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Published online before print May 5, 2008, 10.1148/radiol.2481071120
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(Radiology 2008;248:148-159.)
© RSNA, 2008

Genitourinary Imaging

Epithelial Ovarian Tumors: Value of Dynamic Contrast-enhanced MR Imaging and Correlation with Tumor Angiogenesis1

Isabelle Thomassin-Naggara, MD, Marc Bazot, MD, Emile Daraï, MD, PhD, Patrice Callard, MD, Jeanne Thomassin, MD, and Charles A. Cuenod, MD, PhD

1 From the Departments of Radiology (I.T., M.B.), Obstetrics and Gynecology (E.D.), and Anatomic Pathology (P.C., J.T.), Hôpital Tenon, Assistance Publique Hôpitaux de Paris (APHP), 4 rue de la Chine, 75020 Paris, France; Laboratoire de Recherche en Imagerie (LRI-EA 4062), Université Paris V-René Descartes, Paris, France (I.T., C.A.C.); and Department of Radiology, Hôpital Européen Georges Pompidou, Paris, France (C.A.C.). Received June 26, 2007; revision requested August 30; revision received October 2; accepted December 17; final version accepted February 5, 2008. Address correspondence to I.T. (e-mail: isabelle.thomassin{at}tnn.aphp.fr).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
 References
 
Purpose: To retrospectively evaluate the diagnostic performance of dynamic contrast material–enhanced magnetic resonance (MR) imaging for the characterization of ovarian epithelial tumors, by using histologic findings as the reference standard, and to correlate dynamic contrast-enhanced MR imaging findings with angiogenesis biomarkers.

Materials and Methods: Ethics committee approval was obtained, with waiver of informed consent. Patients consented to having their data used for future retrospective research. Forty-one women (age range, 22–73 years) with 48 epithelial ovarian tumors underwent dynamic contrast-enhanced MR imaging before surgical excision. In case of bilateral tumors (n = 7), only the most complex tumor was analyzed. Thus, 41 tumors (12 benign, 13 borderline, and 16 invasive) were examined with dynamic contrast-enhanced MR imaging and immunohistochemical methods. Dynamic contrast-enhanced MR imaging parameters (enhancement amplitude [EA], time of half rising [Tmax], and maximal slope [MS]) were analyzed according to histopathologic findings, microvessel density, pericyte coverage index (PCI), and vascular endothelial growth factor receptor 2 (VEGFR-2) expression. Statistical analyses were performed by using Kruskal-Wallis, Fisher exact, and Spearman tests and receiver operating curve analysis.

Results: EA was higher for invasive tumors than for benign (P < .001) and borderline (P < .05) tumors. Tmax was longer for benign tumors than for borderline (P < .05) and invasive (P < .01) tumors. MS was steeper for invasive tumors than for benign (P < .001) and borderline (P < .001) tumors. PCI was lower in invasive tumors than in borderline (P < .05) and benign (P < .05) tumors. Microvessels showed stronger immunohistochemical VEGFR-2 expression in invasive tumors than in benign or borderline tumors (P < .05). MS correlated with a lower PCI (r = –0.34, P = .04) and stronger VEGFR-2 expression by using both epithelial (r = 0.41, P < .01) and endothelial (r = 0.66, P < .001) cells.

Conclusion: The early enhancement patterns of ovarian epithelial tumors on dynamic contrast-enhanced MR images can help distinguish among benign, borderline, and invasive tumors and were found to correlate with tumoral angiogenic status.

© RSNA, 2008


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
 References
 
Ovarian tumor is the leading indication for gynecologic surgery (1). Epithelial tumors account for two-thirds of ovarian tumors, and their malignant forms represent more than 90% of ovarian cancers (2). The therapeutic strategy depends on whether the tumor is benign, borderline, or invasive. Preoperative characterization is therefore crucial but, despite advances in imaging techniques, remains difficult.

MR imaging can be useful for characterizing adnexal masses; in particular, gadolinium-enhanced MR imaging can help distinguish between benign and malignant tumors (3,4). However, there are few published data on the use of dynamic contrast material–enhanced magnetic resonance (MR) imaging for the characterization of ovarian tumors (5,6). In other tissues, dynamic contrast-enhanced MR imaging has proved useful for distinguishing malignant from benign tumors on the basis of differences in contrast agent behavior owing to changes in the microcirculation induced by neoangiogenesis (7–9). Applied to ovarian tumors, this technique might permit the assessment of angiogenesis in vivo (10).

