Differentiation of Benign and Malignant Liver Tumors Using Non-Enhanced Spectral CT Quantitative Parameters Combined with Inflammatory Indicators

Differentiation of Benign and Malignant Liver Tumors Using Non-Enhanced Spectral CT Quantitative Parameters Combined with Inflammatory Indicators | 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 Differentiation of Benign and Malignant Liver Tumors Using Non-Enhanced Spectral CT Quantitative Parameters Combined with Inflammatory Indicators Qinghong Yan, Rui Guo, Ke Chang, Xingyue Xu, Yunhang Jing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8010985/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aims to evaluate the effectiveness of using quantitative parameters from non-contrast dual-layer spectral CT combined with inflammatory markers to differentiate between benign and malignant hepatic tumors and to assess their diagnostic performance. Materials and Methods A retrospective analysis was conducted on 119 patients with hepatic lesions who underwent dual-layer spectral CT scanning. Clinical data, including gender, age, albumin-to-total bilirubin ratio (ABR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), were collected. Measurement of spectral CT parameters in lesions on plain scan images, including CT values of virtual monoenergetic images at 40 keV, 70 keV, and 140 keV, effective atomic number, and electron density. Clinical and spectral CT parameters were screened using logistic regression, and the discriminative efficacy of these parameters in differentiating hepatic vascular carcinoma from hepatocellular carcinoma was evaluated using the area under the ROC curve alongside logistic regression models. Results Multivariable logistic regression identified the lymphocyte-to-monocyte ratio (LMR), Zeff-range, and electron density as independent predictors for distinguishing between benign and malignant liver tumors. Receiver operating characteristic (ROC) analysis revealed no significant difference in the area under the curve (AUC) between quantitative spectral CT parameters (AUC= 0.79, 95% CI: 0.70-0.86) and clinical parameters (AUC=0.75, 95% CI: 0.67-0.83; P=0.551). However, an integrated model combining both parameter sets achieved superior diagnostic performance (AUC=0.85, 95% CI: 0.78-0.91). Conclusion Both non-enhanced dual-layer spectral CT quantitative parameters and clinical parameters can effectively distinguish between benign and malignant liver tumors, and their combination can further enhance diagnostic performance. Hepatic Hemangioma Hepatocellular Carcinoma Metastatic Liver Cancer Spectral CT Differential diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Hepatic hemangioma (HH) is the most common benign liver tumor, whereas hepatocellular carcinoma (HCC) represents the most prevalent primary malignant tumor, and metastatic liver cancer (MLC) is the most frequently encountered malignant liver tumor. These entities demonstrate significant differences in clinical management and prognosis, underscoring the importance of accurate differentiation in clinical practice [1–3] . Although computed tomography (CT) imaging plays a crucial role in distinguishing liver lesions, these lesions may exhibit similar morphological features on conventional non-contrast CT scans, considerably complicating accurate differentiation based solely on unenhanced imaging [4] . In contrast, contrast-enhanced CT, with its high sensitivity in detecting malignant tumors, substantially outperforms non-contrast CT and has become the primary diagnostic modality [5] . However, the use of iodinated contrast agents is associated with potential risks, including nephrotoxicity, allergic reactions, and, in some cases, severe, life-threatening adverse events [6] . Additionally, the high cost of multiphase contrast-enhanced CT examinations further contributes to the financial burden on patients. Dual-layer spectral detector computed tomography (SDCT) is an emerging spectral imaging modality that offers multiple image reconstruction options, including virtual monoenergetic images (VMI), effective atomic number (Z_eff) maps, and electron density (ED) maps. These capabilities not only enhance the precision of CT analyses but also provide a foundation for the multiparametric assessment of lesions [7] . Quantitative parameters derived from spectral CT are currently employed widely for the detection, differential diagnosis, and evaluation of treatment response in liver lesions [8] . For example, studies have demonstrated that VMI can enhance the contrast of liver metastases, while spectral CT has been applied to assess Ki-67 expression in hepatocellular carcinoma [9,10] . Furthermore, spectral CT has been utilized to evaluate hepatic steatosis and predict microvascular invasion in hepatocellular carcinoma [11,12] . However, the majority of these studies have relied on contrast-enhanced scans, and the potential of non-contrast spectral CT in differentiating liver lesions remains inadequately explored. Additionally, research has shown that inflammatory markers are significant in the diagnosis and prognostic evaluation of various malignancies, including liver cancer. Although previous investigations have used non-contrast spectral CT parameters to differentiate between benign and malignant liver lesions, these studies have primarily focused on imaging parameters and have not integrated a comprehensive analysis with clinical parameters [13] . Accordingly, this study aims to investigate the efficacy of combining non-contrast spectral CT quantitative parameters with inflammatory markers in differentiating benign from malignant liver tumors, and to assess its diagnostic performance. This approach not only has the potential to streamline the diagnostic workflow for liver lesions—thereby reducing patient exposure to iodine-based contrast agents and alleviating financial burdens—but also to provide a more comprehensive and precise basis for differential diagnosis in clinical practice. 2. Materials and methods 2.1 Patients A retrospective study was performed on patients with liver lesions who underwent abdominal dual-layer detector spectral CT scans at our hospital from January 1, 2025, to June 30, 2025. The inclusion criteria were as follows: (1) patients who received both non-contrast and contrast-enhanced spectral CT scans prior to any treatment; and (2) lesions with a diameter greater than 1 cm. The exclusion criteria comprised: (1) patients with liver lesions other than hepatic hemangioma (HH), hepatocellular carcinoma (HCC), or metastatic liver cancer (MLC); (2) patients who had received radiotherapy or chemotherapy; and (3) patients with infectious conditions other than chronic hepatitis. Ultimately, 119 patients were enrolled. Patients with hepatic hemangioma were classified as the benign group, while those with hepatocellular carcinoma or metastatic liver cancer were assigned to the malignant group. Retrospective clinical data, including gender, age, albumin-to-bilirubin ratio (ABR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), were collected for both groups. This study received approval from the Institutional Review Board of our hospital (ethical approval number: IIT-R-20250174). Owing to the retrospective design of the study, the requirement to obtain informed consent was waived. For liver lesions lacking pathological confirmation, the following imaging-based diagnostic criteria were employed. Hepatic hemangiomas were characterized on multiphase contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) as well-defined masses with peripheral nodular enhancement that progressed centripetally over time. On T2-weighted imaging, these lesions typically appeared hyperintense, often exhibiting the "light bulb" sign [14] . Hepatocellular carcinoma (HCC) was identified by its non-peripheral arterial phase hyperenhancement, followed by non-peripheral washout during the portal venous and delayed phases, with the presence of an enhancing capsule on multiphase imaging further supporting the diagnosis [15] . Metastatic liver lesions were recognized as either hypervascular or hypovascular, contingent on the vascularity of the primary tumor, with peripheral rim enhancement and central necrosis commonly observed on contrast-enhanced CT or MRI. Additionally, all cases of metastatic liver cancer were accompanied by a confirmed history of primary malignancy [16] . 2.2 CT protocol All patients underwent non-contrast and triple-phase dynamic contrast-enhanced abdominal imaging using a dual-layer detector spectral CT scanner (IQon Spectral CT, Philips Healthcare, the Netherlands). The scanning range extended from the dome of the diaphragm to the symphysis pubis. The scanning parameters were as follows: a tube voltage of 120 kVp, automatic tube current modulation (80–210 mAs), a collimator width of 64 × 0.625 mm, a gantry rotation speed of 0.5 s per rotation, and a pitch of 0.985. The non-ionic contrast agent iodixanol (320 mgI/mL) was administered via a dual-syringe power injector at a dose of 1.5 mL/kg and a flow rate of 3 mL/s, followed by a 20 mL saline flush at the same flow rate. For the arterial phase, a region of interest was placed in the abdominal aorta at the level of the diaphragmatic dome, with a trigger threshold set at 150 HU. Venous and delayed phase images were acquired 30 seconds and 120 seconds, respectively, after the arterial phase scan. 2.3 Spectral CT analysis The spectral CT images from non-contrast and triple-phase enhanced scans were imported into the IntelliSpace Portal (ISP) Version 10.1 post-processing workstation for analysis. Using the Spectral CT Viewer application, virtual monoenergetic images (VMIs) at 40 keV, 70 keV, and 140 keV were generated, along with effective atomic number (Zeff) maps and electron density (ED) maps. On the non-contrast images, regions of interest (ROIs) were delineated on the slice with the largest cross-sectional area of the lesion, as well as on the two adjacent slices above and below. For lesions with ill-defined boundaries, the enhanced scan images were used as a reference. During ROI placement, areas corresponding to hemorrhage, necrosis, and calcification were carefully avoided, and the ROI was positioned to encompass as much of the lesion as possible. Two radiology residents independently performed the measurements, and the average of their values was used for subsequent analysis; interobserver consistency was subsequently assessed. In cases where multiple lesions were present, only the largest lesion was measured, given that smaller lesions are often difficult to identify on non-contrast images and challenging to delineate with ROIs. The measured parameters included the mean and range of CT values on the VMIs, as well as the mean, maximum, minimum, and range of effective atomic numbers, electron density values, and the spectral curve slope between 40 keV and 140 keV. The range was defined as the difference between the maximum and minimum values. The spectral curve slope (λHU) was calculated using the following formula: λHU = (CT40 keV − CT140 keV) / 100. 