Prediction of Lymph Node Metastasis in Non-Small Cell Lung Cancer and Its Correlation with Ki67 Expression: A Comparative Study between Intravoxel Incoherent Motion Imaging and 18F-FDG PET

Prediction of Lymph Node Metastasis in Non-Small Cell Lung Cancer and Its Correlation with Ki67 Expression: A Comparative Study between Intravoxel Incoherent Motion Imaging and 18F-FDG PET | 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 Prediction of Lymph Node Metastasis in Non-Small Cell Lung Cancer and Its Correlation with Ki67 Expression: A Comparative Study between Intravoxel Incoherent Motion Imaging and 18F-FDG PET Qianqian Chen, Nan Meng, Xinyu Wang, Yue Liu, Jingwen Zhang, Yaping Wu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7435917/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted 17 You are reading this latest preprint version Abstract Background: Integrated positron emission tomography/magnetic resonance (PET/MR) may have the potential to evaluate lymph node metastasis status and Ki-67 proliferation index in patients with non-small cell lung cancer. Methods: This study enrolled 92 pathologically confirmed NSCLC patients who underwent preoperative integrated PET/MRI. Quantitative analysis was performed for PET metabolic parameters (SUVmax, MTV, TLG) and IVIM parameters (ADC, D, D*, f, DDC). The predictive performance of each parameter for LNM was assessed using receiver operating characteristic (ROC) curve analysis, and a multivariate logistic regression model was constructed to establish the optimal combined predictive model. Spearman correlation analysis was used to explore the relationship between imaging parameters and Ki-67 expression. Results: LNM prediction: The LNM-positive group exhibited lower ADC, D, and DDC (all P < 0.05) compared to the LNM-negative group. Multivariate analysis identified MTV and DDC as independent predictors of LNM. The combined model (MTV + DDC) achieved an AUC of 0.821 (sensitivity 79.49%, specificity 73.59%), significantly outperforming individual parameters (P < 0.05). Ki-67 correlation: SUVmax, MTV, and TLG showed positive correlations with Ki-67 (r= 0.232–0.300, P < 0.05), while ADC and D values exhibited negative correlations (r= −0.327 to −0.240, P 0.05). Conclusion: Integrated PET/MRI, by combining metabolic (MTV) and diffusion (DDC) parameters, significantly improves the predictive accuracy for LNM in NSCLC. Additionally, metabolic and select IVIM parameters correlate with Ki-67 expression. Non-small cell lung cancer Lymph node metastasis Ki-67 PET/MRI Intravoxel incoherent motion imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Non-small cell lung cancer (NSCLC) accounts for approximately 85% [ 1 ] of all lung cancer cases, and its clinical prognosis is closely associated with tumor TNM staging. Among these factors, lymph node metastasis (LNM) status is one of the key determinants of disease staging and therapeutic strategy formulation. According to the 8th edition of the TNM classification by the International Association for the Study of Lung Cancer (IASLC) [ 2 ] , the 5-year survival rate of patients with mediastinal lymph node metastasis (N2/N3 stage) is significantly lower than that of patients without lymph node involvement (N0/N1 stage). For resectable LNM (+) NSCLC patients, systematic lymph node dissection [ 3 ] in addition to primary tumor resection can significantly improve disease-free survival (DFS) and overall survival (OS). However, clinical evidence suggests that for LNM (-) patients, systematic lymph node dissection not only fails to provide significant survival benefits but may also increase the incidence of postoperative complications [ 4 ] (e.g., chylothorax, recurrent laryngeal nerve injury). More importantly, the removal of unaffected lymph nodes may disrupt the homeostasis of the regional immune microenvironment, thereby impairing the body's antitumor immune surveillance function. The nuclear antigen Ki67, a cell cycle-dependent protein, has been widely recognized as a critical biomarker for assessing proliferative activity in malignant tumors. In NSCLC, Ki67 expression levels show a significant positive correlation [ 5 ] with tumor aggressiveness and prognosis. Studies have demonstrated that NSCLC patients with a Ki67 labeling index ≥ 20% exhibit higher rates [ 6 ] of early recurrence and distant metastasis. Given that both lymph node metastasis status and Ki67 expression level serve as independent prognostic factors influencing NSCLC progression, the integration of multimodal imaging evaluation with molecular pathological testing for preoperative accurate lymph node staging and quantitative Ki67 analysis holds substantial clinical value. This approach can optimize surgical decision-making (including but not limited to the extent of lymph node dissection), guide personalized comprehensive treatment strategies, and facilitate the development of precise prognostic assessment models. Currently, the clinically common lymph node staging methods include CT and 18F-FDG PET/CT. However, CT relies solely on morphological criteria (e.g., short-axis diameter ≥ 10 mm) for differentiation, resulting in relatively low diagnostic accuracy. Although 18F-FDG PET/CT has gained widespread recognition in lymph node metastasis evaluation, its specificity is limited because 18F-FDG is a nonspecific tracer. Inflammatory conditions (e.g., tuberculosis, sarcoidosis) or granulomatous lymphadenopathy may also exhibit high metabolic activity, leading to a high false-positive rate (20%-30%) [ 7 ] . Multiple meta-analyses [ 8 , 9 ] have demonstrated that 18F-FDG PET/MRI exhibits comparable or even superior diagnostic performance to PET/CT in T and N staging of non-small cell lung cancer (NSCLC). Furthermore, an IVIM-DWI (intravoxel incoherent motion diffusion-weighted imaging) study [ 10 ] based on a rabbit model demonstrated that IVIM parameters (e.g., ADC value, D value) could effectively differentiate inflammatory lymph nodes from metastatic lymph nodes and dynamically monitor the progression of lymph node metastasis. Currently, preoperative assessment of the Ki67 proliferation index primarily relies on fine-needle aspiration [ 11 ] biopsy and bronchoscopic biopsy. However, these methods are not only invasive but also susceptible to sampling errors. Therefore, noninvasive prediction of Ki67 expression has become a research hotspot. Previous studies have confirmed a positive correlation [ 12 ] between SUVmax and the Ki67 proliferation index in NSCLC, while biexponential DWI-derived parameters also correlate [ 13 ] with Ki67 expression. Nevertheless, a single imaging technique or fragmented scanning is insufficient to comprehensively characterize Ki67 proliferation status, highlighting the urgent need for multimodal imaging integration to improve predictive accuracy. Intravoxel incoherent motion (IVIM) is an advanced functional MRI technique based on the random diffusion characteristics of water molecules. As an extension of conventional diffusion-weighted imaging (DWI), IVIM employs a multib-value biexponential model to noninvasively quantify both tissue water diffusion properties and microcirculatory perfusion information [ 14 ] . This technique provides the following three key quantitative parameters: 1) D reflects the restricted diffusion characteristics of water molecules in tissues, influenced by cellular density, membrane integrity, extracellular matrix structure, and tissue viscosity. 2) f represents the proportional contribution of microvascular networks to the diffusion signal, closely associated with microvessel density and blood perfusion levels. 3) D* reflects capillary blood flow velocity and microcirculatory structural features. Existing studies indicate that IVIM parameters [ 15 , 16 ] significantly correlate with the Ki67 proliferation index in lung adenocarcinoma and lymph node metastasis risk in NSCLC, demonstrating substantial clinical value in assessing tumor proliferative activity and metastatic potential. Integrated 18F-FDG PET/MRI combines the metabolic imaging advantages of positron emission tomography (PET) with the high soft-tissue resolution and functional information of magnetic resonance imaging (MRI), enabling simultaneous assessment of tumor glycolytic metabolism, cellular heterogeneity, and angiogenic features. This approach improves NSCLC diagnostic accuracy and offers a novel strategy for noninvasive preoperative evaluation of lymph node metastasis status and Ki67 expression. This study aims to systematically evaluate the clinical utility of integrated 18F-FDG PET/MRI in determining mediastinal lymph node metastasis (LNM) status and predicting the Ki67 proliferation index in NSCLC. By quantitatively comparing the diagnostic performance of PET metabolic parameters and IVIM functional parameters and constructing an optimal multivariate logistic regression prediction model, we seek to establish a radiomics-based precision staging system for NSCLC. The findings are expected to provide objective imaging biomarkers for guiding individualized treatment strategies and prognostic assessment. Materials and Methods Patients This prospective study was approved by Henan Provincial People's Hospital Ethics Review Board (NO.2021148). Patients with suspected lung tumors who underwent computed tomography (CT) imaging between July 2020 and July 2023 were prospectively recruited, and 128 patients were enrolled in the study based on the following inclusion criteria: 1) maximum diameter of lung lesion ≥1.0 cm on chest CT image; 2) no history of tumor; 3) no contraindications to MRI, such as cardiac pacemakers, ferromagnetic implants, or claustrophobia; Exclusion criteria were as follows: 1) Previous receipt of any form of antitumor therapy; 2) Incomplete imaging data or image quality failing to meet diagnostic requirements; 3) Pathologically confirmed non-NSCLC; 4) Missing clinical follow-up data; 5) Absence of Ki-67 immunohistochemical testing(Figure 1). All enrolled patients provided written informed consent. After screening, 92 patients with pathologically confirmed NSCLC were ultimately included in this study. Demographic and clinical characteristics including age, sex, smoking status, histological type, tumor stage, and maximum tumor diameter were recorded (Table 1). PET-MRI Scanning and Image Acquisition All imaging examinations were performed using an integrated 3.0 T PET/MRI system (uPMR 790, United Imaging, Shanghai, China) equipped with a 12-channel phased-array body coil. Standardized pre-examination protocols were strictly followed: subjects were instructed to avoid strenuous exercise for 24 hours prior to the examination, maintain a fasting state for at least 6 hours, and confirm fasting blood glucose levels <8.0 mmol/L. During the examination, patients were guided to maintain steady breathing patterns to minimize respiratory motion artifacts. The 18F-FDG tracer was administered intravenously at a standard dose of 0.11 mCi/kg (4.07 MBq/kg), followed by a 40-60 minute resting period prior to image acquisition. PET data acquisition was performed in the supine position with head-first orientation, covering a scan range from the lung apex to the diaphragm dome for a duration of 27 minutes, during which respiratory motion was monitored using an abdominal breathing belt. MRI image acquisition was conducted simultaneously with PET scanning. Attenuation correction for gamma rays was performed using a Dixon water-fat separation technique with three-dimensional T1-weighted gradient echo sequences. Image reconstruction employed the ordered subsets expectation maximization (OSEM) method. The MRI sequences included: MRAC, axial T1-weighted imaging (T1WI), axial T2-weighted imaging (T2WI), and multi-b-value DWI, with specific parameters detailed in Table 2. Image Processing and Analysis All imaging data were transferred to the uWS-MR post-processing workstation (United Imaging Healthcare, Shanghai, China) for standardized quantitative analysis. Two radiologists (M.N. and F.F.F., with 8 and 13 years of experience in thoracic oncologic imaging diagnosis, respectively) independently delineated regions of interest (ROIs) encompassing the entire solid tumor component using a double-blind method. For PET image analysis, tumor metabolic active regions were automatically delineated using a 40% SUVmax threshold to generate volumes of interest (VOIs)Simultaneously calculated parameters included TLG, metabolic tumor volume (MTV), and SUVmax. Using fat-suppressed T2-weighted imaging (T2WI) and ultrashort echo time (UTE) images as reference, regions of interest (ROIs) were manually delineated slice-by-slice along the inner margins of solid tumor areas on ADC colored map, while excluding interfering regions such as necrosis, cystic changes, hemorrhage, gas, and calcifications. This ensured coverage of the tumor's largest cross-sectional area while avoiding adjacent normal tissues. The software automatically reproduced the ROIs to parametric color maps (D, D*, f, and DDC) and calculated their mean values (Figure2 and 3). The formula used to determine the IVIM sequence parameters is presented below. Sb/S0 = (1-f) × exp(-bD) + f × exp [-b × (D* + D)] for the bi-exponential IVIM model illustrates the relationship between the DWI signal intensity and the b factor. Sb represents the signal intensity, and b stands for the sensitivity factor. The [17] diffusion coefficient is represented by D, and the perfusion fraction by f. D* is used to represent the pseudo-diffusion factor. Statistical Analysis Data analysis was performed using MedCalc (version 22.0), R (version 4.2.1), and SPSS (version 29.0) software. The intraclass correlation coefficient (ICC) was employed to assess intra- and inter-observer agreement, with interpretation criteria as follows: 0.75-0.90 indicated good agreement, and >0.90 indicated excellent agreement. The normality of each parameter was evaluated using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean ± standard deviation (Mean±SD) and compared between groups using Student's t-test. Non-normally distributed variables were expressed as median (interquartile range) and compared using the Mann-Whitney U test. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated to describe diagnostic performance. The optimal threshold was determined based on the maximum Youden index. The DeLong test was used to compare differences in AUC between parameters (individually or in combination). Logistic regression analysis was performed to identify the optimal predictive model. Spearman's rank correlation coefficient was used to evaluate the correlation between PET/IVIM parameters and Ki-67 expression. The significance level was set at 0.05. Results Consistency Testing Measurements by both observers demonstrated excellent agreement for all parameters. The ICC values for ADCstand, D, D*, f, DDC, SUVmax, MTV, and TLG were 0.831, 0.866, 0.801, 0.823, 0.846, 1, 0.995, and 0.986, respectively. Comparison of Parameters Between Groups The LNM(+) group showed significantly higher SUVmax, MTV, and TLG compared to the LNM(-) group. Conversely, ADCstand, D, and DDC were significantly lower in the LNM(+) group (Table 3). Regression Analyses In the identification of LNM (+) and LNM (-), univariate(Table 4)analysis showed that SUVmax, MTV, TLG, ADCstand, D, and DDC were predictors, and multivariate analysis revealed that only MTV and DDC were independent predictors (Figure 4). Diagnostic Value of Different Parameters and Imaging Techniques for Lymph Node Metastasis in Non-Small Cell Lung Cancer To evaluate the predictive efficacy of different parameters for lymph node metastasis, receiver operating characteristic (ROC) curves were generated for patients with and without lymph node metastasis. The area under the curve (AUC) values were as follows:(MTV+DDC)>MTV>DDC>TLG>ADC>D>SUVmax(AUC=0.821,0.754,0.736,0.726,0.685,0.656, and 0.629). Statistical analysis revealed significant differences between the AUC of the combined model (MTV+DDC) and those of DDC, TLG, ADC, D, and SUVmax individually (Table 5). Although no statistically significant difference was observed between the combined model and MTV alone, the combined approach demonstrated improved both specificity and sensitivity. Correlation Analysis SUVmax, MTV, and TLG were positively correlated（Figure 5）with Ki-67 (r = 0.232, P = 0.026; r = 0.213, P = 0.041; r = 0.3, P = 0.004), while ADC values and D values were negatively correlated with Ki-67 (r = −0.327, P = 0.001; r = −0.24, P = 0.021). Discussion This study compared 18F-FDG PET and IVIM-derived parameters, demonstrating that SUVmax, MTV, TLG, ADCmean, D, D*, f, and DDC could all effectively differentiate LNM status in NSCLC. Among these, the combined diagnostic model of MTV + DDC exhibited the highest predictive efficacy. Furthermore, SUVmax, MTV, TLG, and D values all showed significant correlations with Ki-67 expression. These findings suggest that integrated PET/MRI may serve as a non-invasive biomarker for assisting in the evaluation of lymph node metastasis and proliferative status. Lymph node metastasis (LNM) status constitutes a critical determinant in clinical staging systems, with accurate preoperative evaluation being paramount for therapeutic decision-making and prognostic stratification. This investigation revealed significantly diminished ADC, D, and DDC values in LNM-positive group compared to their LNM-negative counterparts (p < 0.05). ADC, D, and DDC are all diffusion-related parameters of IVIM, which can quantify the diffusion movement of water molecules to indirectly reflect tumor cell proliferation, differentiation, and other information. Currently, several studies [ 10 , 18 , 19 ] have explored the predictive value of different IVIM parameters in different tumor lymph node states. The results show that increased tumor cell density leads to a decrease in ADC values; the decrease in D values, due to the separation of tissue perfusion effects, further supports the limitation of true diffusion within the tumor parenchyma, primarily dependent on cell density and extracellular matrix composition. Studies have shown that D values exhibit a negative correlation with cellular structure and nuclear-to-cytoplasmic ratio, meaning that higher cell numbers and increased nuclear-to-cytoplasmic ratio led to reduced extracellular space, consequently restricting water molecule diffusion and resulting in decreased D values. DDC, as the core parameter of the stretched exponential model, can reflect average diffusion information in all directions and is more sensitive to changes in diffusion characteristics. Therefore, DDC values demonstrate superior predictive performance for LNM status compared to ADC and D values. A previous study [ 20 ] comparing benign and malignant lymph nodes in NSCLC found that metastatic lymph nodes had lower DDC values than non-metastatic lymph nodes, with specificity reaching up to 97%. The research by Ke W et al. [ 21 ] also demonstrated that DDC could more effectively differentiate benign from malignant solitary pulmonary lesions compared to ADC. Hyung C et al. [ 22 ] compared the stretched exponential model with mono-exponential and bi-exponential diffusion-weighted MRI in characterizing focal liver lesions, finding that DDC values had higher diagnostic efficacy than both ADC and D values in distinguishing between benign and malignant hepatic lesions. The multifactorial logistic regression analysis in this study indicated that among diffusion parameters, only DDC is an independent predictor of NSCLC LNM, consistent with the results of the aforementioned studies, fully demonstrating that DDC has a significant advantage over ADC and D values in the assessment of NSCLC. Additionally, there is currently significant controversy regarding the use of primary tumor D* and f values for assessing LN metastasis. In this study, D* and f values showed no statistical significance in assessing LN metastasis. Ye X et al. [ 16 ] also support our findings, but this contradicts previous studies. Several studies [ 23 , 24 ] have suggested that lower D* and f values indicate positive lymph node metastasis, while in a study of rectal adenocarcinoma patients, Jia et al. [ 25 ] found higher f values in the lymph node-positive group, and Hui H et al. [ 26 ] also reported higher f values in LNM (+) NSCLC. The reason for this discrepancy may be due to the low reproducibility and high variability of D* and f values, with significant differences in D* and f values among different tumor patients. Previous studies [ 27 , 28 ] have shown that the expression of glucose transporter-1 (Glut-1) is associated with lymph node metastasis in various tumors, leading to increased FDG uptake and corresponding quantitative indicators. Multiple studies have demonstrated that the SUVmax value of tumors is a predictive marker [ 29 , 30 ] for lymph node metastasis in NSCLC, and in our study, an SUVmax ≥ 6.3 could distinguish between the presence or absence of lymph node metastasis. The research by Li et al. [ 31 ] also supports our findings, as they suggested that a primary tumor SUVmax ≥ 4.3 has value in determining lymph node metastasis in NSCLC. Additionally, our study explored two parameters, MTV and TLG, based on tumor volume. Various studies [ 32 , 33 ] have reported that the MTV and TLG values of primary tumors provide information about tumor volume and activity, which may more accurately assess the metabolic burden of tumors compared to SUVmax. Seong Park et al. [ 32 ] reported in a study of 139 NSCLC patients that MTV demonstrated better predictive performance than other PET parameters in evaluating lymph node status in NSCLC. In this study, multivariate logistic regression analysis revealed that MTV was the only independent metabolic predictor, further confirming its predictive value for lymph node metastasis in NSCLC. This may be related to the fact that SUVmax only reflects the metabolic level of a single voxel within the lesion while ignoring the overall changes in the tumor. Moreover, differences between studies, including variations in the study population, injected radioactivity, image reconstruction parameters, and SUVmax measurement methods, may be important factors contributing to the inconsistencies between our study and previous research. This study utilized multivariate logistic regression analysis to establish a combined prediction model for lymph node metastasis in NSCLC patients, finding that the combined model of MTV + DDC demonstrated superior predictive performance compared to any single model alone. In previous studies, lymph nodes with FDG uptake higher than the metabolic level of background lymph nodes were considered diagnostic criteria for lymph node metastasis. However, inflammatory or granulomatous lesions can also lead to increased uptake. This study confirmed the ability of such criteria to predict lymph node metastasis while also demonstrating its relatively low sensitivity. In contrast, the MTV + DDC combined prediction model exhibited better predictive value (AUC 0.821, sensitivity 79.49%, specificity 73.59%). By integrating metabolic burden and microenvironment heterogeneity information, the MTV + DDC combined model overcomes the biological limitations of single imaging parameters, providing a more reliable predictive tool for lymph node metastasis in NSCLC. Ki-67, as an important indicator for evaluating tumor cell proliferative activity, demonstrated significant correlations with metabolic and diffusion parameters in this study. The results showed that the Ki-67 index was positively correlated with SUVmax, MTV, and TLG, while negatively correlated with ADC and D values. This finding is consistent with previous literature reports [ 12 , 15 ] . The underlying mechanism may be that a higher Ki-67 index reflects increased tumor cell proliferation activity, which, on one hand, leads to elevated energy metabolism demand in tissues, promoting increased 18F-FDG uptake. On the other hand, it results in higher cellular density, thereby restricting water molecule diffusion, ultimately manifesting as increased SUVmax and decreased ADC and D values. Correlation analysis between volumetric metabolic parameters (MTV, TLG) and the Ki-67 index holds significant clinical value, as it can effectively overcome limitations such as tumor heterogeneity, uneven metabolic distribution, and sampling errors from biopsies. Existing studies have confirmed that enhanced tumor cell proliferation activity leads to increased tumor volume and higher cellular density, which in turn elevates 18F-FDG uptake, reflected by higher MTV and TLG values. Notably, this study found no statistically significant correlation