Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas

Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas | 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 Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas Lei Bi, Wei Chen, Shijuan Zhou, Hongzhi Xu, Yushuai Lin, Juntao Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2721327/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Accurate preoperative evaluation of lymph node (LN) status is crucial for selecting optimal individualized treatment strategy and predicting patients’ prognosis. This study aimed to evaluate whether whole-volume apparent diffusion coefficient (ADC) histogram parameters of the primary tumor were useful to predict regional lymph node metastasis (LNM) in periampullary carcinomas. Methods: Thirty-eight patients with periampullary carcinoma who underwent pancreaticoduodenectomy between January 2016 to April 2019 were retrospectively enrolled. Whole-volume ADC histogram analysis of the primary tumor was performed by two radiologists independently. Clinical factors, pathological results and histogram parameters were evaluated. Interclass correlation coefficient (ICC) was used to assess agreement between observers. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of parameters in differentiating LNM-positive group and LNM-negative group. Results: Interobserver agreements were good to excellent for histogram analysis between two radiologists, with ICCs ranging from 0.766 to 0.967. Tumor size, MR-reported LN status and most ADC histogram parameters (including mean, minimum ADC value, 10th, 25th, 50th, 75th, and 90th percentile, and kurtosis) were significantly different between LNM-positive group and LNM-negative group (p < 0.050), and revealed significant correlations with LNM (p < 0.050). At ROC analysis, tumor size and minimum ADC value generated highest area under the curve (AUC) (AUC = 0.764, 95% confidence interval [CI]: 0.599, 0.886). When diagnostic predictive values were calculated with the combined model incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained, with AUC of 0.879 (95% CI: 0.771, 0.986), sensitivity of 100.0%, and specificity of 75.0%. Conclusions: Whole-volume ADC histogram parameters of the primary tumor held great potential in differentiating regional LNM in periampullary carcinomas. Periampullary carcinoma ADC Histogram Lymph node metastasis Figures Figure 1 Figure 2 Figure 3 Background Periampullary carcinomas (PCs) constitute a rare heterogeneous group of malignant tumors originating from pancreas, distal common bile duct, duodenum, and ampulla of Vater. These tumors arise within 2 cm of the major duodenal papilla, and share a common embryologic origin from the foregut [ 1 ]. Although rare, accounting for roughly 0.2% of gastrointestinal tumors [ 2 – 4 ], PC is one of the top five leading causes of cancer-related death worldwide [ 5 ], with the detection rate increasing year by year [ 6 ]. Pancreaticoduodenectomy remains the only potential curative option for resectable PCs, however, when first diagnosed, nearly 70%-80% of the patients have lost the opportunity for surgery because of lymph node metastasis (LNM), adherent tissues or vessels invasion, and distant metastasis [ 7 ]. Even the tumor was completely resected, the patients’ prognosis is still poor due to the local recurrence and/or distant metastases after surgery [ 8 , 9 ]. Patients with PC were prone to be accompanied by LNM, which was reported to be one of the strongest risk factors for poor survival [ 8 , 10 ]. Previous study showed that the prognosis of patients with LNM was significantly worse than patients without LNM [ 11 ]. What’s more, Kim et al found that if patients with LNM received neoadjuvant chemotherapy before surgery, through the fiborsis of metastastic LNs, patient’s prognosis could be improved [ 12 ]. Therefore, accurate preoperative evaluation of LN status is crucial for selecting optimal individualized treatment strategy, and could assist to predict patients’ prognosis. Diffusion-weighted imaging (DWI) is a non-invasive MRI technique to measure molecular Brownian movement inside tissues, and is quantified by the apparent diffusion coefficient (ADC) [ 13 ]. ADC values were proved to be associated with tumor cellularity, proliferation potential, neoangiogenesis, lymphocytes and extracellular matrix invasion [ 13 ], which can be used to distinguish benign and malignant neoplasmas, predict patient prognosis, and assess tumor response to treatment [ 1 ]. Recently, ADC histogram analysis has received extensive recognition as a reproducible approach which can reflect the heterogeneity of molecular diffusion within a tumor region based on pixel distribution [ 14 ]. ADC histogram analyses have yielded great potential for predicting LNM in various malignant tumors, such as gastric cancer [ 15 ], colorectal cancer [ 16 – 18 ], uterine cervical cancer [ 19 ], and epithelial ovarian cancer [ 20 ]. Till now, there has been no published research that has evaluated whether whole-volume ADC histogram analysis would facilitate LNM prediction in PCs. Therefore, the purpose of our study was to assess the diagnostic potential of ADC histogram analysis for predicting LNM of PCs. Methods Study population This retrospective study of anonymous data was approved by the Ethics Committee of our institution, and requirement for informed consent was waived. Preoperative MR images of patients with PC were searched from our database. The inclusion criteria included: 1) patients were treated with curative whole-tumor resection and lymphadenectomy; 2) comprehensive clinical and pathological results were available; 3) time interval between preoperative MR examination and surgery was less than two weeks; and 4) ADC maps had high quality (without artifacts) for segmentation. The exclusion criteria included: 1) patients received radiotherapy or chemotherapy before surgery; and 2) patients suffered from other malignancies simultaneously. From January 2016 to April 2019, a total of 38 patients (mean age, 54.6 years; range, 38–72 years), including 21 men (mean age, 57.2 years; range, 41–72 years) and 17 women (mean age, 52.0 years; range, 38–70 years) were included in our study (Fig. 1 ). MRI examinations MRI examinations were performed on a 3.0-T MRI system (Magnetom Verio, Siemens, German) using an eight-channel phased-array surface coil. Parallel acquisition technique and retrospective image intensity correction (B1 filter) were used to reduce standing wave or dielectric effects. A respiratory-triggered, fat-suppressed, single-shot echo-planar imaging in the transverse position was performed for DWI sequence. Each acquisition was obtained using b values of 0 and 800 s/mm 2 . The overall acquisition time ranged from 3 to 6 minutes depending on patient’s respiratory efficiency. ADC map was generated automatically with a commercially available software workstation system (Syngo Multimodality workplace, Siemens, German). The acquisition parameters of DWI sequence were as follows: TR/TE = 4000/73 ms; field of view = 380 mm × 285 mm; flip angle = 90⁰; matrix = 128 × 78; slice thickness = 5 mm; band width = 2442 Hz/pixel; ETL = 78; echo space = 0.51 ms. In addition to DWI, routine MRI including a T1-weighted dual-echo in- and out-phase sequence, a T1-weighted volume interpolated body examination (VIBE) sequence, and a breath-hold turbo spin-echo T2-weighted sequence were performed before administration of contrast agent. For enhanced imaging, arterial phase (20–25 sec), portal venous phase (60–70 sec), equilibrium phase (3 min), delayed phase (10 min) and hepatobiliary phase (90 min) were obtained using VIBE sequence after injection of gadobenate dimeglumine. With a power injector, the contrast agent was administered intravenously at a rate of 2.5 mL/s for a total dose of 0.1 mmol/kg of body weight, followed by a 20-mL saline flush. MR feature evaluation Two radiologists (observer 1 and observer 2, with 8 and 15 years of experience in abdominal MR interpretation, respectively) reviewed all images in consensus. The following image features were evaluated: 1) tumor size, defined as the lesion’s maximum diameter on axial images; 2) vascular involvement, defined as vessel occlusion, stenosis, or contour deformity due to tumor invasion; and 3) LNM, defined as LN’s short-axis diameter larger than 10 mm, or LNs with central necrosis, or LNs were hyperenhanced than liver parenchyma in portal venous phase [ 21 , 22 ]. The observers knew that all patients were diagnoses of PC but were blind for the clinical and pathological details. Tumor segmentation and histogram analysis Three-dimensional segmentation was performed using ITK-SNAP (v. 3.8.0). Two radiologists performed the tumor segmentation independently. On ADC map, the region of interest (ROI) was manually drawn freehand on each transverse section strictly within the border of the lesion layer by layer, which should include cyst, hematoma, or necrosis within the lesion, but avoid common bile duct, main pancreatic duct and other normal anatomical structures. The ROIs were first localized on DWI with reference to T1-weighted, T2-weighted, and gadolinium-enhanced VIBE images, and mirrored to the ADC maps subsequently. The volume of interest (VOI) of each lesion was then automatically generated for histogram analysis (Fig. 2 ). PyRadiomics (v. 3.1.0) was used for feature extraction. Various first-order parameters including quartiles (25th, 50th, 75th), 10th and 90th percentiles, maximum, minimum and mean ADC values, energy, kurtosis, variance, and skewness were obtained. Statistical analysis Univariate analysis was applied to compare the differences in the clinical factors and ADC histogram parameters between LNM-positive group and LNM-negative group. Categorical variables were compared by using Chi-square test or Fisher