Genitourinar y Imaging • Original Research Oto et al. DWI and DCE-MRI of Prostate Cancer Genitourinary Imaging Original Research Diffusion-Weighted and Dynamic Contrast-Enhanced MRI of Prostate Cancer: Correlation of Quantitative MR Parameters With Gleason Score and Tumor Angiogenesis Aytekin Oto1 Cheng Yang1 Arda Kayhan1 Maria Tretiakova2 Tatjana Antic2 Christine Schmid-Tannwald1 Scott Eggener 3 Gregory S. Karczmar 1 Walter M. Stadler4 Oto A, Yang C, Kayhan A, et al. Keywords: angiogenesis, diffusion-weighted MRI, dynamic contrast-enhanced MRI, Gleason score, prostate cancer DOI:10.2214/AJR.11.6861 Received March 15, 2011; accepted after revision May 6, 2011. Presented at the 2010 annual meeting of the Radiological Society of North America, Chicago, IL. Supported by the Illinois division of the American Cancer Society. 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637. Address correspondence to A. Oto ([email protected]). 2 Department of Pathology, University of Chicago, Chicago, IL. 3 Department of Surgery, Section of Urology, University of Chicago, Chicago, IL. 4 Department of Medicine, Section of Hematology/ Oncology, University of Chicago, Chicago, IL. AJR 2011; 197:1382–1390 0361–803X/11/1976–1382 © American Roentgen Ray Society 1382 OBJECTIVE. The objective of our study was to investigate whether quantitative parameters derived from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) correlate with Gleason score and angiogenesis of prostate cancer. MATERIALS AND METHODS. Seventy-three patients who underwent preoperative MRI and radical prostatectomy were included in our study. A radiologist and pathologist located the dominant tumor on the MR images based on histopathologic correlation. For each dominant tumor, the apparent diffusion coefficient (ADC) value and quantitative DCE-MRI parameters (i.e., contrast agent transfer rate between blood and tissue [Ktrans], extravascular extracellular fractional volume [ve], contrast agent backflux rate constant [kep], and blood plasma fractional volume on a voxel-by-voxel basis [vp]) were calculated and the Gleason score was recorded. The mean blood vessel count, mean vessel area fraction, and vascular endothelial growth factor (VEGF) expression of the dominant tumor were determined using CD31, CD34, and VEGF antibody stains. Spearman correlation analysis between MR and histopathologic parameters was conducted. RESULTS. The mean tumor diameter was 15.2 mm (range, 5–28 mm). Of the 73 prostate cancer tumors, five (6.8%) had a Gleason score of 6, 46 (63%) had a Gleason score of 7, and 22 (30.1%) had a Gleason score of greater than 7. ADC values showed a moderate negative correlation with Gleason score (r = –0.376, p = 0.001) but did not correlate with tumor angiogenesis parameters. Quantitative DCE-MRI parameters did not show a significant correlation with Gleason score or VEGF expression (p > 0.05). Mean blood vessel count and mean vessel area fraction parameters estimated from prostate cancer positively correlated with kep (r = 0.440 and 0.453, respectively; p = 0.001 for both). CONCLUSION. There is a moderate correlation between ADC values and Gleason score and between kep and microvessel density of prostate cancer. Although the strength of the correlations is insufficient for immediate diagnostic utility, these results warrant further investigation on the potential of multiparametric MRI to facilitate noninvasive assessment of prostate cancer aggressiveness and angiogenesis. P rostate cancer is the second leading cause of cancer-related deaths in American men [1]. Since the introduction of prostate-specific antigen (PSA) testing for prostate cancer screening, the lifetime risk of being diagnosed with prostate cancer has doubled from 9% to 18%, but nearly 50% of these cancers have a low risk of progression and will not lead to mortality [1–3]. Unfortunately, reliable noninvasive methods to differentiate significant from insignificant cancers are lacking and most patients with localized prostate cancer are treated with radical treatment methods, which can be associated with serious adverse effects [2]. The most important predictors of prognosis in prostate cancer are Gleason score and tumor staging [4, 5]. Tumor-associated angiogenesis, measured as microvessel density (MVD), can also provide prognostic information in a variety of solid tumors including prostate cancer and can serve as a prognostic indicator of recurrence and metastatic disease [6– 8]. However, accurate estimation of these parameters is possible only after radical prostatectomy. Development of a noninvasive tool that could assess