2.4 Feature Selection and Model Construction All clinical parameters and spectral CT parameters were initially included in a univariate logistic regression analysis. Variables demonstrating significant differences in the univariate analysis were subsequently incorporated into a multivariate logistic regression analysis. Those indicators that maintained statistical significance in the multivariate analysis were identified as independent predictors for distinguishing between benign and malignant liver tumors.Based on these independent predictors, binary logistic regression models were constructed. Specifically, a clinical model was developed using only clinical predictors, an imaging model was constructed exclusively with spectral CT parameters, and a combined model was established by integrating both clinical and imaging predictors. 2.5 Statistical analysis Statistical analyses were conducted using SPSS version 27.0 and MedCalc version 23.1.7. Continuous variables with a normal distribution are expressed as the mean ± standard deviation (x̅ ± S), whereas non-normally distributed data are presented as the median with interquartile range [M (P25, P75)]. Categorical variables are reported as frequencies (percentages). Normality of continuous variables was evaluated separately in both the benign and malignant tumor groups. For normally distributed variables, comparisons between groups were performed using the independent samples t-test or an adjusted t'-test, whereas the Mann-Whitney U test was employed for non-normally distributed variables. Categorical variables were compared using either the chi-square test or Fisher’s exact test, as appropriate. Inter-observer agreement was determined via the intraclass correlation coefficient (ICC). The diagnostic performance of the models was assessed based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Comparisons of diagnostic efficiency between different models were carried out using the DeLong test. A two-sided P-value of less than 0.05 was considered statistically significant. 3. Results 3.1. Study population A total of 119 patients with liver lesions were included in this study, comprising 88 males and 31 females, with a mean age of 60.39 ± 11.59 years (range, 30–90 years). The clinical characteristics of all patients are summarized in Table 1 . The cohort consisted of 63 patients with hepatic hemangioma (HH), 25 with hepatocellular carcinoma (HCC), and 31 with metastatic liver cancer (MLC). Among the 63 cases of HH, the diagnosis was confirmed by biopsy in 2 patients; by a combination of ultrasonography, contrast-enhanced spectral computed tomography (CT), and magnetic resonance imaging (MRI) in 7 patients; by contrast-enhanced spectral CT and MRI in 5 patients; by ultrasonography and contrast-enhanced spectral CT in 23 patients; and by contrast-enhanced spectral CT alone in the remaining cases. Additionally, 26 cases exhibited no lesion progression during a follow-up period exceeding one year. For the 25 cases of HCC, the diagnosis was established via biopsy in 5 patients, surgical resection in 2 patients, a combination of contrast-enhanced ultrasound, spectral CT, and MRI in 4 patients, contrast-enhanced spectral CT and MRI in 8 patients, and contrast-enhanced ultrasound and spectral CT in 6 patients. Among the 31 MLC cases, the diagnosis was confirmed by biopsy in 10 patients; by contrast-enhanced spectral CT and MRI in 2 patients; by ultrasonography and contrast-enhanced spectral CT in 8 patients; and by contrast-enhanced spectral CT alone in the remaining cases. Thirteen cases were newly identified during follow-up, while 18 cases exhibited lesion progression. The primary tumors included colorectal cancer (8 cases), gastric cancer (12 cases), pancreatic cancer (5 cases), gastrointestinal stromal tumor (3 cases), and lung cancer (3 cases). Patients with hepatic hemangioma were assigned to the benign group, whereas those with hepatocellular carcinoma or metastatic liver cancer were classified into the malignant group. Univariate analysis revealed that age, the Albumin-to-Bilirubin Ratio (ABR), the Neutrophil-to-Lymphocyte Ratio (NLR), and the Lymphocyte-to-Monocyte Ratio (LMR) differed significantly between the two groups (P < 0.05), as detailed in Table 1 . Table 1 presents the baseline clinical characteristics of 119 patients diagnosed with liver lesions. Variables Benign Group (n = 63) Malignant Group (n = 56) P value Age (y) 57.98 ± 11.79 63.09 ± 10.83 0.016 Sex Male 43(43/63,68%) 45(45/56,80%) 0.133 Female 20(20/63,32%) 11(11/56,20%) ABR 3.31(2.59,4.51) 2.51(1.20,3.94) 0.012 NLR 1.94(1.39,2.74) 2.80(2.05,4.14) <0.001 LMR 4.45(3.23,6.36) 2.66(1.83,3.98) <0.001 PLR 118.44(97.01,153.85) 119.89(86.28,185.66) 0.936 ABR (albumin-to-bilirubin ratio); NLR (neutrophil-to-lymphocyte ratio); LMR (lymphocyte-to-monocyte ratio); and PLR (platelet-to-lymphocyte ratio). 2.2 Spectral CT analysis The intraclass correlation coefficient (ICC) for spectral CT parameters ranged from 0.77 to 0.84, indicating good to excellent inter-observer agreement. Univariate analysis revealed statistically significant differences (P < 0.05) in several spectral CT parameters between benign and malignant groups. Specifically, these parameters included the range of CT values on 40 keV, 70 keV, and 140 keV virtual monoenergetic images (VMIs); the mean CT value on 140 keV VMIs; the maximum effective atomic number (Zeff-max); the range of Zeff values; and electron density (ED). Detailed results are summarized in Table 2 . Table 2 Comparison of Spectral CT Parameters Between Benign and Malignant Groups Variables Benign Group (n = 63) Malignant Group (n = 56) P value ICC CT 40 keV - mean (HU) 28.69 ± 8.86 27.49 ± 13.42 0.563 0.80 CT 40 keV -range(HU) 64.24 ± 19.64 101.66 ± 108.40 0.008 0.78 CT 70 keV - mean (HU) 41.89 ± 5.37 39.33 ± 8.99 0.066 0.81 CT 70 keV -range(HU) 59.89 ± 16.57 79.38 ± 22.64 <0.001 0.79 CT 140 keV - mean (HU) 46.37 ± 5.71 43.38 ± 8.86 0.033 0.83 CT 140 keV -range(HU) 55.84 ± 17.04 77.96 ± 22.87 <0.001 0.78 λ HU -0.18 ± 0.09 -0.16 ± 0.11 0.332 0.81 Zeff-mean 7.12 ± 0.08 7.13 ± 0.09 0.377 0.84 Zeff-min 7.05 ± 0.10 7.02 ± 0.11 0.146 0.80 Zeff-max 7.19 ± 0.08 7.24 ± 0.10 0.002 0.80 Zeff-range 0.13 ± 0.08 0.23 ± 0.11 <0.001 0.77 ED(%EDW) 104.92 ± 0.60 104.32 ± 0.82 <0.001 0.83 CT40 keV denotes the CT value measured at 40 keV; CT70 keV denotes the CT value measured at 70 keV; CT140 keV denotes the CT value measured at 140 keV; λHU represents the spectral curve slope; Zeff signifies the effective atomic number; ED indicates the electron density; and ICC denotes the intraclass correlation coefficient. 2.4 Model Construction and Evaluation Independent predictors identified via univariate and multivariate logistic regression analyses of all clinical and spectral computed tomography (CT) parameters included the lymphocyte-to-monocyte ratio (LMR) among clinical factors and both the effective atomic number range (Zeff-range) and electron density (ED) among spectral CT parameters (Table 3 ). Based on these predictors, a clinical model was constructed using the clinical parameter, an imaging model was built using the spectral CT parameters, and a combined model was developed integrating both sets of predictors. Receiver operating characteristic (ROC) curve analysis indicated that the combined model achieved the highest diagnostic performance, with an area under the curve (AUC) of 0.85 (95% confidence interval: 0.78–0.91). Comparison using the DeLong test confirmed the superior diagnostic efficacy of the combined model, while no significant difference was observed between the imaging model and the clinical model alone (Table 4 ). Table 3 Univariate and Multivariate Logistic Regression Analysis of Spectral Computed Tomography and Clinical Parameters Variables Univariable analysis P value Multivariable analysis P value OR 95%CI OR 95%CI Age 1.04 1.01 ~ 1.08 0.019 1.03 0.98 ~ 1.08 0.197 Sex 0.63 0.22 ~ 1.84 0.400 — — — ABR 0.87 0.72 ~ 1.05 0.138 — — — NLR 1.33 1.05 ~ 1.67 0.016 0.93 0.72 ~ 1.20 0.582 LMR 0.59 0.46 ~ 0.75 <0.001 0.58 0.41 ~ 0.82 0.002 PLR 1.00 0.99 ~ 1.01 0.557 — — — CT 40 keV- mean 0.99 0.96 ~ 1.02 0.559 — — — CT 40 keV-range 1.05 1.03 ~ 1.07 <0.001 1.08 0.95 ~ 1.23 0.230 CT 70 keV- mean 0.95 0.91 ~ 1.00 0.061 — — — CT 70 keV-range 1.06 1.03 ~ 1.08 <0.001 0.94 0.65 ~ 1.38 0.766 CT140 keV- mean 0.95 0.90 ~ 0.99 0.032 1.91 0.96 ~ 3.82 0.066 CT140 keV-range 1.05 1.03 ~ 1.08 <0.001 1.02 0.76 ~ 1.36 0.919 λHU 1.21 0.85 ~ 1.69 0.330 — — — Zeff-mean 0.95 0.75 ~ 1.26 0.375 — — — Zeff-min 0.08 0.03 ~ 2.42 0.147 — — — Zeff-max 3.95 1.59 ~ 9.80 0.003 1.94 0.48 ~ 7.83 0.844 Zeff-range 3.50 1.73 ~ 7.06 <0.001 2.20 1.01 ~ 4.78 0.046 ED(%EDW) 0.31 0.17 ~ 0.55 <0.001 0.05 0.01 ~ 0.85 0.045 ABR (albumin-to-bilirubin ratio); NLR (neutrophil-to-lymphocyte ratio); LMR (lymphocyte-to-monocyte ratio); and PLR (platelet-to-lymphocyte ratio). CT40 keV, CT70 keV, and CT140 keV refer to the computed tomography (CT) values measured at 40 keV, 70 keV, and 140 keV, respectively. Furthermore, λHU denotes the spectral curve slope; Zeff signifies the effective atomic number; and ED indicates the electron density. Table 4 Univariate and Multivariate Logistic Regression Analyses of Spectral CT and Clinical Parameters Variables AUC(95%CI) P value Sensitivity (%) Speciality (%) Youden index Delong Test Zeff-range 0.72(0.63 ~ 0.80) <0.001 85.71 44.44 0.30 ED 0.73(0.64 ~ 0.81) 0.009 62.50 80.95 0.43 0.551 a Clinical model 0.75(0.67 ~ 0.83) <0.001 67.86 77.78 0.46 Imaging model 0.79(0.70 ~ 0.86) <0.001 64.29 85.71 0.50 0.031 b Combined model 0.85(0.78 ~ 0.91) <0.001 76.79 84.13 0.61 0.002 c Zeff denotes the effective atomic number; ED represents the electron density; AUC refers to the area under the curve; and CI stands for the confidence interval. a clinical model vs. imaging model; b imaging model vs. combination model; c combination model vs. clinical model. 