect caused by mixed energy X-rays, it can affect the accuracy of CT value measurement and easily lead to misdiagnosis or missed diagnosis[ 8 , 9 ]. The advancement of PET-CT has greatly improved the accuracy of tumor diagnosis, and PET-CT function by combining the diagnostic capabilities of PET and CT exams. The advantages of the two are enhanced by the fact that they measure tumor metabolic activity in addition to defining the exact site of lesions. However, the examination costs are substantial, as is the radiation dose, which has a certain impact on clinical popularization[ 10 ]. While MRI is not currently a standard diagnostic technique for non-small cell lung cancer (NSCLC), preliminary findings from a few domestic and international studies have indicated that MRI can be a useful imaging modality for NSCLC staging, radiation target area delineation, therapeutic effect evaluation, and other purposes. MRI scanning time is relatively long, and patients who are claustrophobic or suffering from a critical illness sometimes find it difficult to cooperate during the scan, which limits its clinical applications[ 11 , 12 ]. Various pathological types of lung cancer exhibit distinct biological behaviors and pathological features and therefore require effective and sensitive modalities for diagnosis. Dual-energy CT (DECT) significantly increases the precision and reliability of disease diagnosis by not only offering images of anatomical morphology but also enabling the conversion of single-parameter diagnostic modes to multi-parameter diagnostic modes. Furthermore, it can reveal additional biological details about lesions and differentiate between various tissue components[ 13 – 16 ]. Consequently, we investigated the utility of energy spectrum CT multiparameter preoperative lung cancer pathological categorization prediction. Methods Patients The Ethics Committee of Shaanxi Province's Baoji Central Hospital approved this study, and the experimental protocol was executed according to the approved guidelines. Informed permission was waived in view of the study's retrospective nature. Seventy-one patients with pathologically confirmed LC and SC who had preoperative energy spectrum CT chest scans performed in our hospital between December 2020 and April 2022 had their clinical and imaging data retrospectively analyzed. There were 35 cases of AC and 36 cases of SC, of whom 34 were males and 37 were females. Inclusion criteria: (1) Preoperative chest energy spectrum CT scan was performed, and the CT image could clearly display the lesion; (2) Pathological confirmation of non-small cell lung cancer through puncture biopsy, fiberoptic bronchoscopy, or surgical resection. Exclusion criteria: (1) Acceptance of radiation and chemotherapy before CT examination or biopsy, fiberoptic bronchoscopy, or surgical resection; (2) Missing or incomplete imaging and clinical data; (3) Cases where the lesion was too small or had too much liquefaction necrosis; (4) Cases where the lesion was not clearly displayed due to obstructive atelectasis, pneumonia, pleural effusion, etc. Figure 1 illustrates the flow diagram of the presentation study. CT examination Plain and three-phase enhanced scans using 256-row single source dual energy CT (GE, Revolution CT Xstream Edition) were performed. The scanning range spanned from the apex of the lungs to the lower edge of the liver. The gemstone spectral imaging (GSI) mode was adopted. The parameters of the conventional sequence included instantaneous switching of tube voltage between 80 kVp and 140 kVp, tube current of 200–400 mA, layer thickness and spacing of 5mm, reconstruction layer thickness of 1.25 mm, detector width of 80 mm, tube rotation time of 0.5 s, pitch of 0.992:1, FOV of 35 cmx35 cm. The contrast agent for enhanced scanning was iodohexanol (300 mgI/mL, dose of 1.2 mL/kg body mass), injected through the anterior elbow vein at a rate of 3.0-3.5 mL/s. Arterial (AP), venous (VP), and delayed phase images were obtained after contrast agent injection for 30 s, 60 s, and 120 s, respectively. Imaging analysis Scanned and reconstructed data was transferred to the post-processing workstation (GE, AW 4.6), and arterial and venous phase images were generated using GSI Viewer analysis software. Dual-energy datasets for arterial and venous phases for post-processing were selected. The region of interest (ROI) should be chosen based on the idea of finding the solid section of the largest axial lesion, avoiding calcification and necrosis to the greatest extent possible, and it should not be smaller than 50% of the tumor area. All measurements were taken three times and the average was taken. Quantitative parameter measurement included: (1) The calculation of the slope of the energy spectrum attenuation curve ( k) =(HU40keV HU100keV)/(100 − 40); (2) Effective ordinal number (Zeff); (3) The measurement of the iodine concentration (IC) of arterial phase lesions and the iodine value of the aorta at the same level as the lesion using Iodine substance map, and the calculation of the standardized iodine concentration (NIC). The standardized iodine concentration is equal to the iodine value of the lesion/the iodine value of the aorta at the same level. (4) Measurement of the water concentration (WC). (5) Measurement of the maximum thickness of the largest layer of the lesion axis map (measured sample thickness in this study). Histochemical examination A pathologist with ten years of expertise in immunohistochemical staining examined tumor specimens. The pathologist numbered, assessed, and documented each section's pathological nature without having access to clinical data or spectral CT scan results. The World Health Organization Classification of Lung Tumors was followed in the application of the histopathological criteria for diagnosis. Statistical Analysis SPSS 25.0 was used to perform statistical analysis. The classification count data was reported as a percentage of the total number of cases, and χ 2 Inspection was used to compare the two groups. The measurement data undergoing normality testing and data that followed a normal distribution were presented as mean ± standard deviation (± S). To compare the two groups, two independent sample t-tests were employed. The median (quartile) was used for presenting data that did not fit into a normal distribution, and the Mann-Whitney U test was used to compare the two groups. Quantitative parameters were combined with statistically significant differences between groups, and the diagnostic efficacy of quantitative parameters was analyzed using ROC curves. A P-value of < 0.05 indicates a statistically significant difference. Results Clinical data and routine imaging data of patients The findings from the study showed that the clinical characteristics of the two patient groups—including age, gender, and smoking history—did not differ statistically significantly ( P > 0.05). The maximum diameter of squamous cell carcinoma was larger than that of adenocarcinoma [5 (3.5, 6.62) cm and 2.8 (2.05, 4.05) cm, P 0.05) in the comparison of conventional imaging features such as hair prick sign, lobulation sign, pleural traction, and tumor location. Table 1 illustrates the clinical data. Table 1 Summary of clinical and routine imaging data. Characteristics AC (n = 35) SC (n = 36) P-value Age (years) 65.71 ± 10.44 64.19 ± 7.06 0.476 Gender 0.901 Female 19 (54) 18 (50) Male 16 (46) 18 (50) Smoking history 0.921 No 15 (43) 14 (39) Yes 20 (57) 22 (61) Speculation sign 0.118 No 12 (34) 20 (56) Yes 23 (66) 16 (44) Lobulation sign 0.478 No 3 (9) 6 (17) Yes 32 (91) 30 (83) Pleural indentation 0.188 No 