Multi-parametric MRI Diffusion Models Combined with Clinical Information for Predicting Ki-67 Expression in Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study

Multi-parametric MRI Diffusion Models Combined with Clinical Information for Predicting Ki-67 Expression in Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study | 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 Multi-parametric MRI Diffusion Models Combined with Clinical Information for Predicting Ki-67 Expression in Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study Ke Li, Jing Li, Xiaoling Liu, Jiafei Chen, Wei Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6830573/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Abdominal Radiology → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters combined with clinical information for predicting Ki-67 expression in pancreatic ductal adenocarcinoma (PDAC). Methods This prospective cohort study enrolled 65 patients with histopathologically confirmed PDAC between January 2024 and May 2025. All patients underwent 3.0T MRI including conventional sequences and advanced diffusion-weighted imaging sequences. Clinical data and laboratory parameters were collected within one week before surgery or biopsy. Ki-67 expression was assessed using immunohistochemical staining with 50% as the cutoff value. Two radiologists independently performed quantitative measurements with excellent inter-observer reliability (ICC > 0.85). Univariate and multivariate logistic regression analyses identified independent predictors. ROC curve analysis and DeLong test evaluated diagnostic performance. Results Based on Ki-67 expression threshold of 50%, 48 patients (73.8%) were classified as low expression and 17 patients (26.2%) as high expression. Compared to the low Ki-67 group, the high expression group demonstrated significantly lower monocyte count (0.35 ± 0.09 vs 0.49 ± 0.16×10⁹/L, P = 0.001), higher IVIM perfusion fraction f-value (14.08 ± 3.41% vs 10.90 ± 3.83%, P = 0.004), and lower DKI mean diffusivity MD-value (1.26 ± 0.17 vs 1.65 ± 0.17×10⁻³ mm²/s, P < 0.001). Individual prediction models achieved AUCs of 0.763 (monocyte count), 0.732 (IVIM-f), and 0.800 (DKI-MD). The combined prediction model integrating these three parameters demonstrated excellent diagnostic performance with AUC of 0.913 (95% CI: 0.841–0.985), sensitivity of 82.4%, and specificity of 83.3%, significantly outperforming all individual models (P < 0.001). Conclusion This multi-parametric combined prediction model achieves excellent diagnostic performance for preoperative non-invasive assessment of Ki-67 expression status in PDAC, providing a reliable tool for precision medicine practice and personalized treatment strategies. Pancreatic ductal adenocarcinoma Ki-67 Intravoxel incoherent motion Diffusion kurtosis imaging Magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 Introduction Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy worldwide. According to GLOBOCAN 2022, approximately 495,773 new pancreatic cancer cases and 466,003 deaths occurred globally, ranking as the 14th most common cancer and the 7th leading cause of cancer death. Its mortality-to-incidence ratio approaches 0.94, reflecting its exceptional fatality [ 1 ]. In China, both incidence and mortality rates continue to rise [ 2 ]. The 5-year survival rate remains below 10%, primarily due to difficulties in early diagnosis, rapid disease progression, and limited effective treatments [ 3 , 4 ]. Most patients are diagnosed at advanced stages when surgery is no longer feasible, and even for resectable cases, postoperative recurrence rates remain high [ 5 , 6 ]. Therefore, identifying effective predictive biomarkers and establishing accurate prognostic models are crucial for individualized PDAC treatment. Ki-67, a nuclear protein marker for tumor proliferative activity, has expression levels closely correlated with PDAC aggressiveness, prognosis, and treatment response [ 7 , 8 ]. Studies have shown that PDAC patients with elevated Ki-67 expression have significantly reduced overall and recurrence-free survival rates [ 9 ], with high expression significantly associated with early postoperative recurrence (< 1 year) [ 10 ]. Co-expression of Ki-67 and p53 can more accurately predict prognosis in patients receiving gemcitabine adjuvant therapy [ 11 ]. Currently, Ki-67 expression assessment relies primarily on immunohistochemical testing of biopsy or surgical specimens, which has limitations including sampling bias and complication risks [ 12 , 13 ]. Preoperative non-invasive assessment of Ki-67 expression would facilitate treatment planning, prognostic evaluation, and therapeutic monitoring, advancing precision medicine for PDAC [ 14 ]. Conventional diffusion-weighted imaging (DWI) provides tissue microstructural information by detecting restricted water molecule diffusion, but the apparent diffusion coefficient (ADC) based on a mono-exponential model cannot distinguish between true diffusion and vascular perfusion effects, and assumes Gaussian water molecule diffusion distribution, presenting inherent limitations [ 15 ]. Multi-parametric diffusion models such as intravoxel incoherent motion (IVIM) can separate true diffusion components (D) from perfusion parameters (D*, f), while diffusion kurtosis imaging (DKI) quantifies non-Gaussian water molecule diffusion behavior (K value), together providing more comprehensive information about tissue microstructure and microcirculation [ 15 , 16 ]. These advanced models have demonstrated superior value to conventional DWI in evaluating tumor tissue characteristics [ 17 , 18 ]. Based on PDAC histological heterogeneity and the prognostic significance of Ki-67 expression, we hypothesize that multi-parametric MRI diffusion models combined with clinical information can accurately predict Ki-67 expression status in PDAC patients. This study aims to establish this comprehensive predictive model, providing new methods for non-invasive molecular phenotyping, guiding individualized treatment decisions, and improving patient outcomes. Materials and Methods Study Population This prospective cohort study was conducted at the Department of Radiology in our institution from January 2024 to May 2025. We consecutively recruited 87 patients with clinically suspected pancreatic ductal adenocarcinoma (PDAC), all of whom underwent magnetic resonance imaging including conventional sequences and advanced diffusion-weighted imaging sequences (IVIM and DKI). Inclusion criteria were: (1) age 18–80 years; (2) clinical or radiological suspicion of PDAC; (3) ability to tolerate MRI examination; (4) no prior anti-tumor treatment; (5) scheduled for surgical resection or biopsy; and (6) lesion diameter greater than 1.5 cm to facilitate subsequent analysis. Exclusion criteria included: (1) poor MRI image quality or severe artifacts; (2) final pathological diagnosis other than PDAC; (3) neoadjuvant chemotherapy before surgery or biopsy; (4) presence of other systemic diseases that could affect imaging assessment; and (5) incomplete clinical data. After screening, 65 patients with histopathologically confirmed PDAC were included in the final analysis and the detailed flowchart is shown in Fig. 1 . This study protocol was approved by the Medical Ethics Committee of our institution and conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment. Imaging Technique All MRI examinations were performed on a 3.0T MR scanner (United Imaging 770, Shanghai United Imaging Healthcare, China) with a standard 12-channel phased array body coil. All patients fasted for 6–8 hours prior to examination to minimize pancreatic compression and artifacts from a full stomach. Patients also received breath-holding training before the examination to reduce respiratory motion artifacts. The imaging protocol included conventional T1-weighted and T2-weighted sequences as well as advanced diffusion sequences for IVIM and DKI model analyses. All detailed scanning parameters are presented in Table 1 . Table 1 MRI scanning parameters Parameter T1WI (GRE) T2WI (SSFSE) T2WI (FSE) with FS DWI IVIM DKI Sequence 3D dual-echo GRE Single-shot FSE Fast spin echo EPI EPI EPI Imaging plane Transverse Coronal Transverse Transverse Oblique axial Oblique axial Repetition time (ms) 155 1000 3000 4095 5200 5200 Echo time (ms) 1.45/3.35 103.2 87.6 77.4 77.4 77.4 Flip angle (°) 70 120 90 90 90 90 Field of view (mm²) 400×320 380×380 400×300 380×300 380×300 380×300 Matrix 256×256 320×320 256×256 128×128 128×128 128×128 Section thickness (mm) 5 4 4 5 5 5 Intersection gap (mm) 0 0 10 0 0 0 Voxel size (mm³) 1.84×1.56×5.0 1.48×1.19×4.0 1.74×1.56×4.0 2.97×2.97×5.0 2.97×2.97×5.0 2.97×2.97×5.0 Fat suppression No No SPECIAL (Fat Sat) Spectral adiabatic inversion recovery Spectral adiabatic inversion recovery Spectral adiabatic inversion recovery Bandwidth (Hz/pixel) 1200 600 300 2200 2200 2200 Respiratory control Breath-hold Breath-hold Breath-hold Free breathing Free breathing Free breathing b-values (s/mm²) - - - 0, 800 0, 25, 75, 100, 150, 200, 500, 800, 1200, 2000 0, 800, 1200, 2000 Image Analysis All images were transferred to an advanced post-processing workstation (United Imaging Healthcare, China) for analysis. Two radiologists (with 8 and 11 years of experience in abdominal MRI diagnosis) independently evaluated all images while blinded to clinical and pathological information and each other's results. Regions of interest (ROIs) were placed on the largest cross-sectional area of the solid tumor component, as identified on T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) sequences. ROIs were carefully drawn on DWI (b = 800 s/mm²), IVIM, and DKI images, avoiding blood vessels, necrotic areas, calcifications, and adjacent normal pancreatic tissue (Fig. 2 ). Each radiologist performed three measurements per case, and the resulting average values were used. The final parameters represented the mean values