4. Discussion Hepatocellular carcinoma is the most common primary malignant liver tumor and is particularly prevalent among patients with chronic hepatitis B or liver cirrhosis. In contrast, liver metastases represent the most frequent secondary malignant tumors of the liver and are often identified in patients with a history of other malignancies. These patients typically require long-term follow-up to monitor disease progression, and the differential diagnosis frequently necessitates distinguishing these lesions from hepatic hemangiomas. Although contrast-enhanced computed tomography (CT) is commonly employed for imaging, it increases patients’ financial burden and its reliance on conventional imaging features poses limitations for accurate differentiation [17] . To address these issues, our study analyzed CT values, effective atomic numbers, and electron densities derived from virtual monoenergetic images (at 40 keV, 70 keV, and 140 keV) based on non-contrast CT scans. These parameters were integrated with inflammatory markers—including the albumin-to-total bilirubin ratio (ABR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)—to assess their potential utility for differentiating benign and malignant liver lesions. The objective is to establish a more convenient, cost-effective, and reliable method for clinical differential diagnosis. This study identified statistically significant differences between benign and malignant groups in the range of virtual monoenergetic computed tomography (CT) values at 40, 70, and 140 keV; the mean CT value at 140 keV; the maximum value and range of effective atomic number (Zeff); and electron density (ED). Additionally, the findings indicate that low-energy virtual monoenergetic imaging (VMI) enhances lesion contrast and image quality, thereby improving both the detection rate and diagnostic accuracy for liver lesions [18] . This study found that the range of CT values at 40 keV, 70 keV, and 140 keV was significantly greater in malignant tumors than in benign lesions, with the range increasing at lower energy levels. These findings suggest that low‐energy virtual monoenergetic imaging (VMI) more effectively elucidates the subtle variations in tissue composition arising from the intrinsic heterogeneity of malignant lesions [19] . Low-energy VMI effectively captures tissue heterogeneity, thereby differentiating benign from malignant lesions. Previous research indicated that only the mean CT value at 140 keV exhibited a statistically significant difference between benign and malignant liver tumor groups, with benign lesions displaying higher values—a finding consistent with our study. However, that research did not investigate the significance of the VMI value range between the two groups [13] . Moreover, the effective atomic number (Zeff) reflects the weighted average atomic number of tissues within the X-ray energy spectrum and is closely linked to material composition, thereby facilitating the differentiation of tissues with distinct elemental profiles [20] . The study found no significant differences in the mean and minimum effective atomic number (Zeff) between benign and malignant lesions. However, malignant lesions exhibited significantly higher maximum Zeff values and broader Zeff ranges compared to benign lesions. These findings are consistent with previous research, which suggests that the increased heterogeneity of malignant lesions primarily accounts for their wider Zeff range [13,21] . Malignant tumors often display abnormal angiogenesis and diverse histological changes including hypervascularity, necrosis, hemorrhage, cystic degeneration, and fibrosis, all of which contribute to internal structural and compositional complexity and, consequently, to greater Zeff variability. In contrast, benign lesions generally possess a more homogeneous tissue architecture, resulting in a narrower Zeff range.Additionally, our findings identified electron density (ED) as an independent predictor for differentiating between benign and malignant liver tumors. ED reflects the tissue’s molecular composition and cellular density [23] . Previous studies have demonstrated that ED can enhance the visualization of pulmonary embolism, improve diagnostic efficacy, and effectively discriminate between normal and infarcted myocardium [23,24] . In this study, the benign lesions exhibited higher ED values compared to the malignant lesions, which may be attributable to the more compact or regular microstructural organization observed in benign tumors [25] . Among the inflammatory markers, ABR, NLR, and LMR exhibited statistically significant differences between the two groups. Chen et al. reported that an elevated total bilirubin-to-albumin ratio, which corresponds to a decreased ABR, is associated with severe impairment of liver function [26] . This finding is consistent with our results and may be explained by the fact that hepatocellular carcinoma often arises in the context of liver cirrhosis, a condition known to compromise hepatic function [17] . Moreover, liver metastases can further impair liver function, particularly when there is a substantial tumor burden, resulting in abnormalities in liver function parameters [27] . Previous research suggests that NLR reflects the dynamic balance between neutrophil-mediated tumor inflammation and lymphocyte-dependent antitumor immunity, with higher NLR levels correlating with increased tumor malignancy. This observation aligns with our findings [28] . Additionally, our study identified LMR as an independent predictive factor for differentiating benign from malignant liver tumors. Zhao et al. demonstrated that LMR serves as a significant protective indicator, with each unit increase in LMR associated with an approximately 33% reduction in the risk of developing hepatocellular carcinoma [28] . This observation may be attributable to the role of tumor-associated macrophages, which promote angiogenesis and lymphangiogenesis, thereby accelerating tumor cell proliferation while simultaneously modulating the immune response, which influences tumor initiation, recurrence, and metastasis [28] . This study integrated spectral CT parameters with inflammatory indices, thereby enhancing the reliability and clinical utility of differential diagnoses. The findings indicate that both the imaging model, based on non-contrast spectral CT quantitative parameters (Zeff-range and ED) with an AUC of 0.79 (95% CI, 0.70-0.86), and the clinical model, developed using the inflammatory marker LMR with an AUC of 0.75 (95% CI, 0.67-0.83), effectively distinguished benign from malignant liver lesions. Notably, the combined model, which incorporated both imaging and inflammatory variables, achieved the highest diagnostic performance (AUC=0.85, 95% CI, 0.78-0.91).These results suggest that the integration of spectral CT-derived quantitative data with systemic inflammatory markers offers a robust and non-invasive strategy for the discrimination of liver lesions. Furthermore, the superior performance of the combined model underscores the complementary roles of anatomical/quantitative imaging and pathophysiological inflammatory status in enhancing diagnostic precision. This study has several limitations. First, it is a single-center, retrospective investigation with a relatively limited sample size; future studies should expand the case cohort and incorporate multi-center validation. Furthermore, pathological diagnoses were not available for all cases. However, obtaining pathological confirmation for every case is challenging in clinical practice, as benign lesions typically do not require surgical intervention and not all hepatocellular carcinoma cases undergo resection. Consequently, diagnoses in this study relied on established imaging criteria when pathological confirmation was unavailable. Most malignant liver lesions identified solely through imaging were monitored over time, with confirmation obtained by observing indicators such as tumor growth or changes in size in response to treatment. Finally, although this approach is suitable for screening or initial assessment of liver diseases, contrast-enhanced computed tomography remains essential when clinical intervention is warranted, particularly for evaluating tumor vascularity and its relationship with adjacent vessels. Declarations Author Contribution Y is responsible for designing the research methodology, conducting the experimental investigations, and drafting the initial version of the paper.G, C, and X are responsible for data collection, measurement, analysis, and chart visualization.J is responsible for formulating the overall research objectives and aims, supervising the thesis, and serving as the corresponding author.All authors have read and agreed to the published version of the manuscript. References Toh, M. R., Wong, E. Y. T., Wong, S. H., Ng, A. W. T., Loo, L.-H., Chow, P. K.-H., & Ngeow, J. (2023). Global Epidemiology and Genetics of Hepatocellular Carcinoma. Gastroenterology , 164 (5), 766～782. https://doi.org/10.1053/j.gastro.2023.01.033. Kale, S. R., Karande, G., Gudur, A., Garud, A., Patil, M. S., Patil, S., Kale, S. R., Karande, G., Gudur, A., Garud, A., Patil, M. S., & Patil, S. (2024). Recent trends in liver cancer: Epidemiology, risk factors, and diagnostic techniques. Cureus, 16(10). https://doi.org/10.7759/cureus.72239. Aziz, H., Brown, Z. J., Baghdadi, A., Kamel, I. R., & Pawlik, T. M. (2022). A comprehensive review of hepatic hemangioma management. Journal of Gastrointestinal Surgery, 26(9), 1998～2007. https://doi.org/10.1007/s11605-022-05382-1. Li, W., Li, R., Zhao, X., Lin, X., Yu, Y., Zhang, J., Chen, K., Chai, W., & Yan, F. (2021). Differentiation of hepatocellular carcinoma from hepatic hemangioma and focal nodular hyperplasia using computed tomographic spectral imaging. Journal of Clinical and Translational Hepatology, 0(0), 0～0. https://doi.org/10.14218/JCTH.2020.00173. Ni, Y., Chen, G., Feng, Z., Cui, H., Metaxas, D., Zhang, S., & Zhu, W. (2024). DA-tran: Multiphase liver tumor segmentation with a domain-adaptive transformer network. Pattern Recognition, 149, 110233. https://doi.org/10.1016/j.patcog.2023.110233. Xu, Y., Zhang, J., Wang, R., Cai, W., Zhao, P. & Guo, D. Analysis of adverse reaction reports for iodine contrast media. Chin. J. Med. Imaging. 28, 232–235 (2020). https://doi.org/10.3969/j.issn.1005-5185.2020.03.020. Franco, P. N., Spasiano, C. M., Maino, C., De Ponti, E., Ragusi, M., Giandola, T., Terrani, S., Peroni, M., Corso, R., & Ippolito, D. (2023). Principles and applications of dual-layer spectral CT in gastrointestinal imaging. Diagnostics (Basel, Switzerland), 13(10), 1740. https://doi.org/10.3390/diagnostics13101740. Asmundo, L., Rizzetto, F., Srinivas Rao, S., Sgrazzutti, C., Vicentin, I., Kambadakone, A., Catalano, O. A., & Vanzulli, A. (2024). Dual-energy CT applications on liver imaging: What radiologists and radiographers should know? A systematic review. Abdominal Radiology (New York), 49(11), 3811～3823. https://doi.org/10.1007/s00261-024-04380-y. Reimer, R. P., Große Hokamp, N., Fehrmann Efferoth, A., Krauskopf, A., Zopfs, D., Kröger, J. R., Persigehl, T., Maintz, D., & Bunck, A. C. (2021). Virtual monoenergetic images from spectral detector computed tomography facilitate washout assessment in arterially hyper-enhancing liver lesions. European Radiology, 31(5), 3468～3477. https://doi.org/10.1007/s00330-020-07379-3. Deng, J., Tang, Q., Wei, Q., Ruan, F., Li, X., & Long, L. (2025). Preoperative assessment of ki-67 expression in hepatocellular carcinoma using a multi-parametric spectral CT approach. Quantitative Imaging in Medicine and Surgery, 15(5), 4262273～4264273. https://doi.org/10.21037/qims-24-2313. Wang, M., Chen, H., Ma, Y., Bai, R., Gao, S., Yang, L., Guo, W., Zhang, C., Kang, C., Lan, Y., Sun, Y., Zhang, Y., Xiao, X., & Hou, Y. (2024). Dual-layer spectral-detector CT for detecting liver steatosis by using proton density fat fraction as reference. Insights into Imaging, 15(1), 210. https://doi.org/10.1186/s13244-024-01716-6. Zhu, Y., Feng, B., Cai, W., Wang, B., Meng, X., Wang, S., Ma, X., & Zhao, X. (2023). Prediction of microvascular invasion in solitary AFP-negative hepatocellular carcinoma ≤ 5 cm using a combination of imaging features and quantitative dual-layer spectral-detector CT parameters. Academic Radiology, 30 Suppl 1, S104～S116. https://doi.org/10.1016/j.acra.2023.02.015. Ota, T., Onishi, H., Fukui, H., Tsuboyama, T., Nakamoto, A., Honda, T., Matsumoto, S., Tatsumi, M., & Tomiyama, N. (2024). Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT. European Radiology, 35(3), 1361～1377. https://doi.org/10.1007/s00330-024-11024-8. Kim, T. K., Lee, E., & Jang, H.-J. (2015). Imaging findings of mimickers of hepatocellular carcinoma. Clinical and Molecular Hepatology, 21(4), 326～343. https://doi.org/10.3350/cmh.2015.21.4.326. Chernyak, V., Fowler, K. J., Kamaya, A., Kielar, A. Z., Elsayes, K. M., Bashir, M. R., Kono, Y., Do, R. K., Mitchell, D. G., Singal, A. G., Tang, A., & Sirlin, C. B. (2018). Liver imaging reporting and data system (LI-RADS) version 2018: Imaging of hepatocellular carcinoma in At-risk patients. Radiology , 289 (3), 816～830. https://doi.org/10.1148/radiol.2018181494. Ozaki, K., Higuchi, S., Kimura, H., & Gabata, T. (2022). Liver metastases: Correlation between imaging features and pathomolecular environments. Radiographics: A Review Publication of the Radiological Society of North America, Inc, 42(7), 1994～2013. https://doi.org/10.1148/rg.220056. Jia, C., Liu, G., Wang, X., Zhao, D., Li, R., & Li, H. (2021). Hepatic sclerosed hemangioma and sclerosing cavernous hemangioma: A radiological study. Japanese Journal of Radiology, 39(11), 1059～1068. https://doi.org/10.1007/s11604-021-01139-z. Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Jantawong, H., Krisanachinda, A., & Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. (2023). Hepatic lesion detectability in abdomen computed tomography: Investigation in low kVp single energy and low keV virtual monochromatic images generated from dual energy computed tomography using task-based image quality assessment in phantom. Journal of Associated Medical Sciences, 56(1), 141～149. https://doi.org/10.12982/JAMS.2023.029. Lenga, L., Lange, M., Arendt, C. T., Booz, C., Yel, I., Bodelle, B., D’Angelo, T., Hammerstingl, R. M., Huizinga, N. A., Vogl, T. J., Martin, S. S., & Albrecht, M. H. (2020). Measurement Reliability and Diagnostic Accuracy of Virtual Monoenergetic Dual-Energy CT in Patients with Colorectal Liver Metastases. Academic Radiology, 27(7), e168～e175. https://doi.org/10.1016/j.acra.2019.09.020. Liu, T., Hong, G., & Cai, W. (2021). A comparative study of effective atomic number calculations for dual-energy CT. Medical Physics, 48(10), 5908～5923. https://doi.org/10.1002/mp.15166. Maruyama, M., Hosogoshi, S., Maruyama, M., Araki, H., Yoshida, R., Ando, S., Nakamura, M., Yoshizako, T., & Kaji, Y. (2025). Utility of corrected effective atomic numbers in differentiating hepatocellular carcinomas and liver metastases from hepatic hemangiomas. Cureus, 17(4), e82478. https://doi.org/10.7759/cureus.82478. Maruyama, M., Hosogoshi, S., Maruyama, M., Araki, H., Yoshida, R., Ando, S., Nakamura, M., Yoshizako, T., & Kaji, Y. (2025). Utility of corrected effective atomic numbers in differentiating hepatocellular carcinomas and liver metastases from hepatic hemangiomas. Cureus, 17(4), e82478. https://doi.org/10.7759/cureus.82478. Schaeffer, C. J., Leon, S. M., Olguin, C. A., & Arreola, M. M. (2021). Accuracy and reproducibility of effective atomic number and electron density measurements from sequential dual energy CT. Medical Physics, 48(7), 3525～3539. https://doi.org/10.1002/mp.14916. Funabashi, N., Akaza, S., Shukuya, A., & Asano, H. (2023). Effective atomic number and electron density number acquired using latest spectral CT enable the differentiation of infarcted myocardium and hypertrophied myocardium from normal myocardium. Circulation, 148(Suppl_1), A18374～A18374. https://doi.org/10.1161/circ.148.suppl_1.18374. Kaichi, Y., Tatsugami, F., Nakamura, Y., Baba, Y., Iida, M., Higaki, T., Kiguchi, M., Tsushima, S., Yamasaki, F., Amatya, V. J., Takeshima, Y., Kurisu, K., & Awai, K. (2018). Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT. Medicine, 97(32), e11670. https://doi.org/10.1097/MD.0000000000011670. Chen, X., Xiao, B. & Cheng, H. Clinical value of B-mode ultrasound combined with serum D-dimer, albumin, and total bilirubin levels in evaluating liver cirrhosis and its progression to primary hepatocellular carcinoma. Chin. J. Health Lab. Technol. 30, 2489-2491 (2020). https://doi.org/1004-8685( 2020) 20-2489-03. Wang, S., Ma, Z., Lv, L., Yu, Q., Liu, S., & Lu, Y. (2025). Tumor microenvironment and metabolic reprogramming: Unraveling the complex interplay in gastrointestinal tumor liver metastasis. Frontiers in Endocrinology, 16. https://doi.org/10.3389/fendo.2025.1616661. Zhao, S., Bao, G., Gao, B., Xu, L., Liu, C. & Liu, Y. Diagnostic value of peripheral blood inflammatory markers in hepatocellular carcinoma. Chin. Med. 19, 1801-1805 (2024). https://doi.org/10. 3760 / j. issn. 1673-4777. 2024. 12. 009. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8010985","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541037907,"identity":"a913182e-6eef-4284-9c40-baccf16d6f5f","order_by":0,"name":"Qinghong Yan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qinghong","middleName":"","lastName":"Yan","suffix":""},{"id":541037908,"identity":"1c88f362-8108-491b-aa1c-414da1a13a60","order_by":1,"name":"Rui 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06:58:14","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85203,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8010985/v1/a7164303571cd78650042886.html"},{"id":95823536,"identity":"7dcf5244-1406-4ce0-8a2b-e2aca7e47586","added_by":"auto","created_at":"2025-11-13 11:04:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":442810,"visible":true,"origin":"","legend":"\u003cp\u003eA 36-year-old female patient was diagnosed with hepatic hemangioma (HH). Panel A displays a contrast-enhanced CT image acquired in the portal venous phase, revealing a 53-mm lesion in the right hepatic lobe with peripheral nodular enhancement. Panel B presents the effective atomic number (Z_eff) map obtained from non-contrast spectral CT, and Panel C illustrates the corresponding electron density (ED) map. Panels D–F show virtual monoenergetic images (VMIs) at 40 keV, 70 keV, and 140 keV, with CT value ranges of 110 HU, 95 HU, and 97 HU, respectively. The diagnosis of HH was established on the basis of these characteristic imaging findings on contrast-enhanced CT and remained stable over a 3-year follow-up period.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8010985/v1/2fb55874414b3e4d1e795582.png"},{"id":95823537,"identity":"dba5eb44-a76d-4a46-8108-0b0a0cd03ab7","added_by":"auto","created_at":"2025-11-13 11:04:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":407194,"visible":true,"origin":"","legend":"\u003cp\u003eA 39-year-old male patient diagnosed with hepatocellular carcinoma (HCC). Panel A presents a contrast-enhanced CT image in the arterial phase, revealing a 27-mm lesion in segments 5 and 6 of the liver with arterial phase hyperenhancement. Panel B illustrates the effective atomic number (Z_eff) map derived from non-contrast spectral CT, while Panel C displays the corresponding electron density (ED) map. Panels D–F exhibit virtual monoenergetic images (VMIs) at 40 keV, 70 keV, and 140 keV obtained from non-contrast spectral CT, with CT value ranges of 93 HU, 79 HU, and 74 HU, respectively. Histopathological examination of the biopsy specimen confirmed a diagnosis of moderately differentiated hepatocellular carcinoma.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8010985/v1/cbf66871e6fe56116d17387a.png"},{"id":96239016,"identity":"3266230d-7ded-48f1-9216-209e4a64c6fa","added_by":"auto","created_at":"2025-11-19 07:01:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418296,"visible":true,"origin":"","legend":"\u003cp\u003eA 72-year-old male patient with liver metastases originating from pancreatic cancer.Panel A presents a contrast-enhanced computed tomography (CT) image acquired during the portal venous phase, revealing a 54‐mm rim-enhancing mass located in segments 5 and 8 of the liver. Panel B displays an effective atomic number (Zeff) map derived from non-contrast spectral CT, and Panel C illustrates an electron density (ED) map obtained using the same modality. Panels D–F show virtual monoenergetic images (VMIs) at 40 keV, 70 keV, and 140 keV, respectively, with corresponding CT value ranges of 117 HU, 112 HU, and 110 HU. The diagnosis of pancreatic cancer liver metastasis was confirmed through biopsy in a patient with a known history of primary pancreatic cancer.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8010985/v1/44c032105b370e2d9742bcec.png"},{"id":95823538,"identity":"283fad00-ace4-47ef-9b5b-242cfe62200a","added_by":"auto","created_at":"2025-11-13 11:04:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83010,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the ROC curves for various parameters used to differentiate between benign and malignant liver tumors. (A) The areas under the curve (AUC) for the lymphocyte-to-monocyte ratio (LMR), Zeff range, and electron density are 0.75, 0.72, and 0.73, respectively. (B) The AUCs for the clinical model, imaging model, and combined model are 0.75, 0.79, and 0.85, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8010985/v1/bf1f3817d80a45082801f103.png"},{"id":96254408,"identity":"a68ff0b7-2d34-4fbb-b375-63142fb1092a","added_by":"auto","created_at":"2025-11-19 07:46:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2183401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8010985/v1/aeb068de-871e-40de-a757-6b0a136ee3b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differentiation of Benign and Malignant Liver Tumors Using Non-Enhanced Spectral CT Quantitative Parameters Combined with Inflammatory Indicators","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatic hemangioma (HH) is the most common benign liver tumor, whereas hepatocellular carcinoma (HCC) represents the most prevalent primary malignant tumor, and metastatic liver cancer (MLC) is the most frequently encountered malignant liver tumor. These entities demonstrate significant differences in clinical management and prognosis, underscoring the importance of accurate differentiation in clinical practice \u003csup\u003e[1\u0026ndash;3]\u003c/sup\u003e. Although computed tomography (CT) imaging plays a crucial role in distinguishing liver lesions, these lesions may exhibit similar morphological features on conventional non-contrast CT scans, considerably complicating accurate differentiation based solely on unenhanced imaging \u003csup\u003e[4]\u003c/sup\u003e. In contrast, contrast-enhanced CT, with its high sensitivity in detecting malignant tumors, substantially outperforms non-contrast CT and has become the primary diagnostic modality \u003csup\u003e[5]\u003c/sup\u003e. However, the use of iodinated contrast agents is associated with potential risks, including nephrotoxicity, allergic reactions, and, in some cases, severe, life-threatening adverse events \u003csup\u003e[6]\u003c/sup\u003e. Additionally, the high cost of multiphase contrast-enhanced CT examinations further contributes to the financial burden on patients.