Angiogenesis is usually assessed in terms of microvessel density (MVD) (7), although other factors (pericyte coverage index [PCI] and vascular endothelial growth factor [VEGF] expression) can influence contrast enhancement (11). The pericyte is a mesenchymal-like cell present in the walls of small blood vessels and is now coming into focus as an important regulator of angiogenesis and blood vessel function (12). The PCI appears to be more relevant than MVD for distinguishing benign from malignant ovarian tumors (13). VEGF plays a key role in tumor angiogenesis, and the degree of VEGF expression correlates with dynamic MR imaging enhancement in various tumors (14). VEGF increases microvessel permeability, mainly through VEGF receptor 2 (VEGFR-2), which is also involved in the spread of ovarian cancers (15). So far, dynamic contrast-enhanced MR imaging and immunostaining studies of angiogenesis have been conducted separately. To our knowledge, no data have been published on possible correlations between dynamic contrast-enhanced MR imaging parameters and MVD, PCI, or VEGFR-2 expression in ovarian tumors.

The purpose of our study, therefore, was to retrospectively evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging for the characterization of ovarian epithelial tumors by using histologic findings as the reference standard and to correlate dynamic contrast-enhanced MR imaging findings with angiogenesis biomarkers.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
 References
 
Patients and Tumors
Ethics committee approval (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale-Paris St Louis) was obtained for our study, with waiver of informed consent. However, at the time the patients were examined, they consented to having their data used for future retrospective research. From April 2003 to February 2005, 41 patients with 48 (14 benign, 14 borderline, and 20 invasive) ovarian epithelial tumors underwent dynamic contrast-enhanced MR imaging before surgical excision. To eliminate the potential influence of intraindividual correlation in women with bilateral tumors (n = 7), only the results for the most complex tumor were considered (16). Thus, 41 tumors (12 benign, 13 borderline, 16 invasive) were analyzed both by using dynamic contrast-enhanced MR imaging and by using immunohistochemical methods (MVD, PCI, and VEGFR-2 expression).

The patients' ages ranged from 22 to 73 years. The median ages of the patients with benign, borderline, and invasive ovarian tumors were 50 years (range, 22–72 years), 53 years (range, 27–73 years), and 53 years (range, 37–68 years), respectively (P = .82, Kruskal-Wallis test). All patients underwent surgery, and 41 stored surgical specimens were available for immunohistochemical analysis.

Ovarian cystectomy was performed in three cases, salpingo-oophorectomy was performed in 22 cases (unilateral in four and bilateral in 18), and bilateral salpingo-oophorectomy was performed with hysterectomy, omentectomy, and peritoneal sampling in 16 cases. The stage of invasive malignant tumors was assessed according to the International Federation of Gynecology and Obstetrics standards, and the tumors were classified according to histopathologic type (Tables 1, 2).


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  		Table 1. International Federation of Gynecology and Obstetrics Classification of Malignant Ovarian Lesions

 

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  		Table 2. Histopathologic Type in 41 Epithelial Ovarian Tumors

 
MR Imaging Protocol
The median interval between MR imaging and surgery was 32 days (range, 7–76 days), with no significant difference among the benign, borderline, and invasive groups (P = .08, Kruskal-Wallis test). MR imaging was performed with a 1.5-T device (Sonata; Siemens, Erlangen, Germany). The patients were placed in a phased-array coil in the supine position, with an intravenous access in place. All sequences were performed with saturation bands placed anteriorly and posteriorly to eliminate artifacts induced by the high signal from subcutaneous fat. The patients fasted for 3 hours, had abdominal compression with a belt, and received 10 mg of an antispasmodic drug (tiemonium methylsulfate, Visceralgine; Organon, Livron, France) intravenously immediately before MR imaging to reduce bowel peristalsis.