13 (37) 20 (56) Yes 22 (63) 16 (44) Tumor size (cm) 2.8 (2.05, 4.05) 5 (3.5, 6.62) < 0.001 * Tumor location 0.138 Right upper lobe 16 (46) 7 (19) Right lower lobe 6 (17) 11 (31) Right middle lobe 0 (0) 1 (3) Left upper lobe 8 (23) 10 (28) Left lower lobe 5 (14) 7 (19) *P < 0.05. Comparison of CT parameters of different energy spectra between LAC and SC groups. As shown in Tables 2 and 3 and Figs. 1 and 2 , the Zeff, IC, NIC, and K obtained from AP and VP images in the LAC patients were all higher than those in SC patients ( P < 0.05), whereas the WC was comparatively lower than that in SC ( P < 0.05). Table 2 Comparison of energy spectrum parameters in arterial phase of CT enhanced scanning between lung adenocarcinoma and squamous cell carcinoma patients. Group Zeff WC IC NIC K AC 8.53 (8.2, 8.73) 1023.20 (1009, 1029.78) 15.70 (9.56, 20.53) 0.16 (0.11, 0.2) 1.76 (1.1, 2.42) SC 8.25 (7.98, 8.43) 1027.84 (1023.83, 1033.75) 10.52 (6.4, 13.6) 0.13 (0.08, 0.16) 1.27 (0.75, 1.7) t/Z 8.82 442.5 8.69 801 3..51 <0.01* 0.03* <0.01* 0.05* 0.01* *P < 0.05. AC: adenocarcinoma; SC: Squamous cell carcinoma; WC: water concentration; IC: iodine concentration; NIC: normalized iodine concentration; Zeff :Z effective; K: slope of the curve. Table 3 Comparison of energy spectrum parameters in venous phase of CT enhanced scanning between lung adenocarcinoma and squamous cell carcinoma patients. Group Zeff WC IC NIC K AC 8.53 ± 0.35 1023.60 (1014.26, 1029.65) 15.77 ± 6.13 0.41 (0.32, 0.52) 1.84 ± 0.71 SC 8.23 ± 0.31 1029.28(1024.18, 1033.75) 11.05 ± 5.83 0.34 (0.2, 0.47) 1.24 ± 0.59 t/Z 3.87 431.5 3.32 815 3.84 <0.01 0.02 <0.01 0.03 <0.01 *P < 0.05. AC: adenocarcinoma; SC: Squamous cell carcinoma; WC: water concentration; IC: iodine concentration; NIC: normalized iodine concentration; Zeff :Z effective; K : slope of the curve. ROC curve analysis and display of diagnostic efficiency of spectral CT parameters Logistic regression was used to integrate the spectral CT parameters with statistically significant differences between the two groups based on the weighting coefficient, as seen in Table 4 and Fig. 3 . The regression model was:11.465–3.727*VP_(Intercept) + 0.014*VP_Zeff-0.19*VP_water base value + 10.275*VP_IC-1.875*VP_NIC-0.112*VP_K + 0.004*AP_Zeff + 0.042* AP_water base value − 16.286* AP_ IC + 3.028* AP_NIC + 0.318* AP_K-3.727* tumor diameter. The AUC of the ROC curve was 0.831, with sensitivity of 63.9% and specificity of 94.3%. Table 4 Performance indicators of arterial and venous phase DECT parameters and their combined predictive classification. Parameter AUC Sensitivity (%) Specificity (%) Threshold 95% CI AP Zeff 0.70 0.861 0.514 8.525 0.577 ~ 0.823 WC(mg/cm 3 ) 0.65 0.639 0.657 1026.305 0.517 ~ 0.78 IC(mg/cm 3 ) 0.70 0.806 0.571 14.475 0.565 ~ 0.815 NIC(mg/cm 3 ) 0.64 0.778 0.514 0.162 0.506 ~ 0.766 K 0.68 0.750 0.571 1.698 0.549 ~ 0.802 VP Zeff 0.70 0.861 0.514 8.525 0.577 ~ 0.823 WC(mg/cm 3 ) 0.65 0.639 0.657 1026.305 0.517 ~ 0.780 IC(mg/cm 3 ) 0.69 0.806 0.571 14.475 0.565 ~ 0.815 NIC(mg/cm 3 ) 0.64 0.778 0.514 0.162 0.506 ~ 0.766 K 0.68 0.75 0.571 1.698 0.549 ~ 0.802 Tumor size 0.73 0.694 0.743 3.95 0.605 ~ 0.853 Combined diagnosis 0.83 0.639 0.943 0.60 0.736 ~ 0.926 *P < 0.05. AC: adenocarcinoma; SC: Squamous cell carcinoma; AP: Arterial phase; VP: venous phase. WC: water concentration, IC: iodine concentration, NIC: normalized iodine concentration, Zeff :Z effective; K : slope of the curve. Discussion In addition to identifying and classifying tumor disorders based on their physical features, dual source spectral computed tomography (DSCT) can also provide insight into the underlying biology of the tumor by analyzing its various stages[ 17 – 20 ]. Although the parameters of VP were left out of the study, Wang et al.[ 21 ] demonstrated the diagnostic value of the spectral curve slope of 40 to 70 kev in the AP-K and IC in distinguishing between SC and LAC. By expressing tumor microvessel density, on the other hand, Li [ 22 ] demonstrated that VP-IC can distinguish between SC and LAC By utilizing dual-energy spectral CT scanning technology to investigate the function of dual-phase scanning in lung cancer subtypes, the problems above were addressed, and improved. Comparison of spectral CT quantitative parameters between LAC and SC The term "energy spectrum curve" describes the curve that shows how the energy level of an X-ray affects the CT value of various lesions or tissues. The chemical molecular structure of different substances will change, and different molecules will attenuate energy differently[ 23 ]. Therefore, subgroups of lung cancer can be identified using the K of the energy spectrum curve. According to the research both domestically and internationally, the K value of LAC was greater than that of SC[ 24 ]. This could be connected to the different material makeup or metabolic processes of lung cancer. However, the single energy node of K in this study is not completely consistent with that in the study of Zhang et al.[ 7 ]. The single energy nodes of K in Zhang's study are 40 keV and 110 keV; however, in this investigation, the CT values in the single energy 40 ~ 100 keV between the two groups are significantly different, leading to the final selection of 40 keV and 100 keV as the single energy nodes of K. In this investigation, the Zeff, IC value, and NIC value were successfully measured in addition to the CT value of any level within the 40–190 keV range using the software. The Zeff is the atomic number of elements with the same decay coefficient as compounds or mixtures, which can be utilized to determine the tissue composition of substances, particularly in substances with the same CT values. It is a quantitative index comprising many substances[ 25 , 26 ]. The values of Zeff, IC, and NIC were measured in addition to obtaining the CT value of any level in the 40–190 keV range. The findings from our study indicated that the Zeff of LAC was higher than that of SC. This difference may be caused by the primary characteristics of LAC, which include unclear cell boundaries, solid blocks or strips, and a tendency to form an adenoid structure supported by a fibrous matrix and secreting mucus. Whereas, the internal structure of SC is closely arranged and exhibits the formation of keratinization and keratinized beads (also known as cancer beads) and a unique intercellular bridge structure. The two have different Zeff because of their differing chemical composition and densities[ 27 ]. Comparatively, adenocarcinoma grows in diversification, with rich stroma, relatively few tumor cells per unit volume, and low water content; as a result, its water content (WC) was lower than that of SC. Most SC, on the other hand, grows in aggregation, with more tumor cells per unit volume and greater water content. These results were confirmed by a pathological study conducted by Zhong et al.