exact test, and continuous variables were compared by using Student’s t test or Mann-Whitney U test, as appropriate. Interobserver agreement was assessed by using interclass correlation coefficient (ICC). An ICC value of 1.0 was deemed to indicate perfect agreement; 0.81–0.99, excellent correlation; 0.61–0.80, good correlation; 0.41–0.60, moderate agreement; 0.21–0.40, fair correlation; and 0.20 or less, poor correlation [ 23 ]. Spearman correlation analysis was done to assess the correlation between variables and LN metastasis. Diagnostic performance of variables for differentiating two groups was calculated by using receiver operating characteristic (ROC) curve analysis, and the evaluation indicators included area under the curve (AUC), sensitivity, and specificity. The optimal cutoff value was defined according to Youden index (the value at which the sum of the sensitivity and specificity was maximized). Multivariate model calibration was assessed with the goodness-of-fit Hosmer-Lemeshow test through a calibration plot. All statistical analyses were performed using SPSS software (SPSS for Windows, v. 17.0; Chicago, IL) and MedCalc software (MedCalc for Windows, v. 12.7.0; Mariakerke, Belgium). A p -value < 0.05 was considered statistically significant. Results Patient demographics Among the 38 patients in our study, 10 patients (5 men, 5 women; mean age, 48.0 years; range, 47–69 years) were diagnosed with LNM, and 28 patients (16 men, 12 women; mean age, 55.6 years; range, 38–72 years) without LNM. In total, 5 patients (50.0%; 5 of 10) with LNM were understaged, and 3 patients (10.7%; 3 of 28) without LNM were overstaged based on MR-reported LN status. Nine tumors (23.7%) were located at duodenum, 6 (15.8%) at ampulla of Vater, 5 (13.1%) at common bile duct, and 18 (47.4%) at pancreas. Pathological reports showed that 3 tumors (7.9%) were well differentiated, 23 (60.5%) were moderately differentiated, and 12 (31.6%) were poorly differentiated (Table 1 ). Table 1 Patient and tumor characteristics Characteristic LNM-positive group (n = 10) LNM-negative group (n = 28) p value Age (mean ± SD) 56.00 ± 7.23 55.86 ± 10.28 0.968 Gender 0.697 Male 5 (50.0) 16 (57.1) Female 5 (50.0) 12 (42.9) Tumor size (mean ± SD) 3.68 ± 1.09 2.63 ± 1.21 0.020 MR-reported LN status 0.009 LNM-positive 5 (50.0) 3 (10.7) LNM-negative 5 (50.0) 25 (89.3) Vascular involvement 3 (30.0) 2 (7.1) 0.066 Tumor origin 0.306 Duodenum 2 (20.0) 7 (25.0) Ampulla of Vater 3 (30.0) 3 (10.7) Common bile duct 0 (0.0) 5 (17.9) Pancreas 5 (50.0) 13 (46.4) Degree of differentiation 0.502 High 0 (0.0) 3 (10.7) Moderate 6 (60.0) 17 (60.7) Low 4 (40.0) 8 (28.6) Note: Data are number of patients; data in parentheses are percentage unless otherwise indicated. LN, lymph node; LNM, lymph node metastasis; MR, magnetic resonance; SD, standard deviation. Univariate analysis Comparison of clinical and pathological characteristics The tumor size and MR-reported LN status were significantly different between LNM-positive group and LNM-negative group ( p = 0.020 and 0.019, respectively), and revealed significantly positive correlations with LN metastasis (rs = 0.406, p = 0.011, and rs = 0.424, p = 0.008, respectively). The other parameters did not differ significantly between two groups ( p > 0.050) (Table 1 ). Comparison of ADC histogram parameters Interobserver agreements of histogram parameters were good to excellent, with ICCs ranging from 0.766 to 0.967 (Table 2 ). Table 2 Interobserver Agreement for the Measurement of ADC Histogram Parameters Histogram parameters ICC (95% CI) 10th percentile ADC 0.938 (0.915, 0.961) 25th percentile ADC 0.933 (0.912, 0.953) Median ADC 0.923 (0.901, 0.945) 75th percentile ADC 0.917 (0.890, 0.944) 90th percentile ADC 0.917 (0.892, 0.942) Minimum ADC 0.967 (0.927, 1.000) Mean ADC 0.936 (0.918, 0.953) Maximum ADC 0.935 (0.874, 0.995) Energy 0.911 (0.886, 0.936) Kurtosis 0.766 (0.663, 0.868) Variance 0.871 (0.819, 0.924) Skewness 0.793 (0.722, 0.864) Note: ADC, apparent diffusion coefficient; CI, confidence interval; ICC, interclass correlation coefficient. The mean, minimum ADC values and 10th, 25th, 50th, 75th, and 90th percentile values were significantly lower in LNM-positive group than in LNM-negative group ( p = 0.014–0.047), and revealed significantly negative correlations with LN metastasis (rs = -0.403-[-0.327], all p < 0.050). The kurtosis was significantly higher in LNM-positive group than in LNM-negative group ( p = 0.028), and had a positive correlation (rs = 0.360, p = 0.027) with LN metastasis. The other parameters did not differ significantly between two groups ( p > 0.050) (Table 3 ). Table 3 Histogram parameters between the LNM-positive and LNM-negative groups and ROC curve results Histogram parameters LNM-positive group LNM-negative group p value AUC (95% CI) Sensitivity (%) Specificity (%) Correlation with LN status (rs) p value of correlation test 10th percentile ADC (× 10 − 6 mm 2 /sec) 999 ± 258 1210 ± 339 0.044 0.718 (0.549, 0.851) 80.0 60.7 -0.332 0.041 25th percentile ADC (× 10 − 6 mm 2 /sec) 1111 ± 268 1327 ± 330 0.047 0.714 (0.545, 0.849) 40.0 100.0 -0.327 0.045 Median ADC (× 10 − 6 mm 2 /sec) 1243 ± 288 1482 ± 327 0.034 0.729 (0.560, 0.860) 80.0 64.3 -0.349 0.032 75th percentile ADC (× 10 − 6 mm 2 /sec) 1379 ± 258 1646 ± 333 0.014 0.761 (0.595, 0.884) 80.0 75.0 -0.398 0.013 90th percentile ADC (× 10 − 6 mm 2 /sec) 1531 ± 301 1803 ± 357 0.019 0.750 (0.583, 0.876) 80.0 67.9 -0.381 0.018 Minimum ADC (× 10 − 6 mm 2 /sec) 699 ± 292 1002 ± 443 0.013 0.764 (0.599, 0.886) 90.0 64.3 -0.403 0.012 Mean ADC (× 10 − 6 mm 2 /sec) 1259 ± 280 1497 ± 324 0.026 0.739 (0.572, 0.868) 80.0 64.3 -0.365 0.024 Maximum ADC (× 10 − 6 mm 2 /sec) 2166 ± 452 2200 ± 484 0.961 0.507 (0.340, 0.673) 20.0 53.6 -0.011 0.948 Energy (× 10 − 6 ) 981 ± 129 558 ± 810 0.172 0.650 (0.478, 0.797) 70.0 64.3 0.229 0.167 Kurtosis 4.66 ± 2.27 3.18 ± 1.09 0.028 0.736 (0.568, 0.865) 80.0 64.3 0.360 0.027 Variance (× 10 − 6 ) 0.052 ± 0.019 659 ± 498 0.660 0.550 (0.380, 0.711) 100.0 32.1 -0.076 0.649 Skewness 0.69 ± 0.59 0.35 ± 0.57 0.116 0.671 (0.500, 0.815) 100.0 35.7 0.262 0.113 Note: ADC, apparent diffusion coefficient; AUC, area under the curve; CI, confidence interval; LNM, lymph node metastasis; ROC, receiver operating characteristic. ROC analysis Diagnostic performances of clinical and pathological characteristics The tumor size generated highest AUC in all clinical and pathological parameters for differentiating LNM-positive group and LNM-negative group (AUC = 0.764, 95% confidence interval [CI]: 0.599, 0.886), with sensitivity of 70.0%, and specificity of 82.1%. MR-reported LN status also showed good discrimination capability, with AUC of 0.713, while the tumor location generated lowest AUC of 0.500. Discrimination capabilities of vascular involvement and degree of differentiation were unsatisfactory, with AUC of 0.614 and 0.589, respectively. Diagnostic performances of ADC histogram parameters The minimum ADC value generated highest AUC in all histogram parameters for differentiating LNM-positive group and LNM-negative group (AUC = 0.764, 95% confidence interval [CI]: 0.599, 0.886), with sensitivity of 90.0%, and specificity of 64.3%. The 75th, 90th percentiles, mean ADC value and kurtosis also showed good discrimination capability, with AUC of 0.761, 0.750, 0.739 and 0.736, respectively, while the maximum ADC value generated lowest AUC of 0.507. Diagnostic performances of combined models When diagnostic predictive values were calculated with the combined model incorporating MR-reported LN status and 75th percentile, the AUC increased to 0.843 (95% CI: 0.718, 0.968), with sensitivity of 90.0% and specificity of 71.4%. When incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained (AUC = 0.879, 95% CI: 0.771, 0.986), with sensitivity of 100.0% and specificity of 75.0%. A non-significant statistic ( p = 0.490) of the Hosmer-Lemeshow test suggested no significant deviation from an ideal fitting. The ROC curve analyses were showed in Fig. 3 , and the diagnostic performance of parameters were presented in Table 3 . Discussion In this study, we investigated the diagnostic value of whole-volume ADC histogram analysis in predicting regional lymph node metastasis of periampullary carcinomas. Our results showed that most ADC histogram parameters were significantly different between LNM-positive group and LNM-negative group. At ROC analysis, the tumor size and minimum ADC value generated highest AUC of 0.764. When diagnostic predictive values were calculated with the combined model incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained, with AUC of 0.879, sensitivity of 100%, and specificity of 75%. Due to the region’s anatomical complexity, PCs remain a diagnostic and therapeutic challenge. Preoperative prediction of LNM has received extensive attention due to its prognostic significance and the essential role in patient management. In daily clinical practice, image-based differentiation of metastatic LNs from nonmetastatic LNs mainly depends on morphological features and LN’s sizes, which is inevitably subjective. Moreover, benign LNs with nonspecific inflammatory hyperplasia and metastatic LNs with small sizes also exist. Tseng et al revealed that diagnostic accuracy of conventional CT imaging in assessing PCs’ LNM was less than 81% [ 24 ]. In our present study, half of the patients (5 in 10 patients) with LNM were underestimated based on the conventional MR images. Therefore, conventional imaging modalities’ performance in assessment of LN status is inadequate. It is necessary to explore some new method to reveal the histopathological characteristics of metastatic LNs. Prithivi Raj et al found that PET-CT has a potential ability for detecting PC’ s LNM. In their study, FDG-PET/CT showed a sensitivity of 71.4% and specificity of 77.8% for assessment of