The acquisition protocols, including sequences and parameters, are given in Table 3. Before injection, sagittal and transverse turbo spin-echo T2-weighted and transverse gradient-echo T1-weighted sequences were performed. Then, a dynamic contrast-enhanced T1-weighted gradient-echo sequence (two-dimensional fast low-angle shot) was performed through the tumor at the level of presumed "solid tissue" observed on nonenhanced MR images. Solid tissue, as defined by Timmerman et al (17), consists of vegetations, solid portions, and irregular septa. Gadolinium chelate (Dotarem; Guerbet, Aulnay-sous-Bois, France) was administered at a dose of 0.2 mL per kilogram of body weight by means of a power injector (Medrad, Maastricht, the Netherlands) at a rate of 2 mL/sec, followed by administration of 20 mL of normal saline to flush the tubing. Images were obtained sequentially at 5-second intervals for 2 minutes, beginning simultaneously with the bolus injection. The patient was asked to breathe normally during the dynamic contrast-enhanced acquisition. Finally, contrast-enhanced transverse and sagittal T1-weighted gradient-echo images with breath hold were acquired after gadolinium-based contrast agent injection.


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  		Table 3. Parameters for MR Imaging Sequences

 
Analysis of Dynamic Contrast-enhanced MR Images
Dynamic data were analyzed in consensus at a workstation (Leonardo; Siemens) by two radiologists (I.T. and M.B., who had 5 and 15 years of experience in pelvic MR imaging, respectively), with no knowledge of the histologic findings. Semiquantitative analysis of the signal intensity (SI)–time curve was performed with the region-of-interest technique (7–50 pixels) (Fig 1a). When the tumor had numerous vegetations, solid portions, or septa, several regions of interest were used. In this specific setting, the highest amplitude of tumor enhancement was used to determine the SI-time curves for MR imaging enhancement.


Figure 1A
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  		Figure 1a: (a) Region-of-interest selection in patient with serous borderline cystadenoma. On T2-weighted transverse turbo spin-echo MR image (6790/89), a left multiloculate ovarian tumor with vegetations was found. Region of interest was drawn over vegetation to obtain SI-time curve (red overlay). (b) Graph shows SI plotted against time for Equation (1), where point A corresponds to asymptotic enhancement amplitude (EA), point B corresponds to time of half rising (Tmax), and D (the first derivative of EIt from Equation [1] on point B) corresponds to maximal slope (MS), or the value of the slope of the tangent line to the curve on point B.

 

Figure 1B
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  		Figure 1b: (a) Region-of-interest selection in patient with serous borderline cystadenoma. On T2-weighted transverse turbo spin-echo MR image (6790/89), a left multiloculate ovarian tumor with vegetations was found. Region of interest was drawn over vegetation to obtain SI-time curve (red overlay). (b) Graph shows SI plotted against time for Equation (1), where point A corresponds to asymptotic enhancement amplitude (EA), point B corresponds to time of half rising (Tmax), and D (the first derivative of EIt from Equation [1] on point B) corresponds to maximal slope (MS), or the value of the slope of the tangent line to the curve on point B.

 
The enhancement index for each time, designated as EIt, was calculated by using the MR software as follows: EIt = (SICE – SI0)/SI0, where SI0 is the baseline SI on the nonenhanced T1-weighted image (the first nonenhanced image, which was acquired simultaneously with contrast agent injection, of the dynamic series) and SICE is the SI on dynamic contrast-enhanced MR images. The part of the tumor exhibiting the strongest contrast enhancement on dynamic contrast-enhanced MR images was used for data analysis.

The enhancement curves were fitted (Fig 1b) to a sigmoid equation with a personal computer (Pentium; Dell, Round Rock, Tex) running scientific graphing and data analysis software (KaleidaGraph; Synergy Corporate Technologies, Westport, Conn) as follows:

Formula 1(1)
where A is the asymptotic EA, B is Tmax, t is time, and C is a power constant. The MS (D) was calculated at the inflection point of the curve, which occurs at point B, as the first derivative of Equation (1), as follows:

Formula 2(2)

The three parameters A, B, and D obtained in each case were interpreted with respect to histopathologic and immunohistochemical data.

Immunohistochemical Analysis
Two pathologists (P.C. and J.T., who had 25 and 2 years of experience in pelvic pathology, respectively) reviewed all the slides in consensus. The most representative paraffin-embedded fixed specimens were selected. Sections that were 4 µm thick were cut with a microtome (SM200R; Leica, Nussloch, Germany), and then they were stained successively with specific antibodies, following the guidelines of an international consensus on methods and criteria for quantification of angiogenesis in solid human tumors (18).