[ 28 ], where 50 cancer patients (including 35 cases of adenocarcinoma and 22 cases of squamous cell carcinoma) were analyzed. The study found that the Zeff (7.90 ± 0.14) and IC of LAC were higher than those of SC, while the WC in LAC was substantially lower compared to that in SC. The findings of this investigation align with the findings of other researchers. The quantitative analysis of iodine content reflects the intravascular blood flow distribution and vascular status. However, many factors influence it, including the patient's cardiac output and blood volume, the concentration and flow rate of the contrast agent, and the injection dose and rate. The NIC is the ratio of tumor IC to aortic or subclavian artery IC at the same level. According to various research, NIC can reduce the effect of variations in individual circulation variability in the iodine content of tumors, improving the accuracy of the lesions' blood supply. The sources of tissue vary among clinical forms of lung cancer, and iodine concentrations are influenced by tumor angiogenesis[ 29 , 30 ]. During growth, SC has a relatively weak internal blood supply and expand slowly, whereas LAC tends to form an abundance of ethmoid capillaries[ 31 ]. Thus, following increased scanning, the iodine concentration of LAC was higher than that of SC, which was in line with Zhong's findings. Furthermore, this study found that IC and NIC in VP were higher than those in AP, which may be due to the fact that the contrast agent in AP was not filled with microvessels, while the contrast agent in VP was filled with microvessels and penetrated the basement membrane into the intercellular space. For spectral CT scanning, Mu et al.[ 24 ] collected 127 patients with pulmonary AC and 70 patients with pulmonary SC verified by histology. According to the study, vein IC and NIC were higher than arterial phase NIC, and the spectrum parameters of AC were increased compared to those of SC. These parameters may offer a specific benchmark for the categorization of lung cancer. Nonetheless, we discovered that IC was more effective in AP and VP than NIC when comparing LAC to SC, which was in line with Li et al.'s findings[ 22 ]. This suggests that NIC is dependent on the degree of lesion and aortic enhancement and that NIC deviation may result from changes in aortic diameter Study on the efficacy of quantitative parameters in differentiating AC from SC A ROC curve was drawn to assess the value of the single parameter index and joint prediction of lung cancer categorization according to the findings of the quantitative parameter analysis. In this study, the AUC of the ROC curve predicted by a single parameter was 0.65–0.73. This indicated that when only a certain spectral quantitative parameter of lung cancer is considered, its evaluation value is limited in most cases. To increase prediction efficiency, this study will jointly analyze meaningful quantitative parameters while carefully taking into account the distribution characteristics of iodine concentration and tumor heterogeneity. The findings of this investigation showed that the AUC of the ROC curve was 0.83 when multiple quantitative parameters were combined. This indicates a potential clinical application and significantly raises the prediction accuracy of lung cancer subtypes. Limitations and prospects of the study This study has the following limitations: (1) The sample size of the study was small and needs to be expanded in the follow-up study; (2) There are only squamous cell carcinoma and adenocarcinoma subtypes, and other pathological analyses of rare subtypes need to be studied in the future. In conclusion, the quantitative examination of spectral CT parameters was determined to be useful in identifying lung cancer subtypes and offers important information for assessing prognosis and clinical treatment options. For future studies, the sample size will be extended, examine more pathological types will be examined, and the research will be applied to tumors in other organs and diseases. Abbreviations CT: computed tomography; AC: adenocarcinoma; SC: Squamous cell carcinoma; AP: Arterial phase; VP: venous phase. WC: water concentration, IC: iodine concentration, NIC: normalized iodine concentration, Zeff :Z effective; K : slope of the curve. Declarations Acknowledgements Not applicable. Author Contributions XXZ : Data curation, Methodology, Writing - original draft, Software, Investigation. YHP : Data curation, Methodology , Software, Investigation. HZ T and JL : Visualization, Investigation. WL : Software, Formal analysis. K X : Visualization, Investigation. CWJ and YHP: Conceptualization, Methodology, Writing - review & editing. All authors revised the report and approved the final version before submission. Funding This work was supported by the the Baoji Health Committee Foundation of China (Grant Number 2021-023, Grant Number 2024-035, Grant Number 2019-01). Availability of data and materials The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate Our study followed the Declaration of Helsinki and it was approved by the Ethics Committee of the Baoji Central Hospital (ethical approval number: BZYL2022-14) and the requirement for informed consent from the patients was waived. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References R. Rami-Porta, V. Bolejack, J. Crowley, D. Ball, J. Kim, G. Lyons, T. Rice, K. Suzuki, C.F. Thomas, Jr., W.D. Travis, Y.L. Wu, I. Staging, A.B. Prognostic Factors Committee, I. Participating, The IASLC Lung Cancer Staging Project: Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer, J Thorac Oncol 10(7) (2015) 990-1003. R.S. Zheng, R. Chen, B.F. Han, S.M. Wang, L. Li, K.X. Sun, H.M. Zeng, W.W. Wei, J. He, [Cancer incidence and mortality in China, 2022], Zhonghua Zhong Liu Za Zhi 46(3) (2024) 221-231. C. Allemani, T. 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Kim, Preoperative assessment of cervical lymph node metastases in patients with papillary thyroid carcinoma: Incremental diagnostic value of dual-energy CT combined with ultrasound, PLoS One 16(12) (2021) e0261233. X. Xie, K. Liu, K. Luo, Y. Xu, L. Zhang, M. Wang, W. Shen, Z. Zhou, Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model, Front Oncol 13 (2023) 1147479. L.J. Zhong, N. Yu, X.J. Zhou, L.Z. Fu, D.Q. Zhou, Y. Wang, M. Yan, Differentiating between pulmonary adenocarcinoma and squamous cell carcinoma by spectral CT volumetric quantitative analysis: a comparative study with conventional spectral analysis, J Thorac Dis 15(2) (2023) 679-689. H. Mo, R. Huang, X. Wei, L. Huang, J. Huang, J. Chen, M. Qin, W. Lu, X. Yu, M. Liu, K. Ding, Diagnosis of Metastatic Lymph Nodes in Patients With Hepatocellular Carcinoma Using Dual-Energy Computed Tomography, J Comput Assist Tomogr 47(3) (2023) 355-360. W. Gao, Y. Zhang, Y. Dou, L. Zhao, H. Wu, Z. Yang, A. Liu, L. Zhu, F. Hao, Association between extramural vascular invasion and iodine quantification using dual-energy computed tomography of rectal cancer: a preliminary study, Eur J Radiol 158 (2023) 110618. M. Zheng, Classification and Pathology of Lung Cancer, Surg Oncol Clin N Am 25(3) (2016) 447-68. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4589013","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326617921,"identity":"b59cb2fe-0879-424a-8a64-3f232a55fef4","order_by":0,"name":"Xingxing Zheng","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xingxing","middleName":"","lastName":"Zheng","suffix":""},{"id":326617923,"identity":"dde0a900-8bdf-47b5-8d59-16a897c64d3c","order_by":1,"name":"Hongzhe Tian","email":"","orcid":"","institution":"Baoji Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongzhe","middleName":"","lastName":"Tian","suffix":""},{"id":326617924,"identity":"f37a46c7-8ee4-4b6c-94ea-70df5af6554b","order_by":2,"name":"Wei Li","email":"","orcid":"","institution":"Baoji Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":326617925,"identity":"fc751c83-382d-42a5-8f2c-b4ad22140f59","order_by":3,"name":"Jun Li","email":"","orcid":"","institution":"Baoji Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":326617926,"identity":"1e7f786e-862a-4722-92d5-92bfce9ff6f8","order_by":4,"name":"Kai Xu","email":"","orcid":"","institution":"Baoji Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Xu","suffix":""},{"id":326617928,"identity":"63fd5125-5802-42a2-99e5-810852449cf3","order_by":5,"name":"Chenwang