\u003c/p\u003e\u003cp\u003eDual-layer spectral detector computed tomography (SDCT) is an emerging spectral imaging modality that offers multiple image reconstruction options, including virtual monoenergetic images (VMI), effective atomic number (Z_eff) maps, and electron density (ED) maps. These capabilities not only enhance the precision of CT analyses but also provide a foundation for the multiparametric assessment of lesions \u003csup\u003e[7]\u003c/sup\u003e. Quantitative parameters derived from spectral CT are currently employed widely for the detection, differential diagnosis, and evaluation of treatment response in liver lesions \u003csup\u003e[8]\u003c/sup\u003e. For example, studies have demonstrated that VMI can enhance the contrast of liver metastases, while spectral CT has been applied to assess Ki-67 expression in hepatocellular carcinoma \u003csup\u003e[9,10]\u003c/sup\u003e. Furthermore, spectral CT has been utilized to evaluate hepatic steatosis and predict microvascular invasion in hepatocellular carcinoma \u003csup\u003e[11,12]\u003c/sup\u003e. However, the majority of these studies have relied on contrast-enhanced scans, and the potential of non-contrast spectral CT in differentiating liver lesions remains inadequately explored. Additionally, research has shown that inflammatory markers are significant in the diagnosis and prognostic evaluation of various malignancies, including liver cancer. Although previous investigations have used non-contrast spectral CT parameters to differentiate between benign and malignant liver lesions, these studies have primarily focused on imaging parameters and have not integrated a comprehensive analysis with clinical parameters \u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccordingly, this study aims to investigate the efficacy of combining non-contrast spectral CT quantitative parameters with inflammatory markers in differentiating benign from malignant liver tumors, and to assess its diagnostic performance. This approach not only has the potential to streamline the diagnostic workflow for liver lesions\u0026mdash;thereby reducing patient exposure to iodine-based contrast agents and alleviating financial burdens\u0026mdash;but also to provide a more comprehensive and precise basis for differential diagnosis in clinical practice.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Patients\u003c/h2\u003e\u003cp\u003eA retrospective study was performed on patients with liver lesions who underwent abdominal dual-layer detector spectral CT scans at our hospital from January 1, 2025, to June 30, 2025. The inclusion criteria were as follows: (1) patients who received both non-contrast and contrast-enhanced spectral CT scans prior to any treatment; and (2) lesions with a diameter greater than 1 cm. The exclusion criteria comprised: (1) patients with liver lesions other than hepatic hemangioma (HH), hepatocellular carcinoma (HCC), or metastatic liver cancer (MLC); (2) patients who had received radiotherapy or chemotherapy; and (3) patients with infectious conditions other than chronic hepatitis. Ultimately, 119 patients were enrolled. Patients with hepatic hemangioma were classified as the benign group, while those with hepatocellular carcinoma or metastatic liver cancer were assigned to the malignant group. Retrospective clinical data, including gender, age, albumin-to-bilirubin ratio (ABR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), were collected for both groups.\u003c/p\u003e\u003cp\u003e This study received approval from the Institutional Review Board of our hospital (ethical approval number: IIT-R-20250174). Owing to the retrospective design of the study, the requirement to obtain informed consent was waived.\u003c/p\u003e\u003cp\u003eFor liver lesions lacking pathological confirmation, the following imaging-based diagnostic criteria were employed. Hepatic hemangiomas were characterized on multiphase contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) as well-defined masses with peripheral nodular enhancement that progressed centripetally over time. On T2-weighted imaging, these lesions typically appeared hyperintense, often exhibiting the \"light bulb\" sign\u003csup\u003e[14]\u003c/sup\u003e. Hepatocellular carcinoma (HCC) was identified by its non-peripheral arterial phase hyperenhancement, followed by non-peripheral washout during the portal venous and delayed phases, with the presence of an enhancing capsule on multiphase imaging further supporting the diagnosis\u003csup\u003e[15]\u003c/sup\u003e. Metastatic liver lesions were recognized as either hypervascular or hypovascular, contingent on the vascularity of the primary tumor, with peripheral rim enhancement and central necrosis commonly observed on contrast-enhanced CT or MRI. Additionally, all cases of metastatic liver cancer were accompanied by a confirmed history of primary malignancy\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 CT protocol\u003c/h2\u003e\u003cp\u003eAll patients underwent non-contrast and triple-phase dynamic contrast-enhanced abdominal imaging using a dual-layer detector spectral CT scanner (IQon Spectral CT, Philips Healthcare, the Netherlands). The scanning range extended from the dome of the diaphragm to the symphysis pubis. The scanning parameters were as follows: a tube voltage of 120 kVp, automatic tube current modulation (80\u0026ndash;210 mAs), a collimator width of 64 \u0026times; 0.625 mm, a gantry rotation speed of 0.5 s per rotation, and a pitch of 0.985. The non-ionic contrast agent iodixanol (320 mgI/mL) was administered via a dual-syringe power injector at a dose of 1.5 mL/kg and a flow rate of 3 mL/s, followed by a 20 mL saline flush at the same flow rate. For the arterial phase, a region of interest was placed in the abdominal aorta at the level of the diaphragmatic dome, with a trigger threshold set at 150 HU. Venous and delayed phase images were acquired 30 seconds and 120 seconds, respectively, after the arterial phase scan.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Spectral CT analysis\u003c/h2\u003e\u003cp\u003eThe spectral CT images from non-contrast and triple-phase enhanced scans were imported into the IntelliSpace Portal (ISP) Version 10.1 post-processing workstation for analysis. Using the Spectral CT Viewer application, virtual monoenergetic images (VMIs) at 40 keV, 70 keV, and 140 keV were generated, along with effective atomic number (Zeff) maps and electron density (ED) maps. On the non-contrast images, regions of interest (ROIs) were delineated on the slice with the largest cross-sectional area of the lesion, as well as on the two adjacent slices above and below. For lesions with ill-defined boundaries, the enhanced scan images were used as a reference. During ROI placement, areas corresponding to hemorrhage, necrosis, and calcification were carefully avoided, and the ROI was positioned to encompass as much of the lesion as possible. Two radiology residents independently performed the measurements, and the average of their values was used for subsequent analysis; interobserver consistency was subsequently assessed. In cases where multiple lesions were present, only the largest lesion was measured, given that smaller lesions are often difficult to identify on non-contrast images and challenging to delineate with ROIs. The measured parameters included the mean and range of CT values on the VMIs, as well as the mean, maximum, minimum, and range of effective atomic numbers, electron density values, and the spectral curve slope between 40 keV and 140 keV. The range was defined as the difference between the maximum and minimum values. The spectral curve slope (λHU) was calculated using the following formula: λHU = (CT40 keV\u0026thinsp;\u0026minus;\u0026thinsp;CT140 keV) / 100.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Feature Selection and Model Construction\u003c/h2\u003e\u003cp\u003eAll clinical parameters and spectral CT parameters were initially included in a univariate logistic regression analysis. Variables demonstrating significant differences in the univariate analysis were subsequently incorporated into a multivariate logistic regression analysis. Those indicators that maintained statistical significance in the multivariate analysis were identified as independent predictors for distinguishing between benign and malignant liver tumors.Based on these independent predictors, binary logistic regression models were constructed. Specifically, a clinical model was developed using only clinical predictors, an imaging model was constructed exclusively with spectral CT parameters, and a combined model was established by integrating both clinical and imaging predictors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using SPSS version 27.0 and MedCalc version 23.1.7. Continuous variables with a normal distribution are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̅ \u0026plusmn; S), whereas non-normally distributed data are presented as the median with interquartile range [M (P25, P75)]. Categorical variables are reported as frequencies (percentages). Normality of continuous variables was evaluated separately in both the benign and malignant tumor groups. For normally distributed variables, comparisons between groups were performed using the independent samples t-test or an adjusted t'-test, whereas the Mann-Whitney U test was employed for non-normally distributed variables. Categorical variables were compared using either the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. Inter-observer agreement was determined via the intraclass correlation coefficient (ICC). The diagnostic performance of the models was assessed based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Comparisons of diagnostic efficiency between different models were carried out using the DeLong test. A two-sided P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Study population\u003c/h2\u003e\u003cp\u003eA total of 119 patients with liver lesions were included in this study, comprising 88 males and 31 females, with a mean age of 60.39\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59 years (range, 30\u0026ndash;90 years). The clinical characteristics of all patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cohort consisted of 63 patients with hepatic hemangioma (HH), 25 with hepatocellular carcinoma (HCC), and 31 with metastatic liver cancer (MLC).\u003c/p\u003e\u003cp\u003eAmong the 63 cases of HH, the diagnosis was confirmed by biopsy in 2 patients; by a combination of ultrasonography, contrast-enhanced spectral computed tomography (CT), and magnetic resonance imaging (MRI) in 7 patients; by contrast-enhanced spectral CT and MRI in 5 patients; by ultrasonography and contrast-enhanced spectral CT in 23 patients; and by contrast-enhanced spectral CT alone in the remaining cases. Additionally, 26 cases exhibited no lesion progression during a follow-up period exceeding one year.\u003c/p\u003e\u003cp\u003eFor the 25 cases of HCC, the diagnosis was established via biopsy in 5 patients, surgical resection in 2 patients, a combination of contrast-enhanced ultrasound, spectral CT, and MRI in 4 patients, contrast-enhanced spectral CT and MRI in 8 patients, and contrast-enhanced ultrasound and spectral CT in 6 patients.