from both radiologists, with individual measurements preserved for inter-observer agreement analysis. Quantitative parameters included: apparent diffusion coefficient (ADC) from conventional DWI; true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM; mean kurtosis (MK) and mean diffusivity (MD) from DKI. The IVIM and DKI parameters were automatically calculated through pixel-by-pixel fitting using bi-exponential and non-Gaussian models, respectively. Additionally, maximum tumor diameter was measured on T2-weighted images, and tumor location and lymph node metastasis status were recorded. Clinical Data and Histopathologic Analysis Clinical data were collected within one week before surgery or biopsy, including gender, age, body mass index (BMI), serum tumor markers (carcinoembryonic antigen [CEA], carbohydrate antigen 19 − 9 [CA19-9], carbohydrate antigen 125 [CA125]), and hematological parameters (lymphocyte, neutrophil, monocyte, and platelet counts). Histopathologic evaluation was performed on surgical specimens or biopsy samples. Ki-67 expression was assessed by an experienced pathologist who was blinded to the imaging findings. The percentage of positively stained tumor cell nuclei was calculated in areas with the highest nuclear labeling. Based on previous studies, a threshold of 50% was selected as the cutoff value [ 19 ]. Patients were stratified into two groups: low Ki-67 expression (< 50%) and high Ki-67 expression (≥ 50%). Statistical Analysis Statistical analysis was performed using R software ( http://www.R-project.org ), SPSS software (version 26.0, SPSS, IBM), and MedCalc (version 18.2.1, MedCalc Software). Normality was assessed using the Shapiro-Wilk test. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequencies (percentages). Independent samples t-test or Mann-Whitney U test was used for continuous variables between groups, while chi-square test or Fisher's exact test was applied for categorical variables. Intraclass correlation coefficient (ICC) was used to evaluate inter-observer agreement between two radiologists, with ICC > 0.75 indicating good consistency. Univariate and multivariate logistic regression analyses were performed to identify predictors associated with high Ki-67 expression, with variables showing P < 0.05 in univariate analysis included in multivariate analysis to establish a combined prediction model. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance of individual parameters and the combined model, calculating area under the curve (AUC), sensitivity, specificity, and accuracy, with DeLong test used to compare AUC differences between models. A nomogram was constructed based on the optimal prediction model, and decision curve analysis (DCA) was performed to assess clinical utility, while calibration curves were used to evaluate agreement between predicted and actual probabilities. All statistical tests were two-sided, with P < 0.05 considered statistically significant. Results Population Characteristics A total of 65 patients with histopathologically confirmed PDAC were included in the final analysis, with a mean age of 62.0 ± 9.6 years, comprising 34 males (52.3%) and 31 females (47.7%), and a mean BMI of 22.8 ± 3.6 kg/m². Tumor locations included 44 cases (67.7%) in the pancreatic head and 21 cases (32.3%) in the body/tail region. Based on a Ki-67 expression threshold of 50%, 48 patients (73.8%) were classified as low expression and 17 patients (26.2%) as high expression. Histopathological diagnosis was obtained through surgical resection in 58 cases (89.2%) and percutaneous biopsy in 7 cases (10.8%). Comparison of Clinical and Imaging Features Between Groups Inter-observer reliability assessment between two radiologists for MRI diffusion parameter measurements demonstrated intraclass correlation coefficients (ICC) greater than 0.85, indicating excellent measurement consistency. Table 2 shows the comparison between low Ki-67 expression and high Ki-67 expression groups. No significant differences were observed in clinical characteristics (age, sex, BMI, tumor diameter, location, lymph node metastasis) between groups (P > 0.05). Among laboratory parameters, monocyte count was significantly lower in the high Ki-67 expression group (0.35 ± 0.09 vs 0.49 ± 0.16×10⁹/L, P = 0.001), while other blood parameters and tumor markers showed no differences. MRI diffusion parameter analysis revealed that the high Ki-67 expression group demonstrated significantly higher IVIM perfusion fraction f values (14.08 ± 3.41% vs 10.90 ± 3.83%, P = 0.004) and significantly lower DKI mean diffusivity MD values (1.26 ± 0.17 vs 1.65 ± 0.17×10⁻³ mm²/s, P < 0.001), while ADC, IVIM-D, IVIM-D*, and DKI-MK values showed no statistical differences. Table 2 Comparison of clinical and imaging characteristics between low and high Ki-67 expression groups Parameters Low Ki-67 expression (< 50%) High Ki-67 expression (≥ 50%) Standardized difference (95% CI) P-value Clinical characteristics Age (years) 61.54 ± 9.83 63.24 ± 9.14 0.18 (-0.38, 0.73) 0.537 BMI (kg/m²) 22.83 ± 3.72 22.58 ± 3.27 0.07 (-0.48, 0.62) 0.808 Maximum tumor diameter (cm) 3.09 ± 1.16 3.21 ± 1.46 0.08 (-0.47, 0.64) 0.751 Sex 0.31 (-0.25, 0.86) 0.285 Female 21 (43.75) 10 (58.82) Male 27 (56.25) 7 (41.18) Tumor location 0.09 (-0.47, 0.64) 0.759 Head/neck/body 33 (68.75) 11 (64.71) Tail 15 (31.25) 6 (35.29) Lymph node metastasis 0.26 (-0.30, 0.81) 0.372 Absent 28 (58.33) 12 (70.59) Present 20 (41.67) 5 (29.41) Laboratory parameters Neutrophil count (×10⁹/L) 4.14 ± 1.62 4.33 ± 1.71 0.11 (-0.44, 0.67) 0.685 Lymphocyte count (×10⁹/L) 1.17 ± 0.44 1.12 ± 0.50 0.12 (-0.43, 0.67) 0.662 Monocyte count (×10⁹/L) 0.49 ± 0.16 0.35 ± 0.09 1.06 (0.48, 1.64) 0.001* Platelet count (×10⁹/L) 220.83 ± 88.36 195.06 ± 52.05 0.36 (-0.20, 0.91) 0.262 CEA 0.13 (-0.42, 0.68) 0.645 Normal 28 (58.33) 11 (64.71) Elevated 20 (41.67) 6 (35.29) CA19-9 0.04 (-0.51, 0.60) 0.878 Normal 5 (10.42) 2 (11.76) Elevated 43 (89.58) 15 (88.24) CA125 0.33 (-0.23, 0.88) 0.241 Normal 33 (68.75) 9 (52.94) Elevated 15 (31.25) 8 (47.06) MRI parameters ADC (×10⁻³ mm²/s) 1.55 ± 0.18 1.57 ± 0.25 0.12 (-0.44, 0.67) 0.653 D (×10⁻³ mm²/s) 1.35 ± 0.25 1.25 ± 0.21 0.43 (-0.12, 0.99) 0.146 D* (×10⁻³ mm²/s) 60.39 ± 16.90 57.62 ± 16.05 0.17 (-0.39, 0.72) 0.559 f (%) 10.90 ± 3.83 14.08 ± 3.41 0.88 (0.30, 1.45) 0.004* MD (×10⁻³ mm²/s) 1.65 ± 0.17 1.26 ± 0.17 2.35 (1.66, 3.03) < 0.001* MK (dimensionless) 0.74 ± 0.10 0.71 ± 0.08 0.34 (-0.21, 0.90) 0.249 Establishment and Validation of Ki-67 Expression Prediction Model Based on univariate and multivariate analysis results, monocyte count, IVIM perfusion fraction f, and DKI mean diffusivity MD values with statistical significance were selected to construct individual prediction models. ROC curve analysis revealed AUCs of 0.763 (95% CI: 0.641–0.885) for monocyte count, 0.732 (95% CI: 0.603–0.860) for IVIM-f, and 0.800 (95% CI: 0.682–0.917) for DKI-MD (Fig. 3 A). A combined prediction model was established by integrating the three parameters through logistic regression analysis, achieving an AUC of 0.913 (95% CI: 0.841–0.985) with sensitivity of 82.4% and specificity of 83.3%. DeLong test demonstrated that the combined model significantly outperformed all individual parameter models (all P < 0.001). Table 3 summarizes the detailed performance metrics of each prediction model. A nomogram based on the combined model provided an intuitive visualization tool for clinical application (Fig. 3 B). DCA showed that the combined model demonstrated net benefit across threshold probabilities ranging from 0.01 to 1.0, confirming its favorable clinical utility (Fig. 3 C). The calibration curve indicated good agreement between predicted and actual probabilities, demonstrating high model prediction accuracy (Fig. 3 D). Table 3 ROC curve analysis results of different models for predicting Ki-67 expression Parameters AUC 95% CI Cutoff Sensitivity (%) Specificity (%) Accuracy (%) Monocyte count 0.763 0.641–0.885 0.445 × 10⁹/L 88.2 58.3 66.2 f 0.732 0.603–0.860 10.03% 94.1 50.0 61.5 MD 0.800 0.682–0.917 1.63 × 10⁻³ mm²/s 76.5 77.1 76.9 Combined 0.913 0.841–0.985 -- 82.4 83.3 83.1 Discussion This prospective cohort study enrolled 65 patients with histopathologically confirmed pancreatic ductal adenocarcinoma (PDAC) to evaluate the diagnostic value of MRI multi-parametric diffusion models IVIM and DKI parameters combined with clinical information for predicting Ki-67 expression. The study identified that monocyte count, IVIM perfusion fraction f-value, and DKI mean diffusivity MD-value were significantly associated with Ki-67 expression status. The combined prediction model integrating these three parameters demonstrated excellent diagnostic performance, achieving an AUC of 0.913 (95% CI: 0.841–0.985) with sensitivity of 82.4% and specificity of 83.3%, significantly outperforming individual parameter models. Multiple studies have reported the application of IVIM and DKI parameters in tumor proliferation assessment. A study on soft tissue sarcoma demonstrated significant correlations between DKI and IVIM parameters with Ki-67 labeling index, with MD values showing negative correlation with Ki-67 expression (r=-0.521, P < 0.001) [ 20 ]. Another study on endometrial carcinoma showed that the high Ki-67 expression group exhibited significantly decreased D and MD values, though f values were also significantly reduced [ 21 ]. A study on gliomas reported associations between IVIM and DKI parameters with Ki-67 labeling index [ 22 ]. In pancreatic disease research, one study found that IVIM parameter D values were significantly decreased in high-fibrosis pancreatic ductal adenocarcinoma [ 23 ], while another study explored the value of IVIM parameters in differential diagnosis of pancreatic masses [ 24 ]. A rectal cancer study established correlations between DKI and IVIM parameters and tumor tissue composition, finding MD values negatively correlated with CDX-2 and CD34 