the biologic aggressiveness of prostate cancer at the time of diagnosis would be a major advance and would have a significant impact on the choice of treatment. AJR:197, December 2011 DWI and DCE-MRI of Prostate Cancer Despite its limitations, multiparametric MRI including T2-weighted imaging, MR spectroscopy, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) is currently the best imaging modality for the diagnosis and staging of prostate cancer. One of the limitations of conventional T2-weighted images is their inability to assess tumor aggressiveness. Only one recent study has shown a negative correlation between Gleason scores and tumormuscle signal intensity ratios on T2-weighted images [9]. More recently applied functional MRI sequences such as DWI and DCEMRI have the potential to provide information about the tumor microenvironment and angiogenesis and, hence, about the biologic aggressiveness of the tumor [10–14]. In this study, our purpose was to evaluate the potential of MRI to predict histologic prognostic parameters by investigating the correlation between quantitative DCE-MRI parameters (calculated using a two-compartment model) and the apparent diffusion coefficient (ADC) with histologic parameters including Gleason score, vascular endothelial growth factor (VEGF), and MVD established from surgically resected specimens of prostate cancer. Materials and Methods Study Patients This retrospective study was conducted with an institutional review board–approved waiver of informed consent and was in compliance with HIPAA. Seventy-three consecutive prostate cancer patients (median serum PSA level, 5.5 ng/mL; range of PSA levels, 1.1–65.0 ng/mL; average age, 60.8 years; age range, 47–75 years) who underwent endorectal MRI followed by radical prostatectomy between September 2007 and December 2008 were identified from our institutional imaging database. The average period between MRI and prostatectomy was 44 days (range, 7–118 days). MRI Protocols All MR examinations were performed using a 1.5-T scanner (Excite HD, GE Healthcare [n = 61]; or Achieva, Philips Healthcare [n = 12]). An endorectal coil combined with a phased-array surface coil was used for all examinations except DCE-MRI examinations performed on the GE scanner; for those examinations, only a phased-array coil was used. Immediately before the MR examination, 1 mg of glucagon was injected intramuscularly. We imaged the entire prostate and oriented axial images to be perpendicular to the rectal wall, guided by sagittal images. A parallel imaging factor of 2 was used in all sequences. The following axial, coronal, and sagittal images were obtained: T2-weighted fast spin-echo (FSE) (slice thickness, 3 mm), axial T1 FSE, axial free-breathing DWI (b = 0, 1000, and 1500 s/mm2), and axial free-breathing DCEMRI. Acquisition of DCE-MR images of the entire prostate started 30 seconds before IV administration of 0.1 mmol/kg of gadodiamide (Omniscan, GE Healthcare) followed by a 20-mL saline flush at a rate of 2.0 mL/s. Detailed acquisition protocols are given in Appendix 1. MRI-Histopathology Correlation Surgical specimens of the entire prostate after radical prostatectomy were inked and fixed in 10% neutral buffered formalin for at least 24 hours. After dehydration, each specimen was cut serially into 3-mm-thick sections from the apex to base in transverse planes. Each serial section was then either halved or quartered depending on its size and was put in a cassette for processing, paraffin embedding, and microtome cutting. A genitourinary pathologist with more than 6 years’ experience in genitourinary pathology at the time of the study reviewed the H and E sections of the 73 prostate cancer patients and using a four-quadrant approach (i.e., right anterior, right posterior, left anterior, and left posterior) recorded the size, location, and Gleason score of each carcinoma on a schematic prostate diagram. A radiologist who had 8 years of experience in prostate MRI determined the locations of the carcinoma on T2-weighted images on the basis of these diagrams and in consultation with the pathologist. Each H and E section was then visually matched to a corresponding T2weighted image on the basis of the location of the ejaculatory ducts, the dimension of the prostate, any identifiable benign prostatic hyperplasia nodule, and the approximate distance from the base or apex. To be considered a match, a focus of carcinoma must be in the same anterior or posterior half of the prostate and must be at the same superior-to-inferior level of the prostate. For the portions of tumor foci that were