between the Ki-67 index and perfusion-related parameters (D* and f values). Apart from the two aforementioned lung cancer-related studies, Wang et al. [ 34 ] also reached a similar conclusion in a bladder cancer study, stating that restricted water molecule diffusion effectively reflects cell proliferation levels, whereas blood flow perfusion parameters are susceptible to multiple interfering factors. This may be because benign conditions such as tissue hyperemia and edema can also alter blood perfusion, indicating that D* and f values are influenced by too many confounding factors and do not accurately reflect tumor proliferative status. Limitations This study has several limitations. First, our research adopted a single-center, single-scanner design, which may lead to limitations in technical reproducibility and potential case selection bias. Second, the predictive model was based on the primary tumor rather than lymph nodes, imposing constraints in identifying the location and number of metastatic lymph nodes. Third, PET/MR images for each patient were acquired under free-breathing conditions, introducing respiratory motion artifacts into the images. Fourth, lesions with a maximum diameter smaller than 1 cm were excluded from this study due to noise interference, which may have introduced a certain degree of selection bias. Conclusions Multiparametric PET/MRI can be used to evaluate lymph node metastasis and Ki-67 proliferation status in non-small cell lung cancer (NSCLC). The combined predictive model of MTV and D provides a novel clinical approach for assessing lymph node metastasis status. Some parameters show a certain correlation with Ki-67 expression, which may assist in formulating individualized treatment strategies for NSCLC patients. Wfi3d-trig 3D T1-weighted spoiled gradient-echo sequence with Dixon-based water fat separation imaging Abbreviations DWI Diffusion-weighted imaging IVIM Intravoxel incoherent motion ADCstand Stand diffusion coefficient D True diffusion coefficient DDC Distributed diffusion coefficient D* Pseudo diffusion coefficient f Perfusion fraction MTV Metabolic tumor volume TLG Total lesion glycolysis LNM Lymph Node Metastasis NSCLC Non-small cell lung cancer Declarations 1.Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Research Committee of Henan Provincial People's Hospital and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This prospective study was approved by Henan Provincial People's Hospital Ethics Review Board (NO.2021148). All participants provided written informed consent to participate in this study. All data is analyzed anonymously. 2. Consent for publication Not applicable. 3.Availability of data and materials Due to privacy restrictions, raw data cannot be made available free of charge, but datasets used and/or analyzed during the current study may be obtained from the corresponding authors upon reasonable request. 4.Competing interests The authors of this manuscript declare no relationships with any companies. 5.Funding This work was funded by the National Key R&D Program of China (2023YFC2414200), and the Henan medical science and technology project (LHGJ20210001). 6.Authors' contributions QQC and NM conceptualized the ideas, designed the study, analyzed the data, constructed the models, and authored the manuscript text. XYW, YL, JWZ and JYP were responsible for recruiting and scanning the patients. YPW, ZH, YY and ZW assisted in the statistical analysis and model building. QYL and FFF critically revised the manuscript for important intellectual content. FFF and MYW provided administrative, technical, material, and financial support, and supervised the study. All authors reviewed the manuscript, read, and approved the final version. 7.Acknowledgements We thank the patients and their families who supported and cooperated with this study. References Siegel, RL, Kratzer, TB, Giaquinto, AN, , Sung, H., Jemal, A. Cancer statistics, 2025. CA Cancer J Clin. 2025; 75 (1): 10-45. Asamura, H, Nishimura, KK, Giroux, DJ, et al. IASLC Lung Cancer Staging Project: The New Database to Inform Revisions in the Ninth Edition of the TNM Classification of Lung Cancer. J THORAC ONCOL. 2023; 18 (5): 564-575. Riely, GJ, Wood, DE, Ettinger, DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology J NATL COMPR CANC NE. 2024; 22 (4): 249-274. Jiang C, Zhang Y, Fu F, Deng P, Chen H. A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer. J THORAC ONCOL. 2023; 19 (1): 25-35. Yang, Y, Shao, X, Li, Z, et al. Prognostic heterogeneity of Ki67 in non-small cell lung cancer: A comprehensive reappraisal on immunohistochemistry and transcriptional data. J CELL MOL MED. 2024; 28 (14): e18521. Wei, DM, Chen, WJ, Meng, RM, et al. Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients. Respir Res. 2018; 19 (1): 150. Sommer, G, Wiese, M, Winter, L, et al. Preoperative staging of non-small-cell lung cancer: comparison of whole-body diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography. EUR RADIOL. 2012; 22 (12): 2859-67. Kirchner, J, Sawicki, LM, Nensa, F, et al. Prospective comparison of 18F-FDG PET/MRI and 18F-FDG PET/CT for thoracic staging of non-small cell lung cancer. EUR J NUCL MED MOL I. 2018; 46 (2): 437-445. Fraioli, F, Screaton, NJ, Janes, SM, et al. Non-small-cell lung cancer resectability: diagnostic value of PET/MR. Eur J Nucl Med Mol Imaging. 2014; 42 (1): 49-55. Guo, L, Liu, X, Liu, Z, et al. Differential detection of metastatic and inflammatory lymph nodes using intravoxel incoherent motion diffusion-weighted imaging. MAGN RESON IMAGING. 2019; 65 62-66. Del Gobbo, A, Pellegrinelli, A, Gaudioso, G, et al. Analysis of NSCLC tumour heterogeneity, proliferative and 18F-FDG PET indices reveals Ki67 prognostic role in adenocarcinomas. HISTOPATHOLOGY. 2015; 68 (5): 746-51. Surov, A, Meyer, HJ, Wienke, A. Standardized Uptake Values Derived from 18F-FDG PET May Predict Lung Cancer Microvessel Density and Expression of KI 67, VEGF, and HIF-1α but Not Expression of Cyclin D1, PCNA, EGFR, PD L1, and p53. CONTRAST MEDIA MOL I. 2018; 2018 9257929. Zheng, Y, Huang, W, Zhang, X, et al. A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging. J Cancer. 2021; 12 (1): 190-197. Xiang L, Yang H, Qin Y, Wen Y, Liu X, Zeng W-B. Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions. Front Oncol. 2023; 12 1075072. Huang, Z, Li, X, Wang, Z, et al. Application of Simultaneous 18 F-FDG PET With Monoexponential, Biexponential, and Stretched Exponential Model-Based Diffusion-Weighted MR Imaging in Assessing the Proliferation Status of Lung Adenocarcinoma. J MAGN RESON IMAGING. 2021; 56 (1): 63-74. Ye, X, Chen, S, Tian, Y, et al. A preliminary exploration of the intravoxel incoherent motion applied in the preoperative evaluation of mediastinal lymph node metastasis of lung cancer. J THORAC DIS. 2017; 9 (4): 1073-1080. Fang, T, Meng, N, Feng, P, et al. A Comparative Study of Amide Proton Transfer Weighted Imaging and Intravoxel Incoherent Motion MRI Techniques Versus (18) F-FDG PET to Distinguish Solitary Pulmonary Lesions and Their Subtypes. J MAGN RESON IMAGING. 2021; 55 (5): 1376-1390. Sauer, M, Klene, C, Kaul, M, et al. Preoperative evaluation of pelvine lymph node metastasis in high risk prostate cancer with intravoxel incoherent motion (IVIM) MRI. EUR J RADIOL. 2018; 107 1-6. Song, T, Lu, S, Qu, J, et al. Intravoxel incoherent motion diffusion-weighted imaging in evaluating preoperative staging of esophageal squamous cell carcinoma : Evaluation of preoperative stage of primary tumour and prediction of lymph node metastases from esophageal cancer using IVIM: a prospective study. CANCER IMAGING. 2024; 24 (1): 116. Zheng, Y., Han, N., Huang, W., Jiang, Y., Zhang, J. Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging. J THORAC IMAG. 2023; 39 (5): 285-292. Wang, K, Wu, G. Monoexponential, biexponential, stretched exponential and diffusion kurtosis models of diffusion-weighted imaging: a quantitative differentiation of solitary pulmonary lesion. BMC Med Imaging. 2024; 24 (1): 346. Kim HC, Seo N, Chung YE, Park MS, Choi JY, Kim MJ. Characterization of focal liver lesions using the stretched exponential model: comparison with monoexponential and biexponential diffusion-weighted magnetic resonance imaging. EUR RADIOL. 2019; 29 (9): 5111-5120. Zhang, Y, Zhang, KY, Jia, HD, et al. Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer. ACAD RADIOL. 2021; 29 (7): 1048-1057. Yang, X, Chen, Y, Wen, Z, et al. Non-invasive MR assessment of the microstructure and microcirculation in regional lymph nodes for rectal cancer: a study of intravoxel incoherent motion imaging. CANCER IMAGING. 2019; 19 (1): 70. Jia, H, Jiang, X, Zhang, K, et al. A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively. J MAGN RESON IMAGING. 2022; 56 (3): 658-667. Han, H, Guo, W, Ren, H, et al. Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction. Insights Imaging. 2024; 15 (1): 168. Eilsberger, F, Noltenius, FE, Librizzi, D, et al. Real-Life Performance of F-18-FDG PET/CT in Patients with Cervical Lymph Node Metastasis of Unknown Primary Tumor. Biomedicines. 2022; 10 (9). Todate, Y, Honda, M, Takada, T, et al. The additional diagnostic impact of positron emission tomography-computed tomography for lymph node metastasis from colorectal cancer: A prospective lymph node level analysis. J SURG ONCOL. 2021; 124 (7): 1085-1090. Lee, AY, Choi, SJ, Jung, KP, et al. Characteristics of Metastatic Mediastinal Lymph Nodes of Non-Small Cell Lung Cancer on Preoperative F-18 FDG PET/CT. NUCL MED MOLEC IMAG. 2013; 48 (1): 41-6. Yu X, Wang J, Huang L, Xie L, Su Y. Predictive value of 18F-FDG PET/CT metabolic parameters for lymph node metastasis of non-small cell lung cancer. Biomark Med. 2024; 19 (2): 35-41. Li, L, Ren, S, Zhang, Y, et al. Risk factors for predicting the occult nodal metastasis in T1-2N0M0 NSCLC patients staged by PET/CT: potential value in the clinic. LUNG CANCER. 2013; 81 (2): 213-7. Park SY, Yoon JK, Park KJ, Lee SJ. Prediction of occult lymph node metastasis using volume-based PET parameters in small-sized peripheral non-small cell lung cancer. CANCER IMAGING. 2015; 15 21. Goksel S, Erdivanli OC, Bulbul O, Dursun E. The role of metabolic tumor parameters predicting cervical lymph node metastasis in patients with head and neck squamous cell carcinoma. J CANCER RES THER. 2022; 18 (4): 1045-1051. Wang F, Wu LM, Hua XL, Zhao ZZ, Chen XX, Xu JR. Intravoxel incoherent motion diffusion-weighted imaging in assessing bladder cancer invasiveness and cell proliferation. J MAGN RESON IMAGING. 2017; 47 (4): 1054-1060. Tables Table 1 Patient clinicopathological characteristics Clinical characteristics Value Age(years) 59±9 Sex Smoke Male Female Yes No 43 49 42 50 Histological type SCC 1 AC 14 78 LNM + - 53 39 Maximum diameter (cm) IASLC stage (2024), n (%) Early stage (I-IIB) Advanced stage (IIIA-IV) Ki67, n (%) ≤25% ＞25% 2.7±1.2 62（67.39） 30（32.61） 52（56.52） 40（43.48） Table 2 Parameters used for MR-IVIM imaging 2 Parameters T1WI T2WI DWI IVIM Sequence 2D-FSE 2D-FSE 2D-SS-EPI 2D-SS-EPI Orientation Axial Axial Axial Axial TR/TE(ms) 5.06/2.1 3,315/87.8 1,620/69.6 1,620/69.6 FOV (cm2) 35×50 35×50 35×50 35×50 Matrix 303×456 264×480 202×256 202×256 Bandwidth(Hz/pixel) 260 260 2370 2370 Slice thickness(mm) 5 5 5 5 Interval (mm) 1 1 1 1 NEX 2 2 1.8 1,1,2,2,4,4,6,6,8,10 b-values (s/mm2) / / 0.800 0,25,50,100,150,200 400,600,800,1000 Respiratory compensation Yes Yes Yes Yes Scan time 14s 2min 26s 2min58s 3min 38s Table 3 Comparison of 18 F-FDG PET and IVIM Parameters for LNM status LNM-positive LNM-negative P N 53 39 SUVmax(g/cm3) 6.96(1.14,16.65) 5.45(0.48,13.81) 0.035 MTV(cm3) 18.48(1.87,105.7) 6.47(0.58,24.99) <0.001 TLG(g) 58.06(2.07,259.57) 29.92(0.69,194.99) <0.001 3 ADCstand(10−3 mm2/s) 0.878(0.25,1.8) 1.2(0.29,4.8) 0.003 D(10−3mm2/s) 0.71 (0.014,1.73) 0.95 (0.002,3.67) 0.011 DDC(10−3mm2/s) 2.81(0.54,7.96) 4.65(0.93,9.45) <0.001 D*(10−3 mm2/s) 71.64 (3.66,147.1) 73.79(7.89,186.8) 0.69 f (%) 43.14(17.44,72.22) 47.48(13.63,373.9) 0.965 Table 4 Univariate and multivariate analyses Univariate Analyses Multivariate Analyses Parameters OR (95% CI) P-Value OR (95% CI) P-Value SUVmax 4 1.145(1.0007-1.303) 0.039 1.227(0.975-1.545) 0.081 MTV 1.119(1.047-1.196) <0.001 0.891(0.807-0.983) 0.021 TLG 1.017(1.005-1.029) 0.007 0.993(0.976-1.011) 0.458 ADC 0.206(0.066-0.646) 0.007 3.173 (0.233-43.225) 0.386 D 0.231(0.069-0.775) 0.018 1.769(0.111-28.226) 0.686 DDC 0.618(0.477-0.800) <0.001 1.619(1.182-2.219) 0.003 D* 0.999(0.989-1.009) 0.802 0.990(0.975-1.005) 0.204 f 0.997(0.984-1.010) 0.602 1.001(0.990-1.014) 0.967 Table 5 ROC Analysis of the Diagnostic Performance for Different Parameters Alone or in Combination for Predicting LNM status Parameters AUC (95% CI) Youden Index Cut-off Value Sensitivity (%) Specificity (%) Comparison with Combined Diagnosis (P-Value) SUVmax 0.629 (0.513–0.746) 0.3202 6.26 64.10 67.93 P=0.0006 MTV 0.754 (0.655–0.852) 0.4039 7.16 74.36 66.03 P=0.0998 TLG 0.726 (0.621–0.832) 0.4267 25.45 61.54 81.13 P=0.027 ADC 0.685 (0.568–0.802) 0.3875 0.79 53.87 84.91 P=0.0462 D 0.656 (0.538–0.774) 0.3014 0.80 64.1 66.04 P=0.0161 DDC 0.736 (0.633–0.838) 0.4485 3.51 76.92 67.93 P=0.0135 MTV+DDC 0.821 (0.736–0.907) 0.5307 – 79.49 73.59 – 1 SCC, squamous cell carcinoma; AC, adenocarcinoma; LNM, lymph node metastasis. 