Diffusion Kurtosis Imaging in Identifying the Malignancy of Lymph Node during the Primary Staging of Rectal Cancer

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1 Article type : Original Article Accepted Article R2 Original Article Diffusion Kurtosis Imaging in Identifying the Malignancy of Lymph Node during the Primary Staging of Rectal Cancer Jing Yu 1#, Xin Dai 1#, Hai-Hua Zou 1, Jia-Cheng Song 1, Yan Li 1, Hai-Bin Shi 1, Qing Xu 1*, Hongbing Shen 2* 1 Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China 2 Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China # Jing Yu and Xin Dai contributed equally to this work. *Hongbing Shen and Hai-Bin Shi are co-corresponding authors. Hongbing Shen, PhD, Professor, Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing , China; ; hbshen_njmu@126.com This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: /codi.13835

2 Abstract Accepted Article Aim To assess the diagnostic value of diffusion kurtosis imaging (DKI) for discriminating between benign and malignant lymph nodes (LNs) in patients with rectal carcinoma. Method Eighty-five patients with rectal adenocarcinoma who underwent total mesorectal excision of the rectum were studied. A total of 273 lymph nodes were harvested and subjected to histological analysis.. Quantitative parameters (apparent diffusion parameter of Gaussian distribution (D app ), apparent kurtosis coefficient (K app ) and apparent diffusion coefficient (ADC)) of lymph node were derived from DKI. Differences and diagnostic performance of these parameters were calculated by using independent-samples t test and Receiver operating characteristic curve analyses. Results The median D app and ADC values of metastatic lymph nodes were significantly greater than those of benign lymph nodes, whereas the median K app of metastatic lymph nodes was statistically lesser than that of the normal lymph nodes. D app had the relatively highest AUC of When mm 2 /s was used as a D app threshold value, the sensitivity and specificity were 96.97% and 41.82%, respectively. Conclusion DKI can help differentiate metastatic versus benign lymph nodes during the primary staging of rectal cancer. Keywords Diffusion Kurtosis Imaging, D app, K app, ADC value, lymph node, rectal cancer What does this paper add to the existing literature? Lymph node staging in patients with rectal cancer is crucial for the selection of the appropriate treatment strategy. The DKI technique was first applied in lymph nodes of rectal cancer and DKI can help differentiate metastatic versus benign lymph nodes. Introduction Preoperative detection of lymph node (LN) metastases in patients with rectal cancer is crucial for the selection of the appropriate treatment strategy. Lymph node involvement has also been shown to be a major prognostic factor in determining the risk of recurrence (1, 2).

3 Diffusion-weighted imaging (DWI) is a functional MRI technique utilizing the micro-diffusion of water in the intra and extracellular spaces. Previous studies that have investigated lymph the feasibility of apparent diffusion coefficient (ADC) for the assessment of nodes in patients with rectal cancer (3-6), have yielded conflicting results.. Some of these studies concluded that that the ADC value was not reliable for differentiating between benign and malignant lymph nodes (3, 4), whilst others found that the ADC could be used to discriminate metastatic from non-metastatic LNs (5, 6). (4). Diffusion kurtosis imaging is a recently described technique based on the non-gaussian diffusion of water in biologic systems (7,8). Kurtosis quantifies the deviation of tissue diffusion from a Gaussian pattern. (9). The MR signal intensity decay in tissue is not a simple mono-exponential function of the b-value and the non-gaussian behavior may contain useful information related to tissue structure and pathophysiology (8). Kurtosis modeling has been widely used in different cancers and results suggest a significantly better fit of the data than mono-exponential modeling did (10-13). However, as far as we are aware, few studies have been conducted to evaluate the feasibility of DKI in discriminating between metastatic and benign LNs in patients with primary rectal cancer This study aimed to assess the diagnostic value of kurtosis model and mono-exponential model of DWI for the differentiation between benign and metastatic lymph nodes during the primary staging of rectal cancer. Materials and methods Patients Our institutional review board approved this retrospective study and patient informed consent was waived. Between October 2014 and March 2016, 297 patients with biopsy-proven rectal adenocarcinoma were screened. All patients underwent endoscopy and biopsy. Rectal carcinoma was confirmed by histology in all cases. Exclusion criteria included patients who did not undergo surgery, those with MR images which showed artefacts or were not suitable for quantitative parameter calculations for artefacts, patients with contraindications for MRI or gadolinium contrast agents and finally 85 patients were included (fig.1). patients with mucinous tumors. A total of