invisible on the matched T2-weighted image, the locations of the tumor were determined by its position relative to landmarks, such as the ejaculatory ducts, and its anteroposterior position. The largest tumor focus that could be matched confidently to a T2-weighted MR image by consensus of the radiologist and pathologist was included for analysis in each patient. Regions of interest (ROIs) of prostate cancer were then drawn manually on T2-weighted images and subsequently determined on other MR images with the help of image-registration software in our PACS system (iSite Radiology System, Philips Healthcare). Immunohistochemistry For the immunohistochemical studies, 4-µm sections of paraffin-embedded tissue were stained with H and E and were bound to a secondary antibody (EnvisionTM+ Kit, cat ## K4001 and K4007, Dako) using a horseradish peroxidase–labeled biotin-free dextrose-based polymer complex. In brief, paraffin sections were deparaffinized in xylenes, rehydrated through the addition of graded ethanol solutions to distilled water, and then washed in Tris-buffered saline. Antigens were retrieved using either a hightemperature treatment in citrate buffer for CD31 and VEGF for 15 minutes in a microwave oven or proteinase K digestion for CD34 (0.04 mg/mL) for 5 minutes at 37°C. Endogenous peroxidase activity was quenched by incubation in 3% H2O2 in methanol for 5 minutes. Nonspecific binding sites were blocked using serum-free blocking solution (Protein Block, #X0909, Dako) for 20 minutes. Tissue sections were then incubated for 1 hour at room temperature with the mouse monoclonal antibodies against CD31 (clone JC70A, 1:40, Dako) and CD34 (clone QBEnd, 1:50, Novocastra) and rabbit polyclonal antibody against VEGF (sc-152, 1:100). This step was followed by 30 minutes of incubation with either goat antimouse or antirabbit IgG conjugated to an horseradish peroxidase–labeled polymer (EnvisionTM+ System, Dako). Slides were then developed for 5 minutes with 3-3′-diaminobenzidine chromogen, counterstained with hematoxylin, and covered with a cover slip. Negative control experiments were performed by substituting the primary antibody step with nonimmune mouse or rabbit immunoglobulins. Automated Image Analysis An automated MVD count was performed as described earlier using an automated system (Automated Cellular Imaging System [ACIS], version II, ChromaVision Medical Systems) [15]. The ACIS automatically loads conventional immunohistochemistry microscope slides for scanning at high resolution on a bright field microscope (Olympus BX45, Olympus) using a digital camera (CCD camera, Sony). Then a computer program creates a histologic re con struction of an entire section image composed of all individual microscopic fields. The image analysis system’s MVD application was configured for vessel detection using CD31- and CD34-stained slides. This configuration was based on applying chromogen masks for high chromogenic staining (brown threshold) and hematoxylin counterstaining (blue threshold) and the minimal and maximal sizes of the vessels. In each section, 10 representative nonoverlapping AJR:197, December 20111383 Oto et al. areas of dominant cancer were selected. Each selected area was equal to 100× microscope FOV in diameter (313,841 µm 2) and included at least 2000 cells. In each selected area the following measure ments were captured digitally: the mean vessel count per area; the mean vessel area (MVA), which is the square microns occupied by positively stained vessels; and the MVA fraction, which is the vessel density calculated as ratio of MVA in the total area of counterstained tissue. The parameter used to evaluate the VEGF expression was based on the area and intensity of the brown stain, called “integrated optical density,” or “IOD,” per 10 µm2. MRI Analysis Diffusion-weighted imaging analysis—Apparent diffusion coefficient (ADC) maps were generated from diffusion-weighted (DW) images with commercial diffusion-analysis software (Advantage Windows, version 4.2.3, GE Healthcare; or extended MR work space, version 2.6.3.1, Philips Healthcare). Because of the significantly lower spatial resolution of DW images than T2-weighted images, simply converting the ROIs drawn on T2-weighted images into ROIs on DW images using image-registration software would result in significant partial volume effect on the margin of the ROIs. To remedy this problem, two radiologists, who had 8 years and 1 year of experience with prostate MRI, manually drew ROIs on DW images independently using the ROIs on T2-weighted images as a reference and with guidance of automatic