LNM with cutoff value SUV max ≥ 2.0. However, small lesions that are less than twice the imaging resolution would yield false negative results because of partial volume effect, and high cost of PET scans may limit its utility in clinical practice [ 21 ]. In one previous studies, Bi et al established and validated a radiomics nomogram for preoperatively predicting LNM in PCs. The results showed that the radiomics nomogram incorporating radiomics signature and CT-reported LN status obtained favorable discrimination and calibration ability in both training and validation set, with AUC of 0.853 for each set [ 22 ]. However, although the radiomics nomogram performed well, it was complicated and time-consuming, to some extent. Histogram analysis is a rapidly-emerging noninvasive method in the field of medical imaging, which is capable of quantitatively and objectively assessing tumor heterogeneity, regularity, and image roughness by evaluating the distribution of voxel gray levels without requiring additional invasive procedures [ 25 ]. ADC histogram can reflect tissue components with different diffusion features inside the tumor, which was proved to have favorable repeatability and comprehensiveness [ 20 ]. LNM has been reported to be associated with ADC histogram parameters of the primary tumor [ 15 – 20 ], which is consistent with our results. ADC values based on water molecular diffusion can reflect the microenvironment of tissues. In general terms, acellular regions (such as cystic and necrotic components) that allow free water diffusion would yield higher ADC values, and highly cellular areas in which water diffusion is restricted by the intensive cell membranes always yield lower ADC values [ 14 , 26 , 27 ]. When the primary tumor progresses, the proliferative ability of the tumor cells and their metastatic capacity were enhanced by the changes of tumor microenvironment [ 20 , 28 ]. Therefore, patients with LNM showed lower ADC values than that without LNM. In present study, we not only evaluated various ADC values, but also ADC histogram kurtosis, energy, variance and skewness, which is a more reproducible approach and reflected the distribution of ADC values [ 29 ]. In our study, tumors with LNM showed higher kurtosis than tumors without LNM. Kurtosis reflects the peakedness of the histogram distribution and measures the shape of the probability distribution [ 30 ]. Higher kurtosis indicates that the distribution tended to have heavier tails or outliers, and the mass of the distribution was more concentrated in tumors with LNM. Previous research has showed that kurtosis of ADC histogram could predict LN status in thyroid cancer [ 31 ]. In addition, energy, variance and skewness have also been reported to be correlated with pathological characteristics. Higher energy reflects narrower distribution of intensity levels, higher variance reflects more heterogeneous distribution of ADC values, and higher skewness reflects more asymmetric distribution of ADC values [ 29 , 32 ]. However, in our study, energy, variance and skewness were not significantly different between LNM-positive and LNM-negative group. Further studies focusing on this topic should be proposed. For manual tumor segmentation, how to ensure the reproducibility of histogram analysis is the most important issue. Reports suggested that compared with a representative single cross-sectional lesion segmentation, whole-tumor analysis was more reproducible because ADC values were depended on the selected regions of interest, subjective selection of single section would reduce inter- and intraobserver agreements [ 14 ]. What’s more, since PCs are often heterogeneous, a single region of interest could not comprehensively reflect different pathological characteristics within the entire tumor. Therefore, in our study, we explored whole-volume ADC histogram analysis, and an overall good interobserver agreement was obtained, with ICCs ranging from 0.766 to 0.967. This was in accordance with previous studies which revealed that whole-volume histogram analyses exhibited favorable repeatability in clinical practice [ 14 , 33 , 34 ]. There were several limitations to our study. First, due to the retrospective design of our study, selection bias inevitably existed even with strict inclusion criteria. Patients with tumors of advanced stage who have lost the opportunity for surgery were excluded. Second, this was a single-center research and the patients number was relatively small, especially the number of patients with LNM, so the diagnostic criteria proposed in our study should be validated in other patient cohort. Finally, we only applied two b values of 0 and 800 s/mm 2 for DWI sequence in our study, higher and multiple b values may provide more parameters, which could be further evaluated. Conclusions In conclusion, whole-volume ADC histogram analysis of the primary tumor could be a promising and noninvasive method for predicting regional lymph node metastasis of periampullary carcinomas. Abbreviations ADC, apparent diffusion coefficient AUC, area under the curve CI, confidence interval DWI, diffusion-weighted imaging ICC, interclass correlation coefficient LN, lymph node LNM, lymph node metastasis PC, periampullary carcinomas ROC, receiver operating characteristic ROI, region of interest VIBE, volume interpolated body examination VOI, volume of interest Declarations Ethics approval and consent to participate The need for informed consent was waived by the ethics committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University, because of the retrospective nature of the study. All experimental protocols were approved ethics committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University. All methods were carried out in accordance with relevant guidelines and regulations or declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Academic Promotion Programme of Shandong First Medical University (grant number 2019QL023), and Medical and Health Science and Technology Development Plans of Shandong Province (grant number 202209011102). Authors' contributions Guarantor of integrity of the entire study: XDL and XMW Study concepts and design: LB Literature research: LB and WC Clinical studies: LB and SJZ Data collection: HZX and YSL Experimental studies / data analysis: JTZ and SFD Statistical analysis: JTZ and SFD Manuscript preparation: LB Manuscript editing: SPD and XMW All authors read and approved the final manuscript. Acknowledgements Not applicable. References Bi L, Dong Y, Jing C, Wu Q, Xiu J, Cai S, Huang Z, Zhang J, Han X, Liu Q, Lv S. Differentiation of pancreatobiliary-type from intestinal-type periampullary carcinomas using 3.0T MRI. J Magn Reson Imaging. 2016;43:877-86. He C, Mao Y, Wang J, Huang X, Lin X, Li S. Surgical management of periampullary adenocarcinoma: defining an optimal prognostic lymph node stratification schema. J Cancer. 2018;9:1667-79. Yeo CJ, Sohn TA, Cameron JL, Hruban RH, Lillemoe KD, Pitt HA. Periampullary adenocarcinoma: analysis of 5-year survivors. Ann Surg. 1998;227:821-31. Zakaria H, Sallam AN, Ayoub II, Gad EH, Taha M, Roshdy MR, Sweed D, Gaballa NK, Yassein T. Prognostic factors for long-term survival after pancreaticoduodenectomy for periampullary adenocarcinoma. A retrospective cohort study. Ann Med Surg (Lond). 2020;57:321-7. Malvezzi M, Carioli G, Bertuccio P, Rosso T, Boffetta P, Levi F, La Vecchia C, Negri E. European cancer mortality predictions for the year 2016 with focus on leukaemias. Ann Oncol. 2016;27:725-31. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71:7-33. Raj P, Kaman L, Singh R, Dahyia D, Bhattacharya A, Bal A. Sensitivity and specificity of FDG PET-CT scan in detecting lymph node metastasis in operable periampullary tumours in correlation with the final histopathology after curative surgery. Updates Surg. 2013;65:103-7. Chen SC, Shyr YM, Wang SE. Longterm survival after pancreaticoduodenectomy for periampullary adenocarcinomas. HPB (Oxford). 2013;15:951-7. El Nakeeb A, El Sorogy M, Ezzat H, Said R, El Dosoky M, Abd El Gawad M, Elsabagh AM, El Hanafy E. Predictors of long-term survival after pancreaticoduodenectomy for peri-ampullary adenocarcinoma: A retrospective study of 5-year survivors. Hepatobiliary Pancreat Dis Int. 2018;17:443-49. Wennerblom J, Saksena P, Jönsson C, Thune A. Lymph node 8a as a prognostic marker for poorer prognosis in pancreatic and periampullary carcinoma. Scand J Gastroentero. 2018;53:225-30. Nappo G, Borzomati D, Perrone G, Valeri S, Amato M, Petitti T, Coppola R. Incidence and prognostic impact of para-aortic lymph nodes metastases during pancreaticoduodenectomy for peri-ampullary cancer. HPB (Oxford). 2015;17:1001-8. Kim SM, Eads JR. Adjuvant and neoadjuvant therapy for resectable pancreatic and periampullary cancer. Surg Clin North Am. 2016;96:1287-300. Meyer HJ, Höhn AK, Surov A. Relationships between apparent diffusion coefficient (ADC) histogram analysis parameters and PD-L 1-expression in head and neck squamous cell carcinomas: a preliminary study. Radiol Oncol. 2021;55:150-7. Nakajo M, Fukukura Y, Hakamada H, Yoneyama T, Kamimura K, Nagano S, Nakajo M, Yoshiura T. Whole-tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas. J Magn Reson Imaging. 2018; doi: 10.1002/jmri.25987. Liu S, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, Zhou Z. Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers. BMC cancer. 2017;17:665. Zhou Y, Yang R, Wang Y, Zhou M, Zhou X, Xing J, Wang X, Zhang C. Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma. BMC Med Imaging. 2021;21:176. Li J, Zhou Y, Wang X, Yu Y, Zhou X, Luan K. Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict lymph node metastasis in T3 stage rectal carcinoma. Cancer Manag Res. 2021;13:2983-93. Nerad E, Delli Pizzi A, Lambregts DMJ, Maas M, Wadhwani S, Bakers FCH. The Apparent Diffusion Coefficient (ADC) is a useful biomarker in predicting metastatic colon cancer using the ADC-value of the primary tumor. PloS One. 2019;14:e0211830. Lee J, Kim CK, Park SY. Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer. MAGMA. 2020;33:283-92. Wang F, Wang Y, Zhou Y, Liu C, Liang D, Xie L, Yao Z, Liu J. Apparent diffusion coefficient histogram analysis for assessing tumor staging and detection of lymph node metastasis in epithelial ovarian cancer: correlation with p53 and Ki-67 expression. Mol Imaging Biol. 2019;21:731-9. Ji GW, Zhang YD, Zhang H, Zhu FP, Wang K, Xia YX, Zhang YD, Jiang WJ, Li XC, Wang XH. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology. 2019;290:90-8. Bi L, Liu Y, Xu J, Wang X, Zhang T, Li K, Duan M, Huang C, Meng X, Huang Z. A CT-based radiomics nomogram for preoperative prediction of lymph node metastasis in periampullary carcinomas. Front Oncol. 2021;11:632176. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1997;33:159-74. Tseng DS, van Santvoort HC, Fegrachi S, Besselink MG, Zuithoff NP, Borel Rinkes IH, van Leeuwen MS, Molenaar IQ. Diagnostic accuracy of CT in assessing extra-regional lymphadenopathy in pancreatic and peri-ampullary cancer: a systematic review and meta-analysis. Surg Oncol. 2014;23:229-35. Zhao L, Liang M, Shi Z, Xie L, Zhang H, Zhao X. Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer. Quant Imag Med Surg. 2021;11:1805-16. Subhawong TK, Jacobs MA, Fayad LM. Diffusion-weighted MR imaging for characterizing musculoskeletal lesions. Radiographics. 2014;34:1163-77. Shindo T, Fukukura Y, Umanodan T, Takumi K, Hakamada H, Nakajo M, Umanodan A, Ideue J, Kamimura K, Yoshiura T. Histogram analysis of apparent diffusion coefficient in differentiating pancreatic adenocarcinoma and neuroendocrine tumor. Medicine (Baltimore). 2016;95:e2574. Woo S, Lee JM, Yoon JH, Joo I, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology. 2014;270:758-67. Umanodan T, Fukukura Y, Kumagae Y, Shindo T, Nakajo M, Takumi K, Nakajo M, Hakamada H, Umanodan A, Yoshiura T. ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma. J Magn Reson Imaging. 2017;45:1195-203. Just N. Improving tumour heterogeneity MRI assessment with histograms. Brit J Cancer. 2014;111:2205-13. Schob S, Meyer HJ, Dieckow J, Pervinder B, Pazaitis N, Höhn AK, Garnov N, Horvath-Rizea D, Hoffmann KT, Surov A. Histogram analysis of diffusion weighted imaging at 3T is useful for prediction of lymphatic metastatic spread, proliferative activity, and cellularity in thyroid cancer. Int J Mol Sci. 2017;18:821. Yang L, Liu D, Fang X, Wang Z, Xing Y, Ma L, Wu B. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol. 2019;29:6469-76. Gourtsoyianni S, Doumou G, Prezzi D, Taylor B, Stirling JJ, Taylor NJ, Siddique M, Cook GJR, Glynne-Jones R, Goh V. Primary rectal cancer: repeatability of global and local-regional MR imaging texture features. Radiology. 2017;284:552-61. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883-92. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2721327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":185855350,"identity":"c1751d39-4c0b-4e86-866d-3f77f55ca558","order_by":0,"name":"Lei Bi","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Bi","suffix":""},{"id":185855351,"identity":"eed793ea-8596-46bf-95ab-e562d9fde415","order_by":1,"name":"Wei Chen","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""},{"id":185855352,"identity":"d25815d4-2dd2-4450-901a-779dec42654b","order_by":2,"name":"Shijuan Zhou","email":"","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shijuan","middleName":"","lastName":"Zhou","suffix":""},{"id":185855353,"identity":"97e5e7ff-ed1c-4715-af4f-662a2abeae8d","order_by":3,"name":"Hongzhi Xu","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Xu","suffix":""},{"id":185855354,"identity":"66879f22-829e-4c70-b895-f50346e26f03","order_by":4,"name":"Yushuai Lin","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yushuai","middleName":"","lastName":"Lin","suffix":""},{"id":185855355,"identity":"ae89cb12-d2da-43bd-8755-08827607ad7c","order_by":5,"name":"Juntao Zhang","email":"","orcid":"","institution":"GE Healthcare","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juntao","middleName":"","lastName":"Zhang","suffix":""},{"id":185855356,"identity":"94eff1cf-d736-4149-8a39-c7ca5d93107a","order_by":6,"name":"Shaofeng Duan","email":"","orcid":"","institution":"GE Healthcare","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaofeng","middleName":"","lastName":"Duan","suffix":""},{"id":185855357,"identity":"722fc885-8f9f-4e5c-b3bf-38a9b16bf51b","order_by":7,"name":"Shouping Dai","email":"","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shouping","middleName":"","lastName":"Dai","suffix":""},{"id":185855358,"identity":"7514be7f-83ac-43b0-9ed8-0d518cb0f52f","order_by":8,"name":"Xiaodong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACPgYGwwcJFTZybOzNBw58qCBCCxsDg7HBgzNpxnw8xxIPzjhDnBYzyYdthxPnSeQYH+ZtIUaLRPJmg8S2w8ZsEjkfDvA2MMjzix0goIXnWOGDhHPpcmw8bzcckNzBYDhzdgIBLew9xgYJZdbGbOy5Gw4YnmFIMLhNSAszj5lEAhtzYhtDzoMDQJIILew9QC1tzoltHDkMBw4SpYXnWLFBAjCQgQyDgw1nJAj7hV8ieePDH8ColG9vfvz5T4WNPL80AS3oQII05aNgFIyCUTAKsAMATKtHfPAb/LoAAAAASUVORK5CYII=","orcid":"","institution":"Linyi People’s Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Li","suffix":""},{"id":185855359,"identity":"be472b46-cb9d-4b14-aa29-557e57b8fa32","order_by":9,"name":"Ximing Wang","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ximing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-03-22 05:29:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2721327/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2721327/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34882744,"identity":"8867c5af-f4ac-47c8-8db3-a654e09b5ada","added_by":"auto","created_at":"2023-03-27 20:53:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31562,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient selection procedure. ADC, apparent diffusion coefficient.\u003c/p\u003e","description":"","filename":"Onlinefigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2721327/v1/49dc8afe7d868e1ee42d1fc5.png"},{"id":34882743,"identity":"41679659-a6d0-45cc-b57a-8fc9dad4a412","added_by":"auto","created_at":"2023-03-27 20:53:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84424,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram analyses on apparent diffusion coefficient (ADC) maps of periampullary carcinomas in a 48-year-old woman with lymph node metastasis (a) and a 65-year-old woman without lymph node metastasis (b). On ADC map, the region of interest was manually drawn freehand on each transverse section strictly within the border of the lesion. After whole-volume segmentation, the histogram of gray-level distribution was generated (c, d).\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2721327/v1/d93401a7e4ade66388a971fa.png"},{"id":34882742,"identity":"6fbf5261-a451-4541-93ba-265ffa4b7db0","added_by":"auto","created_at":"2023-03-27 20:53:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68597,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for diagnostic performance in differentiating lymph node metastasis (LNM)-positive group from LNM-negative group periampullary carcinomas. ADC, apparent diffusion coefficient; AUC, area under the receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"Onlinefigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2721327/v1/27e85993198054109535f670.png"},{"id":37465007,"identity":"a0889c78-6825-4ece-ab13-ae68d541d1d2","added_by":"auto","created_at":"2023-05-25 03:29:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2721327/v1/2a4cf621-f3a8-4244-91fc-2ef9de795e7c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas","fulltext":[{"header":"Background","content":"\u003cp\u003ePeriampullary carcinomas (PCs) constitute a rare heterogeneous group of malignant tumors originating from pancreas, distal common bile duct, duodenum, and ampulla of Vater. These tumors arise within 2 cm of the major duodenal papilla, and share a common embryologic origin from the foregut [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although rare, accounting for roughly 0.2% of gastrointestinal tumors [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], PC is one of the top five leading causes of cancer-related death worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], with the detection rate increasing year by year [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Pancreaticoduodenectomy remains the only potential curative option for resectable PCs, however, when first diagnosed, nearly 70%-80% of the patients have lost the opportunity for surgery because of lymph node metastasis (LNM), adherent tissues or vessels invasion, and distant metastasis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Even the tumor was completely resected, the patients\u0026rsquo; prognosis is still poor due to the local recurrence and/or distant metastases after surgery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with PC were prone to be accompanied by LNM, which was reported to be one of the strongest risk factors for poor survival [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous study showed that the prognosis of patients with LNM was significantly worse than patients without LNM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. What\u0026rsquo;s more, Kim et al found that if patients with LNM received neoadjuvant chemotherapy before surgery, through the fiborsis of metastastic LNs, patient\u0026rsquo;s prognosis could be improved [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, accurate preoperative evaluation of LN status is crucial for selecting optimal individualized treatment strategy, and could assist to predict patients\u0026rsquo; prognosis.