4 MR imaging acquisition protocol Accepted Article MR examinations were performed using 3.0-Tesla MRI scanner (MAGNETOM Verio Tim; Siemens, Erlangen, Germany) with a sixteen elements of the pelvic phased-array coil. T1-weighted, T2-weighted images, DWI and the dynamic contrast enhancement (DCE) images were obtained. An automated injector system (Stellant MR Injection System, Medrad, Germany) was used to apply the Omniscan (GE HealthCare, 0.5mmol/ml; Dosage (ml) = weight (kg) 0.2 ml/kg) by bolus injection with a flow rate of 2.5 ml/sec. The DWI and DCE sequences were planned perpendicular to the body axis. The parameters of each sequence were shown in Table 2. Before MR examination, patients did not receive a bowel preparation or spasmolytic. Image analysis Two experienced radiologists, performed ADC and DKI measurements independently. Freehand regions of interest (ROIs) were drawn along the border of the high signal of the LN on the DW images, T2 images were used as morphologic images. Each ROI was variable by considering two criteria: 1) include as much of the nodal parenchyma as possible; 2) diminish the possibility of signal contamination caused by the partial volume averaging effect of the surrounding fat or vessels (14) (Fig. 2 and 3). Small LNs that were less than 2 mm in diameter were excluded because it is technically challenging to locate the ROIs. Quantitative parameters (apparent diffusion parameter of Gaussian distribution (D app, 10-3 mm 2 /s) and apparent kurtosis coefficient (K app, a dimensionless parameter)) were calculated from DKI with b values of 0, 700, 1400, and 2000 sec/mm 2 by using medical image software (FireVoxel; CAI2R, New York University, NY). Fig. 2 and 3 showed the D app and K app maps. The relationship between signal intensity of DKI and b factors can be expressed by S ( 2 2 b D app 6b D appk app. S b) 0 e 1 For ADC measurement, ADC maps were calculated from DWI by using functional tool software at MAGNETOM Verio Tim workstation (Siemens, Erlangen, Germany). The ROIs were applied to the corresponding image of the ADC map to obtain the calculated ADC value

5 (Fig. 2 and 3). ADC maps were calculated from b value of 1000 s/mm 2 using a linear regression model: S(b) = S(0)*exp(-b*ADC), where S(b) represents signal intensity with diffusion gradient, and b the gradient factor (sec/mm 2 ) of the used pulse sequence as a measure of strength of the diffusion gradient (15). Contrast enhancement kinetics of lymph nodes Contrast enhancement kinetics of LNs were evaluated by calculating time contrast intensity curves (TIC). Free hand ROIs were drawn and the TIC was calculated from signal intensities of pre-contrast and post-contrast T1 MR gradient echo VIBE series at different times. Three types of TIC were defined as previously described (16) (Fig. 4): (a) persistent enhancement pattern: signal intensity increases by more than 10%; (b) plateau pattern: signal intensity does not change more than 10%; (c) washout enhancement pattern: signal intensity decreases by more than 10%. Histology All specimens were evaluated by two experienced pathologists. The histopathology reports were made in accordance with the TNM staging system (AJCC Cancer Staging Manual. 6th edit.). Pathological record (Table 1) showed results of hematoxylin and eosin staining and immunehistochemical analysis of surgical specimens. Lesion-by-lesion histological evaluation of lymph nodes Our institution s multidisciplinary team reached a consensus regarding the designated region of LNs and the histopathology report about LNs. Regional LNs were categorized into three regional groups: peri-rectal/ peri-tumoral, peri-colic and inferior mesenteric artery. The different regional LNs were separated by the surgeon before sending to department of pathology. The pathologists analysed each specimen and the histopathological report indicated the number of harvested and metastatic LNs in each group. To create a reference standard map of lymph node distribution in each patient, MR images were reviewed in conjunction with histopathology reports. If all of the harvested LNs were metastatic, then the identified LNs on the MR images were considered metastatic LNs in the