2 T1WI = T1-weighted imaging T2WI = T2-weighted imaging DWI = diffusion-weighted imaging IVIM = intravoxel incoherent motion FOV = field of view TR/TE = repetition time/echo time NEX = number of excitations 3 ADCstand, stand diffusion coefficient; D, true diffusion coefficient; DDC, distributed diffusion coefficient; D*, pseudo diffusion coefficient; f, perfusion fraction; LNM, Lymph Node Metastasis. 4 SUVmax = maximum standardized uptake value; MTV = metabolic tumor volume; TLG = total lesion glycolysis; ADC = apparent diffusion coefficient; D, diffusion coefficient; DDC, distributed diffusion coefficient; D*, pseudo diffusion coefficient; f, perfusion fraction. All factors with P < 0.1 in univariate analyses were included in multivariate regression analyses. OR = odds ratio; CI = confidence interval. The bold typeface in the table indicates the logistic regression analyses with statistical significance (significance level = 0.05). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers invited by journal 27 Aug, 2025 Editor assigned by journal 26 Aug, 2025 Editor invited by journal 25 Aug, 2025 Submission checks completed at journal 25 Aug, 2025 First submitted to journal 25 Aug, 2025 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-7435917","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509260554,"identity":"0c624524-5e84-4724-9bb0-7dbe96762bb6","order_by":0,"name":"Qianqian Chen","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Chen","suffix":""},{"id":509260555,"identity":"4948a402-0edc-4e35-8cf7-c94fb6d2f230","order_by":1,"name":"Nan Meng","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Meng","suffix":""},{"id":509260556,"identity":"ce8ff082-3536-4c69-8a96-4e09bebe1c51","order_by":2,"name":"Xinyu Wang","email":"","orcid":"","institution":"Department of Radiology, People's Hospital of Henan University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Wang","suffix":""},{"id":509260557,"identity":"db80c76e-b8d7-4a56-9085-aed1a5f3e306","order_by":3,"name":"Yue Liu","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Liu","suffix":""},{"id":509260558,"identity":"c2e83e05-c1bf-4f24-ae27-f8762b07244d","order_by":4,"name":"Jingwen Zhang","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Zhang","suffix":""},{"id":509260559,"identity":"10525e27-58ed-45fa-b2ef-6a5a66b700d4","order_by":5,"name":"Yaping Wu","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Wu","suffix":""},{"id":509260560,"identity":"cdd7d3aa-7927-4dd3-a326-6e932033496f","order_by":6,"name":"Jiayin Pan","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiayin","middleName":"","lastName":"Pan","suffix":""},{"id":509260561,"identity":"b33e9f60-887c-43c7-8cb4-c954e7d8b724","order_by":7,"name":"Zhun Huang","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhun","middleName":"","lastName":"Huang","suffix":""},{"id":509260562,"identity":"cf6a4f74-90e9-4161-9d2b-070a8c600ae5","order_by":8,"name":"Yang Yang","email":"","orcid":"","institution":"Beijing United Imaging Healthcare Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":509260563,"identity":"2d89ab85-2344-45bd-aff6-077aee5ed5c5","order_by":9,"name":"Zhe Wang","email":"","orcid":"","institution":"Central Research Institute, United Imaging Healthcare Group","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Wang","suffix":""},{"id":509260564,"identity":"56e0fb76-eade-494d-aa99-4ea519b92f9d","order_by":10,"name":"Qiuyu Liu","email":"","orcid":"","institution":"Department of Pathology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiuyu","middleName":"","lastName":"Liu","suffix":""},{"id":509260565,"identity":"876da7ab-1cc5-456e-868a-55eebab62369","order_by":11,"name":"Meiyun Wang","email":"","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meiyun","middleName":"","lastName":"Wang","suffix":""},{"id":509260566,"identity":"d403f794-b052-4252-bc2b-abc90b02dbf8","order_by":12,"name":"Fangfang Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBAC9gbGB0BKTo6NvfkAcVp4DjAbACljYz6eYwmkaUmcJ5GjQKQWiWTGzwW/DNLbGHIYGH5UbCNKC7P0zD6D3DaGswcYe87cJqzFXiL/gDRvz5/cNsa+BGbGNiK0gGz5zdtjkM7GzGNAtBY2aZ4fBglsbERr4XnMZs3bYGDYxsOWcJAov/CwJzPf5vljIC8///HBBz8qiNACBoxtEPoAkepB4A8JakfBKBgFo2DkAQCKBTUJXyWDKAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Radiology, Provincial Medical Clinical School of Zhengzhou University \u0026 Henan Provincial People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-08-22 15:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7435917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7435917/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-025-02131-z","type":"published","date":"2026-01-03T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90795979,"identity":"83477e0b-2814-4005-8f8d-c4244e00d524","added_by":"auto","created_at":"2025-09-08 08:57:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3257510,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Selection Flowchart\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7435917/v1/f4ee374a59b45766a492e182.png"},{"id":90794986,"identity":"7b6abf2d-3f65-476e-86b0-264ad5c6906c","added_by":"auto","created_at":"2025-09-08 08:49:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":815747,"visible":true,"origin":"","legend":"\u003cp\u003eMultiparametric PET-MRI images and pathological images of different patients：(a-h) A patient with adenocarcinoma (white arrow, non-smoker, size approximately 2.5cm × 2cm × 1.5cm, stage IIB, Ki67 =60%, lymph node metastasis positive).(i-p) A patient with squamous cell carcinoma (white arrow, smoker, size approximately 2.5cm × 4cm × 1cm, stage IB, Ki67=30%, lymph node metastasis negative).(a, i) T2-weighted images (T2WI); (b, j) 18F-FDG PET and UTE fusion images; (c, k) Pseudocolored ADC maps; (d, l) Pseudocolored D maps; (e, m) Pseudocolored DDC maps; (f, n) Pseudocolored D* maps; (g, o) Pseudocolored f maps; (h, p) Pathological images (×200).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7435917/v1/14eb5a4b4903eca918ad3d6b.png"},{"id":90794093,"identity":"d2141015-0be3-45bc-86f1-f6c35c08ed52","added_by":"auto","created_at":"2025-09-08 08:41:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11824227,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of different parameters and methods, (a) SUVmax, MTV, TLG, and the combination of independent predictors (MTV+DDC) (the AUC of each parameter is 0.629, 0.754, 0.726, and 0.821, respectively). (b) ADCstand, D, DDC, and the combination of three methods (MTV+DDC) (the AUC of each parameter is 0.685, 0.656, 0.736, and 0.821, respectively).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7435917/v1/a7db6d2271ab02acfc87df85.png"},{"id":90794991,"identity":"86a2b994-22e7-4171-bc8d-dc2f2379cbd4","added_by":"auto","created_at":"2025-09-08 08:49:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7273337,"visible":true,"origin":"","legend":"\u003cp\u003e(a-f) Showing the comparison of various parameters in the LNM- positive and LNM- negative groups, respectively (SUVmax, MTV, TLG, ADCstand, D, and DDC). * stands for P \u0026lt; 0.05 and *** for P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7435917/v1/b2efddb27c76fc2f1d66f9de.png"},{"id":90794098,"identity":"0a1497bd-6808-4126-a559-2d982fdc0522","added_by":"auto","created_at":"2025-09-08 08:41:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13927875,"visible":true,"origin":"","legend":"\u003cp\u003e(a–f) Correlation between Ki67 and SUVmax, MTV, TLG, ADCstand, D, and DDC (r=0.232, 0.213, 0.3, −0.327, −0.24, and -0.204, P = 0.026,0.041,0.004, 0.001,0.021, and 0.051).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7435917/v1/f3a2c49f78b31ea4804bfdfd.png"},{"id":99545230,"identity":"8c777e16-6040-4692-89a8-634c5aed08f5","added_by":"auto","created_at":"2026-01-05 16:02:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27549273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7435917/v1/8555de73-4c1a-45b2-abbb-f02c1511bd5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Lymph Node Metastasis in Non-Small Cell Lung Cancer and Its Correlation with Ki67 Expression: A Comparative Study between Intravoxel Incoherent Motion Imaging and 18F-FDG PET","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC) accounts for approximately 85%\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e of all lung cancer cases, and its clinical prognosis is closely associated with tumor TNM staging. Among these factors, lymph node metastasis (LNM) status is one of the key determinants of disease staging and therapeutic strategy formulation. According to the 8th edition of the TNM classification by the International Association for the Study of Lung Cancer (IASLC)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, the 5-year survival rate of patients with mediastinal lymph node metastasis (N2/N3 stage) is significantly lower than that of patients without lymph node involvement (N0/N1 stage). For resectable LNM (+) NSCLC patients, systematic lymph node dissection\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e in addition to primary tumor resection can significantly improve disease-free survival (DFS) and overall survival (OS). However, clinical evidence suggests that for LNM (-) patients, systematic lymph node dissection not only fails to provide significant survival benefits but may also increase the incidence of postoperative complications\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e (e.g., chylothorax, recurrent laryngeal nerve injury). More importantly, the removal of unaffected lymph nodes may disrupt the homeostasis of the regional immune microenvironment, thereby impairing the body's antitumor immune surveillance function.\u003c/p\u003e\u003cp\u003eThe nuclear antigen Ki67, a cell cycle-dependent protein, has been widely recognized as a critical biomarker for assessing proliferative activity in malignant tumors. In NSCLC, Ki67 expression levels show a significant positive correlation\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e with tumor aggressiveness and prognosis. Studies have demonstrated that NSCLC patients with a Ki67 labeling index\u0026thinsp;\u0026ge;\u0026thinsp;20% exhibit higher rates\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e of early recurrence and distant metastasis.\u003c/p\u003e\u003cp\u003eGiven that both lymph node metastasis status and Ki67 expression level serve as independent prognostic factors influencing NSCLC progression, the integration of multimodal imaging evaluation with molecular pathological testing for preoperative accurate lymph node staging and quantitative Ki67 analysis holds substantial clinical value. This approach can optimize surgical decision-making (including but not limited to the extent of lymph node dissection), guide personalized comprehensive treatment strategies, and facilitate the development of precise prognostic assessment models.