image-registration software. Based on the image coordinates information recorded in the DICOM head files of T2-weighted images and DW images, the imaging-registration software enabled us to pinpoint the tumor region on DW images corresponding to the tumor ROI selected on the T2-weighted images. In drawing the ROIs, the DW images were magnified and the largest possible oval-shaped ROI was placed on the area of interest excluding the blurred margin caused by lower spatial resolution. Each radiologist drew one ROI per each focus. The mean of the two ADC measurements was accepted as the ADC of the tumor focus. DCE-MRI analysis—We used a previously published approximation [16] to convert the measured signal S(t), where S is signal and t is time, from T1-weighted DCE-MRI data acquired with a small TR/TE, median flip angle, and standard contrast agent dosage into contrast agent concentration (Ct): where r1 is the relaxivity coefficient (~ 4.5 1/s mM at normal body temperature and 1.5 T for Omniscan) and T1 is T1 relaxation time. The conversion coefficient in the first set of brackets is usually calculated using a T1 value reported in the literature and a baseline signal, both of a reference tissue (RT). This method basically uses normal tissue as the reference to infer the unenhanced T1 value of other regions, which is then used to calculate contrast agent concentration. In this study, we used normal prostate tissue as the reference tissue and used the T1 value of 1317 ± 85 ms [17]. The Tofts model [18] of time dependence of contrast agent concentration Ct (t) was applied to calculate the contrast agent transfer rate between blood and tissue, Ktrans ; the extravascular extracellular fractional volume, νe; and the blood plasma fractional volume, νp, on a voxel-by-voxel basis. The contrast agent backflux rate constant kep was also calculated as follows: kep = Ktrans / νe. Average parameters were then calculated within each ROI. When applying the Tofts model, for contrast agent arterial input function (AIF) we used the average AIFs estimated for the GE Healthcare and Philips Healthcare scanners based on a previous clinical DCE-MRI study that used a multiple reference tissue method [19]. It has been shown previously that a realistic population AIF value can produce reproducible estimates of the kinetic parameters from DCE-MRI data [20] that correlate strongly with reference standard estimates from dynamic CT data [19]. The tumor ROI on DCE-MR images was automatically converted from the ROI on T2-weighted images using image-registration software, and the average DCE-MRI parameters were calculated for each tumor ROI. Statistical Analysis Statistical analysis was performed using statistics software (SPSS, version 17, SPSS) for Microsoft Windows. Bivariate plots were generated and analyses were conducted using the Spearman rank correlation coefficient to evaluate the associations of quantitative MRI parameters with commonly used predictors and prognostic markers including the Gleason score, MVD, and VEGF expression. The significance level was set at 0.00167 = 0.05 / 30 based on Bonferroni correction, where 30 was the total number of correlation tests we explored (Table 1). Internal consistency for ADC measurements was assessed using Cronbach’s alpha statistic. TABLE 1: Spearman Rank Correlation Coefficients Between Histopathologic Parameters and Quantitative DiffusionWeighted Imaging and Dynamic Contrast-Enhanced MRI Parameters Ktrans ADC Parameters r p ve na r p na kep r p na r vp p na r p na Gleason score –0.376 0.001 70 −0.075 0.549 66 0.015 0.906 66 −0.091 0.469 66 0.061 0.625 66 VEGF −0.160 0.217 60 0.182 0.180 55 −0.092 0.502 55 0.230 0.088 55 0.252 0.061 55 Mean vessel counts per areab −0.060 0.649 60 0.146 0.289 55 −0.327 0.015 55 0.440 0.001 55 0.312 0.020 55 Mean microvessel area fraction −0.021 0.875 60 0.146 0.286 55 −0.328 0.014 55 0.453 0.001 55 0.393 0.003 55 CD31 CD34 Mean vessel counts per areab Mean microvessel area fraction −0.022 0.869 60 0.024 0.863 55 −0.045 0.746 55 0.177 0.197 55 0.029 0.836 55 0.033 0.805 60 0.046 0.739 55 −0.051 0.712 55 0.192 0.160 55 0.070 0.611 55 Note—The significance level was set at 0.05 / 30 = 0.00167 based on Bonferroni correction. Statistically significant correlation coefficients are highlighted in boldface. ADC = apparent diffusion coefficient, Ktrans = contrast agent transfer rate between blood and tissue, ve = extravascular extracellular fractional volume, kep = contrast agent backflux rate constant, vp = blood plasma fractional volume on a voxel-by-voxel basis, VEGF = vascular endothelial growth factor. aNumber of samples. bArea = 313,841 µm2 (≥ 200 cells). 