6 corresponding group. This rule was same for non-metastatic LNs. In instances of mixed LNs, they were excluded in the final analysis to avoid selection bias. The cell number was counted using software of Image J (Rawak Software, Inc. Germany) as illustrated in Fig.5. Statistical Analyses Statistical packages (PASW Statistics 18.0 SPSS Inc., Chicago, IL and MedCalc, version 11.5, Mariakerke, Belgium) were used. The normality and homoscedasticity of the data was tested using the Q-Q plots and the Levene tests. Data satisfying the assumption was subjected to independent-sample t test or Analysis of Variance (ANOVA). Conversely, data was analyzed using the Mann-Whitney U test or Kruskal-Wallis H(K) test. The accuracy of D app or K app and ADC values for diagnose of malignant LNs was assessed by the receiver operating characteristic curve (ROC). The corresponding area under the curve (AUC), sensitivity and specificity were calculated by using a threshold criterion determined as that value would maximize the average of sensitivity and specificity. Differences in diagnostic performance were analyzed by comparing the ROC curves (17). P values<0.05 were considered statistically significant. Results Patients and Histopathological Results A total of 273 lymph nodes were detected in 85 patients (50 men and 35 women; mean age, 60.4±9.9years; age range, years). Characteristics of the study population and histopathological results are summarized in Table 1. Morphological Features of Non-metastatic and Metastatic Lymph Nodes Figures 2 and 3 show representative images of the metastatic and non-metastatic lymph nodes. Supplemental Table 1 shows the distribution of all metastatic and non-metastatic lymph nodes with regard to the size and morphology. Of 273 lymph nodes, 197 were benign and 76 were malignant. The sizes of 76 malignant lymph nodes ranged from 3.2 to 11.2 mm (in minimal transverse diameter), The mean minimal transverse diameter of the benign and

7 malignant lymph nodes were 4.27 ± 1.24 mm (range, 3, mm) and 5.78 ± 2.08 mm (range, 3, mm), respectively. Accepted Article DKI Parameter and ADC Measurements of Non-metastatic and Metastatic Lymph Nodes Excellent inter-observer agreement (ICC) between the two observers in measuring all DKI parameters and ADC values was found. The ICC for D app, K app and ADC was 0.833, 0.791, and respectively. A summary of the DKI parameters and ADC values is provided in Table 3 and Fig. 6. Median D app and ADC values of metastatic lymph nodes were significantly greater than those of normal lymph nodes (p< 0.001), whereas the median K app of metastatic lymph nodes was statistically less than that of the normal lymph nodes (p< 0.001). Diagnostic Efficacy of DKI Parameter (D app, K app ) and ADC Values The ROC curves of D app, K app and ADC values in differentiating normal from metastatic lymph nodes are shown in Table 4 and Fig. 7. D app had the highest AUC. When mm 2 /s was used as a D app threshold value, the sensitivity and specificity were 96.97% and 41.82%, respectively. When was used as a K app threshold value, the sensitivity and specificity were 72.73% and 59.39%, respectively. Using an ADC value cutoff of mm 2 /s, metastatic lymph nodes could be diagnosed with 65.15% sensitivity and 67.27% specificity. When comparing the diagnostic efficacy, there was no significantly difference between D app and ADC value (p > 0.05). Time contrast intensity curves of Normal and Metastatic Lymph Nodes The mean distribution of TIC in metastatic lymph nodes showed persistent curve type in 39.47%, plateau curve in 51.32%, and washout enhancement curve in 9.21% (Supplemental Table 1). In comparison, benign lymph nodes demonstrated persistent curve type in 9.65% (p<0.001), plateau curve type in 46.19% (p=0.458), and washout curve type in 44.16% (p<0.001).