Jin","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Chenwang","middleName":"","lastName":"Jin","suffix":""},{"id":326617930,"identity":"16ae18de-9ce3-4b21-89b9-cf00c0650b3c","order_by":6,"name":"Yuhui Pang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYPCCAwxszMwHH0hUSMjJE6+FnS3ZwOKMhbFhA7FaGPh5zCQq2yoSQWy8wJz97OHXvG13EvuYgVpuzpNIYGxgfvjoBh4tlj15ada8bc8S25jZii1nbpPIY2dgMzbOwaPF4ECOmTFv22GgFuaNtyW3SRQzNvCwSePVcv4NTAuDgfTfORKJDQcIabmRY/wYooXFSEKygQgtljPemDHOOXfYGOiXZAOJYxLGhs0E/GLOn2P84U3ZYdn5/YeBUVlTJyfP3vzwMV6HMTCwSfGgCDHjUQ7VwvzxBwFFo2AUjIJRMMIBAC9fS+Z6zuSHAAAAAElFTkSuQmCC","orcid":"","institution":"Baoji Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Pang","suffix":""}],"badges":[],"createdAt":"2024-06-16 08:36:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4589013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4589013/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-024-03370-6","type":"published","date":"2024-10-30T16:20:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60621016,"identity":"31ad194c-6142-4a0a-bdbb-6fe8c006c48a","added_by":"auto","created_at":"2024-07-18 20:58:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256765,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the inclusion and exclusion of patients.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589013/v1/f20c3793bee0676830cd433f.jpg"},{"id":60621017,"identity":"6eb6a0c9-c15d-4763-8f1b-8a83d834f29f","added_by":"auto","created_at":"2024-07-18 20:58:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":775324,"visible":true,"origin":"","legend":"\u003cp\u003e(1) Quantitative parameters of arterial (A-D) and venous phases (E-H) in 70 year old male patients with AC. Iodine concentrationimages (A, E); Water concentration mages (B, F); Slope of the curve (C, G); Z effective images (D, H); In the arterial phase, IC, NIC, WC, K and Zeff are 11.38, 0.15, 1029.72, 1.32 and 8.19. In the venous phase, IC, NIC, WC, K and Zeff are 12.38, 0.40, 1033.56, 1.53 and\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589013/v1/e816f1e12677d0a02d845fec.jpg"},{"id":60621014,"identity":"6de93b15-3131-41b6-a22a-ee817c62f6d9","added_by":"auto","created_at":"2024-07-18 20:58:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":727338,"visible":true,"origin":"","legend":"\u003cp\u003e(1) Quantitative parameters of arterial (A-D) and venous phases (E-H) in 61 year old male patients with SC. Iodine concentrationimages (A, E); Water concentration mages (B, F); Slope of the curve (C, G); Z effective images (D, H); In the arterial phase, IC, NIC, WC, K and Zeff are 10.66, 0.12, 1034.50, 1.15 and 8.13. In the venous phase, IC, NIC, WC, K and Zeff are 11.06, 0.30, 1040.17, 1.20 and 8.22.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589013/v1/68db90fefbb1f08a27ac715c.jpg"},{"id":60621013,"identity":"e2183b50-b57a-48dc-9ce2-3a986ec116c0","added_by":"auto","created_at":"2024-07-18 20:58:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100027,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of ROC curves between lung adenocarcinoma and squamous cell carcinoma using combined energy spectrum CT parameters.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4589013/v1/967ee3d81364bde9b9d31ab9.jpg"},{"id":68207287,"identity":"7d72b914-002b-46ff-8682-8f14eaabff1e","added_by":"auto","created_at":"2024-11-04 16:36:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2541150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4589013/v1/f7c8eef6-aa53-45e6-b064-e3b003f785db.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The application value of dual-energy computed tomography (DECT) multi- parameter imaging in lung adenocarcinoma and squamous cell carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer is one of the world\u0026rsquo;s most prevalent and a major contributor to cancer-related mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The most pervasive pathological forms of lung cancer are lung adenocarcinoma (AC) and squamous cell carcinoma (SC), and each has a unique therapeutic treatment strategy, with notable variations in chemotherapy regimens[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The identification of the pathological type of lung cancer before beginning therapy might therefore aid in the development of precise treatment strategies and prognostication. The majority of pathological diagnoses of lung cancer currently depend on cytological analysis, fiberoptic bronchoscopy, or fine needle aspiration biopsy[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Tumor tissue may be difficult to collect due to the occurrence of some tumors. Some tumors are situated deep within the lungs or close to large blood vessels and bones. Additionally, some patients have physical limitations that prevent them from undergoing invasive procedures, and some struggle to get tissue specimens and incur high testing costs, thus rendering the above methods unsuitable[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A non-invasive, safe, and economical technique is an essential tool to assist in identifying the histological type of malignancies.\u003c/p\u003e \u003cp\u003eAt present, conventional Computed tomography (CT) is a commonly used method for diagnosing lung cancer, but due to the beam hardening effect caused by mixed energy X-rays, it can affect the accuracy of CT value measurement and easily lead to misdiagnosis or missed diagnosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The advancement of PET-CT has greatly improved the accuracy of tumor diagnosis, and PET-CT function by combining the diagnostic capabilities of PET and CT exams. The advantages of the two are enhanced by the fact that they measure tumor metabolic activity in addition to defining the exact site of lesions. However, the examination costs are substantial, as is the radiation dose, which has a certain impact on clinical popularization[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While MRI is not currently a standard diagnostic technique for non-small cell lung cancer (NSCLC), preliminary findings from a few domestic and international studies have indicated that MRI can be a useful imaging modality for NSCLC staging, radiation target area delineation, therapeutic effect evaluation, and other purposes. MRI scanning time is relatively long, and patients who are claustrophobic or suffering from a critical illness sometimes find it difficult to cooperate during the scan, which limits its clinical applications[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Various pathological types of lung cancer exhibit distinct biological behaviors and pathological features and therefore require effective and sensitive modalities for diagnosis.