expression [ 25 ]. Additionally, a musculoskeletal tumor study showed that ADC and D values correlated negatively with Ki-67 index (r=-0.711, P < 0.001) [ 26 ], and a thyroid papillary carcinoma study confirmed significant correlations between IVIM-derived parameter D and DKI-derived parameter Dapp with Ki-67 expression [ 27 ]. Our study is the first to integrate IVIM and DKI parameters with clinical information for Ki-67 expression prediction in pancreatic ductal adenocarcinoma, constructing a multi-parametric combined prediction model. Mechanistically, we speculate that decreased monocyte count may be related to immunosuppressive states in the tumor microenvironment, where highly proliferative tumors often activate immune escape mechanisms, promoting recruitment and polarization of circulating monocytes to tumor tissues [ 28 , 29 ]. The elevated IVIM perfusion fraction f-value may reflect enhanced angiogenesis in high Ki-67 expression tumors, as rapidly proliferating tumor cells require increased oxygen and nutrient supply, stimulating expression of pro-angiogenic factors like vascular endothelial growth factor (VEGF), thereby increasing microvascular density and vascular permeability [ 30 , 31 ]. The decreased DKI mean diffusivity MD-value is speculated to be related to increased cell density and reduced extracellular space, where high proliferative activity leads to enlarged cell volume, increased nuclear-to-cytoplasmic ratio, and enhanced tight junctions between cells, restricting free water molecule diffusion [ 32 , 33 ]. Furthermore, high Ki-67 expression may activate key signaling pathways such as phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mechanistic target of rapamycin (mTOR), altering cell membrane permeability, mitochondrial function, and protein synthesis, further affecting tissue microstructure and water molecule diffusion characteristics [ 34 , 35 ]. These molecular mechanism changes ultimately manifest as imaging feature alterations that can be sensitively detected through IVIM and DKI parameters, providing a solid biological foundation for non-invasive Ki-67 expression assessment [ 15 ]. The multi-parametric combined prediction model constructed in this study demonstrates significant clinical value in Ki-67 expression assessment for pancreatic ductal adenocarcinoma. This model, based on routine MRI diffusion imaging and basic blood tests, provides a reliable tool for preoperative non-invasive assessment of Ki-67 expression status, avoiding tissue sampling-related risks and heterogeneity bias. Compared to previous studies that mostly employed single imaging parameters or relied on complex molecular marker detection limitations, this study is the first to integrate IVIM and DKI parameters with clinical information, significantly enhancing prediction accuracy and establishing a foundation for precision medicine practice in pancreatic ductal adenocarcinoma. High Ki-67 expression often indicates tumors with greater aggressiveness and poorer prognosis, and accurate identification of this molecular phenotype assists clinicians in optimizing treatment strategies, including neoadjuvant therapy selection, surgical timing decisions, and postoperative monitoring protocol formulation. The high diagnostic performance of this model positions it as a potential clinical decision support tool, particularly in resource-limited settings or when pathological examination is challenging, providing important reference for patient stratification management. Furthermore, the reproducibility and standardization characteristics of this approach facilitate its widespread application across different medical institutions, supporting standardized diagnosis and treatment of pancreatic ductal adenocarcinoma. The methodological innovation of this study provides important reference for non-invasive assessment of Ki-67 expression in other solid tumors, demonstrating broad clinical translational prospects. This study has several limitations. As a single-center prospective study, the relatively limited sample size (n = 65) and lack of validation cohorts may affect result generalizability and model robustness. The pathological heterogeneity of PDAC may introduce sampling bias, with Ki-67 expression from a single section potentially not fully representing the proliferative status of the entire tumor. Additionally, IVIM and DKI parameter measurements are influenced by scanning parameters, image quality, and post-processing methods, while ROI selection subjectivity may introduce measurement errors. Future studies will address these limitations by expanding sample size, conducting multi-center validation, establishing standardized scanning protocols, and introducing automated segmentation techniques. In conclusion, this study successfully constructed a multi-parametric combined prediction model based on IVIM and DKI parameters integrated with clinical information, achieving the first preoperative non-invasive assessment of Ki-67 expression status in pancreatic ductal adenocarcinoma. The model demonstrated excellent diagnostic performance (AUC = 0.913), and the integration of monocyte count, IVIM perfusion fraction f-value, and DKI mean diffusivity MD-value not only enhanced prediction accuracy but also provided new insights into understanding the molecular pathological characteristics of pancreatic ductal adenocarcinoma. This innovative approach holds promise for improving patient stratification management, guiding individualized treatment strategies, and making important contributions to precision medicine development for pancreatic cancer. Declarations Author Contribution Ke Li and Jing Li contributed equally to this work. Conception and design of the project: W.C.; Supervision: J.C.; Data collection: J.L., X.L.; Statistical analysis: K.L.; Manuscript writing: K.L. All authors reviewed the manuscript. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2024;74(3):229-63. doi: 10.3322/caac.21834. Xia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chinese medical journal. 2022;135(5):584-90. doi: 10.1097/cm9.0000000000002108. Mizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet (London, England). 2020;395(10242):2008-20. doi: 10.1016/s0140-6736(20)30974-0. Park W, Chawla A, O'Reilly EM. Pancreatic Cancer: A Review. Jama. 2021;326(9):851-62. doi: 10.1001/jama.2021.13027. Neoptolemos JP, Kleeff J, Michl P, Costello E, Greenhalf W, Palmer DH. Therapeutic developments in pancreatic cancer: current and future perspectives. Nature reviews Gastroenterology & hepatology. 2018;15(6):333-48. doi: 10.1038/s41575-018-0005-x. Rawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World journal of oncology. 2019;10(1):10-27. doi: 10.14740/wjon1166. Li LT, Jiang G, Chen Q, Zheng JN. Ki67 is a promising molecular target in the diagnosis of cancer (review). Molecular medicine reports. 2015;11(3):1566-72. doi: 10.3892/mmr.2014.2914. Kuwabara S, Tsuchikawa T, Nakamura T, Hatanaka Y, Hatanaka KC, Sasaki K, et al. Prognostic relevance of tertiary lymphoid organs following neoadjuvant chemoradiotherapy in pancreatic ductal adenocarcinoma. Cancer science. 2019;110(6):1853-62. doi: 10.1111/cas.14023. Pezzilli R, Partelli S, Cannizzaro R, Pagano N, Crippa S, Pagnanelli M, et al. Ki-67 prognostic and therapeutic decision driven marker for pancreatic neuroendocrine neoplasms (PNENs): A systematic review. Advances in medical sciences. 2016;61(1):147-53. doi: 10.1016/j.advms.2015.10.001. Hui AM, Shi YZ, Li X, Sun L, Guido T, Takayama T, et al. Proliferative marker Ki-67 in gallbladder carcinomas: high expression level predicts early recurrence after surgical resection. Cancer letters. 2002;176(2):191-8. doi: 10.1016/s0304-3835(01)00749-2. Striefler JK, Sinn M, Pelzer U, Jühling A, Wislocka L, Bahra M, et al. P53 overexpression and Ki67-index are associated with outcome in ductal pancreatic adenocarcinoma with adjuvant gemcitabine treatment. Pathology, research and practice. 2016;212(8):726-34. doi: 10.1016/j.prp.2016.06.001. Huang Z, Lyu M, Ai Z, Chen Y, Liang Y, Xiang Z. Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features. Frontiers in surgery. 2021;8:736737. doi: 10.3389/fsurg.2021.736737. Besusparis J, Plancoulaine B, Rasmusson A, Augulis R, Green AR, Ellis IO, et al. Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer. Diagnostic pathology. 2016;11(1):82. doi: 10.1186/s13000-016-0525-z. Li B, Yin X, Ding X, Zhang G, Jiang H, Chen C, et al. Combined utility of Ki-67 index and tumor grade to stratify patients with pancreatic ductal adenocarcinoma who underwent upfront surgery. BMC surgery. 2023;23(1):370. doi: 10.1186/s12893-023-02256-4. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine. 2005;53(6):1432-40. doi: 10.1002/mrm.20508. Rosenkrantz AB, Padhani AR, Chenevert TL, Koh DM, De Keyzer F, Taouli B, et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice. Journal of magnetic resonance imaging : JMRI. 2015;42(5):1190-202. doi: 10.1002/jmri.24985. Zhu Q, Zhu W, Ye J, Wu J, Chen W, Hao Z. Value of intravoxel incoherent motion for differential diagnosis of renal tumors. Acta radiologica (Stockholm, Sweden : 1987). 2019;60(3):382-7. doi: 10.1177/0284185118778884. Iima M, Kataoka M, Kanao S, Onishi N, Kawai M, Ohashi A, et al. Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions. Radiology. 2018;287(2):432-41. doi: 10.1148/radiol.2017162853. Pergolini I, Crippa S, Pagnanelli M, Belfiori G, Pucci A, Partelli S, et al. Prognostic impact of Ki-67 proliferative index in resectable pancreatic ductal adenocarcinoma. BJS open. 2019;3(5):646-55. doi: 10.1002/bjs5.50175. Zhang K, Dai Y, Liu Y, Tao J, Pan Z, Xie L, et al. Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices. European radiology. 2022;32(8):5659-68. doi: 10.1007/s00330-022-08646-1. Meng N, Wang X, Sun J, Huang Z, Yang Z, Shang J, et al. Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging. European radiology. 