8 Discussion Accepted Article In this study we successfully performed DK imaging to differentiate benign from metastatic lymph nodes during the primary staging of rectal cancer. D app and ADC values of metastatic lymph node were significantly greater than those of benign lymph node. An earlier study from other s group also demonstrated that the D and ADC values from IVIM were significantly higher in metastatic lymph nodes. (18). However, the explanation was partially different. The the greater D value of metastatic nodes in the earlier study might be accounted for by low cellular density and the heterogeneity of metastatic nodes may be partly caused by the presence of necrotic tissue in the nodes (19). In current study, we did not find a significant difference between cellular density comparing metastatic and non-metastatic nodes (data not shown). As normal/benign lymph nodes have a high cellular density, they usually show restricted diffusion and are easily detected on DWI (20, 21). The previous study suggested that quantification of the ADC could be useful to discriminate between benign and metastatic nodes based on the assumption that increased cellular density occurs when nodes are invaded with tumor. Other studies have reported different results finding lower ADC values in metastatic LNs (6). In our opinion, this difference could be attributed to either selection bias or measurement methods. We consider that the higher D app and ADC values of that they are metastatic lymph nodes demonstrates more heterogeneous compared to non-metastatic nodes. In addition, the metastatic lymph nodes may have an increased number of blood vessels and other tumor-associated vascular changes.. The contrast behavior (time contrast intensity curves, TIC) differences between metastatic and non-metastatic nodes support our interpretation as as previously reported (16), We observed that the percentage of persistent enhancing voxels in malignant lymph nodes is significantly higher compared to uninvolved lymph nodes (39.47% vs. 9.65%; p<0.001). Conversely, the percentage distribution of washout-type enhancing voxels in benign nodes is significantly higher than in metastatic nodes (44.16% vs. 9.21%; p<0.001). Angiogenesis appears to be a key factor concerning enhancement type. Previous studies have found a significant correlation between mean vessel density (MVD) and TIC in DCE-MRI (22, 23). Further aspects including the fenestration of capillaries (24), which are dependent on different molecular factors like VEGF (25), are also known to influence contrast dynamics.. Our future work will focus on the angiogenesis of metastatic and non-metastatic lymph nodes.

9 Although the size, border and signal intensity of LN was investigated and reported (26), there are some pitfalls in predicting LN status on morphology alone. As has been discussed, the reactive LN swelling makes it difficult to be differentiated from metastatic nodes, leading to the false-positive results. Another factor of clinical significance in the present study is the identification of those lymph nodes outside the mesorectal fascia, because those nodes that are not resected by extended lymphadenectomy (27) may be responsible for local recurrence despite the apparent free surgical resection margin (28). Another important prognostic variable is distant metastases (29). A major study on lateral pelvic lymph node dissection (LPLD) based on the multi-institutional data registry in Japan found that LP lymph node metastasis (internal and external iliac) was present in 14.6% of patients with T3 or T4 tumors who had undergone LPLD (30). Although LPLD is not regularly performed in Western countries because preoperative radiation followed by total mesorectal excision (TME) is the standard treatment for advanced rectal cancer, the selection of patients with lateral pelvic lymph node involvement is clinically significant which may lead to the identification of a subgroup for individual treatment. For DKI is firstly applied to rectal cancer node, the image and data quality should be assured. In this study, DKI method with four b values was employed which makes this approach clinically viable. The highest b value was determined based on previous studies (31, 32) and the signal-to-noise ratio (SNR) remained high because of restricted diffusion within the tumor. The low signal to noise data was excluded by software (FireVoxel; CAI2R, New York University, NY) when D app and K app values were measured. In this study, we did not include the starting value and lower and upper limits. The number of b-values and the detailed highest b-value of DKI should be determined with the comparison with different fit. Our DKI method with four b-values applied in three orthogonal directions which based on other reported studies (33). The numbers of excitation (NEXs) were three. However, the use of fewer diffusion directions causes a loss of rotational invariance (34) and fewer number of signals acquired causes low SNR. Therefore, a further test and comparison study for parameters optimization about DKI method is necessary. In this study, ADC and DKI calculation were used in different sequences in accordance with one study (35).

10 The present study had some limitations. Firstly, the study population was from one institution. Large, multicenter and prospective studies with strict standardization of DKI protocols are required to strengthen the statistical power of our data and to be developed as a routine clinical application in the near future. Secondly, the selection bias. The assessment was performed retrospectively on a per-patient basis on the pathology report. Any lymph node which did not meet the inclusion criteria (lesion-by-lesion correlation of nodal imaging results with pathological confirmation) was excluded. LNs with size less than 2mm in diameter were also excluded in the current study.. This could also result in selection bias. Future work about DKI should include such small lesions. In addition, patients with pre-operation chemotherapy/radiotherapy was not included in this study. We will further analyze the changes in these quantitative parameters and the value in lymph node judgment after chemotherapy/radiotherapy. Thirdly, the diagnostic performance comparison between DKI and contrast enhancement kinetics of lymph nodes was not accessed in this present study. Our future work will focus on the comparison and combination of multimodal imaging. Fourthly, this is the first application of DKI for assessing lymph nodes, and, as such, DKI standardization is required to improve hardware, software, kinetic model application, and analysis methodology.. In conclusion, the kurtosis model described is useful for the differentiation between metastatic and benign lymph nodes during the initial staging of rectal cancer. Lymph nodes with higher D app and ADC values were significantly more likely to be metastatic. The use of DKI in rectal carcinoma may provide a new mean of improving the ability to predict the presence of metastatic lymph nodes. References: 1. Kwon TS, Choi SB, Lee YS, Kim JG, Oh ST, Lee IK. Novel Methods of Lymph Node Evaluation for Predicting the Prognosis of Colorectal Cancer Patients with Inadequate Lymph Node Harvest. CANCER RES TREAT 2016;48: Leonard D, Penninckx F, Laenen A, Kartheuser A. Quantitative contribution of prognosticators to oncologic outcome after rectal cancer resection. DIS COLON RECTUM 2015;58:

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13 IMAGING 1997;7: Accepted Article 24. Faye N, Fournier L, Balvay D, et al. Dynamic contrast enhanced optical imaging of capillary leakage. Technol Cancer Res Treat 2011;10: Kamba T, Tam BY, Hashizume H, et al. VEGF-dependent plasticity of fenestrated capillaries in the normal adult microvasculature. Am J Physiol Heart Circ Physiol 2006;290:H560-H Brown G, Richards CJ, Bourne MW, et al. Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. RADIOLOGY 2003;227: Suzuki K, Muto T, Sawada T. Prevention of local recurrence by extended lymphadenectomy for rectal cancer. SURG TODAY 1995;25: Moreira LF, Kenmotsu M, Gochi A, Tanaka N, Orita K. Lymphovascular and neural invasion in low-lying rectal carcinoma. Cancer Detect Prev 1999;23: Ueno H, Mochizuki H, Hashiguchi Y, Hase K. Prognostic determinants of patients with lateral nodal involvement by rectal cancer. ANN SURG 2001;234: Akiyoshi T, Watanabe T, Miyata S, Kotake K, Muto T, Sugihara K. Results of a Japanese nationwide multi-institutional study on lateral pelvic lymph node metastasis in low rectal cancer: is it regional or distant disease? ANN SURG 2012;255: Yu J, Xu Q, Song JC, et al. The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer. EUR RADIOL 2017;27(5): Yu J, Huang DY, Li Y, Dai X, Shi HB. Correlation of standard diffusion-weighted imaging and diffusion kurtosis imaging with distant metastases of rectal carcinoma. J MAGN RESON IMAGING 2016;44: Zhu L, Pan Z, Ma Q, et al. Diffusion Kurtosis Imaging Study of Rectal Adenocarcinoma Associated with Histopathologic Prognostic Factors: Preliminary Findings. RADIOLOGY 2016:160094[Epup ahead of print] 34. Jensen JH, Helpern JA. MRI quantification of non-gaussian water diffusion by kurtosis

14 analysis. NMR BIOMED 2010;23: Accepted Article 35. Matthias C. Roethke, Tristan A. Kuder, Timur H. Kuru, Michael Fenchel, Boris A. Hadaschik, Frederik B. Laun, Heinz-Peter Schlemmer, and Bram Stieltjes. Evaluation of Diffusion Kurtosis Imaging Versus Standard Diffusion Imaging for Detection and Grading of Peripheral Zone Prostate Cancer. Invest Radiol. 2015;50(8):483-9 Table 1. Summary of Clinicopathologic characteristics of included study population. Clinicopathologic characteristics No. (%) a Gender Male 50 (58.82%) Female 35 (41.18%) Age (51.76%) > (48.24%) Tumor location upper 32 (37.65%) middle 47 (55.29%) lower 6 (7.06%) Histological T Stage pt1 2 (2.35%) pt2 46 (54.12%) pt3 37 (43.53%) pt4 0 (0%) Histological grade Well differentiated 2 (2.35%) Moderately differentiated 49 (57.65%) Poorly differentiated 34 (40%) plvi negative 78 (91.76%) positive 7 (8.24%) Histological N stage negative 67 (78.82%) positive 18 (21.18%) plvi: pathologic lymphovascular invasion; a Data are no. of patients, with percentages in parentheses.