Tumor vessels were identified by means of staining for the endothelial cell marker CD34 with a monoclonal mouse antihuman antibody (QBEnd-10; Dako, Trappes, France). Any brown-stained endothelial cell or cell cluster that was clearly distinct from adjacent tumor cells and connective tissue was considered to represent a single countable microvessel. The pathologists analyzed the anti-CD34–stained slides at low power (x40 and x100) to identify areas of strongest vascularization (so-called hot spots) in each tumor. The three most vascularized areas of each slide were chosen for further study. The microvessel count in one x200 field of each of these three areas was first assessed with the Chalkley technique (19). Then these three areas were digitized and analyzed at a personal computer running image-editing software (PhotoShop 9.0; Adobe Systems, San Jose, Calif) and a public-domain Java-based image-processing program called ImageJ developed at the National Institutes of Health, Bethesda, Md, and available at http://rsb.info.nih.gov/ij/. The average number of microvessels counted in each of the three areas (x200 fields) was recorded as the MVD, and the count in the field with the largest number of vessels among the three x200 fields examined was recorded as high vessel density (HVD). The total endothelial area (TEA) was calculated as the sum of all microvessel areas defined in the three x200 fields, and the mean area per microvessel was calculated as the TEA divided by the number of microvessels in all three x200 fields.

The PCI was determined by using dual immunostaining with anti-CD34 to locate endothelial cells and anti-{alpha} smooth-muscle actin to identify pericytes (monoclonal mouse antihuman 1A4 antibody, 1/400 dilution; Dako) (18). The immunostaining was analyzed at a magnification of x200. The PCI was recorded as the ratio of the number of microvessels simultaneously positive for CD34 and anti-{alpha} smooth-muscle actin to the number of microvessels positive for CD34 alone. The tumors were classified in three groups according to the PCI: less than , –, and more than .

A monoclonal antibody (flk-1 ref sc-6251; Santa Cruz Biotechnology, Santa Cruz, Calif) was used to detect VEGFR-2. Expression of VEGFR-2 was measured with a previously reported method (14), with three-level categorization that was based on the staining intensity of epithelial cells as follows: category 0, epithelial cells were negative for expression of VEGFR-2; category 1, VEGFR-2 was clearly identified at x100; and category 2, VEGFR-2 was clearly identified at x40. Areas that were positive for expression were coded with four levels, as follows: score 0, none of the tumor or epithelial cells were stained; score 1, one-third or less of the tumor or epithelial cells was stained; score 2, two-thirds or less of the tumor or epithelial cells was stained; and score 3, two-thirds or more of the tumor or epithelial cells was stained. When the total score (the sum of the staining intensity and quantification measurements) was 4 or more, the sample was considered positive for VEGFR-2. For endothelial cells, a simple three-level categorization that was based on the staining intensity was used, as follows: category 0, endothelial cells were negative for VEGFR-2 expression; category 1, endothelial cells were clearly identified at x400 (considered as low VEGFR-2 expression); and category 2, endothelial cells were clearly identified at x100 (considered as intense VEGFR-2 expression).

Statistical Analysis
The nonparametric Kruskal-Wallis test was used to identify significant differences in dynamic contrast-enhanced MR imaging parameters according to histopathologic type. We used nonparametric analyses because of the small sample size. The Kruskal-Wallis test is a one-way analysis of variance according to ranks. In a set of k independent samples, the Kruskal-Wallis test analyzes the null hypothesis of no differences in the medians of the k populations. The alternative hypothesis is that not all medians are equal. Consequently, if the result is significant, we can conclude that there is a high likelihood that at least two of the samples represent populations with different median values. When the result was deemed significant, we performed 2 x 2 multiple comparisons, controlling for type I error, to identify which of the histopathologic groups were different.

Receiver operating characteristic curves were used to determine the dynamic contrast-enhanced MR image parameter cutoffs with the best sensitivity and specificity for distinguishing between benign and malignant tumors and between invasive and noninvasive tumors. The best cutoff point was the value with the highest sensitivity and specificity combined. The relevance of each parameter was evaluated by comparing the areas under the receiver operating characteristic curves. Positive likelihood ratios (PLRs) for different histopathologic types were calculated for the highest sensitivity and specificity of each dynamic contrast-enhanced MR imaging parameter.