2021;31(11):8388-98. doi: 10.1007/s00330-021-07966-y. Wang C, Dong H. Ki-67 labeling index and the grading of cerebral gliomas by using intravoxel incoherent motion diffusion-weighted imaging and three-dimensional arterial spin labeling magnetic resonance imaging. Acta radiologica (Stockholm, Sweden : 1987). 2020;61(8):1057-63. doi: 10.1177/0284185119891694. Liu Q, Zhang J, Jiang M, Zhang Y, Chen T, Zhang J, et al. Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging. Frontiers in oncology. 2021;11:670085. doi: 10.3389/fonc.2021.670085. Klauss M, Lemke A, Grünberg K, Simon D, Re TJ, Wente MN, et al. Intravoxel incoherent motion MRI for the differentiation between mass forming chronic pancreatitis and pancreatic carcinoma. Investigative radiology. 2011;46(1):57-63. doi: 10.1097/RLI.0b013e3181fb3bf2. Yuan J, Gong Z, Liu K, Song J, Wen Q, Tan W, et al. Correlation between diffusion kurtosis and intravoxel incoherent motion derived (IVIM) parameters and tumor tissue composition in rectal cancer: a pilot study. Abdominal radiology (New York). 2022;47(4):1223-31. doi: 10.1007/s00261-022-03426-3. Zhan J, Hao D, Wang D, Yue B, Zhou R, Tian N, et al. Standard diffusion-weighted, intravoxel incoherent motion, and dynamic contrast-enhanced MRI of musculoskeletal tumours: correlations with Ki67 proliferation status. Clinical radiology. 2021;76(12):941.e11-.e18. doi: 10.1016/j.crad.2021.09.004. Jiang L, Chen J, Huang H, Wu J, Zhang J, Lan X, et al. Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules. Frontiers in oncology. 2022;12:895972. doi: 10.3389/fonc.2022.895972. Mantovani A, Marchesi F, Malesci A, Laghi L, Allavena P. Tumour-associated macrophages as treatment targets in oncology. Nature reviews Clinical oncology. 2017;14(7):399-416. doi: 10.1038/nrclinonc.2016.217. DeNardo DG, Brennan DJ, Rexhepaj E, Ruffell B, Shiao SL, Madden SF, et al. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer discovery. 2011;1(1):54-67. doi: 10.1158/2159-8274.Cd-10-0028. Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature. 2000;407(6801):249-57. doi: 10.1038/35025220. Liu ZL, Chen HH, Zheng LL, Sun LP, Shi L. Angiogenic signaling pathways and anti-angiogenic therapy for cancer. Signal transduction and targeted therapy. 2023;8(1):198. doi: 10.1038/s41392-023-01460-1. Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia (New York, NY). 2009;11(2):102-25. doi: 10.1593/neo.81328. Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR American journal of roentgenology. 2007;188(6):1622-35. doi: 10.2214/ajr.06.1403. Manning BD, Toker A. AKT/PKB Signaling: Navigating the Network. Cell. 2017;169(3):381-405. doi: 10.1016/j.cell.2017.04.001. Saxton RA, Sabatini DM. mTOR Signaling in Growth, Metabolism, and Disease. Cell. 2017;169(2):361-71. doi: 10.1016/j.cell.2017.03.035. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 05 Jun, 2025 First submitted to journal 05 Jun, 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-6830573","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470224873,"identity":"dca4add4-65cf-44fa-b96e-c9e11e214030","order_by":0,"name":"Ke Li","email":"","orcid":"","institution":"Southwest Hospital, Army Medical University (Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Li","suffix":""},{"id":470224875,"identity":"d4f0f19a-9c85-431e-a4dc-888c3d50de54","order_by":1,"name":"Jing Li","email":"","orcid":"","institution":"Southwest Hospital, Army Medical University (Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":470224882,"identity":"4182e73d-f66d-4495-add0-bebdef549a39","order_by":2,"name":"Xiaoling Liu","email":"","orcid":"","institution":"Southwest Hospital, Army Medical University (Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Liu","suffix":""},{"id":470224883,"identity":"833609b3-11d0-4042-a10e-bb17cc2cd843","order_by":3,"name":"Jiafei Chen","email":"","orcid":"","institution":"Southwest Hospital, Army Medical University (Third Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Jiafei","middleName":"","lastName":"Chen","suffix":""},{"id":470224885,"identity":"b25a41c5-7480-4c43-af13-618fdedd04ef","order_by":4,"name":"Wei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACPiCWYGCwgXLZiNDCBtGSJkGylsOkaGE/e/DGz7bzdbrTzhgwfCg7zMA/u4GAFp68ZMvettsSZrdzDBhnnDvMIHHnACGH5ZhJM26DaGHmbTvMYCCRQEAL/xuQlnMQLX+J0iIBtuUARAsjcVreGFv2/kuW3HY7reBgz7l0HokbBLTw8+cY3vhxxo7f7Hbyxgc/yqzl+GcQ0IICDgAxDwnqR8EoGAWjYBTgAgDBbjzBAhEKlQAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest Hospital, Army Medical University (Third Military Medical University)","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-05 15:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6830573/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6830573/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-025-05164-8","type":"published","date":"2025-08-29T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84818606,"identity":"a5010ab0-effc-475a-9a56-573d882b6414","added_by":"auto","created_at":"2025-06-17 15:53:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57694,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient population\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6830573/v1/683271bbad1f4c9b69f026e5.png"},{"id":84818629,"identity":"ce7ddcf6-7064-42e7-be1e-feaf07746ee0","added_by":"auto","created_at":"2025-06-17 15:53:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":555285,"visible":true,"origin":"","legend":"\u003cp\u003eA 71-year-old male patient with PDAC demonstrating high Ki-67 expression (≥50%). A-G represent T1WI, T2WI, D, D*, f, MD, and MK parametric maps, respectively. The white box delineates the tumor ROI. Diffusion parameters measured: D value 1.04×10⁻³ mm²/s, D* value 25.16×10⁻³ mm²/s, f value 17.77%, MD value 1.45×10⁻³ mm²/s, and MK value 0.67. H shows HE staining demonstrating tumor histological features. I displays Ki-67 immunohistochemical staining with positive nuclear staining in ≥50% of tumor cells.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6830573/v1/1404cdc84f38468c43db9dd0.png"},{"id":84818605,"identity":"bc65afb1-dd50-4793-9568-fd782cfbc132","added_by":"auto","created_at":"2025-06-17 15:53:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83233,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation of Ki-67 expression prediction models. (A) ROC curve analysis showing diagnostic efficacy of each parameter: monocyte count with AUC of 0.763 (95% CI: 0.641-0.885), IVIM-f value with AUC of 0.732 (95% CI: 0.603-0.860), DKI-MD value with AUC of 0.800 (95% CI: 0.682-0.917), and combined model with AUC of 0.913 (95% CI: 0.841-0.985); (B) Nomogram based on the combined model integrating monocyte count, IVIM-f value, and DKI-MD value for clinical application; (C) Decision curve analysis (DCA) demonstrating net benefit of the combined model across threshold probabilities ranging from 0.01 to 1.0, confirming favorable clinical utility; (D) Calibration curve showing good agreement between predicted and actual probabilities, indicating high model prediction accuracy.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6830573/v1/d88cb2201bf1610d26c7038f.png"},{"id":90344929,"identity":"a01f0563-77bc-40cc-9325-6814c0d5b484","added_by":"auto","created_at":"2025-09-01 16:07:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1463362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6830573/v1/4305065d-6c78-426b-8afb-c23a46da25d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-parametric MRI Diffusion Models Combined with Clinical Information for Predicting Ki-67 Expression in Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy worldwide. According to GLOBOCAN 2022, approximately 495,773 new pancreatic cancer cases and 466,003 deaths occurred globally, ranking as the 14th most common cancer and the 7th leading cause of cancer death. Its mortality-to-incidence ratio approaches 0.94, reflecting its exceptional fatality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, both incidence and mortality rates continue to rise [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The 5-year survival rate remains below 10%, primarily due to difficulties in early diagnosis, rapid disease progression, and limited effective treatments [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Most patients are diagnosed at advanced stages when surgery is no longer feasible, and even for resectable cases, postoperative recurrence rates remain high [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, identifying effective predictive biomarkers and establishing accurate prognostic models are crucial for individualized PDAC treatment.