\u003c/p\u003e\u003cp\u003eCurrently, the clinically common lymph node staging methods include CT and 18F-FDG PET/CT. However, CT relies solely on morphological criteria (e.g., short-axis diameter\u0026thinsp;\u0026ge;\u0026thinsp;10 mm) for differentiation, resulting in relatively low diagnostic accuracy. Although 18F-FDG PET/CT has gained widespread recognition in lymph node metastasis evaluation, its specificity is limited because 18F-FDG is a nonspecific tracer. Inflammatory conditions (e.g., tuberculosis, sarcoidosis) or granulomatous lymphadenopathy may also exhibit high metabolic activity, leading to a high false-positive rate (20%-30%)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Multiple meta-analyses\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e have demonstrated that 18F-FDG PET/MRI exhibits comparable or even superior diagnostic performance to PET/CT in T and N staging of non-small cell lung cancer (NSCLC). Furthermore, an IVIM-DWI (intravoxel incoherent motion diffusion-weighted imaging) study\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e based on a rabbit model demonstrated that IVIM parameters (e.g., ADC value, D value) could effectively differentiate inflammatory lymph nodes from metastatic lymph nodes and dynamically monitor the progression of lymph node metastasis. Currently, preoperative assessment of the Ki67 proliferation index primarily relies on fine-needle aspiration\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e biopsy and bronchoscopic biopsy. However, these methods are not only invasive but also susceptible to sampling errors. Therefore, noninvasive prediction of Ki67 expression has become a research hotspot. Previous studies have confirmed a positive correlation\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e between SUVmax and the Ki67 proliferation index in NSCLC, while biexponential DWI-derived parameters also correlate\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e with Ki67 expression. Nevertheless, a single imaging technique or fragmented scanning is insufficient to comprehensively characterize Ki67 proliferation status, highlighting the urgent need for multimodal imaging integration to improve predictive accuracy.\u003c/p\u003e\u003cp\u003eIntravoxel incoherent motion (IVIM) is an advanced functional MRI technique based on the random diffusion characteristics of water molecules. As an extension of conventional diffusion-weighted imaging (DWI), IVIM employs a multib-value biexponential model to noninvasively quantify both tissue water diffusion properties and microcirculatory perfusion information\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This technique provides the following three key quantitative parameters: 1) D reflects the restricted diffusion characteristics of water molecules in tissues, influenced by cellular density, membrane integrity, extracellular matrix structure, and tissue viscosity. 2) f represents the proportional contribution of microvascular networks to the diffusion signal, closely associated with microvessel density and blood perfusion levels. 3) D* reflects capillary blood flow velocity and microcirculatory structural features. Existing studies indicate that IVIM parameters\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e significantly correlate with the Ki67 proliferation index in lung adenocarcinoma and lymph node metastasis risk in NSCLC, demonstrating substantial clinical value in assessing tumor proliferative activity and metastatic potential. Integrated 18F-FDG PET/MRI combines the metabolic imaging advantages of positron emission tomography (PET) with the high soft-tissue resolution and functional information of magnetic resonance imaging (MRI), enabling simultaneous assessment of tumor glycolytic metabolism, cellular heterogeneity, and angiogenic features. This approach improves NSCLC diagnostic accuracy and offers a novel strategy for noninvasive preoperative evaluation of lymph node metastasis status and Ki67 expression.\u003c/p\u003e\u003cp\u003eThis study aims to systematically evaluate the clinical utility of integrated 18F-FDG PET/MRI in determining mediastinal lymph node metastasis (LNM) status and predicting the Ki67 proliferation index in NSCLC. By quantitatively comparing the diagnostic performance of PET metabolic parameters and IVIM functional parameters and constructing an optimal multivariate logistic regression prediction model, we seek to establish a radiomics-based precision staging system for NSCLC. The findings are expected to provide objective imaging biomarkers for guiding individualized treatment strategies and prognostic assessment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;prospective\u0026nbsp;study was approved by Henan Provincial People\u0026apos;s Hospital Ethics Review Board (NO.2021148). Patients with suspected lung tumors who underwent computed tomography (CT) imaging between July 2020 and July 2023 were prospectively recruited, and 128 patients were enrolled in the study based on the following inclusion criteria:\u0026nbsp;1) maximum diameter of lung lesion \u0026ge;1.0 cm on chest CT image; 2) no history of tumor; 3)\u0026nbsp;no contraindications to MRI, such as cardiac pacemakers, ferromagnetic implants, or claustrophobia;\u0026nbsp;Exclusion criteria were as follows: 1) Previous receipt of any form of antitumor therapy; 2) Incomplete imaging data or image quality failing to meet diagnostic requirements; 3) Pathologically confirmed non-NSCLC; 4) Missing clinical follow-up data; 5) Absence of Ki-67 immunohistochemical testing(Figure 1). All enrolled patients provided written informed consent.\u0026nbsp;After screening, 92 patients with pathologically confirmed NSCLC were ultimately included in this study. Demographic and clinical characteristics including age, sex, smoking status, histological type, tumor stage, and maximum tumor diameter were recorded (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePET-MRI Scanning and Image Acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll imaging examinations were performed using an integrated 3.0 T PET/MRI system (uPMR 790, United Imaging, Shanghai, China) equipped with a 12-channel phased-array body coil. Standardized pre-examination protocols were strictly followed: subjects were instructed to avoid strenuous exercise for 24 hours prior to the examination, maintain a fasting state for at least 6 hours, and confirm fasting blood glucose levels \u0026lt;8.0 mmol/L. During the examination, patients were guided to maintain steady breathing patterns to minimize respiratory motion artifacts. The 18F-FDG tracer was administered intravenously at a standard dose of 0.11 mCi/kg (4.07 MBq/kg), followed by a 40-60 minute\u0026nbsp;resting period prior to image acquisition. PET data acquisition was performed in the supine position with head-first orientation, covering a scan range from the lung apex to the diaphragm dome for a duration of 27 minutes, during which respiratory motion was monitored using an abdominal breathing belt. MRI image acquisition was conducted simultaneously with PET scanning. Attenuation correction for gamma rays was performed using a Dixon water-fat separation technique with three-dimensional T1-weighted gradient echo sequences. Image reconstruction employed the ordered subsets expectation maximization (OSEM) method. The MRI sequences included: MRAC, axial T1-weighted imaging (T1WI), axial T2-weighted imaging (T2WI), and multi-b-value DWI, with specific parameters detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImage Processing and Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll imaging data were transferred to the uWS-MR post-processing workstation (United Imaging Healthcare, Shanghai, China) for standardized quantitative analysis. Two radiologists (M.N. and F.F.F., with 8 and 13 years of experience in thoracic oncologic imaging diagnosis, respectively) independently delineated regions of interest (ROIs) encompassing the entire solid tumor component using a double-blind method.\u0026nbsp;For PET image analysis, tumor metabolic active regions were automatically delineated using a 40% SUVmax threshold to generate volumes of interest (VOIs)Simultaneously calculated parameters included TLG, metabolic tumor volume (MTV), and SUVmax.\u0026nbsp;Using fat-suppressed T2-weighted imaging (T2WI) and ultrashort echo time (UTE) images as reference, regions of interest (ROIs) were manually delineated slice-by-slice along the inner margins of solid tumor areas on ADC\u0026nbsp;colored\u0026nbsp;map, while excluding interfering regions such as necrosis, cystic changes, hemorrhage, gas, and calcifications. This ensured coverage of the tumor\u0026apos;s largest cross-sectional area while avoiding adjacent normal tissues.\u0026nbsp;The software automatically reproduced the ROIs to parametric color maps (D, D*, f, and DDC) and calculated their mean values (Figure2 and 3).\u003c/p\u003e\n\u003cp\u003eThe formula used to determine the IVIM sequence parameters is presented below. Sb/S0 = (1-f) \u0026times; exp(-bD) + f \u0026times; exp [-b \u0026times; (D* + D)] for the bi-exponential IVIM model illustrates the relationship between the DWI signal intensity and the b factor. Sb represents the signal intensity, and b stands for the sensitivity factor. The \u003csup\u003e[17]\u003c/sup\u003ediffusion coefficient is represented by D, and the perfusion fraction by f. D* is used to represent the pseudo-diffusion factor.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed using MedCalc (version 22.0), R (version 4.2.1), and SPSS (version 29.0) software. The intraclass correlation coefficient (ICC) was employed to assess intra- and inter-observer agreement, with interpretation criteria as follows: 0.75-0.90 indicated good agreement, and \u0026gt;0.90 indicated excellent agreement. The normality of each parameter was evaluated using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean \u0026plusmn; standard deviation (Mean\u0026plusmn;SD) and compared between groups using Student\u0026apos;s t-test. Non-normally distributed variables were expressed as median (interquartile range) and compared using the Mann-Whitney U test. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated to describe diagnostic performance. The optimal threshold was determined based on the maximum Youden index. The DeLong test was used to compare differences in AUC between parameters (individually or in combination). Logistic regression analysis was performed to identify the optimal predictive model. Spearman\u0026apos;s rank correlation coefficient was used to evaluate the correlation between PET/IVIM parameters and Ki-67 expression. The significance level was set at 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eConsistency Testing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMeasurements by both observers demonstrated excellent agreement for all parameters. The ICC values for ADCstand, D, D*, f, DDC, SUVmax, MTV, and TLG were 0.831, 0.866, 0.801, 0.823, 0.846, 1, 0.995, and 0.986, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison of Parameters Between Groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe LNM(+) group showed significantly higher SUVmax, MTV, and TLG compared to the LNM(-) group. Conversely, ADCstand, D, and DDC were significantly lower in the LNM(+) group (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRegression Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the identification of LNM (+) and LNM (-), univariate(Table 4)analysis showed that SUVmax, MTV, TLG, ADCstand, D, and DDC were predictors, and multivariate analysis revealed that only MTV and DDC were independent predictors (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiagnostic Value of Different Parameters and Imaging Techniques for Lymph Node Metastasis in Non-Small Cell Lung Cancer\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive efficacy of different parameters for lymph node metastasis, receiver operating characteristic (ROC) curves were generated for patients with and without lymph node metastasis. The area under the curve (AUC) values were as follows:(MTV+DDC)\u0026gt;MTV\u0026gt;DDC\u0026gt;TLG\u0026gt;ADC\u0026gt;D\u0026gt;SUVmax(AUC=0.821,0.754,0.736,0.726,0.685,0.656, and 0.629). Statistical analysis revealed significant differences between the AUC of the combined model (MTV+DDC) and those of DDC, TLG, ADC, D, and SUVmax individually (Table 5). Although no statistically significant difference was observed between the combined model and MTV alone, the combined approach demonstrated improved both specificity and sensitivity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelation Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSUVmax, MTV, and TLG were positively correlated（Figure 5）with Ki-67 (r = 0.232, P = 0.026; r = 0.213, P = 0.041; r = 0.3, P = 0.004), while ADC values and D values were negatively correlated with Ki-67 (r = \u0026minus;0.327, P = 0.001; r = \u0026minus;0.24, P = 0.021).