\u003c/p\u003e \u003cp\u003eDual-energy CT (DECT) significantly increases the precision and reliability of disease diagnosis by not only offering images of anatomical morphology but also enabling the conversion of single-parameter diagnostic modes to multi-parameter diagnostic modes. Furthermore, it can reveal additional biological details about lesions and differentiate between various tissue components[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, we investigated the utility of energy spectrum CT multiparameter preoperative lung cancer pathological categorization prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e The Ethics Committee of Shaanxi Province's Baoji Central Hospital approved this study, and the experimental protocol was executed according to the approved guidelines. Informed permission was waived in view of the study's retrospective nature. Seventy-one patients with pathologically confirmed LC and SC who had preoperative energy spectrum CT chest scans performed in our hospital between December 2020 and April 2022 had their clinical and imaging data retrospectively analyzed. There were 35 cases of AC and 36 cases of SC, of whom 34 were males and 37 were females. Inclusion criteria: (1) Preoperative chest energy spectrum CT scan was performed, and the CT image could clearly display the lesion; (2) Pathological confirmation of non-small cell lung cancer through puncture biopsy, fiberoptic bronchoscopy, or surgical resection. Exclusion criteria: (1) Acceptance of radiation and chemotherapy before CT examination or biopsy, fiberoptic bronchoscopy, or surgical resection; (2) Missing or incomplete imaging and clinical data; (3) Cases where the lesion was too small or had too much liquefaction necrosis; (4) Cases where the lesion was not clearly displayed due to obstructive atelectasis, pneumonia, pleural effusion, etc. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the flow diagram of the presentation study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCT examination\u003c/h2\u003e \u003cp\u003ePlain and three-phase enhanced scans using 256-row single source dual energy CT (GE, Revolution CT Xstream Edition) were performed. The scanning range spanned from the apex of the lungs to the lower edge of the liver. The gemstone spectral imaging (GSI) mode was adopted. The parameters of the conventional sequence included instantaneous switching of tube voltage between 80 kVp and 140 kVp, tube current of 200\u0026ndash;400 mA, layer thickness and spacing of 5mm, reconstruction layer thickness of 1.25 mm, detector width of 80 mm, tube rotation time of 0.5 s, pitch of 0.992:1, FOV of 35 cmx35 cm. The contrast agent for enhanced scanning was iodohexanol (300 mgI/mL, dose of 1.2 mL/kg body mass), injected through the anterior elbow vein at a rate of 3.0-3.5 mL/s. Arterial (AP), venous (VP), and delayed phase images were obtained after contrast agent injection for 30 s, 60 s, and 120 s, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImaging analysis\u003c/h2\u003e \u003cp\u003eScanned and reconstructed data was transferred to the post-processing workstation (GE, AW 4.6), and arterial and venous phase images were generated using GSI Viewer analysis software. Dual-energy datasets for arterial and venous phases for post-processing were selected. The region of interest (ROI) should be chosen based on the idea of finding the solid section of the largest axial lesion, avoiding calcification and necrosis to the greatest extent possible, and it should not be smaller than 50% of the tumor area. All measurements were taken three times and the average was taken.\u003c/p\u003e \u003cp\u003eQuantitative parameter measurement included: (1) The calculation of the slope of the energy spectrum attenuation curve (\u003cem\u003ek)\u003c/em\u003e=(HU40keV HU100keV)/(100\u0026thinsp;\u0026minus;\u0026thinsp;40); (2) Effective ordinal number (Zeff); (3) The measurement of the iodine concentration (IC) of arterial phase lesions and the iodine value of the aorta at the same level as the lesion using Iodine substance map, and the calculation of the standardized iodine concentration (NIC). The standardized iodine concentration is equal to the iodine value of the lesion/the iodine value of the aorta at the same level. (4) Measurement of the water concentration (WC). (5) Measurement of the maximum thickness of the largest layer of the lesion axis map (measured sample thickness in this study).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHistochemical examination\u003c/h2\u003e \u003cp\u003eA pathologist with ten years of expertise in immunohistochemical staining examined tumor specimens. The pathologist numbered, assessed, and documented each section's pathological nature without having access to clinical data or spectral CT scan results. The World Health Organization Classification of Lung Tumors was followed in the application of the histopathological criteria for diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSPSS 25.0 was used to perform statistical analysis. The classification count data was reported as a percentage of the total number of cases, and χ 2 Inspection was used to compare the two groups.\u003c/p\u003e \u003cp\u003eThe measurement data undergoing normality testing and data that followed a normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026plusmn;\u0026thinsp;S). To compare the two groups, two independent sample t-tests were employed. The median (quartile) was used for presenting data that did not fit into a normal distribution, and the Mann-Whitney U test was used to compare the two groups. Quantitative parameters were combined with statistically significant differences between groups, and the diagnostic efficacy of quantitative parameters was analyzed using ROC curves. A P-value of \u0026lt;\u0026thinsp;0.05 indicates a statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical data and routine imaging data of patients\u003c/h2\u003e \u003cp\u003eThe findings from the study showed that the clinical characteristics of the two patient groups\u0026mdash;including age, gender, and smoking history\u0026mdash;did not differ statistically significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The maximum diameter of squamous cell carcinoma was larger than that of adenocarcinoma [5 (3.5, 6.62) cm and 2.8 (2.05, 4.05) cm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05]. However, there was no statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the comparison of conventional imaging features such as hair prick sign, lobulation sign, pleural traction, and tumor location. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the clinical data.\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\u003eSummary of clinical and routine imaging data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSC (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.71\u0026thinsp;\u0026plusmn;\u0026thinsp;10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpeculation sign\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLobulation sign\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePleural indentation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor size (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 (2.05, 4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.5, 6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight upper lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight lower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight middle lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft upper lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft lower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of CT parameters of different energy spectra between LAC and SC groups.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Zeff, IC, NIC, and \u003cem\u003eK\u003c/em\u003e obtained from AP and VP images in the LAC patients were all higher than those in SC patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the WC was comparatively lower than that in SC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eComparison of energy spectrum parameters in arterial phase of CT enhanced scanning between lung adenocarcinoma and squamous cell carcinoma patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.53 (8.2, 8.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1023.20 (1009, 1029.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15.70 (9.56, 20.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16 (0.11, 0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76 (1.1, 2.