The proportions of tumors with low PCI values or intense VEGFR-2 expression were compared between histopathologic groups by using the Fisher exact test. When the result was significant, we used a Tukey-type 2 x 2 multiple-comparison procedure for proportions. We used Spearman correlation coefficient analysis to identify correlations between dynamic contrast-enhanced MR imaging parameters and immunohistochemical data. All tests were two sided. A P value of less than .05 was considered to indicate a significant difference. Statistical analyses were performed by using software (SPSS 14.0 for Windows, SPSS, Chicago, Ill; GraphPad Prism for Windows, GraphPad Software, San Diego Calif).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
 References
 
Dynamic Contrast-enhanced MR Imaging
The sigmoid equation, Equation (1), fitted all tumor enhancement curves, with R2 values between 0.93 and 0.99. Benign, borderline, and invasive ovarian tumors displayed highly suggestive patterns of enhancement (Fig 2).


Figure 2A
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  		Figure 2a: SI-time curves of (a) benign, (b) borderline, and (c) invasive ovarian tumors. Enhancement curves were fitted to a sigmoid equation.

 

Figure 2B
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  		Figure 2b: SI-time curves of (a) benign, (b) borderline, and (c) invasive ovarian tumors. Enhancement curves were fitted to a sigmoid equation.

 

Figure 2C
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  		Figure 2c: SI-time curves of (a) benign, (b) borderline, and (c) invasive ovarian tumors. Enhancement curves were fitted to a sigmoid equation.

 
The EA was higher for invasive tumors than for benign (P < .001) and borderline (P < .05) tumors, but no significant difference was observed between benign and borderline tumors (Table 4). The Tmax was longer for benign tumors than for borderline (P < .05) and invasive (P < .01) tumors, but no significant difference was observed between borderline and invasive tumors. The MS was steeper for invasive tumors than for benign (P < .001) and borderline (P < .001) tumors, but no significant difference was observed between benign and borderline tumors (Fig 3).


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  		Table 4. Comparison of Dynamic Contrast-enhanced MR Imaging Parameters for Ovarian Tumors

 

Figure 3A
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  		Figure 3a: Box-and-whisker plots for (a) EA, (b) Tmax, and (c) MS for benign, borderline, and invasive ovarian tumors. Box represents values from lower to upper quartiles. Central line represents median. Whiskers extend from minimum to maximal value, excluding extreme values (outliers). Each point represents a tumor.

 

Figure 3B
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  		Figure 3b: Box-and-whisker plots for (a) EA, (b) Tmax, and (c) MS for benign, borderline, and invasive ovarian tumors. Box represents values from lower to upper quartiles. Central line represents median. Whiskers extend from minimum to maximal value, excluding extreme values (outliers). Each point represents a tumor.

 

Figure 3C
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  		Figure 3c: Box-and-whisker plots for (a) EA, (b) Tmax, and (c) MS for benign, borderline, and invasive ovarian tumors. Box represents values from lower to upper quartiles. Central line represents median. Whiskers extend from minimum to maximal value, excluding extreme values (outliers). Each point represents a tumor.

 
Between benign and malignant tumors (borderline and invasive), the EA cutoff was about 114% for maximum sensitivity (83%, 10 of 12) and specificity (72%, 21 of 29). The Tmax cutoff was 29.7 seconds for maximum sensitivity (92%, 11 of 12) and specificity (79%, 23 of 29). The MS cutoff was 2.2% per second for maximum sensitivity (83%, 10 of 12) and specificity (90%, 26 of 29).

Between invasive and noninvasive (benign and borderline) tumors, the EA cutoff was 114% for maximum sensitivity (100%, 16 of 16) and specificity (72%, 18 of 25). The Tmax cutoff was 27.9 seconds for maximum sensitivity (75%, 12 of 16) and specificity (72%, 18 of 25). The MS cutoff was 3.9% per second for maximum sensitivity (100%, 16 of 16) and specificity (92%, 23 of 25).