\u003c/p\u003e \u003cp\u003eDiffusion-weighted imaging (DWI) is a non-invasive MRI technique to measure molecular Brownian movement inside tissues, and is quantified by the apparent diffusion coefficient (ADC) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. ADC values were proved to be associated with tumor cellularity, proliferation potential, neoangiogenesis, lymphocytes and extracellular matrix invasion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which can be used to distinguish benign and malignant neoplasmas, predict patient prognosis, and assess tumor response to treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recently, ADC histogram analysis has received extensive recognition as a reproducible approach which can reflect the heterogeneity of molecular diffusion within a tumor region based on pixel distribution [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. ADC histogram analyses have yielded great potential for predicting LNM in various malignant tumors, such as gastric cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], colorectal cancer [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], uterine cervical cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and epithelial ovarian cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Till now, there has been no published research that has evaluated whether whole-volume ADC histogram analysis would facilitate LNM prediction in PCs.\u003c/p\u003e \u003cp\u003eTherefore, the purpose of our study was to assess the diagnostic potential of ADC histogram analysis for predicting LNM of PCs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e This retrospective study of anonymous data was approved by the Ethics Committee of our institution, and requirement for informed consent was waived. Preoperative MR images of patients with PC were searched from our database. The inclusion criteria included: 1) patients were treated with curative whole-tumor resection and lymphadenectomy; 2) comprehensive clinical and pathological results were available; 3) time interval between preoperative MR examination and surgery was less than two weeks; and 4) ADC maps had high quality (without artifacts) for segmentation. The exclusion criteria included: 1) patients received radiotherapy or chemotherapy before surgery; and 2) patients suffered from other malignancies simultaneously. From January 2016 to April 2019, a total of 38 patients (mean age, 54.6 years; range, 38\u0026ndash;72 years), including 21 men (mean age, 57.2 years; range, 41\u0026ndash;72 years) and 17 women (mean age, 52.0 years; range, 38\u0026ndash;70 years) were included in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMRI examinations\u003c/h2\u003e \u003cp\u003eMRI examinations were performed on a 3.0-T MRI system (Magnetom Verio, Siemens, German) using an eight-channel phased-array surface coil. Parallel acquisition technique and retrospective image intensity correction (B1 filter) were used to reduce standing wave or dielectric effects. A respiratory-triggered, fat-suppressed, single-shot echo-planar imaging in the transverse position was performed for DWI sequence. Each acquisition was obtained using b values of 0 and 800 s/mm\u003csup\u003e2\u003c/sup\u003e. The overall acquisition time ranged from 3 to 6 minutes depending on patient\u0026rsquo;s respiratory efficiency. ADC map was generated automatically with a commercially available software workstation system (Syngo Multimodality workplace, Siemens, German). The acquisition parameters of DWI sequence were as follows: TR/TE\u0026thinsp;=\u0026thinsp;4000/73 ms; field of view\u0026thinsp;=\u0026thinsp;380 mm \u0026times; 285 mm; flip angle\u0026thinsp;=\u0026thinsp;90⁰; matrix\u0026thinsp;=\u0026thinsp;128 \u0026times; 78; slice thickness\u0026thinsp;=\u0026thinsp;5 mm; band width\u0026thinsp;=\u0026thinsp;2442 Hz/pixel; ETL\u0026thinsp;=\u0026thinsp;78; echo space\u0026thinsp;=\u0026thinsp;0.51 ms.\u003c/p\u003e \u003cp\u003eIn addition to DWI, routine MRI including a T1-weighted dual-echo in- and out-phase sequence, a T1-weighted volume interpolated body examination (VIBE) sequence, and a breath-hold turbo spin-echo T2-weighted sequence were performed before administration of contrast agent. For enhanced imaging, arterial phase (20\u0026ndash;25 sec), portal venous phase (60\u0026ndash;70 sec), equilibrium phase (3 min), delayed phase (10 min) and hepatobiliary phase (90 min) were obtained using VIBE sequence after injection of gadobenate dimeglumine. With a power injector, the contrast agent was administered intravenously at a rate of 2.5 mL/s for a total dose of 0.1 mmol/kg of body weight, followed by a 20-mL saline flush.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMR feature evaluation\u003c/h2\u003e \u003cp\u003eTwo radiologists (observer 1 and observer 2, with 8 and 15 years of experience in abdominal MR interpretation, respectively) reviewed all images in consensus. The following image features were evaluated: 1) tumor size, defined as the lesion\u0026rsquo;s maximum diameter on axial images; 2) vascular involvement, defined as vessel occlusion, stenosis, or contour deformity due to tumor invasion; and 3) LNM, defined as LN\u0026rsquo;s short-axis diameter larger than 10 mm, or LNs with central necrosis, or LNs were hyperenhanced than liver parenchyma in portal venous phase [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The observers knew that all patients were diagnoses of PC but were blind for the clinical and pathological details.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTumor segmentation and histogram analysis\u003c/h2\u003e \u003cp\u003eThree-dimensional segmentation was performed using ITK-SNAP (v. 3.8.0). Two radiologists performed the tumor segmentation independently. On ADC map, the region of interest (ROI) was manually drawn freehand on each transverse section strictly within the border of the lesion layer by layer, which should include cyst, hematoma, or necrosis within the lesion, but avoid common bile duct, main pancreatic duct and other normal anatomical structures. The ROIs were first localized on DWI with reference to T1-weighted, T2-weighted, and gadolinium-enhanced VIBE images, and mirrored to the ADC maps subsequently. The volume of interest (VOI) of each lesion was then automatically generated for histogram analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PyRadiomics (v. 3.1.0) was used for feature extraction. Various first-order parameters including quartiles (25th, 50th, 75th), 10th and 90th percentiles, maximum, minimum and mean ADC values, energy, kurtosis, variance, and skewness were obtained.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUnivariate analysis was applied to compare the differences in the clinical factors and ADC histogram parameters between LNM-positive group and LNM-negative group. Categorical variables were compared by using Chi-square test or Fisher exact test, and continuous variables were compared by using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test or Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, as appropriate. Interobserver agreement was assessed by using interclass correlation coefficient (ICC). An ICC value of 1.0 was deemed to indicate perfect agreement; 0.81\u0026ndash;0.99, excellent correlation; 0.61\u0026ndash;0.80, good correlation; 0.41\u0026ndash;0.60, moderate agreement; 0.21\u0026ndash;0.40, fair correlation; and 0.20 or less, poor correlation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Spearman correlation analysis was done to assess the correlation between variables and LN metastasis. Diagnostic performance of variables for differentiating two groups was calculated by using receiver operating characteristic (ROC) curve analysis, and the evaluation indicators included area under the curve (AUC), sensitivity, and specificity. The optimal cutoff value was defined according to Youden index (the value at which the sum of the sensitivity and specificity was maximized). Multivariate model calibration was assessed with the goodness-of-fit Hosmer-Lemeshow test through a calibration plot. All statistical analyses were performed using SPSS software (SPSS for Windows, v. 17.0; Chicago, IL) and MedCalc software (MedCalc for Windows, v. 12.7.0; Mariakerke, Belgium). A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient demographics\u003c/h2\u003e \u003cp\u003eAmong the 38 patients in our study, 10 patients (5 men, 5 women; mean age, 48.0 years; range, 47\u0026ndash;69 years) were diagnosed with LNM, and 28 patients (16 men, 12 women; mean age, 55.6 years; range, 38\u0026ndash;72 years) without LNM. In total, 5 patients (50.0%; 5 of 10) with LNM were understaged, and 3 patients (10.7%; 3 of 28) without LNM were overstaged based on MR-reported LN status. Nine tumors (23.7%) were located at duodenum, 6 (15.8%) at ampulla of Vater, 5 (13.1%) at common bile duct, and 18 (47.4%) at pancreas. Pathological reports showed that 3 tumors (7.9%) were well differentiated, 23 (60.5%) were moderately differentiated, and 12 (31.6%) were poorly differentiated (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\u003ePatient and tumor characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNM-positive group (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLNM-negative group (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.86\u0026thinsp;\u0026plusmn;\u0026thinsp;10.