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared 18F-FDG PET and IVIM-derived parameters, demonstrating that SUVmax, MTV, TLG, ADCmean, D, D*, f, and DDC could all effectively differentiate LNM status in NSCLC. Among these, the combined diagnostic model of MTV\u0026thinsp;+\u0026thinsp;DDC exhibited the highest predictive efficacy. Furthermore, SUVmax, MTV, TLG, and D values all showed significant correlations with Ki-67 expression. These findings suggest that integrated PET/MRI may serve as a non-invasive biomarker for assisting in the evaluation of lymph node metastasis and proliferative status.\u003c/p\u003e\u003cp\u003eLymph node metastasis (LNM) status constitutes a critical determinant in clinical staging systems, with accurate preoperative evaluation being paramount for therapeutic decision-making and prognostic stratification. This investigation revealed significantly diminished ADC, D, and DDC values in LNM-positive group compared to their LNM-negative counterparts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ADC, D, and DDC are all diffusion-related parameters of IVIM, which can quantify the diffusion movement of water molecules to indirectly reflect tumor cell proliferation, differentiation, and other information. Currently, several studies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e have explored the predictive value of different IVIM parameters in different tumor lymph node states. The results show that increased tumor cell density leads to a decrease in ADC values; the decrease in D values, due to the separation of tissue perfusion effects, further supports the limitation of true diffusion within the tumor parenchyma, primarily dependent on cell density and extracellular matrix composition. Studies have shown that D values exhibit a negative correlation with cellular structure and nuclear-to-cytoplasmic ratio, meaning that higher cell numbers and increased nuclear-to-cytoplasmic ratio led to reduced extracellular space, consequently restricting water molecule diffusion and resulting in decreased D values. DDC, as the core parameter of the stretched exponential model, can reflect average diffusion information in all directions and is more sensitive to changes in diffusion characteristics. Therefore, DDC values demonstrate superior predictive performance for LNM status compared to ADC and D values. A previous study\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e comparing benign and malignant lymph nodes in NSCLC found that metastatic lymph nodes had lower DDC values than non-metastatic lymph nodes, with specificity reaching up to 97%. The research by Ke W et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e also demonstrated that DDC could more effectively differentiate benign from malignant solitary pulmonary lesions compared to ADC. Hyung C et al.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e compared the stretched exponential model with mono-exponential and bi-exponential diffusion-weighted MRI in characterizing focal liver lesions, finding that DDC values had higher diagnostic efficacy than both ADC and D values in distinguishing between benign and malignant hepatic lesions. The multifactorial logistic regression analysis in this study indicated that among diffusion parameters, only DDC is an independent predictor of NSCLC LNM, consistent with the results of the aforementioned studies, fully demonstrating that DDC has a significant advantage over ADC and D values in the assessment of NSCLC. Additionally, there is currently significant controversy regarding the use of primary tumor D* and f values for assessing LN metastasis. In this study, D* and f values showed no statistical significance in assessing LN metastasis. Ye X et al.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e also support our findings, but this contradicts previous studies. Several studies\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e have suggested that lower D* and f values indicate positive lymph node metastasis, while in a study of rectal adenocarcinoma patients, Jia et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e found higher f values in the lymph node-positive group, and Hui H et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e also reported higher f values in LNM (+) NSCLC. The reason for this discrepancy may be due to the low reproducibility and high variability of D* and f values, with significant differences in D* and f values among different tumor patients.\u003c/p\u003e\u003cp\u003ePrevious studies\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e have shown that the expression of glucose transporter-1 (Glut-1) is associated with lymph node metastasis in various tumors, leading to increased FDG uptake and corresponding quantitative indicators. Multiple studies have demonstrated that the SUVmax value of tumors is a predictive marker\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e for lymph node metastasis in NSCLC, and in our study, an SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;6.3 could distinguish between the presence or absence of lymph node metastasis. The research by Li et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e also supports our findings, as they suggested that a primary tumor SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4.3 has value in determining lymph node metastasis in NSCLC. Additionally, our study explored two parameters, MTV and TLG, based on tumor volume. Various studies\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e have reported that the MTV and TLG values of primary tumors provide information about tumor volume and activity, which may more accurately assess the metabolic burden of tumors compared to SUVmax. Seong Park et al.\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e reported in a study of 139 NSCLC patients that MTV demonstrated better predictive performance than other PET parameters in evaluating lymph node status in NSCLC. In this study, multivariate logistic regression analysis revealed that MTV was the only independent metabolic predictor, further confirming its predictive value for lymph node metastasis in NSCLC. This may be related to the fact that SUVmax only reflects the metabolic level of a single voxel within the lesion while ignoring the overall changes in the tumor. Moreover, differences between studies, including variations in the study population, injected radioactivity, image reconstruction parameters, and SUVmax measurement methods, may be important factors contributing to the inconsistencies between our study and previous research.\u003c/p\u003e\u003cp\u003eThis study utilized multivariate logistic regression analysis to establish a combined prediction model for lymph node metastasis in NSCLC patients, finding that the combined model of MTV\u0026thinsp;+\u0026thinsp;DDC demonstrated superior predictive performance compared to any single model alone. In previous studies, lymph nodes with FDG uptake higher than the metabolic level of background lymph nodes were considered diagnostic criteria for lymph node metastasis. However, inflammatory or granulomatous lesions can also lead to increased uptake. This study confirmed the ability of such criteria to predict lymph node metastasis while also demonstrating its relatively low sensitivity. In contrast, the MTV\u0026thinsp;+\u0026thinsp;DDC combined prediction model exhibited better predictive value (AUC 0.821, sensitivity 79.49%, specificity 73.59%). By integrating metabolic burden and microenvironment heterogeneity information, the MTV\u0026thinsp;+\u0026thinsp;DDC combined model overcomes the biological limitations of single imaging parameters, providing a more reliable predictive tool for lymph node metastasis in NSCLC.\u003c/p\u003e\u003cp\u003eKi-67, as an important indicator for evaluating tumor cell proliferative activity, demonstrated significant correlations with metabolic and diffusion parameters in this study. The results showed that the Ki-67 index was positively correlated with SUVmax, MTV, and TLG, while negatively correlated with ADC and D values. This finding is consistent with previous literature reports\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The underlying mechanism may be that a higher Ki-67 index reflects increased tumor cell proliferation activity, which, on one hand, leads to elevated energy metabolism demand in tissues, promoting increased 18F-FDG uptake. On the other hand, it results in higher cellular density, thereby restricting water molecule diffusion, ultimately manifesting as increased SUVmax and decreased ADC and D values. Correlation analysis between volumetric metabolic parameters (MTV, TLG) and the Ki-67 index holds significant clinical value, as it can effectively overcome limitations such as tumor heterogeneity, uneven metabolic distribution, and sampling errors from biopsies. Existing studies have confirmed that enhanced tumor cell proliferation activity leads to increased tumor volume and higher cellular density, which in turn elevates 18F-FDG uptake, reflected by higher MTV and TLG values. Notably, this study found no statistically significant correlation between the Ki-67 index and perfusion-related parameters (D* and f values). Apart from the two aforementioned lung cancer-related studies, Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e also reached a similar conclusion in a bladder cancer study, stating that restricted water molecule diffusion effectively reflects cell proliferation levels, whereas blood flow perfusion parameters are susceptible to multiple interfering factors. This may be because benign conditions such as tissue hyperemia and edema can also alter blood perfusion, indicating that D* and f values are influenced by too many confounding factors and do not accurately reflect tumor proliferative status.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, our research adopted a single-center, single-scanner design, which may lead to limitations in technical reproducibility and potential case selection bias. Second, the predictive model was based on the primary tumor rather than lymph nodes, imposing constraints in identifying the location and number of metastatic lymph nodes. Third, PET/MR images for each patient were acquired under free-breathing conditions, introducing respiratory motion artifacts into the images. Fourth, lesions with a maximum diameter smaller than 1 cm were excluded from this study due to noise interference, which may have introduced a certain degree of selection bias.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMultiparametric PET/MRI can be used to evaluate lymph node metastasis and Ki-67 proliferation status in non-small cell lung cancer (NSCLC). The combined predictive model of MTV and D provides a novel clinical approach for assessing lymph node metastasis status. Some parameters show a certain correlation with Ki-67 expression, which may assist in formulating individualized treatment strategies for NSCLC patients.\u003c/p\u003e\u003cp\u003eWfi3d-trig 3D T1-weighted spoiled gradient-echo sequence with Dixon-based water fat separation imaging\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDWI Diffusion-weighted imaging \u003c/p\u003e\n\u003cp\u003eIVIM Intravoxel incoherent motion \u003c/p\u003e\n\u003cp\u003eADCstand Stand diffusion coefficient \u003c/p\u003e\n\u003cp\u003eD True diffusion coefficient\u003c/p\u003e\n\u003cp\u003eDDC Distributed diffusion coefficient \u003c/p\u003e\n\u003cp\u003eD* Pseudo diffusion coefficient\u003c/p\u003e\n\u003cp\u003ef Perfusion fraction \u003c/p\u003e\n\u003cp\u003eMTV Metabolic tumor volume \u003c/p\u003e\n\u003cp\u003eTLG Total lesion glycolysis \u003c/p\u003e\n\u003cp\u003eLNM Lymph Node Metastasis\u003c/p\u003e\n\u003cp\u003eNSCLC Non-small cell lung cancer\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e1.Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Research Committee of Henan Provincial People\u0026apos;s Hospital and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This prospective study was approved by Henan Provincial People\u0026apos;s Hospital Ethics Review Board (NO.2021148).\u0026nbsp;All participants provided written informed consent to participate in this study.