Differentiation of ovarian cancers from borderline ovarian tumors on the basis of evaluation of tumor vascularity in multi-row detector computed tomography--comparison with histopathology.

ORIGINAL STUDY

Differentiation of Ovarian Cancers From Borderline Ovarian Tumors on the Basis of Evaluation of Tumor Vascularity in MultiYRow Detector Computed TomographyVComparison With Histopathology Laretta Grabowska-Derlatka, MD, PhD,* Pawel Derlatka, MD, PhD,Þ Piotr Palczewski, MD, PhD,* Anna Danska-Bidzinska, MD, PhD,Þ and Ryszard Pacho, MD, PhD*

Objective: The aim of this study was to evaluate the feasibility of multiYdetector row computed tomography (MDCT) in the differentiation between borderline ovarian tumors and ovarian cancer on the basis of tumor morphology and specific features of tumor vascularity in correlation with the results at pathology. Methods: A triphasic MDCT protocol was used for the analysis of tumor vascularity. The following features were taken into account: (1) The number of vessels in papillary projections, solid-tissue component, and septa (2 vs 92), (2) serpentine and chaotic configuration of vessels, (3) presence of microaneurysms, and (4) presence of arteriovenous microfistulas. Masses with at least 3 of 4 features were considered ovarian cancer (group A) and masses with 2 features or less as borderline tumor (group B). Radiological findings were compared with results of postoperative pathology. Results: Pathologic vessels were found in all 56 patients. Thirty-two patients were included in group A and 24 in group B. The results of pathology were as follows: in group A: 31 malignant tumors, including 31 ovarian carcinomas and 1 benign cystadenoma; in group B: 22 borderline ovarian tumors, 1 benign cystadenoma, and 1 ovarian cancer. Conclusions: Morphological evaluation of tumor vascularity in MDCT seems to be an efficient method of differentiating between borderline ovarian tumors and ovarian carcinomas. Because of a small number of cases in the current study, a further research seems justified to confirm our results. The presented MDCT-angiographic criteria showed high sensitivity (97%) and specificity (96%) in differentiation of borderline ovarian tumors and ovarian cancers as compared with pathology. The presented CT-angiographic criteria of malignancy showed an excellent interobserver agreement. Key Words: Borderline ovarian tumor, Ovarian cancer, MDCT, Tumor vascularity, Volume rendering (VR), Maximum intensity projection (MIP) Received April 14, 2013, and in revised form July 29, 2013. Accepted for publication August 4, 2013. (Int J Gynecol Cancer 2013;23: 1597Y1602) *2nd Department of Radiology and †2nd Department of Obstetrics and Gynecology, Medical University of Warsaw, Warsaw, Poland. Address correspondence and reprint requests to Pawel Derlatka, MD, PhD, 2nd Department of Obstetrics and Gynecology, Medical University of Warsaw, Karowa 2, 00-315 Warsaw, Poland. E-mail: [email protected]. The authors declare no conflicts of interest. Copyright * 2013 by IGCS and ESGO ISSN: 1048-891X DOI: 10.1097/IGC.0b013e3182a80a41 International Journal of Gynecological Cancer

tumors pose a significant problem in gynecologic O varian oncology. Ovarian carcinoma constitutes a third of all

cases of neoplasms of the female genitourinary tract. The mortality rate associated with this neoplasm is higher than that in all other neoplasms of the female reproductive organs and reaches 55%.1 According to the statistics of the American Cancer Society, ovarian cancer is in the group of 5 most common female cancers and is the fifth leading cause of female cancer death in the United States.2

& Volume 23, Number 9, November 2013

Copyright © 2013 by IGCS and ESGO. Unauthorized reproduction of this article is prohibited.