1384 AJR:197, December 2011 DWI and DCE-MRI of Prostate Cancer Results Seventy-three cancer foci, 64 peripheral zone (PZ) and nine transition zone (TZ) cancer, with an average greatest dimension of 15.2 mm (range, 5–28 mm) were included in the analysis. A Gleason score of 6 was assigned in five cases (6.8%), a Gleason score of 7 in 46 (63.0%), a Gleason score of 8 in 13 (17.8%), and a Gleason score of 9 in nine (12.3%). Of 73 patients, 11 patients underwent prostatectomy at another institution so histology slides were available for review but tissue samples were not available for immunohistochemical staining. We could not ob- tain quantitative DCE-MRI parameters in seven patients: High-temporal-resolution DCE-MRI data were not found in the data archive because of impaired renal function in four patients and there were excessive motion artifacts in another three patients. DW images were not available in three patients to calculate ADC. Figure 1 shows the MR data and the H and E– and immunohistochemistry-stained prostate samples from a representative 53-yearold patient. The MRI parameters and immunohistochemical parameters are described in Table 2. Table 1 shows the Spearman rank correlation coefficients of the histopathologic parameters and quantitative DWI and DCE-MRI parameters for comparison. Correlation Between ADC Values and Gleason Scores Internal consistency of ADC measurements for both readers was excellent (Cronbach’s alpha = 0.982). Gleason scores had a statistically significant and moderate negative correlation with ADC measurements (r = –0.376, p = 0.001). There was substantial overlap between the ADC values of tumors with different Gleason scores; however, Fig. 1—53-year-old man with prostate cancer (Gleason score for dominant tumor = 7). A, Photomicrograph of H and E–stained prostate sample shows prostate cancer in right peripheral zone (arrow). B–D, Axial T2-weighted MR image (B), apparent diffusion coefficient map derived from diffusionweighted imaging (C), and map of contrast agent transfer rate between blood and tissue (Ktrans ) (D) derived from dynamic contrast-enhanced MRI (DCEMRI) show similar anatomic area. Dominant tumor is indicated by arrows. Tumor region of interest on DCEMRI is shown by white contour in D. E–G, Photomicrographs of serial histologic sections from dominant prostate cancer tumor nodule shown in A–D immunostained with CD31 (E), CD34 (F), and vascular endothelial growth factor (G). In E and F, vascular endothelium is stained dark brown. Original magnification was ×100. AJR:197, December 20111385 Oto et al. TABLE 2: Descriptive Statistics for Immunohistochemical Parameters and MRI Parameters Parameters No. of Samples VEGFa Mean SD Minimum Maximum 62 113.9 90.7 2.5 600.0 Mean vessel counts per areab 62 41.7 17.5 11.7 90.6 Mean microvessel area fraction 62 CD31 0.016 0.009 0.004 0.044 CD34 Mean vessel counts per areab 62 Mean microvessel area fraction 62 0.028 0.028 0.000 0.113 70 1.05 0.25 0.63 1.67 ADC (10 −3 mm2 /s) 62.6 46.8 0.7 199.3 Ktrans (min−1) 66 0.107 0.023 0.059 0.151 ve 66 0.189 0.045 0.099 0.303 kep (min−1) 66 0.59 0.15 0.20 0.95 vp 66 0.010 0.006 0.000 0.034 Note—VEGF = vascular endothelial growth factor, ADC = apparent diffusion coefficient, Ktrans = contrast agent transfer rate between blood and tissue, ve = extravascular extracellular fractional volume, kep = contrast agent backflux rate constant, vp = blood plasma fractional volume on a voxel-by-voxel basis. aVEGF expression was based on the area and intensity of the brown stain, called “integrated optical density,” or “IOD,” per 10 µm2 . bArea = 313,841 µm2 (≥ 200 cells). ADC values of tumors with a Gleason score of 6 and those of tumors with a Gleason score of 9 were relatively well separated. The scatterplot of ADC values versus the Gleason scores of 70 dominant tumors in Figure 2 shows the trend that ADC values decreased as Gleason scores increased. Correlation Between Dynamic Contrast-Enhanced MRI Parameters and Histologic Findings No significant correlation was observed between any of the DCE-MRI parameters and Gleason score. The mean blood vessel count and mean vessel area fraction parameters calculated by CD31 staining positively 1.8 ADC (10-3 mm2/s) 1.6 1.4 1.2 1.0 0.8 0.6 6 7 8 9 Gleason Score Fig. 2—Scatterplot of apparent diffusion coefficient (ADC) values versus Gleason scores for 70 prostate tumors in study group. ADC values were negatively correlated with Gleason scores. Spearman rank correlation coefficient, r, was –0.376 (p = 0.001). 