\u0026nbsp;All data is analyzed anonymously.\u003c/p\u003e\n\u003cp\u003e2. Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e3.Availability of data and materials\u003c/p\u003e\n\u003cp\u003eDue to privacy restrictions, raw data cannot be made available free of charge, but datasets used and/or analyzed during the current study may be obtained from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e4.Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no relationships with any companies.\u003c/p\u003e\n\u003cp\u003e5.Funding\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Key R\u0026amp;D Program of China (2023YFC2414200), and the Henan medical science and technology project (LHGJ20210001).\u003c/p\u003e\n\u003cp\u003e6.Authors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eQQC and NM conceptualized the ideas, designed the study, analyzed the data, constructed the models, and authored the manuscript text. XYW, YL, JWZ and JYP were responsible for recruiting and scanning the patients. YPW, ZH, YY and ZW assisted in the statistical analysis and model building. QYL and FFF critically revised the manuscript for important intellectual content. FFF and MYW provided administrative, technical, material, and financial support, and supervised the study. All authors reviewed the manuscript, read, and approved the final version. 7.Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank the patients and their families who supported and cooperated with this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, RL, Kratzer, TB, Giaquinto, AN, , Sung, H., Jemal, A. Cancer statistics, 2025. CA Cancer J Clin. 2025; 75 (1): 10-45.\u003c/li\u003e\n\u003cli\u003eAsamura, H, Nishimura, KK, Giroux, DJ, et al. IASLC Lung Cancer Staging Project: The New Database to Inform Revisions in the Ninth Edition of the TNM Classification of Lung Cancer. J THORAC ONCOL. 2023; 18 (5): 564-575.\u003c/li\u003e\n\u003cli\u003eRiely, GJ, Wood, DE, Ettinger, DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology J NATL COMPR CANC NE. 2024; 22 (4): 249-274.\u003c/li\u003e\n\u003cli\u003eJiang C, Zhang Y, Fu F, Deng P, Chen H. A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer. J THORAC ONCOL. 2023; 19 (1): 25-35.\u003c/li\u003e\n\u003cli\u003eYang, Y, Shao, X, Li, Z, et al. Prognostic heterogeneity of Ki67 in non-small cell lung cancer: A comprehensive reappraisal on immunohistochemistry and transcriptional data. J CELL MOL MED. 2024; 28 (14): e18521.\u003c/li\u003e\n\u003cli\u003eWei, DM, Chen, WJ, Meng, RM, et al. Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients. Respir Res. 2018; 19 (1): 150.\u003c/li\u003e\n\u003cli\u003eSommer, G, Wiese, M, Winter, L, et al. Preoperative staging of non-small-cell lung cancer: comparison of whole-body diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography. EUR RADIOL. 2012; 22 (12): 2859-67.\u003c/li\u003e\n\u003cli\u003eKirchner, J, Sawicki, LM, Nensa, F, et al. Prospective comparison of 18F-FDG PET/MRI and 18F-FDG PET/CT for thoracic staging of non-small cell lung cancer. EUR J NUCL MED MOL I. 2018; 46 (2): 437-445.\u003c/li\u003e\n\u003cli\u003eFraioli, F, Screaton, NJ, Janes, SM, et al. Non-small-cell lung cancer resectability: diagnostic value of PET/MR. Eur J Nucl Med Mol Imaging. 2014; 42 (1): 49-55.\u003c/li\u003e\n\u003cli\u003eGuo, L, Liu, X, Liu, Z, et al. Differential detection of metastatic and inflammatory lymph nodes using intravoxel incoherent motion diffusion-weighted imaging. MAGN RESON IMAGING. 2019; 65 62-66.\u003c/li\u003e\n\u003cli\u003eDel Gobbo, A, Pellegrinelli, A, Gaudioso, G, et al. Analysis of NSCLC tumour heterogeneity, proliferative and 18F-FDG PET indices reveals Ki67 prognostic role in adenocarcinomas. HISTOPATHOLOGY. 2015; 68 (5): 746-51.\u003c/li\u003e\n\u003cli\u003eSurov, A, Meyer, HJ, Wienke, A. Standardized Uptake Values Derived from 18F-FDG PET May Predict Lung Cancer Microvessel Density and Expression of KI 67, VEGF, and HIF-1\u0026alpha; but Not Expression of Cyclin D1, PCNA, EGFR, PD L1, and p53. CONTRAST MEDIA MOL I. 2018; 2018 9257929.\u003c/li\u003e\n\u003cli\u003eZheng, Y, Huang, W, Zhang, X, et al. A Noninvasive Assessment of Tumor Proliferation in Lung cancer Patients using Intravoxel Incoherent Motion Magnetic Resonance Imaging. J Cancer. 2021; 12 (1): 190-197.\u003c/li\u003e\n\u003cli\u003eXiang L, Yang H, Qin Y, Wen Y, Liu X, Zeng W-B. Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions. Front Oncol. 2023; 12 1075072.\u003c/li\u003e\n\u003cli\u003eHuang, Z, Li, X, Wang, Z, et al. Application of Simultaneous 18 F-FDG PET With Monoexponential, Biexponential, and Stretched Exponential Model-Based Diffusion-Weighted MR Imaging in Assessing the Proliferation Status of Lung Adenocarcinoma. J MAGN RESON IMAGING. 2021; 56 (1): 63-74.\u003c/li\u003e\n\u003cli\u003eYe, X, Chen, S, Tian, Y, et al. A preliminary exploration of the intravoxel incoherent motion applied in the preoperative evaluation of mediastinal lymph node metastasis of lung cancer. J THORAC DIS. 2017; 9 (4): 1073-1080.\u003c/li\u003e\n\u003cli\u003eFang, T, Meng, N, Feng, P, et al. A Comparative Study of Amide Proton Transfer Weighted Imaging and Intravoxel Incoherent Motion MRI Techniques Versus (18) F-FDG PET to Distinguish Solitary Pulmonary Lesions and Their Subtypes. J MAGN RESON IMAGING. 2021; 55 (5): 1376-1390.\u003c/li\u003e\n\u003cli\u003eSauer, M, Klene, C, Kaul, M, et al. Preoperative evaluation of pelvine lymph node metastasis in high risk prostate cancer with intravoxel incoherent motion (IVIM) MRI. EUR J RADIOL. 2018; 107 1-6.\u003c/li\u003e\n\u003cli\u003eSong, T, Lu, S, Qu, J, et al. Intravoxel incoherent motion diffusion-weighted imaging in evaluating preoperative staging of esophageal squamous cell carcinoma : Evaluation of preoperative stage of primary tumour and prediction of lymph node metastases from esophageal cancer using IVIM: a prospective study. CANCER IMAGING. 2024; 24 (1): 116.\u003c/li\u003e\n\u003cli\u003eZheng, Y., Han, N., Huang, W., Jiang, Y., Zhang, J. Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging. J THORAC IMAG. 2023; 39 (5): 285-292.\u003c/li\u003e\n\u003cli\u003eWang, K, Wu, G. Monoexponential, biexponential, stretched exponential and diffusion kurtosis models of diffusion-weighted imaging: a quantitative differentiation of solitary pulmonary lesion. BMC Med Imaging. 2024; 24 (1): 346.\u003c/li\u003e\n\u003cli\u003eKim HC, Seo N, Chung YE, Park MS, Choi JY, Kim MJ. Characterization of focal liver lesions using the stretched exponential model: comparison with monoexponential and biexponential diffusion-weighted magnetic resonance imaging. EUR RADIOL. 2019; 29 (9): 5111-5120.\u003c/li\u003e\n\u003cli\u003eZhang, Y, Zhang, KY, Jia, HD, et al. Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer. ACAD RADIOL. 2021; 29 (7): 1048-1057.\u003c/li\u003e\n\u003cli\u003eYang, X, Chen, Y, Wen, Z, et al. Non-invasive MR assessment of the microstructure and microcirculation in regional lymph nodes for rectal cancer: a study of intravoxel incoherent motion imaging. CANCER IMAGING. 2019; 19 (1): 70.\u003c/li\u003e\n\u003cli\u003eJia, H, Jiang, X, Zhang, K, et al. A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively. J MAGN RESON IMAGING. 2022; 56 (3): 658-667.\u003c/li\u003e\n\u003cli\u003eHan, H, Guo, W, Ren, H, et al. Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction. Insights Imaging. 2024; 15 (1): 168.\u003c/li\u003e\n\u003cli\u003eEilsberger, F, Noltenius, FE, Librizzi, D, et al. Real-Life Performance of F-18-FDG PET/CT in Patients with Cervical Lymph Node Metastasis of Unknown Primary Tumor. Biomedicines. 2022; 10 (9).\u003c/li\u003e\n\u003cli\u003eTodate, Y, Honda, M, Takada, T, et al. The additional diagnostic impact of positron emission tomography-computed tomography for lymph node metastasis from colorectal cancer: A prospective lymph node level analysis. J SURG ONCOL. 2021; 124 (7): 1085-1090.\u003c/li\u003e\n\u003cli\u003eLee, AY, Choi, SJ, Jung, KP, et al. Characteristics of Metastatic Mediastinal Lymph Nodes of Non-Small Cell Lung Cancer on Preoperative F-18 FDG PET/CT. NUCL MED MOLEC IMAG. 2013; 48 (1): 41-6.\u003c/li\u003e\n\u003cli\u003eYu X, Wang J, Huang L, Xie L, Su Y. Predictive value of 18F-FDG PET/CT metabolic parameters for lymph node metastasis of non-small cell lung cancer. Biomark Med. 2024; 19 (2): 35-41.\u003c/li\u003e\n\u003cli\u003eLi, L, Ren, S, Zhang, Y, et al. Risk factors for predicting the occult nodal metastasis in T1-2N0M0 NSCLC patients staged by PET/CT: potential value in the clinic. LUNG CANCER. 2013; 81 (2): 213-7.\u003c/li\u003e\n\u003cli\u003ePark SY, Yoon JK, Park KJ, Lee SJ. Prediction of occult lymph node metastasis using volume-based PET parameters in small-sized peripheral non-small cell lung cancer. CANCER IMAGING. 2015; 15 21.\u003c/li\u003e\n\u003cli\u003eGoksel S, Erdivanli OC, Bulbul O, Dursun E. The role of metabolic tumor parameters predicting cervical lymph node metastasis in patients with head and neck squamous cell carcinoma. J CANCER RES THER. 2022; 18 (4): 1045-1051.\u003c/li\u003e\n\u003cli\u003eWang F, Wu LM, Hua XL, Zhao ZZ, Chen XX, Xu JR. Intravoxel incoherent motion diffusion-weighted imaging in assessing bladder cancer invasiveness and cell proliferation. J MAGN RESON IMAGING. 2017; 47 (4): 1054-1060.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Patient clinicopathological characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e59\u0026plusmn;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSCC\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMaximum diameter (cm)\u003c/p\u003e\n \u003cp\u003eIASLC stage (2024), n (%) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEarly stage (I-IIB) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAdvanced stage (IIIA-IV)\u003c/p\u003e\n \u003cp\u003eKi67, n (%)\u003c/p\u003e\n \u003cp\u003e\u0026le;25%\u003c/p\u003e\n \u003cp\u003e＞25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2.7\u0026plusmn;1.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62（67.39）\u003c/p\u003e\n \u003cp\u003e30（32.61）\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52（56.52）\u003c/p\u003e\n \u003cp\u003e40（43.48）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2\u0026nbsp;Parameters used for MR-IVIM imaging\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"459\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eT1WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIVIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eSequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2D-FSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2D-FSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2D-SS-EPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2D-SS-EPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eOrientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTR/TE(ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.06/2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3,315/87.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1,620/69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1,620/69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eFOV (cm2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e35\u0026times;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35\u0026times;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35\u0026times;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e35\u0026times;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eMatrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e303\u0026times;456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e264\u0026times;480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e202\u0026times;256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e202\u0026times;256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eBandwidth(Hz/pixel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSlice thickness(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eInterval (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1,1,2,2,4,4,6,6,8,10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eb-values (s/mm2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0,25,50,100,150,200\u003c/p\u003e\n \u003cp\u003e400,600,800,1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eRespiratory compensation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eScan time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e14s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2min 26s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2min58s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3min 38s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Comparison of \u003csup\u003e18\u003c/sup\u003eF-FDG PET and IVIM Parameters for LNM status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eLNM-positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eLNM-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSUVmax(g/cm3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e6.96(1.14,16.