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.697\u003c/p\u003e \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\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR-reported LN status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNM-positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNM-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuodenum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmpulla of Vater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon bile duct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Data are number of patients; data in parentheses are percentage unless otherwise indicated. LN, lymph node; LNM, lymph node metastasis; MR, magnetic resonance; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eComparison of clinical and pathological characteristics\u003c/h2\u003e \u003cp\u003eThe tumor size and MR-reported LN status were significantly different between LNM-positive group and LNM-negative group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020 and 0.019, respectively), and revealed significantly positive correlations with LN metastasis (rs\u0026thinsp;=\u0026thinsp;0.406, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, and rs\u0026thinsp;=\u0026thinsp;0.424, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, respectively). The other parameters did not differ significantly between two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eComparison of ADC histogram parameters\u003c/h2\u003e \u003cp\u003eInterobserver agreements of histogram parameters were good to excellent, with ICCs ranging from 0.766 to 0.967 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterobserver Agreement for the Measurement of ADC Histogram Parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistogram parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th percentile ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.938 (0.915, 0.961)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th percentile ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.933 (0.912, 0.953)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923 (0.901, 0.945)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75th percentile ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917 (0.890, 0.944)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90th percentile ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917 (0.892, 0.942)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.967 (0.927, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.936 (0.918, 0.953)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum ADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.935 (0.874, 0.995)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911 (0.886, 0.936)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.766 (0.663, 0.868)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871 (0.819, 0.924)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.793 (0.722, 0.864)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: ADC, apparent diffusion coefficient; CI, confidence interval; ICC, interclass correlation coefficient.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean, minimum ADC values and 10th, 25th, 50th, 75th, and 90th percentile values were significantly lower in LNM-positive group than in LNM-negative group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014\u0026ndash;0.047), and revealed significantly negative correlations with LN metastasis (rs = -0.403-[-0.327], all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). The kurtosis was significantly higher in LNM-positive group than in LNM-negative group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), and had a positive correlation (rs\u0026thinsp;=\u0026thinsp;0.360, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) with LN metastasis. The other parameters did not differ significantly between two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.050) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHistogram parameters between the LNM-positive and LNM-negative groups and ROC curve results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistogram parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLNM-positive group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLNM-negative group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCorrelation with LN status (rs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value of correlation test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th percentile ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e999\u0026thinsp;\u0026plusmn;\u0026thinsp;258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1210\u0026thinsp;\u0026plusmn;\u0026thinsp;339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718 (0.549, 0.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th percentile ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1111\u0026thinsp;\u0026plusmn;\u0026thinsp;268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1327\u0026thinsp;\u0026plusmn;\u0026thinsp;330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.714 (0.545, 0.849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1243\u0026thinsp;\u0026plusmn;\u0026thinsp;288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1482\u0026thinsp;\u0026plusmn;\u0026thinsp;327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.729 (0.560, 0.860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75th percentile ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1379\u0026thinsp;\u0026plusmn;\u0026thinsp;258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1646\u0026thinsp;\u0026plusmn;\u0026thinsp;333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.761 (0.595, 0.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90th percentile ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1531\u0026thinsp;\u0026plusmn;\u0026thinsp;301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1803\u0026thinsp;\u0026plusmn;\u0026thinsp;357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.750 (0.583, 0.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e699\u0026thinsp;\u0026plusmn;\u0026thinsp;292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1002\u0026thinsp;\u0026plusmn;\u0026thinsp;443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764 (0.599, 0.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1259\u0026thinsp;\u0026plusmn;\u0026thinsp;280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1497\u0026thinsp;\u0026plusmn;\u0026thinsp;324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739 (0.572, 0.868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum ADC\u003c/p\u003e \u003cp\u003e(\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2166\u0026thinsp;\u0026plusmn;\u0026thinsp;452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2200\u0026thinsp;\u0026plusmn;\u0026thinsp;484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.507 (0.340, 0.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e981\u0026thinsp;\u0026plusmn;\u0026thinsp;129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e558\u0026thinsp;\u0026plusmn;\u0026thinsp;810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650 (0.478, 0.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.736 (0.568, 0.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance (\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.052\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e659\u0026thinsp;\u0026plusmn;\u0026thinsp;498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.550 (0.380, 0.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671 (0.500, 0.815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: ADC, apparent diffusion coefficient; AUC, area under the curve; CI, confidence interval; LNM, lymph node metastasis; ROC, receiver operating characteristic.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic performances of clinical and pathological characteristics\u003c/h2\u003e \u003cp\u003eThe tumor size generated highest AUC in all clinical and pathological parameters for differentiating LNM-positive group and LNM-negative group (AUC\u0026thinsp;=\u0026thinsp;0.764, 95% confidence interval [CI]: 0.599, 0.886), with sensitivity of 70.0%, and specificity of 82.1%. MR-reported LN status also showed good discrimination capability, with AUC of 0.713, while the tumor location generated lowest AUC of 0.500. Discrimination capabilities of vascular involvement and degree of differentiation were unsatisfactory, with AUC of 0.614 and 0.589, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic performances of ADC histogram parameters\u003c/h2\u003e \u003cp\u003eThe minimum ADC value generated highest AUC in all histogram parameters for differentiating LNM-positive group and LNM-negative group (AUC\u0026thinsp;=\u0026thinsp;0.764, 95% confidence interval [CI]: 0.599, 0.886), with sensitivity of 90.0%, and specificity of 64.3%. The 75th, 90th percentiles, mean ADC value and kurtosis also showed good discrimination capability, with AUC of 0.761, 0.750, 0.739 and 0.736, respectively, while the maximum ADC value generated lowest AUC of 0.507.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic performances of combined models\u003c/h2\u003e \u003cp\u003eWhen diagnostic predictive values were calculated with the combined model incorporating MR-reported LN status and 75th percentile, the AUC increased to 0.843 (95% CI: 0.718, 0.968), with sensitivity of 90.0% and specificity of 71.4%. When incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained (AUC\u0026thinsp;=\u0026thinsp;0.879, 95% CI: 0.771, 0.986), with sensitivity of 100.0% and specificity of 75.0%. A non-significant statistic (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.490) of the Hosmer-Lemeshow test suggested no significant deviation from an ideal fitting. The ROC curve analyses were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the diagnostic performance of parameters were presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the diagnostic value of whole-volume ADC histogram analysis in predicting regional lymph node metastasis of periampullary carcinomas. Our results showed that most ADC histogram parameters were significantly different between LNM-positive group and LNM-negative group. At ROC analysis, the tumor size and minimum ADC value generated highest AUC of 0.764. When diagnostic predictive values were calculated with the combined model incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained, with AUC of 0.879, sensitivity of 100%, and specificity of 75%.