\u003c/p\u003e\u003cp\u003eAmong the 31 MLC cases, the diagnosis was confirmed by biopsy in 10 patients; by contrast-enhanced spectral CT and MRI in 2 patients; by ultrasonography and contrast-enhanced spectral CT in 8 patients; and by contrast-enhanced spectral CT alone in the remaining cases. Thirteen cases were newly identified during follow-up, while 18 cases exhibited lesion progression. The primary tumors included colorectal cancer (8 cases), gastric cancer (12 cases), pancreatic cancer (5 cases), gastrointestinal stromal tumor (3 cases), and lung cancer (3 cases).\u003c/p\u003e\u003cp\u003ePatients with hepatic hemangioma were assigned to the benign group, whereas those with hepatocellular carcinoma or metastatic liver cancer were classified into the malignant group. Univariate analysis revealed that age, the Albumin-to-Bilirubin Ratio (ABR), the Neutrophil-to-Lymphocyte Ratio (NLR), and the Lymphocyte-to-Monocyte Ratio (LMR) differed significantly between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003epresents the baseline clinical characteristics of 119 patients diagnosed with liver lesions.\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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign Group (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMalignant Group (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.98\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.09\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43(43/63,68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45(45/56,80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.133\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\u003e20(20/63,32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(11/56,20%)\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\u003eABR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.31(2.59,4.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.51(1.20,3.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.94(1.39,2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.80(2.05,4.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.45(3.23,6.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.66(1.83,3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118.44(97.01,153.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.89(86.28,185.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eABR (albumin-to-bilirubin ratio); NLR (neutrophil-to-lymphocyte ratio); LMR (lymphocyte-to-monocyte ratio); and PLR (platelet-to-lymphocyte ratio).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Spectral CT analysis\u003c/h2\u003e\u003cp\u003eThe intraclass correlation coefficient (ICC) for spectral CT parameters ranged from 0.77 to 0.84, indicating good to excellent inter-observer agreement. Univariate analysis revealed statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in several spectral CT parameters between benign and malignant groups. Specifically, these parameters included the range of CT values on 40 keV, 70 keV, and 140 keV virtual monoenergetic images (VMIs); the mean CT value on 140 keV VMIs; the maximum effective atomic number (Zeff-max); the range of Zeff values; and electron density (ED). Detailed results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Spectral CT Parameters Between Benign and Malignant Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign Group (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMalignant Group (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003csub\u003e40 keV\u003c/sub\u003e- mean (HU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e28.69\u0026thinsp;\u0026plusmn;\u0026thinsp;8.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e27.49\u0026thinsp;\u0026plusmn;\u0026thinsp;13.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003csub\u003e40 keV\u003c/sub\u003e-range(HU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e64.24\u0026thinsp;\u0026plusmn;\u0026thinsp;19.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e101.66\u0026thinsp;\u0026plusmn;\u0026thinsp;108.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003csub\u003e70 keV\u003c/sub\u003e- mean (HU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e41.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e39.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003csub\u003e70 keV\u003c/sub\u003e-range(HU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e59.89\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e79.38\u0026thinsp;\u0026plusmn;\u0026thinsp;22.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003csub\u003e140 keV\u003c/sub\u003e- mean (HU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e46.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e43.38\u0026thinsp;\u0026plusmn;\u0026thinsp;8.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT\u003csub\u003e140 keV\u003c/sub\u003e-range(HU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e55.84\u0026thinsp;\u0026plusmn;\u0026thinsp;17.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e77.96\u0026thinsp;\u0026plusmn;\u0026thinsp;22.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eλ\u003csub\u003eHU\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e-0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED(%EDW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e104.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e104.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCT40 keV denotes the CT value measured at 40 keV; CT70 keV denotes the CT value measured at 70 keV; CT140 keV denotes the CT value measured at 140 keV; λHU represents the spectral curve slope; Zeff signifies the effective atomic number; ED indicates the electron density; and ICC denotes the intraclass correlation coefficient.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Model Construction and Evaluation\u003c/h2\u003e\u003cp\u003eIndependent predictors identified via univariate and multivariate logistic regression analyses of all clinical and spectral computed tomography (CT) parameters included the lymphocyte-to-monocyte ratio (LMR) among clinical factors and both the effective atomic number range (Zeff-range) and electron density (ED) among spectral CT parameters (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on these predictors, a clinical model was constructed using the clinical parameter, an imaging model was built using the spectral CT parameters, and a combined model was developed integrating both sets of predictors. Receiver operating characteristic (ROC) curve analysis indicated that the combined model achieved the highest diagnostic performance, with an area under the curve (AUC) of 0.85 (95% confidence interval: 0.78\u0026ndash;0.91). Comparison using the DeLong test confirmed the superior diagnostic efficacy of the combined model, while no significant difference was observed between the imaging model and the clinical model alone (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eUnivariate and Multivariate Logistic Regression Analysis of Spectral Computed Tomography and Clinical Parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariable analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003cp\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariable analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\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\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026thinsp;~\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u0026thinsp;~\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u0026thinsp;~\u0026thinsp;1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eABR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u0026thinsp;~\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u0026thinsp;~\u0026thinsp;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72\u0026thinsp;~\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.46\u0026thinsp;~\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.41\u0026thinsp;~\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99\u0026thinsp;~\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT 40 keV- mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u0026thinsp;~\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT 40 keV-range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03\u0026thinsp;~\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u0026thinsp;~\u0026thinsp;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT 70 keV- mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u0026thinsp;~\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT 70 keV-range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03\u0026thinsp;~\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65\u0026thinsp;~\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT140 keV- mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u0026thinsp;~\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u0026thinsp;~\u0026thinsp;3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT140 keV-range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03\u0026thinsp;~\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76\u0026thinsp;~\u0026thinsp;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eλHU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u0026thinsp;~\u0026thinsp;1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u0026thinsp;~\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.59\u0026thinsp;~\u0026thinsp;9.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u0026thinsp;~\u0026thinsp;7.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.73\u0026thinsp;~\u0026thinsp;7.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.01\u0026thinsp;~\u0026thinsp;4.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED(%EDW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u0026thinsp;~\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026thinsp;~\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eABR (albumin-to-bilirubin ratio); NLR (neutrophil-to-lymphocyte ratio); LMR (lymphocyte-to-monocyte ratio); and PLR (platelet-to-lymphocyte ratio). CT40 keV, CT70 keV, and CT140 keV refer to the computed tomography (CT) values measured at 40 keV, 70 keV, and 140 keV, respectively. Furthermore, λHU denotes the spectral curve slope; Zeff signifies the effective atomic number; and ED indicates the electron density.\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\u003eUnivariate and Multivariate Logistic Regression Analyses of Spectral CT and Clinical Parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpeciality\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYouden index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDelong\u003c/p\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZeff-range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72(0.63\u0026thinsp;~\u0026thinsp;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73(0.64\u0026thinsp;~\u0026thinsp;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.551\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75(0.67\u0026thinsp;~\u0026thinsp;0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImaging model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79(0.70\u0026thinsp;~\u0026thinsp;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85(0.78\u0026thinsp;~\u0026thinsp;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eZeff denotes the effective atomic number; ED represents the electron density; AUC refers to the area under the curve; and CI stands for the confidence interval.