1386 correlated with kep (r = 0.440 and 0.453, respectively; both, p = 0.001). We should note that even though mean blood vessel count and mean vessel area fraction parameters showed a moderate negative correlation with ve (r = –0.327 and –0.328; p = 0.015 and 0.014, respectively) and a moderate positive correlation with vp (r = 0.312 and 0.393; p = 0.020 and 0.003, respectively), these correlations did not reach a statistical significance after Bonferroni correction. VEGF expression and MVD parameters calculated by CD34 staining had no significant correlations with any of the MRI parameters. Discussion Our results show that there is a moderate negative correlation between ADC values calculated from DW images and Gleason score of prostate cancer obtained from radical prostatectomy specimen. The kep (i.e., contrast agent backflux rate constant) derived from DCE-MRI moderately correlates with angiogenesis parameters (mean blood vessel count and mean vessel area fraction), but no correlation was found between any of the quantitative DCE-MRI parameters and the Gleason score or VEGF expression of the prostate cancer. The association between lower ADC values and higher Gleason scores has been previously described in studies correlating DW images with transrectal ultrasound–guided biopsy results [10, 11]. Tamada et al. [10] reported a correlation coefficient, r, between ADC and Gleason score of –0.497 in PZ cancer and of –0.343 in TZ cancer. More recently, Woodfield et al. [11] found statistically significant differences between the ADC values of PZ tumors with Gleason scores of 6 and 7 and those with Gleason scores of 6 and 8. In another study performed in 44 patients with prostate cancer, significant differences in tumor ADC values were reported between patients with low-risk disease and those with higher-risk disease [21]. The major limitations of these studies were the unreliable Gleason score obtained from needle biopsies and difficulties in accurate localization of the biopsied tumor on MRI. Needle biopsy leads to underestimation of Gleason score in approximately 25% of the cases compared with Gleason score established from prostatectomy specimen because of biopsy sampling error and tumor heterogeneity [22, 23]. To overcome this limitation, we used prostatectomy specimens for imaging correlation and establishing Gleason score. Our results are similar to the results of Tamada et al. [10]. Mazaheri et al. [24] also observed lower ADC values for prostate tumors with higher Gleason scores from prostatectomy. The correlation coefficient we found was –0.376 in 70 prostate tumors composed of 61 PZ and nine TZ tumors. A lower ADC value with a higher Gleason score is most likely because of the increased cellularity of tumors with a higher Gleason score. Gibbs et al. [25] reported a trend of increasing cell density of the prostate cancer with increasing Gleason score. Cell density increased from a mean of 14.5% for tumors AJR:197, December 2011 DWI and DCE-MRI of Prostate Cancer with a Gleason score of 6 to 21.9% for those with a Gleason score of 8 or greater [25]. A significant negative correlation between ADC and cellular density, or percentage area of nuclei and cytoplasm, and a positive correlation between ADC and percentage area of luminal space have also been previously reported for prostate cancer [26–28]. Interestingly, even though ve is the extravascular extracellular space fractional volume (thus, a marker of cellular density), we did not find a significant correlation between Gleason score and any of the DCE-MRI parameters including ve. This may be due to the fact that tissue architecture parameters such as nucleus-cytoplasm ratio and area of glandular luminal space can affect ADC measurements whereas they are not included in the calculation of ve. Microvascularity is considered an important marker for neoangiogenesis, which in turn is responsible for local growth and metastasis in tumors [29]. MVD has been reported to be associated with Gleason score, tumor staging, recurrence, metastatic potential, and patient outcome in prostate cancer [6–8, 30–34]. However, somewhat contradictory results have also been reported regarding the association between MVD and the biologic behavior of prostate cancer [35, 36]. These contradictory results may be attributed to the differences in the composition of the study groups and the method used for quantification of MVD. The choice of antibody to stain vessels (CD31 vs CD34), the method of selection of the area for vessel count (selection of “hot spots,” the areas with the highest MVD in the tumor, vs random sampling of entire tumor region), and the actual