15 ccepted Article Table 2. MRI sequences and parameters. T2w 2D T2w 2D T2w 2D T1w 2D CE T1w 3D Standard DWI DKI Sequence TSE TSE TSE TSE spoiled gradient echo Single-shot echo planar Single-shot echo planar Orientation sagittal oblique axial oblique coronal axial axial axial axial Repetition time (msec) Echo time (msec) Field of view (mm 2 ) Matrix Section thickness (mm) No. of phase / / / / 25 / / Acquisition time (min:s) 2:32 3:20 4:10 2:43 5:05 2:47 3:30 No. of signals acquired / / / / / 3 3 b values (sec/mm 2 ) / / / / / 0, , 700, 1400, 2000 CE: contrast enhancement; DWI: diffusion-weighted imaging; DKI: diffusion kurtosis imaging; TSE: Turbo spin-echo.

16 Table 3. ADC, D app, and K app among metastatic LN and non-metastatic LN. Variable metastatic non-metastatic p value ADC( 10-6 mm 2 /s) ± ±177.3 <0.001 D app ( 10-6 mm 2 /s) ± ±226.2 <0.001 K app ( 10-3 ) 874.6± ±250.7 <0.001 ADC: apparent diffusion coefficient; D app : apparent diffusion for Gaussian distribution; K app : apparent kurtosis coefficient; LN: lymph nodes.

17 ccepted Article Table 4. Effectiveness of ADC, D app, and K app for discriminating metastatic LN and non-metastatic LN. Variable Cutoff value AUC Standard Error 95%CI Sensitivity (%) Specificity (%) LR (+) LR (-) p value ADC( 10-6 mm 2 /s) > ( ) ( ) <0.001 D app ( 10-6 mm 2 /s) > ( ) ( ) <0.001 K app ( 10-3 ) ( ) ( ) <0.001 ADC: apparent diffusion coefficient; D app : apparent diffusion for Gaussian distribution; K app : apparent kurtosis coefficient; AUC = area under the curve; LR = likelihood ratio; LN: lymph nodes.

18 Figure 1. Flowchart of the study population.

19 Figure 2. A 54-year-old man with rectal carcinoma who have peri-tumoral lymph Nodes metastasis. a, On T2-weighted turbo spin-echo (TSE) axial image, the metastatic LN (arrow) was seen. b, Signal of the metastatic LN was high on DW image (circle). c, The ADC map with mean ADC value of s/mm 2. The color-coded D app map (d) and K app map (e) shows mixed green and blue color with D app of s/mm 2 and K app value of f, the corresponding kurtosis model fit.

20 Figure 3. A 68-year-old man with rectal carcinoma who have peri-tumoral lymph nodes swelling but not metastatic. a, On T2-weighted axial image, the swelling LN (arrow) was seen. b, Signal of the metastatic LN was high on DW image (circle). c, The ADC map with mean ADC value of s/mm 2. The color-coded D app map (d) and K app map (e) shows mixed green and blue color with D app of s/mm 2 and K app value of f, the corresponding kurtosis model fit.

21 Figure 4. Time contrast intensity curves of benign and metastatic lymph nodes. The peri-tumoral benign lymph nodes on T1-weighted axial image (a) and the corresponding postcontrast T1 MR gradient echo VIBE image (b). The metastatic lymph nodes on T1-weighted axial image (c) and the corresponding postcontrast T1 MR gradient echo VIBE image (d). e, diagrammatic drawing of time contrast intensity curves (TIC). f, the corresponding TIC of benign and metastatic lymph nodes.

22 Figure 5. Lesion-by-lesion histological evaluation of lymph nodes. The metastatic and benign lymph nodes on T2-weighted axial image (a, c) and the corresponding hematoxylin and eosin staining slice (b, d). The cell number of LNs were counted using software of Image J (e).

23 Figure 6. Boxplots show D app, K app and ADC values of metastatic and benign lymph nodes. The median ADC value (a) and D app (b) were significantly higher in the metastatic LNs than benign lymph nodes (1093.1±203.0 vs ± mm 2 /s, p<0.001; ±418.1 vs ±226.2, p<0.001; respectively). The median K app (c) of metastatic lymph nodes was statistically lesser than that of the normal lymph nodes (874.6±212.0 vs ±250.7, p< 0.001).

24 Figure 7. Graph shows receiver operating characteristic curves to assess utility of D app, K app and ADC values for discriminating lesions with metastatic from benign lymph nodes. AUC was for ADC, for D app, and 0.7 for K app.

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