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e clinical model vs. imaging model; \u003csup\u003eb\u003c/sup\u003e imaging model vs. combination model; \u003csup\u003ec\u003c/sup\u003e combination model vs. clinical model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHepatocellular carcinoma is the most common primary malignant liver tumor and is particularly prevalent among patients with chronic hepatitis B or liver cirrhosis. In contrast, liver metastases represent the most frequent secondary malignant tumors of the liver and are often identified in patients with a history of other malignancies. These patients typically require long-term follow-up to monitor disease progression, and the differential diagnosis frequently necessitates distinguishing these lesions from hepatic hemangiomas. Although contrast-enhanced computed tomography (CT) is commonly employed for imaging, it increases patients’\u0026nbsp;financial burden and its reliance on conventional imaging features poses limitations for accurate differentiation\u003csup\u003e[17]\u003c/sup\u003e. To address these issues, our study analyzed CT values, effective atomic numbers, and electron densities derived from virtual monoenergetic images (at 40 keV, 70 keV, and 140 keV) based on non-contrast CT scans. These parameters were integrated with inflammatory markers—including the albumin-to-total bilirubin ratio (ABR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)—to assess their potential utility for differentiating benign and malignant liver lesions. The objective is to establish a more convenient, cost-effective, and reliable method for clinical differential diagnosis.\u003c/p\u003e\n\u003cp\u003eThis study identified statistically significant differences between benign and malignant groups in the range of virtual monoenergetic computed tomography (CT) values at 40, 70, and 140 keV; the mean CT value at 140 keV; the maximum value and range of effective atomic number (Zeff); and electron density (ED). Additionally, the findings indicate that low-energy virtual monoenergetic imaging (VMI) enhances lesion contrast and image quality, thereby improving both the detection rate and diagnostic accuracy for liver lesions \u003csup\u003e[18]\u003c/sup\u003e.\u0026nbsp;This study found that the range of CT values at 40 keV, 70 keV, and 140 keV was significantly greater in malignant tumors than in benign lesions, with the range increasing at lower energy levels. These findings suggest that low‐energy virtual monoenergetic imaging (VMI) more effectively elucidates the subtle variations in tissue composition arising from the intrinsic heterogeneity of malignant lesions \u003csup\u003e[19]\u003c/sup\u003e.\u0026nbsp;Low-energy VMI effectively captures tissue heterogeneity, thereby differentiating benign from malignant lesions. Previous research indicated that only the mean CT value at 140 keV exhibited a statistically significant difference between benign and malignant liver tumor groups, with benign lesions displaying higher values—a finding consistent with our study. However, that research did not investigate the significance of the VMI value range between the two groups\u003csup\u003e[13]\u003c/sup\u003e. Moreover, the effective atomic number (Zeff) reflects the weighted average atomic number of tissues within the X-ray energy spectrum and is closely linked to material composition, thereby facilitating the differentiation of tissues with distinct elemental profiles\u003csup\u003e[20]\u003c/sup\u003e.\u0026nbsp;The study found no significant differences in the mean and minimum effective atomic number (Zeff) between benign and malignant lesions. However, malignant lesions exhibited significantly higher maximum Zeff values and broader Zeff ranges compared to benign lesions. These findings are consistent with previous research, which suggests that the increased heterogeneity of malignant lesions primarily accounts for their wider Zeff range\u003csup\u003e[13,21]\u003c/sup\u003e. Malignant tumors often display abnormal angiogenesis and diverse histological changes including hypervascularity, necrosis, hemorrhage, cystic degeneration, and fibrosis, all of which contribute to internal structural and compositional complexity and, consequently, to greater Zeff variability. In contrast, benign lesions generally possess a more homogeneous tissue architecture, resulting in a narrower Zeff range.Additionally, our findings identified electron density (ED) as an independent predictor for differentiating between benign and malignant liver tumors. ED reflects the tissue’s molecular composition and cellular density\u003csup\u003e[23]\u003c/sup\u003e. Previous studies have demonstrated that ED can enhance the visualization of pulmonary embolism, improve diagnostic efficacy, and effectively discriminate between normal and infarcted myocardium\u003csup\u003e[23,24]\u003c/sup\u003e. In this study, the benign lesions exhibited higher ED values compared to the malignant lesions, which may be attributable to the more compact or regular microstructural organization observed in benign tumors\u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAmong the inflammatory markers, ABR, NLR, and LMR exhibited statistically significant differences between the two groups. Chen et al. reported that an elevated total bilirubin-to-albumin ratio, which corresponds to a decreased ABR, is associated with severe impairment of liver function\u003csup\u003e[26]\u003c/sup\u003e. This finding is consistent with our results and may be explained by the fact that hepatocellular carcinoma often arises in the context of liver cirrhosis, a condition known to compromise hepatic function\u003csup\u003e[17]\u003c/sup\u003e. Moreover, liver metastases can further impair liver function, particularly when there is a substantial tumor burden, resulting in abnormalities in liver function parameters\u003csup\u003e[27]\u003c/sup\u003e. Previous research suggests that NLR reflects the dynamic balance between neutrophil-mediated tumor inflammation and lymphocyte-dependent antitumor immunity, with higher NLR levels correlating with increased tumor malignancy. This observation aligns with our findings\u003csup\u003e[28]\u003c/sup\u003e. Additionally, our study identified LMR as an independent predictive factor for differentiating benign from malignant liver tumors. Zhao et al. demonstrated that LMR serves as a significant protective indicator, with each unit increase in LMR associated with an approximately 33% reduction in the risk of developing hepatocellular carcinoma\u003csup\u003e[28]\u003c/sup\u003e. This observation may be attributable to the role of tumor-associated macrophages, which promote angiogenesis and lymphangiogenesis, thereby accelerating tumor cell proliferation while simultaneously modulating the immune response, which influences tumor initiation, recurrence, and metastasis\u003csup\u003e[28]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study integrated spectral CT parameters with inflammatory indices, thereby enhancing the reliability and clinical utility of differential diagnoses. The findings indicate that both the imaging model, based on non-contrast spectral CT quantitative parameters (Zeff-range and ED) with an AUC of 0.79 (95% CI, 0.70-0.86), and the clinical model, developed using the inflammatory marker LMR with an AUC of 0.75 (95% CI, 0.67-0.83), effectively distinguished benign from malignant liver lesions. Notably, the combined model, which incorporated both imaging and inflammatory variables, achieved the highest diagnostic performance (AUC=0.85, 95% CI, 0.78-0.91).These results suggest that the integration of spectral CT-derived quantitative data with systemic inflammatory markers offers a robust and non-invasive strategy for the discrimination of liver lesions. Furthermore, the superior performance of the combined model underscores the complementary roles of anatomical/quantitative imaging and pathophysiological inflammatory status in enhancing diagnostic precision.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, it is a single-center, retrospective investigation with a relatively limited sample size; future studies should expand the case cohort and incorporate multi-center validation. Furthermore, pathological diagnoses were not available for all cases. However, obtaining pathological confirmation for every case is challenging in clinical practice, as benign lesions typically do not require surgical intervention and not all hepatocellular carcinoma cases undergo resection. Consequently, diagnoses in this study relied on established imaging criteria when pathological confirmation was unavailable. Most malignant liver lesions identified solely through imaging were monitored over time, with confirmation obtained by observing indicators such as tumor growth or changes in size in response to treatment. Finally, although this approach is suitable for screening or initial assessment of liver diseases, contrast-enhanced computed tomography remains essential when clinical intervention is warranted, particularly for evaluating tumor vascularity and its relationship with adjacent vessels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY is responsible for designing the research methodology, conducting the experimental investigations, and drafting the initial version of the paper.G, C, and X are responsible for data collection, measurement, analysis, and chart visualization.J is responsible for formulating the overall research objectives and aims, supervising the thesis, and serving as the corresponding author.All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eToh, M. R., Wong, E. Y. T., Wong, S. H., Ng, A. W. T., Loo, L.-H., Chow, P. K.-H., \u0026amp; Ngeow, J. (2023). Global Epidemiology and Genetics of Hepatocellular Carcinoma. \u003cem\u003eGastroenterology\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e(5), 766～782. https://doi.org/10.1053/j.gastro.2023.01.033.\u003c/li\u003e\n\u003cli\u003eKale, S. R., Karande, G., Gudur, A., Garud, A., Patil, M. S., Patil, S., Kale, S. R., Karande, G., Gudur, A., Garud, A., Patil, M. S., \u0026amp; Patil, S. (2024). Recent trends in liver cancer: Epidemiology, risk factors, and diagnostic techniques. Cureus, 16(10). https://doi.org/10.7759/cureus.72239.\u003c/li\u003e\n\u003cli\u003eAziz, H., Brown, Z. J., Baghdadi, A., Kamel, I. R., \u0026amp; Pawlik, T. M. (2022). A comprehensive review of hepatic hemangioma management. Journal of Gastrointestinal Surgery, 26(9), 1998～2007. https://doi.org/10.1007/s11605-022-05382-1.\u003c/li\u003e\n\u003cli\u003eLi, W., Li, R., Zhao, X., Lin, X., Yu, Y., Zhang, J., Chen, K., Chai, W., \u0026amp; Yan, F. (2021). Differentiation of hepatocellular carcinoma from hepatic hemangioma and focal nodular hyperplasia using computed tomographic spectral imaging. Journal of Clinical and Translational Hepatology, 0(0), 0～0. https://doi.org/10.14218/JCTH.2020.00173.