\u003c/p\u003e \u003cp\u003eKi-67, a nuclear protein marker for tumor proliferative activity, has expression levels closely correlated with PDAC aggressiveness, prognosis, and treatment response [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Studies have shown that PDAC patients with elevated Ki-67 expression have significantly reduced overall and recurrence-free survival rates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with high expression significantly associated with early postoperative recurrence (\u0026lt;\u0026thinsp;1 year) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Co-expression of Ki-67 and p53 can more accurately predict prognosis in patients receiving gemcitabine adjuvant therapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Currently, Ki-67 expression assessment relies primarily on immunohistochemical testing of biopsy or surgical specimens, which has limitations including sampling bias and complication risks [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Preoperative non-invasive assessment of Ki-67 expression would facilitate treatment planning, prognostic evaluation, and therapeutic monitoring, advancing precision medicine for PDAC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional diffusion-weighted imaging (DWI) provides tissue microstructural information by detecting restricted water molecule diffusion, but the apparent diffusion coefficient (ADC) based on a mono-exponential model cannot distinguish between true diffusion and vascular perfusion effects, and assumes Gaussian water molecule diffusion distribution, presenting inherent limitations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Multi-parametric diffusion models such as intravoxel incoherent motion (IVIM) can separate true diffusion components (D) from perfusion parameters (D*, f), while diffusion kurtosis imaging (DKI) quantifies non-Gaussian water molecule diffusion behavior (K value), together providing more comprehensive information about tissue microstructure and microcirculation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These advanced models have demonstrated superior value to conventional DWI in evaluating tumor tissue characteristics [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Based on PDAC histological heterogeneity and the prognostic significance of Ki-67 expression, we hypothesize that multi-parametric MRI diffusion models combined with clinical information can accurately predict Ki-67 expression status in PDAC patients. This study aims to establish this comprehensive predictive model, providing new methods for non-invasive molecular phenotyping, guiding individualized treatment decisions, and improving patient outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis prospective cohort study was conducted at the Department of Radiology in our institution from January 2024 to May 2025. We consecutively recruited 87 patients with clinically suspected pancreatic ductal adenocarcinoma (PDAC), all of whom underwent magnetic resonance imaging including conventional sequences and advanced diffusion-weighted imaging sequences (IVIM and DKI). Inclusion criteria were: (1) age 18\u0026ndash;80 years; (2) clinical or radiological suspicion of PDAC; (3) ability to tolerate MRI examination; (4) no prior anti-tumor treatment; (5) scheduled for surgical resection or biopsy; and (6) lesion diameter greater than 1.5 cm to facilitate subsequent analysis. Exclusion criteria included: (1) poor MRI image quality or severe artifacts; (2) final pathological diagnosis other than PDAC; (3) neoadjuvant chemotherapy before surgery or biopsy; (4) presence of other systemic diseases that could affect imaging assessment; and (5) incomplete clinical data. After screening, 65 patients with histopathologically confirmed PDAC were included in the final analysis and the detailed flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study protocol was approved by the Medical Ethics Committee of our institution and conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging Technique\u003c/h3\u003e\n\u003cp\u003eAll MRI examinations were performed on a 3.0T MR scanner (United Imaging 770, Shanghai United Imaging Healthcare, China) with a standard 12-channel phased array body coil. All patients fasted for 6\u0026ndash;8 hours prior to examination to minimize pancreatic compression and artifacts from a full stomach. Patients also received breath-holding training before the examination to reduce respiratory motion artifacts. The imaging protocol included conventional T1-weighted and T2-weighted sequences as well as advanced diffusion sequences for IVIM and DKI model analyses. All detailed scanning parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRI scanning parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1WI (GRE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2WI (SSFSE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2WI (FSE) with FS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDWI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIVIM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDKI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3D dual-echo GRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle-shot FSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFast spin echo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEPI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImaging plane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoronal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOblique axial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOblique axial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepetition time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcho time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45/3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlip angle (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField of view (mm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026times;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380\u0026times;380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u0026times;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e380\u0026times;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e380\u0026times;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e380\u0026times;300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u0026times;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256\u0026times;256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u0026times;128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128\u0026times;128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128\u0026times;128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntersection gap (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoxel size (mm\u0026sup3;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84\u0026times;1.56\u0026times;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u0026times;1.19\u0026times;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u0026times;1.56\u0026times;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.97\u0026times;2.97\u0026times;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u0026times;2.97\u0026times;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.97\u0026times;2.97\u0026times;5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat suppression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPECIAL (Fat Sat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpectral adiabatic inversion recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpectral adiabatic inversion recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpectral adiabatic inversion recovery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBandwidth (Hz/pixel)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreath-hold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath-hold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBreath-hold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree breathing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFree breathing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFree breathing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb-values (s/mm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0, 800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0, 25, 75, 100, 150, 200, 500, 800, 1200, 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 800, 1200, 2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eAll images were transferred to an advanced post-processing workstation (United Imaging Healthcare, China) for analysis. Two radiologists (with 8 and 11 years of experience in abdominal MRI diagnosis) independently evaluated all images while blinded to clinical and pathological information and each other's results.\u003c/p\u003e \u003cp\u003eRegions of interest (ROIs) were placed on the largest cross-sectional area of the solid tumor component, as identified on T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) sequences. ROIs were carefully drawn on DWI (b\u0026thinsp;=\u0026thinsp;800 s/mm\u0026sup2;), IVIM, and DKI images, avoiding blood vessels, necrotic areas, calcifications, and adjacent normal pancreatic tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each radiologist performed three measurements per case, and the resulting average values were used. The final parameters represented the mean values from both radiologists, with individual measurements preserved for inter-observer agreement analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantitative parameters included: apparent diffusion coefficient (ADC) from conventional DWI; true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM; mean kurtosis (MK) and mean diffusivity (MD) from DKI. The IVIM and DKI parameters were automatically calculated through pixel-by-pixel fitting using bi-exponential and non-Gaussian models, respectively. Additionally, maximum tumor diameter was measured on T2-weighted images, and tumor location and lymph node metastasis status were recorded.\u003c/p\u003e\n\u003ch3\u003eClinical Data and Histopathologic Analysis\u003c/h3\u003e\n\u003cp\u003eClinical data were collected within one week before surgery or biopsy, including gender, age, body mass index (BMI), serum tumor markers (carcinoembryonic antigen [CEA], carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 [CA19-9], carbohydrate antigen 125 [CA125]), and hematological parameters (lymphocyte, neutrophil, monocyte, and platelet counts).\u003c/p\u003e \u003cp\u003eHistopathologic evaluation was performed on surgical specimens or biopsy samples. Ki-67 expression was assessed by an experienced pathologist who was blinded to the imaging findings. The percentage of positively stained tumor cell nuclei was calculated in areas with the highest nuclear labeling. Based on previous studies, a threshold of 50% was selected as the cutoff value [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients were stratified into two groups: low Ki-67 expression (\u0026lt;\u0026thinsp;50%) and high Ki-67 expression (\u0026ge;\u0026thinsp;50%).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), SPSS software (version 26.0, SPSS, IBM), and MedCalc (version 18.2.1, MedCalc Software). Normality was assessed using the Shapiro-Wilk test. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables as frequencies (percentages). Independent samples t-test or Mann-Whitney U test was used for continuous variables between groups, while chi-square test or Fisher's exact test was applied for categorical variables. Intraclass correlation coefficient (ICC) was used to evaluate inter-observer agreement between two radiologists, with ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 indicating good consistency. Univariate and multivariate logistic regression analyses were performed to identify predictors associated with high Ki-67 expression, with variables showing P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis included in multivariate analysis to establish a combined prediction model. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic performance of individual parameters and the combined model, calculating area under the curve (AUC), sensitivity, specificity, and accuracy, with DeLong test used to compare AUC differences between models. A nomogram was constructed based on the optimal prediction model, and decision curve analysis (DCA) was performed to assess clinical utility, while calibration curves were used to evaluate agreement between predicted and actual probabilities. All statistical tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Characteristics\u003c/h2\u003e \u003cp\u003eA total of 65 patients with histopathologically confirmed PDAC were included in the final analysis, with a mean age of 62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years, comprising 34 males (52.3%) and 31 females (47.7%), and a mean BMI of 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 kg/m\u0026sup2;. Tumor locations included 44 cases (67.7%) in the pancreatic head and 21 cases (32.3%) in the body/tail region. Based on a Ki-67 expression threshold of 50%, 48 patients (73.8%) were classified as low expression and 17 patients (26.2%) as high expression. Histopathological diagnosis was obtained through surgical resection in 58 cases (89.2%) and percutaneous biopsy in 7 cases (10.8%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of Clinical and Imaging Features Between Groups\u003c/h3\u003e\n\u003cp\u003eInter-observer reliability assessment between two radiologists for MRI diffusion parameter measurements demonstrated intraclass correlation coefficients (ICC) greater than 0.85, indicating excellent measurement consistency. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the comparison between low Ki-67 expression and high Ki-67 expression groups. No significant differences were observed in clinical characteristics (age, sex, BMI, tumor diameter, location, lymph node metastasis) between groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among laboratory parameters, monocyte count was significantly lower in the high Ki-67 expression group (0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 vs 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u0026times;10⁹/L, P\u0026thinsp;=\u0026thinsp;0.001), while other blood parameters and tumor markers showed no differences. MRI diffusion parameter analysis revealed that the high Ki-67 expression group demonstrated significantly higher IVIM perfusion fraction f values (14.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41% vs 10.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83%, P\u0026thinsp;=\u0026thinsp;0.004) and significantly lower DKI mean diffusivity MD values (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 vs 1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while ADC, IVIM-D, IVIM-D*, and DKI-MK values showed no statistical differences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical and imaging characteristics between low and high Ki-67 expression groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Ki-67 expression (\u0026lt;\u0026thinsp;50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Ki-67 expression (\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized difference (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18 (-0.38, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (-0.48, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum tumor diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08 (-0.47, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31 (-0.25, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (43.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (58.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (56.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (41.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09 (-0.47, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead/neck/body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (64.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (35.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (-0.30, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (58.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (70.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (41.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (29.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (-0.44, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (-0.43, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.48, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220.83\u0026thinsp;\u0026plusmn;\u0026thinsp;88.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195.06\u0026thinsp;\u0026plusmn;\u0026thinsp;52.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36 (-0.20, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13 (-0.42, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (58.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (64.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (41.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (35.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04 (-0.51, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (10.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (89.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (88.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33 (-0.23, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (52.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (47.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADC (\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (-0.44, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD (\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (-0.12, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD* (\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.39\u0026thinsp;\u0026plusmn;\u0026thinsp;16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.62\u0026thinsp;\u0026plusmn;\u0026thinsp;16.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17 (-0.39, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.30, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD (\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35 (1.66, 3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMK (dimensionless)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34 (-0.21, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and Validation of Ki-67 Expression Prediction Model\u003c/h2\u003e \u003cp\u003eBased on univariate and multivariate analysis results, monocyte count, IVIM perfusion fraction f, and DKI mean diffusivity MD values with statistical significance were selected to construct individual prediction models. ROC curve analysis revealed AUCs of 0.763 (95% CI: 0.641\u0026ndash;0.885) for monocyte count, 0.732 (95% CI: 0.603\u0026ndash;0.860) for IVIM-f, and 0.800 (95% CI: 0.682\u0026ndash;0.917) for DKI-MD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A combined prediction model was established by integrating the three parameters through logistic regression analysis, achieving an AUC of 0.913 (95% CI: 0.841\u0026ndash;0.985) with sensitivity of 82.4% and specificity of 83.3%. DeLong test demonstrated that the combined model significantly outperformed all individual parameter models (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the detailed performance metrics of each prediction model. A nomogram based on the combined model provided an intuitive visualization tool for clinical application (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). DCA showed that the combined model demonstrated net benefit across threshold probabilities ranging from 0.01 to 1.0, confirming its favorable clinical utility (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The calibration curve indicated good agreement between predicted and actual probabilities, demonstrating high model prediction accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC curve analysis results of different models for predicting Ki-67 expression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u0026ndash;0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.445 \u0026times; 10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.603\u0026ndash;0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.682\u0026ndash;0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u0026ndash;0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective cohort study enrolled 65 patients with histopathologically confirmed pancreatic ductal adenocarcinoma (PDAC) to evaluate the diagnostic value of MRI multi-parametric diffusion models IVIM and DKI parameters combined with clinical information for predicting Ki-67 expression. The study identified that monocyte count, IVIM perfusion fraction f-value, and DKI mean diffusivity MD-value were significantly associated with Ki-67 expression status. The combined prediction model integrating these three parameters demonstrated excellent diagnostic performance, achieving an AUC of 0.913 (95% CI: 0.841\u0026ndash;0.985) with sensitivity of 82.4% and specificity of 83.3%, significantly outperforming individual parameter models.