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e5.45(0.48,13.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMTV(cm3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e18.48(1.87,105.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e6.47(0.58,24.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTLG(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e58.06(2.07,259.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e29.92(0.69,194.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003eADCstand(10\u0026minus;3 mm2/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.878(0.25,1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.2(0.29,4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eD(10\u0026minus;3mm2/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.71 (0.014,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.95 (0.002,3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eDDC(10\u0026minus;3mm2/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e2.81(0.54,7.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e4.65(0.93,9.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eD*(10\u0026minus;3 mm2/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e71.64 (3.66,147.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e73.79(7.89,186.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ef (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e43.14(17.44,72.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e47.48(13.63,373.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Univariate and multivariate analyses\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eUnivariate Analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eMultivariate Analyses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eSUVmax\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1.145(1.0007-1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.227(0.975-1.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eMTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1.119(1.047-1.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.891(0.807-0.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eTLG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1.017(1.005-1.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.993(0.976-1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.206(0.066-0.646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3.173 (0.233-43.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.231(0.069-0.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.769(0.111-28.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eDDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.618(0.477-0.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.619(1.182-2.219)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eD*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.999(0.989-1.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.990(0.975-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.997(0.984-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.001(0.990-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 ROC Analysis of the Diagnostic Performance for Different Parameters Alone or in Combination for Predicting LNM status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"784\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYouden Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCut-off Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eComparison with Combined Diagnosis (P-Value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.629 (0.513\u0026ndash;0.746)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.3202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e64.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e67.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eP=0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.754 (0.655\u0026ndash;0.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.4039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e74.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e66.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eP=0.0998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTLG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.726 (0.621\u0026ndash;0.832)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.4267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e25.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e61.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e81.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eP=0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.685 (0.568\u0026ndash;0.802)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.3875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e53.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e84.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eP=0.0462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.656 (0.538\u0026ndash;0.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.3014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e66.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eP=0.0161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eDDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.736 (0.633\u0026ndash;0.838)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.4485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e76.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e67.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eP=0.0135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMTV+DDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.821 (0.736\u0026ndash;0.907)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.5307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e79.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e73.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 13.3333px;\"\u003e\u0026nbsp;\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e\u003c/span\u003eSCC, squamous cell carcinoma; AC, adenocarcinoma; LNM, lymph node metastasis.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eT1WI = T1-weighted imaging T2WI = T2-weighted imaging DWI = diffusion-weighted imaging IVIM = intravoxel incoherent motion FOV = field of view TR/TE = repetition time/echo time NEX = number of excitations\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eADCstand, stand diffusion coefficient; D, true diffusion coefficient; DDC, distributed diffusion coefficient; D*, pseudo diffusion coefficient; f, perfusion fraction; LNM, Lymph Node Metastasis.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eSUVmax = maximum standardized uptake value; MTV = metabolic tumor volume; TLG = total lesion glycolysis; ADC = apparent diffusion coefficient; D, diffusion coefficient; DDC, distributed diffusion coefficient; D*, pseudo diffusion coefficient; f, perfusion fraction. All factors with P \u0026lt; 0.1 in univariate analyses were included in multivariate regression analyses. OR = odds ratio; CI = confidence interval. The bold typeface in the table indicates the logistic regression analyses with statistical significance (significance level = 0.05).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer, Lymph node metastasis, Ki-67, PET/MRI, Intravoxel incoherent motion imaging","lastPublishedDoi":"10.21203/rs.3.rs-7435917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7435917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Integrated positron emission tomography/magnetic resonance (PET/MR) may have the potential to evaluate lymph node metastasis status and Ki-67 proliferation index in patients with non-small cell lung cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study enrolled 92 pathologically confirmed NSCLC patients who underwent preoperative integrated PET/MRI. Quantitative analysis was performed for PET metabolic parameters (SUVmax, MTV, TLG) and IVIM parameters (ADC, D, D*, f, DDC). The predictive performance of each parameter for LNM was assessed using receiver operating characteristic (ROC) curve analysis, and a multivariate logistic regression model was constructed to establish the optimal combined predictive model. Spearman correlation analysis was used to explore the relationship between imaging parameters and Ki-67 expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e LNM prediction: The LNM-positive group exhibited lower ADC, D, and DDC (all P \u0026lt; 0.05) compared to the LNM-negative group. Multivariate analysis identified MTV and DDC as independent predictors of LNM. The combined model (MTV + DDC) achieved an AUC of 0.821 (sensitivity 79.49%, specificity 73.59%), significantly outperforming individual parameters (P \u0026lt; 0.05). Ki-67 correlation: SUVmax, MTV, and TLG showed positive correlations with Ki-67 (r= 0.232–0.300, P \u0026lt; 0.05), while ADC and D values exhibited negative correlations (r= −0.327 to −0.240, P \u0026lt; 0.05). No significant association was observed between D*, f, and Ki-67 (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Integrated PET/MRI, by combining metabolic (MTV) and diffusion (DDC) parameters, significantly improves the predictive accuracy for LNM in NSCLC. Additionally, metabolic and select IVIM parameters correlate with Ki-67 expression.\u003c/p\u003e","manuscriptTitle":"Prediction of Lymph Node Metastasis in Non-Small Cell Lung Cancer and Its Correlation with Ki67 Expression: A Comparative Study between Intravoxel Incoherent Motion Imaging and 18F-FDG PET","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 08:41:18","doi":"10.21203/rs.3.rs-7435917/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T14:49:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T22:04:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T13:39:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T07:50:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317028030935997229651914202194991524740","date":"2025-10-01T05:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86473746300725938313541676480031001815","date":"2025-10-01T04:09:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92566307907088265506379977330306672655","date":"2025-09-29T02:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31452103197475840237131644533074536098","date":"2025-09-29T02:52:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301680850765622442832637257822609856597","date":"2025-09-07T09:21:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T20:01:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54220054931682191151192523931567445070","date":"2025-08-30T16:44:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232261253646321252605411116515359919995","date":"2025-08-29T02:00:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-28T01:00:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T13:53:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-25T11:11:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-25T10:53:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-08-25T10:50:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32477b8c-6981-47ba-aac4-70b3e5f4bb05","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T15:59:15+00:00","versionOfRecord":{"articleIdentity":"rs-7435917","link":"https://doi.org/10.1186/s12880-025-02131-z","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2026-01-03 15:57:07","publishedOnDateReadable":"January 3rd, 2026"},"versionCreatedAt":"2025-09-08 08:41:18","video":"","vorDoi":"10.1186/s12880-025-02131-z","vorDoiUrl":"https://doi.org/10.1186/s12880-025-02131-z","workflowStages":[]},"version":"v1","identity":"rs-7435917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7435917","identity":"rs-7435917","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}