\u003c/li\u003e\n\u003cli\u003eNi, Y., Chen, G., Feng, Z., Cui, H., Metaxas, D., Zhang, S., \u0026amp; Zhu, W. (2024). DA-tran: Multiphase liver tumor segmentation with a domain-adaptive transformer network. Pattern Recognition, 149, 110233. https://doi.org/10.1016/j.patcog.2023.110233.\u003c/li\u003e\n\u003cli\u003eXu, Y., Zhang, J., Wang, R., Cai, W., Zhao, P. \u0026amp; Guo, D. Analysis of adverse reaction reports for iodine contrast media. Chin. J. Med. Imaging. 28, 232\u0026ndash;235 (2020). https://doi.org/10.3969/j.issn.1005-5185.2020.03.020.\u003c/li\u003e\n\u003cli\u003eFranco, P. N., Spasiano, C. M., Maino, C., De Ponti, E., Ragusi, M., Giandola, T., Terrani, S., Peroni, M., Corso, R., \u0026amp; Ippolito, D. (2023). Principles and applications of dual-layer spectral CT in gastrointestinal imaging. Diagnostics (Basel, Switzerland), 13(10), 1740. https://doi.org/10.3390/diagnostics13101740.\u003c/li\u003e\n\u003cli\u003eAsmundo, L., Rizzetto, F., Srinivas Rao, S., Sgrazzutti, C., Vicentin, I., Kambadakone, A., Catalano, O. A., \u0026amp; Vanzulli, A. (2024). Dual-energy CT applications on liver imaging: What radiologists and radiographers should know? A systematic review. Abdominal Radiology (New York), 49(11), 3811～3823. https://doi.org/10.1007/s00261-024-04380-y.\u003c/li\u003e\n\u003cli\u003eReimer, R. P., Gro\u0026szlig;e Hokamp, N., Fehrmann Efferoth, A., Krauskopf, A., Zopfs, D., Kr\u0026ouml;ger, J. R., Persigehl, T., Maintz, D., \u0026amp; Bunck, A. C. (2021). Virtual monoenergetic images from spectral detector computed tomography facilitate washout assessment in arterially hyper-enhancing liver lesions. European Radiology, 31(5), 3468～3477. https://doi.org/10.1007/s00330-020-07379-3.\u003c/li\u003e\n\u003cli\u003eDeng, J., Tang, Q., Wei, Q., Ruan, F., Li, X., \u0026amp; Long, L. (2025). Preoperative assessment of ki-67 expression in hepatocellular carcinoma using a multi-parametric spectral CT approach. Quantitative Imaging in Medicine and Surgery, 15(5), 4262273～4264273. https://doi.org/10.21037/qims-24-2313.\u003c/li\u003e\n\u003cli\u003eWang, M., Chen, H., Ma, Y., Bai, R., Gao, S., Yang, L., Guo, W., Zhang, C., Kang, C., Lan, Y., Sun, Y., Zhang, Y., Xiao, X., \u0026amp; Hou, Y. (2024). Dual-layer spectral-detector CT for detecting liver steatosis by using proton density fat fraction as reference. Insights into Imaging, 15(1), 210. https://doi.org/10.1186/s13244-024-01716-6.\u003c/li\u003e\n\u003cli\u003eZhu, Y., Feng, B., Cai, W., Wang, B., Meng, X., Wang, S., Ma, X., \u0026amp; Zhao, X. (2023). Prediction of microvascular invasion in solitary AFP-negative hepatocellular carcinoma \u0026le; 5 cm using a combination of imaging features and quantitative dual-layer spectral-detector CT parameters. Academic Radiology, 30 Suppl 1, S104～S116. https://doi.org/10.1016/j.acra.2023.02.015.\u003c/li\u003e\n\u003cli\u003eOta, T., Onishi, H., Fukui, H., Tsuboyama, T., Nakamoto, A., Honda, T., Matsumoto, S., Tatsumi, M., \u0026amp; Tomiyama, N. (2024). Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT. European Radiology, 35(3), 1361～1377. https://doi.org/10.1007/s00330-024-11024-8.\u003c/li\u003e\n\u003cli\u003eKim, T. K., Lee, E., \u0026amp; Jang, H.-J. (2015). Imaging findings of mimickers of hepatocellular carcinoma. Clinical and Molecular Hepatology, 21(4), 326～343. https://doi.org/10.3350/cmh.2015.21.4.326.\u003c/li\u003e\n\u003cli\u003eChernyak, V., Fowler, K. J., Kamaya, A., Kielar, A. Z., Elsayes, K. M., Bashir, M. R., Kono, Y., Do, R. K., Mitchell, D. G., Singal, A. G., Tang, A., \u0026amp; Sirlin, C. B. (2018). Liver imaging reporting and data system (LI-RADS) version 2018: Imaging of hepatocellular carcinoma in At-risk patients. \u003cem\u003eRadiology\u003c/em\u003e, \u003cem\u003e289\u003c/em\u003e(3), 816～830. https://doi.org/10.1148/radiol.2018181494.\u003c/li\u003e\n\u003cli\u003eOzaki, K., Higuchi, S., Kimura, H., \u0026amp; Gabata, T. (2022). Liver metastases: Correlation between imaging features and pathomolecular environments. Radiographics: A Review Publication of the Radiological Society of North America, Inc, 42(7), 1994～2013. https://doi.org/10.1148/rg.220056.\u003c/li\u003e\n\u003cli\u003eJia, C., Liu, G., Wang, X., Zhao, D., Li, R., \u0026amp; Li, H. (2021). Hepatic sclerosed hemangioma and sclerosing cavernous hemangioma: A radiological study. Japanese Journal of Radiology, 39(11), 1059～1068. https://doi.org/10.1007/s11604-021-01139-z.\u003c/li\u003e\n\u003cli\u003eFaculty of Medicine, Chulalongkorn University, Bangkok, Thailand., Jantawong, H., Krisanachinda, A., \u0026amp; Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. (2023). Hepatic lesion detectability in abdomen computed tomography: Investigation in low kVp single energy and low keV virtual monochromatic images generated from dual energy computed tomography using task-based image quality assessment in phantom. Journal of Associated Medical Sciences, 56(1), 141～149. https://doi.org/10.12982/JAMS.2023.029.\u003c/li\u003e\n\u003cli\u003eLenga, L., Lange, M., Arendt, C. T., Booz, C., Yel, I., Bodelle, B., D\u0026rsquo;Angelo, T., Hammerstingl, R. M., Huizinga, N. A., Vogl, T. J., Martin, S. S., \u0026amp; Albrecht, M. H. (2020). Measurement Reliability and Diagnostic Accuracy of Virtual Monoenergetic Dual-Energy CT in Patients with Colorectal Liver Metastases. Academic Radiology, 27(7), e168～e175. https://doi.org/10.1016/j.acra.2019.09.020.\u003c/li\u003e\n\u003cli\u003eLiu, T., Hong, G., \u0026amp; Cai, W. (2021). A comparative study of effective atomic number calculations for dual-energy CT. Medical Physics, 48(10), 5908～5923. https://doi.org/10.1002/mp.15166.\u003c/li\u003e\n\u003cli\u003eMaruyama, M., Hosogoshi, S., Maruyama, M., Araki, H., Yoshida, R., Ando, S., Nakamura, M., Yoshizako, T., \u0026amp; Kaji, Y. (2025). Utility of corrected effective atomic numbers in differentiating hepatocellular carcinomas and liver metastases from hepatic hemangiomas. Cureus, 17(4), e82478. https://doi.org/10.7759/cureus.82478.\u003c/li\u003e\n\u003cli\u003eMaruyama, M., Hosogoshi, S., Maruyama, M., Araki, H., Yoshida, R., Ando, S., Nakamura, M., Yoshizako, T., \u0026amp; Kaji, Y. (2025). Utility of corrected effective atomic numbers in differentiating hepatocellular carcinomas and liver metastases from hepatic hemangiomas. Cureus, 17(4), e82478. https://doi.org/10.7759/cureus.82478.\u003c/li\u003e\n\u003cli\u003eSchaeffer, C. J., Leon, S. M., Olguin, C. A., \u0026amp; Arreola, M. M. (2021). Accuracy and reproducibility of effective atomic number and electron density measurements from sequential dual energy CT. Medical Physics, 48(7), 3525～3539. https://doi.org/10.1002/mp.14916.\u003c/li\u003e\n\u003cli\u003eFunabashi, N., Akaza, S., Shukuya, A., \u0026amp; Asano, H. (2023). Effective atomic number and electron density number acquired using latest spectral CT enable the differentiation of infarcted myocardium and hypertrophied myocardium from normal myocardium. Circulation, 148(Suppl_1), A18374～A18374. https://doi.org/10.1161/circ.148.suppl_1.18374.\u003c/li\u003e\n\u003cli\u003eKaichi, Y., Tatsugami, F., Nakamura, Y., Baba, Y., Iida, M., Higaki, T., Kiguchi, M., Tsushima, S., Yamasaki, F., Amatya, V. J., Takeshima, Y., Kurisu, K., \u0026amp; Awai, K. (2018). Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT. Medicine, 97(32), e11670. https://doi.org/10.1097/MD.0000000000011670.\u003c/li\u003e\n\u003cli\u003eChen, X., Xiao, B. \u0026amp; Cheng, H. Clinical value of B-mode ultrasound combined with serum D-dimer, albumin, and total bilirubin levels in evaluating liver cirrhosis and its progression to primary hepatocellular carcinoma. Chin. J. Health Lab. Technol. 30, 2489-2491 (2020). https://doi.org/1004-8685( 2020) 20-2489-03.\u003c/li\u003e\n\u003cli\u003eWang, S., Ma, Z., Lv, L., Yu, Q., Liu, S., \u0026amp; Lu, Y. (2025). Tumor microenvironment and metabolic reprogramming: Unraveling the complex interplay in gastrointestinal tumor liver metastasis. Frontiers in Endocrinology, 16. https://doi.org/10.3389/fendo.2025.1616661.\u003c/li\u003e\n\u003cli\u003eZhao, S., Bao, G., Gao, B., Xu, L., Liu, C. \u0026amp; Liu, Y. Diagnostic value of peripheral blood inflammatory markers in hepatocellular carcinoma. Chin. Med. 19, 1801-1805 (2024). https://doi.org/10. 3760 / j. issn. 1673-4777. 2024. 12. 009.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatic Hemangioma, Hepatocellular Carcinoma, Metastatic Liver Cancer, Spectral CT, Differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8010985/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8010985/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eThis study aims to evaluate the effectiveness of using quantitative parameters from non-contrast dual-layer spectral CT combined with inflammatory markers to differentiate between benign and malignant hepatic tumors and to assess their diagnostic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods \u003c/strong\u003eA retrospective analysis was conducted on 119 patients with hepatic lesions who underwent dual-layer spectral CT scanning. Clinical data, including gender, age, albumin-to-total bilirubin ratio (ABR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), were collected. Measurement of spectral CT parameters in lesions on plain scan images, including CT values of virtual monoenergetic images at 40 keV, 70 keV, and 140 keV, effective atomic number, and electron density. Clinical and spectral CT parameters were screened using logistic regression, and the discriminative efficacy of these parameters in differentiating hepatic vascular carcinoma from hepatocellular carcinoma was evaluated using the area under the ROC curve alongside logistic regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eMultivariable logistic regression identified the lymphocyte-to-monocyte ratio (LMR), Zeff-range, and electron density as independent predictors for distinguishing between benign and malignant liver tumors. Receiver operating characteristic (ROC) analysis revealed no significant difference in the area under the curve (AUC) between quantitative spectral CT parameters (AUC= 0.79, 95% CI: 0.70-0.86) and clinical parameters (AUC=0.75, 95% CI: 0.67-0.83; P=0.551). However, an integrated model combining both parameter sets achieved superior diagnostic performance (AUC=0.85, 95% CI: 0.78-0.91).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Both non-enhanced dual-layer spectral CT quantitative parameters and clinical parameters can effectively distinguish between benign and malignant liver tumors, and their combination can further enhance diagnostic performance.\u003c/p\u003e","manuscriptTitle":"Differentiation of Benign and Malignant Liver Tumors Using Non-Enhanced Spectral CT Quantitative Parameters Combined with Inflammatory Indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 11:04:22","doi":"10.21203/rs.3.rs-8010985/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f4fd1c6-7e96-481f-bdd5-ac9a8bcd0e88","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T13:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 11:04:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8010985","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8010985","identity":"rs-8010985","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}