counting method (manual vs automated counting methods) may all influence the MVD measurements [37]. In our study, we used both CD31 and CD34 for immunohistochemical staining and performed sampling of an entire tumor region with 10 randomly placed ROIs over each tumor for MVD calculations rather than selectively including the hot spots [6]. Localization of these hot spots on histology slides is prone to observer bias. By sampling the entire tumor region, we aimed to improve the reproducibility of our results by minimizing this bias [37, 38]. We also used automated digital quantification of MVD using ACIS. Application of automated digital image analysis has been shown to enhance reproducibility of both the selection of the measurement area and the actual vessel counts [38, 39]. Limited studies evaluating the correlation between DCE-MRI parameters and angiogenesis markers of prostate cancer have provided contradicting results [12, 13]. Schlem mer et al. [12] reported significant correlation between contrast exchange rate constant (k21) (also called kep) and MVD, whereas Kiessling et al. [13] found no such correlation. Both studies used the Brix model for DCE-MRI analysis but used different antibodies (Schlemmer et al., CD31; Kiessling et al., CD34) to calculate MVD. In our study, only the MVD measured by CD31 staining showed moderate but significant correlation with kep, similar to the results of Schlemmer et al. Our results suggest that the conflicting results of two previous studies may at least partly be due to the different antibodies used in MVD calculation. CD31 is the most sensitive pan-endothelial marker: It stains large and small vessels with equal signal intensity as well as blood vessels in normal tissue and tumor tissue [7]. On the other hand, CD34 is known to stain perivascular stromal cells and some lymphatic vessels, which may have contributed to falsely elevated MVD [40]. In prostate specimens, the widely used CD34 monoclonal antibody is suggested to be less optimal for microvessel staining because of abundant stromal staining [37]. More recently, Franiel et al. [14, 41] studied the correlation between histologic parameters and the quantitative parameters obtained from their dynamic dual-contrastenhanced MR data of prostate cancer, chronic prostatitis, and normal prostate tissue from 35 patients. Similar to our study, they used CD31 antibody and randomly selected ROIs for MVD calculation. However, they used a more complex three-compartmental model to analyze the DCE-MRI data [41]. They found poor correlation between blood volume and MVD (r = 0.252) and no correlation between blood volume and MVA or between interstitial volume measured by MRI and histologic mean interstitial area. In our study, correlations between quantitative perfusion parameters and histologic parameters were moderate at best and only a correlation between kep and MVD assessed by CD31 reached a statistical significance. The ve parameter had a moderate negative correlation and Ktrans had a very weak positive correlation with MVD. The kep is a composite parameter, equal to Ktrans / ve. The compounding effect of Ktrans and ve may be the main reason for better correlation of kep with MVD. We also observed that, even though it did not reach statistical significance, vp correlated to MVD assessed by CD31 better than Ktrans. These findings may be explained by the more direct relation between vp, the blood plasma fractional volume, and MVD, whereas Ktrans can be affected by both the perfusion and permeability of the vessels in tumor, so its relation to the MVD is more distant. VEGF is a potent cytokine that supports development of tumor vessels and its expression in the tumor correlates with cancer prognosis [42]. Immunohistochemical studies have shown that human prostate cancer cell tissues stained positively for VEGF, whereas benign prostate tissue displayed little VEGF staining [43]. Studies using prostate cancer models also implied an association between tumor metastatic capacity and VEGF expression [44]. Its usefulness as a prognostic factor is strongly suggested but remains to be clarified, despite the strong evidence indicating its involvement in the growth process of prostate cancer [45]. In our study, we did not find any significant correlation between the DCE-MRI parameters and VEGF expression. This may be partly explained by the complex nature of the angiogenesis regulation involving many angiogenic factors and inhibitors in addition to VEGF. Our study has several limitations. First, it is a retrospective study with a relatively small sample size that could have been