\u003c/p\u003e \u003cp\u003eDue to the region\u0026rsquo;s anatomical complexity, PCs remain a diagnostic and therapeutic challenge. Preoperative prediction of LNM has received extensive attention due to its prognostic significance and the essential role in patient management. In daily clinical practice, image-based differentiation of metastatic LNs from nonmetastatic LNs mainly depends on morphological features and LN\u0026rsquo;s sizes, which is inevitably subjective. Moreover, benign LNs with nonspecific inflammatory hyperplasia and metastatic LNs with small sizes also exist. Tseng et al revealed that diagnostic accuracy of conventional CT imaging in assessing PCs\u0026rsquo; LNM was less than 81% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our present study, half of the patients (5 in 10 patients) with LNM were underestimated based on the conventional MR images. Therefore, conventional imaging modalities\u0026rsquo; performance in assessment of LN status is inadequate. It is necessary to explore some new method to reveal the histopathological characteristics of metastatic LNs.\u003c/p\u003e \u003cp\u003ePrithivi Raj et al found that PET-CT has a potential ability for detecting PC\u0026rsquo; s LNM. In their study, FDG-PET/CT showed a sensitivity of 71.4% and specificity of 77.8% for assessment of LNM with cutoff value SUV max\u0026thinsp;\u0026ge;\u0026thinsp;2.0. However, small lesions that are less than twice the imaging resolution would yield false negative results because of partial volume effect, and high cost of PET scans may limit its utility in clinical practice [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In one previous studies, Bi et al established and validated a radiomics nomogram for preoperatively predicting LNM in PCs. The results showed that the radiomics nomogram incorporating radiomics signature and CT-reported LN status obtained favorable discrimination and calibration ability in both training and validation set, with AUC of 0.853 for each set [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, although the radiomics nomogram performed well, it was complicated and time-consuming, to some extent.\u003c/p\u003e \u003cp\u003eHistogram analysis is a rapidly-emerging noninvasive method in the field of medical imaging, which is capable of quantitatively and objectively assessing tumor heterogeneity, regularity, and image roughness by evaluating the distribution of voxel gray levels without requiring additional invasive procedures [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. ADC histogram can reflect tissue components with different diffusion features inside the tumor, which was proved to have favorable repeatability and comprehensiveness [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. LNM has been reported to be associated with ADC histogram parameters of the primary tumor [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which is consistent with our results. ADC values based on water molecular diffusion can reflect the microenvironment of tissues. In general terms, acellular regions (such as cystic and necrotic components) that allow free water diffusion would yield higher ADC values, and highly cellular areas in which water diffusion is restricted by the intensive cell membranes always yield lower ADC values [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. When the primary tumor progresses, the proliferative ability of the tumor cells and their metastatic capacity were enhanced by the changes of tumor microenvironment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, patients with LNM showed lower ADC values than that without LNM.\u003c/p\u003e \u003cp\u003eIn present study, we not only evaluated various ADC values, but also ADC histogram kurtosis, energy, variance and skewness, which is a more reproducible approach and reflected the distribution of ADC values [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study, tumors with LNM showed higher kurtosis than tumors without LNM. Kurtosis reflects the peakedness of the histogram distribution and measures the shape of the probability distribution [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Higher kurtosis indicates that the distribution tended to have heavier tails or outliers, and the mass of the distribution was more concentrated in tumors with LNM. Previous research has showed that kurtosis of ADC histogram could predict LN status in thyroid cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, energy, variance and skewness have also been reported to be correlated with pathological characteristics. Higher energy reflects narrower distribution of intensity levels, higher variance reflects more heterogeneous distribution of ADC values, and higher skewness reflects more asymmetric distribution of ADC values [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, in our study, energy, variance and skewness were not significantly different between LNM-positive and LNM-negative group. Further studies focusing on this topic should be proposed.\u003c/p\u003e \u003cp\u003eFor manual tumor segmentation, how to ensure the reproducibility of histogram analysis is the most important issue. Reports suggested that compared with a representative single cross-sectional lesion segmentation, whole-tumor analysis was more reproducible because ADC values were depended on the selected regions of interest, subjective selection of single section would reduce inter- and intraobserver agreements [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. What\u0026rsquo;s more, since PCs are often heterogeneous, a single region of interest could not comprehensively reflect different pathological characteristics within the entire tumor. Therefore, in our study, we explored whole-volume ADC histogram analysis, and an overall good interobserver agreement was obtained, with ICCs ranging from 0.766 to 0.967. This was in accordance with previous studies which revealed that whole-volume histogram analyses exhibited favorable repeatability in clinical practice [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere were several limitations to our study. First, due to the retrospective design of our study, selection bias inevitably existed even with strict inclusion criteria. Patients with tumors of advanced stage who have lost the opportunity for surgery were excluded. Second, this was a single-center research and the patients number was relatively small, especially the number of patients with LNM, so the diagnostic criteria proposed in our study should be validated in other patient cohort. Finally, we only applied two b values of 0 and 800 s/mm\u003csup\u003e2\u003c/sup\u003e for DWI sequence in our study, higher and multiple b values may provide more parameters, which could be further evaluated.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, whole-volume ADC histogram analysis of the primary tumor could be a promising and noninvasive method for predicting regional lymph node metastasis of periampullary carcinomas.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADC, apparent diffusion coefficient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC, area under the curve\u003c/p\u003e\n\u003cp\u003eCI, confidence interval\u003c/p\u003e\n\u003cp\u003eDWI, diffusion-weighted imaging\u003c/p\u003e\n\u003cp\u003eICC, interclass correlation coefficient\u003c/p\u003e\n\u003cp\u003eLN, lymph node\u003c/p\u003e\n\u003cp\u003eLNM, lymph node metastasis\u003c/p\u003e\n\u003cp\u003ePC, periampullary carcinomas\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eROI, region of interest\u003c/p\u003e\n\u003cp\u003eVIBE, volume interpolated body examination\u003c/p\u003e\n\u003cp\u003eVOI, volume of interest\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe need for informed consent was waived by the ethics committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University, because of the retrospective nature of the study. All experimental protocols were approved ethics committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University. All methods were carried out in accordance with relevant guidelines and regulations or declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Academic Promotion Programme of Shandong First Medical University (grant number 2019QL023), and Medical and Health Science and Technology Development Plans of Shandong Province (grant number 202209011102).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuarantor of integrity of the entire study: XDL and XMW\u003c/p\u003e\n\u003cp\u003eStudy concepts and design: LB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiterature research: LB and WC\u003c/p\u003e\n\u003cp\u003eClinical studies: LB and SJZ\u003c/p\u003e\n\u003cp\u003eData collection: HZX and YSL\u003c/p\u003e\n\u003cp\u003eExperimental studies / data analysis: JTZ and SFD\u003c/p\u003e\n\u003cp\u003eStatistical analysis: JTZ and SFD\u003c/p\u003e\n\u003cp\u003eManuscript preparation: LB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eManuscript editing: SPD and XMW\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBi L, Dong Y, Jing C, Wu Q, Xiu J, Cai S, Huang Z, Zhang J, Han X, Liu Q, Lv S. Differentiation of pancreatobiliary-type from intestinal-type periampullary carcinomas using 3.0T MRI. J Magn Reson Imaging. 2016;43:877-86.\u003c/li\u003e\n\u003cli\u003eHe C, Mao Y, Wang J, Huang X, Lin X, Li S. Surgical management of periampullary adenocarcinoma: defining an optimal prognostic lymph node stratification schema. J Cancer. 2018;9:1667-79.\u003c/li\u003e\n\u003cli\u003eYeo CJ, Sohn TA, Cameron JL, Hruban RH, Lillemoe KD, Pitt HA. Periampullary adenocarcinoma: analysis of 5-year survivors. Ann Surg. 1998;227:821-31.