An MS of less than 2.2% per second had a PLR of 8.1 for prediction of a benign ovarian tumor (sensitivity, 83% [10 of 12]; specificity, 90% [26 of 29]) (Table 5). An MS of between 2.2% and 3.9% per second had a PLR of 19.2 for prediction of borderline tumors (sensitivity, 69% [nine of 13]; specificity, 96% [27 of 28]). An MS of more than 3.9% per second had a PLR of 12.5 for prediction of invasive tumors (sensitivity, 100% [16 of 16]; specificity, 92% [23 of 25]). The MS was the best criterion for distinguishing invasive from noninvasive tumors (Fig 4).


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  		Table 5. PLR of Dynamic Contrast-enhanced MR Imaging Parameters for Discriminating Benign, Borderline, and Invasive Ovarian Tumors

 

Figure 4A
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  		Figure 4a: Comparative diagnostic values of quantitative enhancement parameters. (a) For discriminating benign from malignant tumors, area under the curve (AUC) for MS was higher than that for EA and Tmax, but difference was not significant. (b) For discriminating invasive from noninvasive tumors, MS was the best criterion: Area under the curve for MS (P < .05) was higher than that for EA and Tmax (P < .001), and differences were significant. CI = confidence interval.

 

Figure 4B
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  		Figure 4b: Comparative diagnostic values of quantitative enhancement parameters. (a) For discriminating benign from malignant tumors, area under the curve (AUC) for MS was higher than that for EA and Tmax, but difference was not significant. (b) For discriminating invasive from noninvasive tumors, MS was the best criterion: Area under the curve for MS (P < .05) was higher than that for EA and Tmax (P < .001), and differences were significant. CI = confidence interval.

 
Immunohistochemical Analysis of CD34, PCI, and VEGFR-2 Expression
Microvascular analysis.—No significant difference in the Chalkley count, MVD, HVD, TEA, or mean area per microvessel was found among the three types of ovarian tumors (Table 6).


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  		Table 6. MVD, Microvessel Area, PCI, and VEGFR-2 Expression Level

 
Pericyte coverage index.—The proportion of tumors with low PCI values was significantly higher among invasive tumors than among borderline (P < .05) and benign (P < .05) tumors, but no significant difference was noted between borderline and benign tumors.

Expression level of VEGFR-2.—The proportion of tumors in which epithelial cells expressed VEGFR-2 was significantly higher among invasive than benign tumors (P < .05). No significant difference was noted between benign and borderline tumors or between invasive and borderline tumors. The proportion of tumors with intense VEGFR-2 expression by endothelial cells was significantly higher among invasive tumors than among benign (P < .05) and borderline (P < .05) tumors, but no significant difference was noted between borderline and benign tumors.

Correlations between CD34, PCI, and VEGFR-2 Expression and Dynamic Contrast-enhanced MR Imaging Parameters
The EA did not correlate with the Chalkley count, MVD, HVD, TEA, mean area per microvessel, or the PCI (Table 7). A positive correlation was noted between the EA and VEGFR-2 expression by both epithelial (r = 0.41, P < .01) and endothelial (r = 0.54, P < .001) cells (Fig 5). The Tmax did not correlate with any of the immunohistochemical parameters. The MS did not correlate with the Chalkley count, MVD, HVD, TEA, or mean area per microvessel. A negative correlation was noted between the MS and the PCI (r = –0.34, P = .04). In addition, a positive correlation was found between the MS and VEGFR-2 expression by both epithelial (r = 0.41, P < .01) and endothelial (r = 0.66, P < .001) cells (Fig 6).


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  		Table 7. Correlation between Dynamic Contrast-enhanced MR Imaging Parameters and Immunohistochemical Data

 

Figure 5A
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  		Figure 5a: Box-and-whisker plots show correlation between EA and VEGFR-2 expression. Correlation between EA and VEGFR-2 expression by both (a) epithelial (P < .01, r = 0.41) and (b) endothelial (P < .001, r = 0.54) cells was positive.

 

Figure 5B
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  		Figure 5b: Box-and-whisker plots show correlation between EA and VEGFR-2 expression. Correlation between EA and VEGFR-2 expression by both (a) epithelial (P < .01, r = 0.41) and (b) endothelial (P < .001, r = 0.54) cells was positive.