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.25 (7.98, 8.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1027.84 (1023.83, 1033.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10.52 (6.4, 13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13 (0.08, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27 (0.75, 1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/Z\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3..51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. AC: adenocarcinoma; SC: Squamous cell carcinoma; WC: water concentration; IC: iodine concentration; NIC: normalized iodine concentration; Zeff :Z effective; K: slope of the curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of energy spectrum parameters in venous phase of CT enhanced scanning between lung adenocarcinoma and squamous cell carcinoma patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZeff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1023.60 (1014.26, 1029.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.32, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1029.28(1024.18, 1033.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e11.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34 (0.2, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et/Z\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e431.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. AC: adenocarcinoma; SC: Squamous cell carcinoma; WC: water concentration; IC: iodine concentration; NIC: normalized iodine concentration; Zeff :Z effective; \u003cem\u003eK\u003c/em\u003e: slope of the curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eROC curve analysis and display of diagnostic efficiency of spectral CT parameters\u003c/h2\u003e \u003cp\u003eLogistic regression was used to integrate the spectral CT parameters with statistically significant differences between the two groups based on the weighting coefficient, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The regression model was:11.465\u0026ndash;3.727*VP_(Intercept)\u0026thinsp;+\u0026thinsp;0.014*VP_Zeff-0.19*VP_water base value\u0026thinsp;+\u0026thinsp;10.275*VP_IC-1.875*VP_NIC-0.112*VP_K\u0026thinsp;+\u0026thinsp;0.004*AP_Zeff\u0026thinsp;+\u0026thinsp;0.042* AP_water base value \u0026minus;\u0026thinsp;16.286* AP_ IC\u0026thinsp;+\u0026thinsp;3.028* AP_NIC\u0026thinsp;+\u0026thinsp;0.318* AP_K-3.727* tumor diameter. The AUC of the ROC curve was 0.831, with sensitivity of 63.9% and specificity of 94.3%.\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\u003ePerformance indicators of arterial and venous phase DECT parameters and their combined predictive classification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.577\u0026thinsp;~\u0026thinsp;0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC(mg/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1026.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.517\u0026thinsp;~\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC(mg/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.565\u0026thinsp;~\u0026thinsp;0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIC(mg/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.506\u0026thinsp;~\u0026thinsp;0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.549\u0026thinsp;~\u0026thinsp;0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZeff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.577\u0026thinsp;~\u0026thinsp;0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC(mg/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1026.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.517\u0026thinsp;~\u0026thinsp;0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIC(mg/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.565\u0026thinsp;~\u0026thinsp;0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIC(mg/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.506\u0026thinsp;~\u0026thinsp;0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.549\u0026thinsp;~\u0026thinsp;0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.605\u0026thinsp;~\u0026thinsp;0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.736\u0026thinsp;~\u0026thinsp;0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. AC: adenocarcinoma; SC: Squamous cell carcinoma; AP: Arterial phase; VP: venous phase. WC: water concentration, IC: iodine concentration, NIC: normalized iodine concentration, Zeff :Z effective; \u003cem\u003eK\u003c/em\u003e: slope of the curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn addition to identifying and classifying tumor disorders based on their physical features, dual source spectral computed tomography (DSCT) can also provide insight into the underlying biology of the tumor by analyzing its various stages[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the parameters of VP were left out of the study, Wang et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] demonstrated the diagnostic value of the spectral curve slope of 40 to 70 kev in the AP-K and IC in distinguishing between SC and LAC. By expressing tumor microvessel density, on the other hand, Li [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] demonstrated that VP-IC can distinguish between SC and LAC By utilizing dual-energy spectral CT scanning technology to investigate the function of dual-phase scanning in lung cancer subtypes, the problems above were addressed, and improved.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of spectral CT quantitative parameters between LAC and SC\u003c/h2\u003e \u003cp\u003eThe term \"energy spectrum curve\" describes the curve that shows how the energy level of an X-ray affects the CT value of various lesions or tissues. The chemical molecular structure of different substances will change, and different molecules will attenuate energy differently[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, subgroups of lung cancer can be identified using the K of the energy spectrum curve. According to the research both domestically and internationally, the K value of LAC was greater than that of SC[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This could be connected to the different material makeup or metabolic processes of lung cancer. However, the single energy node of K in this study is not completely consistent with that in the study of Zhang et al.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The single energy nodes of K in Zhang's study are 40 keV and 110 keV; however, in this investigation, the CT values in the single energy 40\u0026thinsp;~\u0026thinsp;100 keV between the two groups are significantly different, leading to the final selection of 40 keV and 100 keV as the single energy nodes of K. In this investigation, the Zeff, IC value, and NIC value were successfully measured in addition to the CT value of any level within the 40\u0026ndash;190 keV range using the software. The Zeff is the atomic number of elements with the same decay coefficient as compounds or mixtures, which can be utilized to determine the tissue composition of substances, particularly in substances with the same CT values. It is a quantitative index comprising many substances[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The values of Zeff, IC, and NIC were measured in addition to obtaining the CT value of any level in the 40\u0026ndash;190 keV range. The findings from our study indicated that the Zeff of LAC was higher than that of SC. This difference may be caused by the primary characteristics of LAC, which include unclear cell boundaries, solid blocks or strips, and