\u003c/p\u003e \u003cp\u003eMultiple studies have reported the application of IVIM and DKI parameters in tumor proliferation assessment. A study on soft tissue sarcoma demonstrated significant correlations between DKI and IVIM parameters with Ki-67 labeling index, with MD values showing negative correlation with Ki-67 expression (r=-0.521, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Another study on endometrial carcinoma showed that the high Ki-67 expression group exhibited significantly decreased D and MD values, though f values were also significantly reduced [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A study on gliomas reported associations between IVIM and DKI parameters with Ki-67 labeling index [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In pancreatic disease research, one study found that IVIM parameter D values were significantly decreased in high-fibrosis pancreatic ductal adenocarcinoma [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while another study explored the value of IVIM parameters in differential diagnosis of pancreatic masses [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A rectal cancer study established correlations between DKI and IVIM parameters and tumor tissue composition, finding MD values negatively correlated with CDX-2 and CD34 expression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, a musculoskeletal tumor study showed that ADC and D values correlated negatively with Ki-67 index (r=-0.711, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and a thyroid papillary carcinoma study confirmed significant correlations between IVIM-derived parameter D and DKI-derived parameter Dapp with Ki-67 expression [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our study is the first to integrate IVIM and DKI parameters with clinical information for Ki-67 expression prediction in pancreatic ductal adenocarcinoma, constructing a multi-parametric combined prediction model.\u003c/p\u003e \u003cp\u003eMechanistically, we speculate that decreased monocyte count may be related to immunosuppressive states in the tumor microenvironment, where highly proliferative tumors often activate immune escape mechanisms, promoting recruitment and polarization of circulating monocytes to tumor tissues [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The elevated IVIM perfusion fraction f-value may reflect enhanced angiogenesis in high Ki-67 expression tumors, as rapidly proliferating tumor cells require increased oxygen and nutrient supply, stimulating expression of pro-angiogenic factors like vascular endothelial growth factor (VEGF), thereby increasing microvascular density and vascular permeability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The decreased DKI mean diffusivity MD-value is speculated to be related to increased cell density and reduced extracellular space, where high proliferative activity leads to enlarged cell volume, increased nuclear-to-cytoplasmic ratio, and enhanced tight junctions between cells, restricting free water molecule diffusion [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, high Ki-67 expression may activate key signaling pathways such as phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/mechanistic target of rapamycin (mTOR), altering cell membrane permeability, mitochondrial function, and protein synthesis, further affecting tissue microstructure and water molecule diffusion characteristics [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These molecular mechanism changes ultimately manifest as imaging feature alterations that can be sensitively detected through IVIM and DKI parameters, providing a solid biological foundation for non-invasive Ki-67 expression assessment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe multi-parametric combined prediction model constructed in this study demonstrates significant clinical value in Ki-67 expression assessment for pancreatic ductal adenocarcinoma. This model, based on routine MRI diffusion imaging and basic blood tests, provides a reliable tool for preoperative non-invasive assessment of Ki-67 expression status, avoiding tissue sampling-related risks and heterogeneity bias. Compared to previous studies that mostly employed single imaging parameters or relied on complex molecular marker detection limitations, this study is the first to integrate IVIM and DKI parameters with clinical information, significantly enhancing prediction accuracy and establishing a foundation for precision medicine practice in pancreatic ductal adenocarcinoma. High Ki-67 expression often indicates tumors with greater aggressiveness and poorer prognosis, and accurate identification of this molecular phenotype assists clinicians in optimizing treatment strategies, including neoadjuvant therapy selection, surgical timing decisions, and postoperative monitoring protocol formulation. The high diagnostic performance of this model positions it as a potential clinical decision support tool, particularly in resource-limited settings or when pathological examination is challenging, providing important reference for patient stratification management. Furthermore, the reproducibility and standardization characteristics of this approach facilitate its widespread application across different medical institutions, supporting standardized diagnosis and treatment of pancreatic ductal adenocarcinoma. The methodological innovation of this study provides important reference for non-invasive assessment of Ki-67 expression in other solid tumors, demonstrating broad clinical translational prospects.\u003c/p\u003e \u003cp\u003eThis study has several limitations. As a single-center prospective study, the relatively limited sample size (n\u0026thinsp;=\u0026thinsp;65) and lack of validation cohorts may affect result generalizability and model robustness. The pathological heterogeneity of PDAC may introduce sampling bias, with Ki-67 expression from a single section potentially not fully representing the proliferative status of the entire tumor. Additionally, IVIM and DKI parameter measurements are influenced by scanning parameters, image quality, and post-processing methods, while ROI selection subjectivity may introduce measurement errors. Future studies will address these limitations by expanding sample size, conducting multi-center validation, establishing standardized scanning protocols, and introducing automated segmentation techniques.\u003c/p\u003e \u003cp\u003eIn conclusion, this study successfully constructed a multi-parametric combined prediction model based on IVIM and DKI parameters integrated with clinical information, achieving the first preoperative non-invasive assessment of Ki-67 expression status in pancreatic ductal adenocarcinoma. The model demonstrated excellent diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.913), and the integration of monocyte count, IVIM perfusion fraction f-value, and DKI mean diffusivity MD-value not only enhanced prediction accuracy but also provided new insights into understanding the molecular pathological characteristics of pancreatic ductal adenocarcinoma. This innovative approach holds promise for improving patient stratification management, guiding individualized treatment strategies, and making important contributions to precision medicine development for pancreatic cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKe Li and Jing Li contributed equally to this work. Conception and design of the project: W.C.; Supervision: J.C.; Data collection: J.L., X.L.; Statistical analysis: K.L.; Manuscript writing: K.L. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2024;74(3):229-63. doi: 10.3322/caac.21834.\u003c/li\u003e\n\u003cli\u003eXia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chinese medical journal. 2022;135(5):584-90. doi: 10.1097/cm9.0000000000002108.\u003c/li\u003e\n\u003cli\u003eMizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet (London, England). 2020;395(10242):2008-20. doi: 10.1016/s0140-6736(20)30974-0.\u003c/li\u003e\n\u003cli\u003ePark W, Chawla A, O\u0026apos;Reilly EM. Pancreatic Cancer: A Review. Jama. 2021;326(9):851-62. doi: 10.1001/jama.2021.13027.\u003c/li\u003e\n\u003cli\u003eNeoptolemos JP, Kleeff J, Michl P, Costello E, Greenhalf W, Palmer DH. Therapeutic developments in pancreatic cancer: current and future perspectives. Nature reviews Gastroenterology \u0026amp; hepatology. 2018;15(6):333-48. doi: 10.1038/s41575-018-0005-x.\u003c/li\u003e\n\u003cli\u003eRawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World journal of oncology. 2019;10(1):10-27. doi: 10.14740/wjon1166.\u003c/li\u003e\n\u003cli\u003eLi LT, Jiang G, Chen Q, Zheng JN. Ki67 is a promising molecular target in the diagnosis of cancer (review). Molecular medicine reports. 2015;11(3):1566-72. doi: 10.3892/mmr.2014.2914.\u003c/li\u003e\n\u003cli\u003eKuwabara S, Tsuchikawa T, Nakamura T, Hatanaka Y, Hatanaka KC, Sasaki K, et al. Prognostic relevance of tertiary lymphoid organs following neoadjuvant chemoradiotherapy in pancreatic ductal adenocarcinoma. Cancer science. 2019;110(6):1853-62. doi: 10.1111/cas.14023.\u003c/li\u003e\n\u003cli\u003ePezzilli R, Partelli S, Cannizzaro R, Pagano N, Crippa S, Pagnanelli M, et al. Ki-67 prognostic and therapeutic decision driven marker for pancreatic neuroendocrine neoplasms (PNENs): A systematic review. Advances in medical sciences. 2016;61(1):147-53. doi: 10.1016/j.advms.2015.10.001.\u003c/li\u003e\n\u003cli\u003eHui AM, Shi YZ, Li X, Sun L, Guido T, Takayama T, et al. Proliferative marker Ki-67 in gallbladder carcinomas: high expression level predicts early recurrence after surgical resection. Cancer letters. 2002;176(2):191-8. doi: 10.1016/s0304-3835(01)00749-2.\u003c/li\u003e\n\u003cli\u003eStriefler JK, Sinn M, Pelzer U, J\u0026uuml;hling A, Wislocka L, Bahra M, et al. P53 overexpression and Ki67-index are associated with outcome in ductal pancreatic adenocarcinoma with adjuvant gemcitabine treatment. Pathology, research and practice. 2016;212(8):726-34. doi: 10.1016/j.prp.2016.06.001.