 

Figure 6A
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  		Figure 6a: Box-and-whisker plots show correlation between MS and PCI and between MS and VEGFR-2 expression. (a) Correlation between MS and PCI (r = –0.34, P = .04) was negative. Correlation between MS and VEGFR-2 expression by both (b) epithelial (r = 0.41, P < .01) and (c) endothelial (r = 0.66, P < .001) cells was positive.

 

Figure 6B
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  		Figure 6b: Box-and-whisker plots show correlation between MS and PCI and between MS and VEGFR-2 expression. (a) Correlation between MS and PCI (r = –0.34, P = .04) was negative. Correlation between MS and VEGFR-2 expression by both (b) epithelial (r = 0.41, P < .01) and (c) endothelial (r = 0.66, P < .001) cells was positive.

 

Figure 6C
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  		Figure 6c: Box-and-whisker plots show correlation between MS and PCI and between MS and VEGFR-2 expression. (a) Correlation between MS and PCI (r = –0.34, P = .04) was negative. Correlation between MS and VEGFR-2 expression by both (b) epithelial (r = 0.41, P < .01) and (c) endothelial (r = 0.66, P < .001) cells was positive.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
 References
 
Our study findings show the diagnostic performance of dynamic contrast-enhanced MR imaging for distinguishing among benign, borderline, and invasive ovarian tumors on the basis of their distinct enhancement patterns. Moreover, dynamic contrast-enhanced MR imaging findings correlated with the PCI and with VEGF receptor expression.

MR imaging is widely used to diagnose a variety of tumors, usually with nonenhanced and delayed contrast-enhanced MR sequences, including contrast-enhanced sequences at arterial and venous phases. Dynamic contrast medium enhancement has been shown to increase the relevance of MR imaging for many tissues (7,11,20–22). Enhancement curves have been reported for breast, head and neck, prostate, and soft-tissue tumors (23–26). Dynamic contrast-enhanced MR imaging also is useful for characterizing gynecologic masses (27,28), but few data are available about ovarian tumors. In a small series with 11 tumors, Van Vierzen et al (5) showed that early enhancement might be a better diagnostic factor than CA 125 levels and ultrasonographic (US) findings for borderline tumors.

Sohaib et al (29) confirmed that malignant tumors exhibited greater early enhancement than benign tumors. We confirm that dynamic contrast-enhanced MR imaging is capable of differentiating benign, borderline, and invasive ovarian epithelial tumors. Each tumor type displays highly suggestive patterns of enhancement. The criteria used to show the malignancy of ovarian tumors on US and morphologic MR images are septations, vegetations, and solid portions (4,6,29,30). It was subsequently shown that the T2 signal and delayed enhancement observed on MR images after gadolinium-based contrast agent injection strengthen the diagnosis of malignancy (4,31). Moreover (4,32), we have shown that dynamic contrast-enhanced MR imaging can be used to analyze the perfusion of solid tissues contained in ovarian tumors and can aid in the discrimination among benign, borderline, and malignant tumors. In our study, the MS was the best criterion for distinguishing invasive from noninvasive tumors. A cutoff of 3.9% per second helped to distinguish between invasive and benign or borderline tumors, with a sensitivity of 100% (16 of 16) and a specificity of 92% (23 of 25).

The mechanisms that explain the different enhancement curves during intravenous gadoterate meglumine (Dotarem; Guerbet, Roissy, France) administration are complex and depend on tissue-specific factors such as the number and maturity of microvessels. Researchers in other studies (32,33) have evaluated the MVD and HVD of ovarian tumors. The results were variable, however, probably owing to differences in the choice of endothelial markers (CD31, CD34, and factor VIII) and immunohistochemical protocols (32,33). By using CD34 and the Chalkley count technique (18,34), we obtained results in line with those of Orre et al (32,35); these results showed no significant difference in MVD or HVD between benign and malignant ovarian tumors.