a tendency to form an adenoid structure supported by a fibrous matrix and secreting mucus. Whereas, the internal structure of SC is closely arranged and exhibits the formation of keratinization and keratinized beads (also known as cancer beads) and a unique intercellular bridge structure. The two have different Zeff because of their differing chemical composition and densities[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Comparatively, adenocarcinoma grows in diversification, with rich stroma, relatively few tumor cells per unit volume, and low water content; as a result, its water content (WC) was lower than that of SC. Most SC, on the other hand, grows in aggregation, with more tumor cells per unit volume and greater water content. These results were confirmed by a pathological study conducted by Zhong et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], where 50 cancer patients (including 35 cases of adenocarcinoma and 22 cases of squamous cell carcinoma) were analyzed. The study found that the Zeff (7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14) and IC of LAC were higher than those of SC, while the WC in LAC was substantially lower compared to that in SC. The findings of this investigation align with the findings of other researchers.\u003c/p\u003e \u003cp\u003eThe quantitative analysis of iodine content reflects the intravascular blood flow distribution and vascular status. However, many factors influence it, including the patient's cardiac output and blood volume, the concentration and flow rate of the contrast agent, and the injection dose and rate. The NIC is the ratio of tumor IC to aortic or subclavian artery IC at the same level.\u003c/p\u003e \u003cp\u003eAccording to various research, NIC can reduce the effect of variations in individual circulation variability in the iodine content of tumors, improving the accuracy of the lesions' blood supply. The sources of tissue vary among clinical forms of lung cancer, and iodine concentrations are influenced by tumor angiogenesis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. During growth, SC has a relatively weak internal blood supply and expand slowly, whereas LAC tends to form an abundance of ethmoid capillaries[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, following increased scanning, the iodine concentration of LAC was higher than that of SC, which was in line with Zhong's findings. Furthermore, this study found that IC and NIC in VP were higher than those in AP, which may be due to the fact that the contrast agent in AP was not filled with microvessels, while the contrast agent in VP was filled with microvessels and penetrated the basement membrane into the intercellular space. For spectral CT scanning, Mu et al.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] collected 127 patients with pulmonary AC and 70 patients with pulmonary SC verified by histology. According to the study, vein IC and NIC were higher than arterial phase NIC, and the spectrum parameters of AC were increased compared to those of SC. These parameters may offer a specific benchmark for the categorization of lung cancer. Nonetheless, we discovered that IC was more effective in AP and VP than NIC when comparing LAC to SC, which was in line with Li et al.'s findings[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This suggests that NIC is dependent on the degree of lesion and aortic enhancement and that NIC deviation may result from changes in aortic diameter\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy on the efficacy of quantitative parameters in differentiating AC from SC\u003c/h2\u003e \u003cp\u003eA ROC curve was drawn to assess the value of the single parameter index and joint prediction of lung cancer categorization according to the findings of the quantitative parameter analysis. In this study, the AUC of the ROC curve predicted by a single parameter was 0.65\u0026ndash;0.73. This indicated that when only a certain spectral quantitative parameter of lung cancer is considered, its evaluation value is limited in most cases. To increase prediction efficiency, this study will jointly analyze meaningful quantitative parameters while carefully taking into account the distribution characteristics of iodine concentration and tumor heterogeneity. The findings of this investigation showed that the AUC of the ROC curve was 0.83 when multiple quantitative parameters were combined. This indicates a potential clinical application and significantly raises the prediction accuracy of lung cancer subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and prospects of the study\u003c/h2\u003e \u003cp\u003eThis study has the following limitations: (1) The sample size of the study was small and needs to be expanded in the follow-up study; (2) There are only squamous cell carcinoma and adenocarcinoma subtypes, and other pathological analyses of rare subtypes need to be studied in the future.\u003c/p\u003e \u003cp\u003eIn conclusion, the quantitative examination of spectral CT parameters was determined to be useful in identifying lung cancer subtypes and offers important information for assessing prognosis and clinical treatment options. For future studies, the sample size will be extended, examine more pathological types will be examined, and the research will be applied to tumors in other organs and diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCT: computed tomography; AC:\u0026nbsp;adenocarcinoma; SC:\u0026nbsp;Squamous cell carcinoma; AP:\u0026nbsp;Arterial phase; VP:\u0026nbsp;venous phase. WC: water concentration, IC: iodine concentration, NIC: normalized iodine concentration, Zeff :Z effective; \u003cem\u003eK\u003c/em\u003e: slope of the curve.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXXZ\u003c/strong\u003e: Data curation, Methodology, Writing - original draft, Software, Investigation. \u003cstrong\u003eYHP\u003c/strong\u003e: Data curation, Methodology , Software, Investigation. \u003cstrong\u003eHZ\u003c/strong\u003e\u003cstrong\u003eT and JL\u003c/strong\u003e: Visualization, Investigation. \u003cstrong\u003eWL\u003c/strong\u003e: Software, Formal analysis. \u003cstrong\u003eK\u003c/strong\u003e\u003cstrong\u003eX\u003c/strong\u003e: Visualization, Investigation. \u003cstrong\u003eCWJ and YHP:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Writing - review \u0026amp; editing. All authors revised the report and approved the final version before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the the Baoji Health Committee Foundation of China (Grant Number 2021-023, Grant Number 2024-035, Grant Number 2019-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets during and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study followed the Declaration of Helsinki and it was approved by the Ethics Committee of the Baoji Central Hospital (ethical approval number: BZYL2022-14) and the requirement for informed consent from the patients was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR. 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Ouyang, The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma, Technol Cancer Res Treat 23 (2024) 15330338241235554.\u003c/li\u003e\n\u003cli\u003eA. Bousse, V.S.S. Kandarpa, S. Rit, A. Perelli, M. Li, G. Wang, J. Zhou, G. Wang, Systematic Review on Learning-based Spectral CT, IEEE Trans Radiat Plasma Med Sci 8(2) (2024) 113-137.