\u003c/li\u003e\n\u003cli\u003eHuang Z, Lyu M, Ai Z, Chen Y, Liang Y, Xiang Z. Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features. Frontiers in surgery. 2021;8:736737. doi: 10.3389/fsurg.2021.736737.\u003c/li\u003e\n\u003cli\u003eBesusparis J, Plancoulaine B, Rasmusson A, Augulis R, Green AR, Ellis IO, et al. Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer. Diagnostic pathology. 2016;11(1):82. doi: 10.1186/s13000-016-0525-z.\u003c/li\u003e\n\u003cli\u003eLi B, Yin X, Ding X, Zhang G, Jiang H, Chen C, et al. Combined utility of Ki-67 index and tumor grade to stratify patients with pancreatic ductal adenocarcinoma who underwent upfront surgery. BMC surgery. 2023;23(1):370. doi: 10.1186/s12893-023-02256-4.\u003c/li\u003e\n\u003cli\u003eJensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic resonance in medicine. 2005;53(6):1432-40. doi: 10.1002/mrm.20508.\u003c/li\u003e\n\u003cli\u003eRosenkrantz AB, Padhani AR, Chenevert TL, Koh DM, De Keyzer F, Taouli B, et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice. Journal of magnetic resonance imaging : JMRI. 2015;42(5):1190-202. doi: 10.1002/jmri.24985.\u003c/li\u003e\n\u003cli\u003eZhu Q, Zhu W, Ye J, Wu J, Chen W, Hao Z. Value of intravoxel incoherent motion for differential diagnosis of renal tumors. Acta radiologica (Stockholm, Sweden : 1987). 2019;60(3):382-7. doi: 10.1177/0284185118778884.\u003c/li\u003e\n\u003cli\u003eIima M, Kataoka M, Kanao S, Onishi N, Kawai M, Ohashi A, et al. Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions. Radiology. 2018;287(2):432-41. doi: 10.1148/radiol.2017162853.\u003c/li\u003e\n\u003cli\u003ePergolini I, Crippa S, Pagnanelli M, Belfiori G, Pucci A, Partelli S, et al. Prognostic impact of Ki-67 proliferative index in resectable pancreatic ductal adenocarcinoma. BJS open. 2019;3(5):646-55. doi: 10.1002/bjs5.50175.\u003c/li\u003e\n\u003cli\u003eZhang K, Dai Y, Liu Y, Tao J, Pan Z, Xie L, et al. Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices. European radiology. 2022;32(8):5659-68. doi: 10.1007/s00330-022-08646-1.\u003c/li\u003e\n\u003cli\u003eMeng N, Wang X, Sun J, Huang Z, Yang Z, Shang J, et al. Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging. European radiology. 2021;31(11):8388-98. doi: 10.1007/s00330-021-07966-y.\u003c/li\u003e\n\u003cli\u003eWang C, Dong H. Ki-67 labeling index and the grading of cerebral gliomas by using intravoxel incoherent motion diffusion-weighted imaging and three-dimensional arterial spin labeling magnetic resonance imaging. Acta radiologica (Stockholm, Sweden : 1987). 2020;61(8):1057-63. doi: 10.1177/0284185119891694.\u003c/li\u003e\n\u003cli\u003eLiu Q, Zhang J, Jiang M, Zhang Y, Chen T, Zhang J, et al. Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging. Frontiers in oncology. 2021;11:670085. doi: 10.3389/fonc.2021.670085.\u003c/li\u003e\n\u003cli\u003eKlauss M, Lemke A, Gr\u0026uuml;nberg K, Simon D, Re TJ, Wente MN, et al. Intravoxel incoherent motion MRI for the differentiation between mass forming chronic pancreatitis and pancreatic carcinoma. Investigative radiology. 2011;46(1):57-63. doi: 10.1097/RLI.0b013e3181fb3bf2.\u003c/li\u003e\n\u003cli\u003eYuan J, Gong Z, Liu K, Song J, Wen Q, Tan W, et al. Correlation between diffusion kurtosis and intravoxel incoherent motion derived (IVIM) parameters and tumor tissue composition in rectal cancer: a pilot study. Abdominal radiology (New York). 2022;47(4):1223-31. doi: 10.1007/s00261-022-03426-3.\u003c/li\u003e\n\u003cli\u003eZhan J, Hao D, Wang D, Yue B, Zhou R, Tian N, et al. Standard diffusion-weighted, intravoxel incoherent motion, and dynamic contrast-enhanced MRI of musculoskeletal tumours: correlations with Ki67 proliferation status. Clinical radiology. 2021;76(12):941.e11-.e18. doi: 10.1016/j.crad.2021.09.004.\u003c/li\u003e\n\u003cli\u003eJiang L, Chen J, Huang H, Wu J, Zhang J, Lan X, et al. Comparison of the Differential Diagnostic Performance of Intravoxel Incoherent Motion Imaging and Diffusion Kurtosis Imaging in Malignant and Benign Thyroid Nodules. Frontiers in oncology. 2022;12:895972. doi: 10.3389/fonc.2022.895972.\u003c/li\u003e\n\u003cli\u003eMantovani A, Marchesi F, Malesci A, Laghi L, Allavena P. Tumour-associated macrophages as treatment targets in oncology. Nature reviews Clinical oncology. 2017;14(7):399-416. doi: 10.1038/nrclinonc.2016.217.\u003c/li\u003e\n\u003cli\u003eDeNardo DG, Brennan DJ, Rexhepaj E, Ruffell B, Shiao SL, Madden SF, et al. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer discovery. 2011;1(1):54-67. doi: 10.1158/2159-8274.Cd-10-0028.\u003c/li\u003e\n\u003cli\u003eCarmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature. 2000;407(6801):249-57. doi: 10.1038/35025220.\u003c/li\u003e\n\u003cli\u003eLiu ZL, Chen HH, Zheng LL, Sun LP, Shi L. Angiogenic signaling pathways and anti-angiogenic therapy for cancer. Signal transduction and targeted therapy. 2023;8(1):198. doi: 10.1038/s41392-023-01460-1.\u003c/li\u003e\n\u003cli\u003ePadhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia (New York, NY). 2009;11(2):102-25. doi: 10.1593/neo.81328.\u003c/li\u003e\n\u003cli\u003eKoh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR American journal of roentgenology. 2007;188(6):1622-35. doi: 10.2214/ajr.06.1403.\u003c/li\u003e\n\u003cli\u003eManning BD, Toker A. AKT/PKB Signaling: Navigating the Network. Cell. 2017;169(3):381-405. doi: 10.1016/j.cell.2017.04.001.\u003c/li\u003e\n\u003cli\u003eSaxton RA, Sabatini DM. mTOR Signaling in Growth, Metabolism, and Disease. Cell. 2017;169(2):361-71. doi: 10.1016/j.cell.2017.03.035.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pancreatic ductal adenocarcinoma, Ki-67, Intravoxel incoherent motion, Diffusion kurtosis imaging, Magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-6830573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6830573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters combined with clinical information for predicting Ki-67 expression in pancreatic ductal adenocarcinoma (PDAC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective cohort study enrolled 65 patients with histopathologically confirmed PDAC between January 2024 and May 2025. All patients underwent 3.0T MRI including conventional sequences and advanced diffusion-weighted imaging sequences. Clinical data and laboratory parameters were collected within one week before surgery or biopsy. Ki-67 expression was assessed using immunohistochemical staining with 50% as the cutoff value. Two radiologists independently performed quantitative measurements with excellent inter-observer reliability (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.85). Univariate and multivariate logistic regression analyses identified independent predictors. ROC curve analysis and DeLong test evaluated diagnostic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on Ki-67 expression threshold of 50%, 48 patients (73.8%) were classified as low expression and 17 patients (26.2%) as high expression. Compared to the low Ki-67 group, the high expression group demonstrated significantly lower monocyte count (0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 vs 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u0026times;10⁹/L, P\u0026thinsp;=\u0026thinsp;0.001), higher IVIM perfusion fraction f-value (14.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41% vs 10.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83%, P\u0026thinsp;=\u0026thinsp;0.004), and lower DKI mean diffusivity MD-value (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 vs 1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Individual prediction models achieved AUCs of 0.763 (monocyte count), 0.732 (IVIM-f), and 0.800 (DKI-MD). The combined prediction model integrating these three parameters demonstrated excellent diagnostic performance with AUC of 0.913 (95% CI: 0.841\u0026ndash;0.985), sensitivity of 82.4%, and specificity of 83.3%, significantly outperforming all individual models (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis multi-parametric combined prediction model achieves excellent diagnostic performance for preoperative non-invasive assessment of Ki-67 expression status in PDAC, providing a reliable tool for precision medicine practice and personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Multi-parametric MRI Diffusion Models Combined with Clinical Information for Predicting Ki-67 Expression in Pancreatic Ductal Adenocarcinoma: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 15:53:19","doi":"10.21203/rs.3.rs-6830573/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-14T13:35:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T10:29:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127653588166304267647598716195504178257","date":"2025-06-18T08:19:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T09:27:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282530606339361566139322893672661789760","date":"2025-06-12T08:04:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T05:11:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T02:32:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T02:30:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2025-06-05T15:20:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c536707f-e92f-4e14-86f9-9edf154ce321","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:02:34+00:00","versionOfRecord":{"articleIdentity":"rs-6830573","link":"https://doi.org/10.1007/s00261-025-05164-8","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2025-08-29 15:57:58","publishedOnDateReadable":"August 29th, 2025"},"versionCreatedAt":"2025-06-17 15:53:19","video":"","vorDoi":"10.1007/s00261-025-05164-8","vorDoiUrl":"https://doi.org/10.1007/s00261-025-05164-8","workflowStages":[]},"version":"v1","identity":"rs-6830573","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6830573","identity":"rs-6830573","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}