Malignant tumors are characterized by the presence of poorly formed and fragile neoangiogenic vessels. In addition, microvessels of malignant tumors lack a muscular coat and are highly permeable (36). One way to assess the muscular coat of microvessels is to determine the PCI. Our results confirm that the density, which is based on the PCI, of smooth-muscle cells within microvessels is significantly lower in malignant ovarian tumors than in benign tumors (13). Another major determinant of microvascular permeability is VEGFR-2 expression. VEGFR-2 is expressed by both tumor cells and endothelial cells of blood vessels in the stroma adjacent to tumor nests. VEGF secreted by tumor cells binds to VEGFR-2 on endothelial cells, thereby promoting hematogenous and lymphogenous metastasis. VEGF also binds to VEGFR-2 expressed by tumor cells, promoting their proliferation (37). VEGFR-2 is an endothelial VEGF receptor specifically involved in ovarian tumor metastasis (15). Overexpression of VEGF, VEGF receptor 1, and VEGFR-2 has been observed in malignant ovarian tumors compared with benign tumors (38–40). We also observed VEGFR-2 overexpression in malignant tumors relative to benign tumors (38–40).

To our knowledge, this is the first study in which dynamic contrast-enhanced MR parameters and angiogenesis biomarkers in ovarian tumors were compared. In contrast to reports on other tissues (breast or cervical uterine tumors), we found no correlation between dynamic contrast-enhanced MR imaging parameters and MVD (25,41,42). The dynamic contrast enhancement of ovarian epithelial tumors seems to correlate more with markers of vascular immaturity than with the number of microvessels. In our study, a high EA was correlated with high VEGFR-2 expression on endothelial and epithelial cells, and a high MS was associated with low anti-{alpha} smooth-muscle–actin expression and high VEGFR-2 expression on endothelial and epithelial cells. VEGF and its receptors, VEGF receptor 1 and VEGFR-2, are angiogenic factors that influence MR signal enhancement. Knopp et al (11) reported that breast tumor vascular permeability to contrast media correlated closely with tissue VEGF expression.

Some limitations of our study must be addressed. First, the patient selection may have been biased because only patients referred for MR imaging were examined. In our institution, patients with small unilocular masses (<6 cm in diameter) at US and no criteria of malignancy do not undergo MR imaging.

Second, the enhancement values were calculated automatically by our workstation by using the single first image as the reference. A more robust reference with averaging of several nonenhanced images would be better to minimize SI variations from the baseline.

Finally, as in the first published studies on head and neck lesions and breast tumors (43), we used semiquantitative parameters for this first study of ovarian tumors. Our curve analysis was based on data fitting to an empiric descriptive mathematic model, which describes enhancement behavior without taking into account the individual variability of the arterial input function. The analysis, therefore, relies on the assumed similarity of the arterial kinetics and the injection protocol in all of the women. This approach has the advantage of being efficient and straightforward, but it does not allow one to extract quantitative physiologic microcirculation parameters, such as perfusion, blood volume, and the capillary permeability product, during early enhancement. Therefore, it cannot be simply extended across different platforms because of the different acquisition parameters. Specific thresholds will have to be determined for each specific platform, depending on available sequences and parameters.

In conclusion, the early enhancement patterns on dynamic contrast-enhanced MR images can help accurately distinguish among benign, borderline, and invasive ovarian tumors and were found to correlate with tumor angiogenic status, as determined with immunohistochemical staining. The acquisition parameters will now have to be optimized to extract quantitative parameters, and these preliminary results must be confirmed in a larger series.


   ADVANCES IN KNOWLEDGE
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
IMPLICATIONS FOR PATIENT CARE
 References
 


   IMPLICATIONS FOR PATIENT CARE
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
References
 


   ACKNOWLEDGMENTS
 
The authors thank Ludovic Trinquart, MD, Epidemiology and Clinical Research Unit, Institut National de la Santé et de la Recherche Médicale CIE4, Georges Pompidou European Hospital, Paris, France, for reviewing statistical analysis results.


   FOOTNOTES
 

Abbreviations: EA = enhancement amplitude • HVD = high vessel density • MS = maximal slope • MVD = microvessel density • PCI = pericyte coverage index • PLR = positive likelihood ratio • SI = signal intensity • Tmax = time of half rising • TEA = total endothelial area • VEGF = vascular endothelial growth factor • VEGFR-2 = VEGF receptor 2

Author contributions: Guarantors of integrity of entire study, I.T., M.B., C.A.C.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, I.T., M.B., E.D., P.C., C.A.C.; clinical studies, all authors; statistical analysis, I.T., C.A.C.; and manuscript editing, I.T., M.B., C.A.C.

Authors stated no financial relationship to disclose.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ADVANCES IN KNOWLEDGE
 IMPLICATIONS FOR PATIENT CARE
 References
 

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