\u003c/li\u003e\n\u003cli\u003eS.P. Ng, C.E. Cardenas, H. Elhalawani, C. Pollard, 3rd, B. Elgohari, P. Fang, M. Meheissen, N. Guha-Thakurta, H. Bahig, J.M. Johnson, M. Kamal, A.S. Garden, J.P. Reddy, S.Y. Su, R. Ferrarotto, S.J. Frank, G. Brandon Gunn, A.C. Moreno, D.I. Rosenthal, C.D. Fuller, J. Phan, Comparison of tumor delineation using dual energy computed tomography versus magnetic resonance imaging in head and neck cancer re-irradiation cases, Phys Imaging Radiat Oncol 14 (2020) 1-5.\u003c/li\u003e\n\u003cli\u003eJ.X. Liu, Y. Zhang, H. Zhou, Y.J. Zhao, Y.S. Dai, Z.W. Zuo, J.N. 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Zhu, Diagnostic value of dual-layer spectral detector CT in differentiating lung adenocarcinoma from squamous cell carcinoma, Front Oncol 12 (2022) 868216.\u003c/li\u003e\n\u003cli\u003eP. Wang, Z. Tang, Z. Xiao, L. Wu, R. Hong, J. Wang, Dual-energy CT for differentiating early glottic squamous cell carcinoma from chronic inflammation and leucoplakia of vocal cord: comparison with simulated conventional 120 kVp CT, Clin Radiol 76(3) (2021) 238 e17-238 e24.\u003c/li\u003e\n\u003cli\u003eJ. Yoon, Y. Choi, J. Jang, N.Y. Shin, K.J. Ahn, B.S. Kim, Preoperative assessment of cervical lymph node metastases in patients with papillary thyroid carcinoma: Incremental diagnostic value of dual-energy CT combined with ultrasound, PLoS One 16(12) (2021) e0261233.\u003c/li\u003e\n\u003cli\u003eX. Xie, K. Liu, K. Luo, Y. Xu, L. Zhang, M. Wang, W. Shen, Z. Zhou, Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model, Front Oncol 13 (2023) 1147479.\u003c/li\u003e\n\u003cli\u003eL.J. Zhong, N. Yu, X.J. Zhou, L.Z. Fu, D.Q. Zhou, Y. Wang, M. Yan, Differentiating between pulmonary adenocarcinoma and squamous cell carcinoma by spectral CT volumetric quantitative analysis: a comparative study with conventional spectral analysis, J Thorac Dis 15(2) (2023) 679-689.\u003c/li\u003e\n\u003cli\u003eH. Mo, R. Huang, X. Wei, L. Huang, J. Huang, J. Chen, M. Qin, W. Lu, X. Yu, M. Liu, K. Ding, Diagnosis of Metastatic Lymph Nodes in Patients With Hepatocellular Carcinoma Using Dual-Energy Computed Tomography, J Comput Assist Tomogr 47(3) (2023) 355-360.\u003c/li\u003e\n\u003cli\u003eW. Gao, Y. Zhang, Y. Dou, L. Zhao, H. Wu, Z. Yang, A. Liu, L. Zhu, F. Hao, Association between extramural vascular invasion and iodine quantification using dual-energy computed tomography of rectal cancer: a preliminary study, Eur J Radiol 158 (2023) 110618.\u003c/li\u003e\n\u003cli\u003eM. Zheng, Classification and Pathology of Lung Cancer, Surg Oncol Clin N Am 25(3) (2016) 447-68.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Energy spectrum CT, Lung adenocarcinoma, Lung squamous cell carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-4589013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4589013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung adenocarcinoma (AC), and lung squamous cell carcinoma (SC) are the two primary types of NSCLC. Determining the pathological type of NSCLC is important in establishing the most effective treatment method. Dual-energy computed tomography (DECT) multi-parameter imaging is an imaging technology that provides accurate and reliable disease diagnosis, and its uses are utilized for the combined diagnostic efficacy of AC and SC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed 71 lung cancer patients (36 squamous cell carcinomas; 35 adenocarcinomas) who had undergone enhanced DECT scans, including arterial and venous phases in this single-center retrospective study. The tumor diameter, water concentration (WC), iodine concentration (IC), normalized iodine concentration (NIC), Z effective (Zeff), and slope of the curve (\u003cem\u003eK\u003c/em\u003e) in lesions were evaluated during two scanning phases in the two separate pathological types of lung cancers. Statistical analysis was used to determine the diagnostic efficacy of morphological parameters alone, and the combined efficacy of spectral parameters and morphological parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In a univariate analysis involving 71 lung cancer patients, the results from Zeff, IC, NIC, and K from the AC's arterial and venous phase images were more elevated than those from the SC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). In contrast, the WC results were lower than those from SC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The area under the ROC curve (AUC) for multi-parameter joint prediction typing was 0.831, with a corresponding sensitivity of 63.9% and specificity of 94.3%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIt is possible to distinguish between central SC and AC using the spectrum characteristics of DECT-enhanced scanning (Zeff, IC, NIC, K, WC, and tumor diameter). Diagnostic effectiveness can be greatly improved when multiple variables are included, and practical treatment plans can be formulated, as well as predicting prognosis in clinical settings.\u003c/p\u003e","manuscriptTitle":"The application value of dual-energy computed tomography (DECT) multi- parameter imaging in lung adenocarcinoma and squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:58:22","doi":"10.21203/rs.3.rs-4589013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-17T10:56:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-13T01:05:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T21:53:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T07:34:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T21:19:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110267556395618128683378818798285372495","date":"2024-09-10T08:58:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-04T18:32:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279706197028146179774818372315958959490","date":"2024-09-02T19:28:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163595885173062440482669325285652782489","date":"2024-09-02T15:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177301789982248591655213441692839974503","date":"2024-09-01T10:17:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291436984549223369176388586457674145781","date":"2024-08-31T20:49:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131791930052704828008950538478636079925","date":"2024-08-31T18:08:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12005690174205446792888736256730223708","date":"2024-08-31T15:11:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-08-31T15:11:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262111034387767179424766712477514376982","date":"2024-08-23T16:29:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191438860106820581794567672119732070260","date":"2024-08-15T15:17:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64800070012459749350280489613410354189","date":"2024-07-16T01:21:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-13T11:05:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-10T10:19:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-24T18:10:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-24T11:37:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-06-16T08:35:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7aca08b7-6473-4da8-88fb-49b153f2d6d2","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:27:38+00:00","versionOfRecord":{"articleIdentity":"rs-4589013","link":"https://doi.org/10.1186/s12890-024-03370-6","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2024-10-30 16:20:22","publishedOnDateReadable":"October 30th, 2024"},"versionCreatedAt":"2024-07-18 20:58:22","video":"","vorDoi":"10.1186/s12890-024-03370-6","vorDoiUrl":"https://doi.org/10.1186/s12890-024-03370-6","workflowStages":[]},"version":"v1","identity":"rs-4589013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4589013","identity":"rs-4589013","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}