influenced by selection and verification biases. The numbers of tumors with Gleason scores of 6 and 9 were also small. At our institution, preoperative MRI is used as the standard of care for high-risk patients and this protocol explains the relatively small percentage of tumors with a Gleason score of 6 in this retrospective cohort. Second, the spatial correlation of a lesion between MR images and histologic sections carries inherent limitations. Third, the moderate correlation or the lack of correlation between MRI and histopathologic parameters might also have contributed to the errors in their measurement. For example, Gleason score was subjectively graded and human factors such as experience will surely play a role in the accuracy of its reading. For our DCE-MRI data, we used a population AIF from a previous study to estimate the quantitative parameters. This analysis method was robust and easy to apply but is less ideal than using individualized AIFs and it inevitably contributed to some errors in the estimated quantitative parameters. The lack of unenhanced T1 mapping is an additional limitation of this study. As a result, the AJR:197, December 20111387 Oto et al. method of contrast agent concentration calculation relied on using normal prostate as the reference tissue, which inevitably introduced observer variability. Fourth, despite the similar protocols used, MRI studies were performed using different scanners made by different manufacturers (GE Healthcare and Philips Healthcare) with different sequence parameters. All of our MR scanners are routinely calibrated as a part of quality assurance study; however, there may be variation between the MRI parameters derived from different scanners. Furthermore, there is evidence that at least for ADC parameters interscanner variation is reasonable (± 5%) [46]. In conclusion, our results showed that there is a moderate correlation between ADC values derived from DWI and Gleason score and between kep and MVD of prostate cancer. 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APPENDIX 1: MRI Acquisition Protocols Protocol for GE Healthcare Scanner Array spatial sensitivity-encoding technique (parallel imaging) factor of 2 was used in all sequences. Protocol for Philips Healthcare Scanner Effective sensitivity-encoding (parallel imaging) factor of 2 was used in all sequences. T2-Weighted Imaging Parameters • TR range/TE range = 3200–3500/90–100 • Matrix size = 192 × 256 • Echo-train length = 19 • Number of signals acquired = 4 • Section thickness = 3 mm • Intersection gap = 0 mm • FOV = 14–16 cm T2-Weighted Imaging Parameters • Resolution = 0.8 × 0.8 × 3 mm • TR range/TE = 4300–5000/120 • Matrix size = 204 × 256 • Echo-train length = 24 • Number of signals acquired = 4 • Section thickness = 3 mm • Intersection gap = 0 mm • FOV = 14–18 cm Diffusion-Weighted Imaging Parameters • TR range/TE range = 7000–8000/80–90 • Matrix size = 128 × 128–224 • b values = 0, 1000, and 1500 s/mm 2 • Number of signals acquired = 4 • Slice thickness = 4 mm • Gap = 0 mm • FOV = 14–18 cm T1-weighted, 3D, gradient-echo, and free-breathing axial dynamic contrast-enhanced MR images covering the entire prostate were acquired starting 30 seconds before the IV administration of gadodiamide (Omniscan, GE Healthcare) at a dose of 0.1 mmol/kg, followed by a 20-mL saline flush at a rate of 2.0 mL/s. Dynamic Contrast-Enhanced MRI Parameters • TR range/TE range = 3.5–3.9/1.6–1.9 • Matrix size = 160 × 256 • Flip angle = 10° • Interpolated slice thickness = 3 mm with temporal resolution of 5–12 seconds for approximately 5–7 minutes Approximately 30–50 sets of images were acquired to monitor the time course of contrast agent uptake and clearance within the prostate. The entire scanning protocol including patient setup was performed in less than 1 hour in all patients. 1390 Diffusion-Weighted Imaging Parameters • TR range/TE range = 3800–4200/80–90 • Matrix size = 128 × 128 • b values = 0, 1000, and 1500 s/mm 2 • Number of signals acquired = 4 • Slice thickness = 4 mm • Gap = 0 mm • FOV = 14–18 cm Dynamic Contrast-Enhanced MRI Parameters • Three-dimensional fast-field echo • TR/TE = 5.5/2.1 • Matrix size = 199 × 292 • Interpolated section thickness = 3 mm with a temporal resolution of 3–5 seconds for approximately 7–9 minutes The dose and administration of IV gadolinium was similar to the GE protocol. Approximately 70–100 sets of images were acquired and the entire scanning protocol including patient setup was performed in less than 1 hour in all patients. AJR:197, December 2011
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