\u003c/li\u003e\n\u003cli\u003eZakaria H, Sallam AN, Ayoub II, Gad EH, Taha M, Roshdy MR, Sweed D, Gaballa NK, Yassein T. Prognostic factors for long-term survival after pancreaticoduodenectomy for periampullary adenocarcinoma. A retrospective cohort study. Ann Med Surg (Lond). 2020;57:321-7.\u003c/li\u003e\n\u003cli\u003eMalvezzi M, Carioli G, Bertuccio P, Rosso T, Boffetta P, Levi F, La Vecchia C, Negri E. European cancer mortality predictions for the year 2016 with focus on leukaemias. Ann Oncol. 2016;27:725-31.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71:7-33.\u003c/li\u003e\n\u003cli\u003eRaj P, Kaman L, Singh R, Dahyia D, Bhattacharya A, Bal A. Sensitivity and specificity of FDG PET-CT scan in detecting lymph node metastasis in operable periampullary tumours in correlation with the final histopathology after curative surgery. Updates Surg. 2013;65:103-7.\u003c/li\u003e\n\u003cli\u003eChen SC, Shyr YM, Wang SE. Longterm survival after pancreaticoduodenectomy for periampullary adenocarcinomas. HPB (Oxford). 2013;15:951-7.\u003c/li\u003e\n\u003cli\u003eEl Nakeeb A, El Sorogy M, Ezzat H, Said R, El Dosoky M, Abd El Gawad M, Elsabagh AM, El Hanafy E. Predictors of long-term survival after pancreaticoduodenectomy for peri-ampullary adenocarcinoma: A retrospective study of 5-year survivors. Hepatobiliary Pancreat Dis Int. 2018;17:443-49. \u003c/li\u003e\n\u003cli\u003eWennerblom J, Saksena P, J\u0026ouml;nsson C, Thune A. Lymph node 8a as a prognostic marker for poorer prognosis in pancreatic and periampullary carcinoma. Scand J Gastroentero. 2018;53:225-30.\u003c/li\u003e\n\u003cli\u003eNappo G, Borzomati D, Perrone G, Valeri S, Amato M, Petitti T, Coppola R. Incidence and prognostic impact of para-aortic lymph nodes metastases during pancreaticoduodenectomy for peri-ampullary cancer. HPB (Oxford). 2015;17:1001-8. \u003c/li\u003e\n\u003cli\u003eKim SM, Eads JR. Adjuvant and neoadjuvant therapy for resectable pancreatic and periampullary cancer. Surg Clin North Am. 2016;96:1287-300.\u003c/li\u003e\n\u003cli\u003eMeyer HJ, H\u0026ouml;hn AK, Surov A. Relationships between apparent diffusion coefficient (ADC) histogram analysis parameters and PD-L 1-expression in head and neck squamous cell carcinomas: a preliminary study. Radiol Oncol. 2021;55:150-7.\u003c/li\u003e\n\u003cli\u003eNakajo M, Fukukura Y, Hakamada H, Yoneyama T, Kamimura K, Nagano S, Nakajo M, Yoshiura T. Whole-tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas. J Magn Reson Imaging. 2018; doi: 10.1002/jmri.25987.\u003c/li\u003e\n\u003cli\u003eLiu S, Zhang Y, Chen L, Guan W, Guan Y, Ge Y, He J, Zhou Z. Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers. BMC cancer. 2017;17:665.\u003c/li\u003e\n\u003cli\u003eZhou Y, Yang R, Wang Y, Zhou M, Zhou X, Xing J, Wang X, Zhang C. Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma. BMC Med Imaging. 2021;21:176. \u003c/li\u003e\n\u003cli\u003eLi J, Zhou Y, Wang X, Yu Y, Zhou X, Luan K. Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict lymph node metastasis in T3 stage rectal carcinoma. Cancer Manag Res. 2021;13:2983-93. \u003c/li\u003e\n\u003cli\u003eNerad E, Delli Pizzi A, Lambregts DMJ, Maas M, Wadhwani S, Bakers FCH. The Apparent Diffusion Coefficient (ADC) is a useful biomarker in predicting metastatic colon cancer using the ADC-value of the primary tumor. PloS One. 2019;14:e0211830.\u003c/li\u003e\n\u003cli\u003eLee J, Kim CK, Park SY. Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer. MAGMA. 2020;33:283-92.\u003c/li\u003e\n\u003cli\u003eWang F, Wang Y, Zhou Y, Liu C, Liang D, Xie L, Yao Z, Liu J. Apparent diffusion coefficient histogram analysis for assessing tumor staging and detection of lymph node metastasis in epithelial ovarian cancer: correlation with p53 and Ki-67 expression. Mol Imaging Biol. 2019;21:731-9. \u003c/li\u003e\n\u003cli\u003eJi GW, Zhang YD, Zhang H, Zhu FP, Wang K, Xia YX, Zhang YD, Jiang WJ, Li XC, Wang XH. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology. 2019;290:90-8. \u003c/li\u003e\n\u003cli\u003eBi L, Liu Y, Xu J, Wang X, Zhang T, Li K, Duan M, Huang C, Meng X, Huang Z. A CT-based radiomics nomogram for preoperative prediction of lymph node metastasis in periampullary carcinomas. Front Oncol. 2021;11:632176.\u003c/li\u003e\n\u003cli\u003eLandis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1997;33:159-74.\u003c/li\u003e\n\u003cli\u003eTseng DS, van Santvoort HC, Fegrachi S, Besselink MG, Zuithoff NP, Borel Rinkes IH, van Leeuwen MS, Molenaar IQ. Diagnostic accuracy of CT in assessing extra-regional lymphadenopathy in pancreatic and peri-ampullary cancer: a systematic review and meta-analysis. Surg Oncol. 2014;23:229-35.\u003c/li\u003e\n\u003cli\u003eZhao L, Liang M, Shi Z, Xie L, Zhang H, Zhao X. Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer. Quant Imag Med Surg. 2021;11:1805-16. \u003c/li\u003e\n\u003cli\u003eSubhawong TK, Jacobs MA, Fayad LM. Diffusion-weighted MR imaging for characterizing musculoskeletal lesions. Radiographics. 2014;34:1163-77. \u003c/li\u003e\n\u003cli\u003eShindo T, Fukukura Y, Umanodan T, Takumi K, Hakamada H, Nakajo M, Umanodan A, Ideue J, Kamimura K, Yoshiura T. Histogram analysis of apparent diffusion coefficient in differentiating pancreatic adenocarcinoma and neuroendocrine tumor. Medicine (Baltimore). 2016;95:e2574. \u003c/li\u003e\n\u003cli\u003eWoo S, Lee JM, Yoon JH, Joo I, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology. 2014;270:758-67.\u003c/li\u003e\n\u003cli\u003eUmanodan T, Fukukura Y, Kumagae Y, Shindo T, Nakajo M, Takumi K, Nakajo M, Hakamada H, Umanodan A, Yoshiura T. ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma. J Magn Reson Imaging. 2017;45:1195-203.\u003c/li\u003e\n\u003cli\u003eJust N. Improving tumour heterogeneity MRI assessment with histograms. Brit J Cancer. 2014;111:2205-13.\u003c/li\u003e\n\u003cli\u003eSchob S, Meyer HJ, Dieckow J, Pervinder B, Pazaitis N, H\u0026ouml;hn AK, Garnov N, Horvath-Rizea D, Hoffmann KT, Surov A. Histogram analysis of diffusion weighted imaging at 3T is useful for prediction of lymphatic metastatic spread, proliferative activity, and cellularity in thyroid cancer. Int J Mol Sci. 2017;18:821. \u003c/li\u003e\n\u003cli\u003eYang L, Liu D, Fang X, Wang Z, Xing Y, Ma L, Wu B. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol. 2019;29:6469-76. \u003c/li\u003e\n\u003cli\u003eGourtsoyianni S, Doumou G, Prezzi D, Taylor B, Stirling JJ, Taylor NJ, Siddique M, Cook GJR, Glynne-Jones R, Goh V. Primary rectal cancer: repeatability of global and local-regional MR imaging texture features. Radiology. 2017;284:552-61.\u003c/li\u003e\n\u003cli\u003eGerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883-92. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Periampullary carcinoma, ADC, Histogram, Lymph node metastasis","lastPublishedDoi":"10.21203/rs.3.rs-2721327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2721327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAccurate preoperative evaluation of lymph node (LN) status is crucial for selecting optimal individualized treatment strategy and predicting patients’ prognosis. This study aimed to evaluate whether whole-volume apparent diffusion coefficient (ADC) histogram parameters of the primary tumor were useful to predict regional lymph node metastasis (LNM) in periampullary carcinomas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThirty-eight patients with periampullary carcinoma who underwent pancreaticoduodenectomy between January 2016 to April 2019 were retrospectively enrolled. Whole-volume ADC histogram analysis of the primary tumor was performed by two radiologists independently. Clinical factors, pathological results and histogram parameters were evaluated. Interclass correlation coefficient (ICC) was used to assess agreement between observers. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of parameters in differentiating LNM-positive group and LNM-negative group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eInterobserver agreements were good to excellent for histogram analysis between two radiologists, with ICCs ranging from 0.766 to 0.967. Tumor size, MR-reported LN status and most ADC histogram parameters (including mean, minimum ADC value, 10th, 25th, 50th, 75th, and 90th percentile, and kurtosis) were significantly different between LNM-positive group and LNM-negative group (p \u0026lt; 0.050), and revealed significant correlations with LNM (p \u0026lt; 0.050). At ROC analysis, tumor size and minimum ADC value generated highest area under the curve (AUC) (AUC = 0.764, 95% confidence interval [CI]: 0.599, 0.886). When diagnostic predictive values were calculated with the combined model incorporating tumor size, MR-reported LN status and 75th percentile, the best diagnosis performance was obtained, with AUC of 0.879 (95% CI: 0.771, 0.986), sensitivity of 100.0%, and specificity of 75.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWhole-volume ADC histogram parameters of the primary tumor held great potential in differentiating regional LNM in periampullary carcinomas.\u003c/p\u003e","manuscriptTitle":"Whole-volume apparent diffusion coefficient histogram analysis for prediction of regional lymph node metastasis in periampullary carcinomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-27 20:53:36","doi":"10.21203/rs.3.rs-2721327/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3423c46-508b-49cf-b0e3-359b932617a4","owner":[],"postedDate":"March 27th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-25T03:29:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-27 20:53:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2721327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2721327","identity":"rs-2721327","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}