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Grabowska-Derlatka et al

International Journal of Gynecological Cancer

Borderline ovarian tumors represent approximately 15% to 20% of ovarian tumors, and the 1973 World Health Organization classification lists them as a separate group. In literature, they are also referred to as ‘‘tumors of low malignant potential’’ or ‘‘atypical proliferative tumors’’; however, according to the statement of conference of pathologists in Bethesda (2004), the term ‘‘borderline ovarian tumor’’ has been accepted as the most appropriate.2,3 The diagnosis of borderline ovarian tumor affects significantly further treatment. The histological criteria for the diagnosis of borderline tumor according to World Health Organization include epithelial proliferation, nuclear abnormalities and mitotic activity similar to malignant tumor, nests of tumor cells outside the primary tumor, and no obvious destructive infiltration of the ovarian stroma. The prognosis is favorable in most cases. In 80% of patients, the tumor is diagnosed as stage I disease. The 5-year survival rate is higher than 90% in tumors limited to the ovary and remains high, approximately 87%, when other cases are included. Twenty to thirty percent of patients are diagnosed with tumor spread outside the ovary, mostly with peritoneal seeding. The 5-year survival rate in this group of patients is between 45% and 70%.3,4 Surgical excision is the primary treatment for borderline ovarian tumors; however, less aggressive approach is advocated than in radical cytoreduction or staging surgery in ovarian carcinoma. Uterine-ovarianYsparing surgery is possible in many cases and can be performed through a minimally invasive laparoscopic approach.3,4 Adjuvant chemotherapy is not required in early-stage disease. Transvaginal sonography is the main imaging method for evaluation of tumors of female reproductive organs, allowing for a structural and topographic evaluation of the ovary. Unfortunately, despite the possibility of 3-dimensional imaging and assessment of blood flow with Doppler imaging, the differentiation between ovarian carcinoma and borderline ovarian tumor is not always possible. Computed tomography characterizes the tissues based on the differences in the attenuation of x-ray beam expressed as Hounsfield units (HU). The measurement of HU allows identification of fluid (from 0 to 20 HU), fatty tissue (approximately j110 to j70 HU), and calcifications (usually 9200 HU), all those components being important in the characterization of pathologic masses in the abdomen and pelvis. In multiYdetector row computed tomography (MDCT), the x-ray beam is simultaneously collected by several rows of detectors (from 4 to 320). This modification of CT technique allows acquisition of very thin (submillimeter) sections in a very short time, which potentially improves the sensitivity of this method in detecting small lesions, such as peritoneal implants of malignant tumors.5 MultiYdetector row CT is especially beneficial for dynamic studies with intravenous contrast medium administration and CT angiography. MultiYdetector row CT offers a possibility of precise reconstruction of vascular morphology of ovarian tumors with evaluation of inflow of contrast medium in the arterial and venous-parenchymal phase of examination. Thus, the distinction between normal and pathologic vessels characteristic of malignant masses can be made.6 Moreover, it has been shown that CT is better than sonography in the evaluation of

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infiltration of adjacent organs and intraperitoneal and retroperitoneal spread.7

AIMS OF THE STUDY The aim of this study was to evaluate the feasibility of MDCT in the differentiation between borderline ovarian tumors and ovarian carcinoma on the basis of tumor morphology and specific features of tumor vascularity in correlation with the results at pathology.

MATERIALS AND METHODS The Central Clinical Hospital’s Investigation Review Board approved this prospective study, and as the MDCT study protocol was not different from the one routinely used in assessment of patients with suspected ovarian mass, the need for individual patient consent was waived. Between March 2011 and January 2013, 69 women aged from 28 to 67 years with sonographic diagnosis of ovarian tumor with morphology of a cyst with papillary projections, multilocular cyst, or cyst with solid-tissue component were seen in gynecologic outpatient clinic. All patients had elevated serum levels of CA-125 or CA-19-9. Thirteen patients with a morphology of lesions more suggestive of a benign disease (thin septa, no flow in Doppler examination) were qualified for laparoscopic surgery on the basis of sonography alone. In the remaining 56 women, aged 28 to 65 years, MDCT of the abdomen and pelvis was performed. All CT examinations were performed with a 16-detectorrow CT scanner (Light Speed; GE) using a trphasic protocol: & phase I: native CT of the abdomen and pelvis; slice thickness 2.5 mm; initial tumor evaluation, presence of calcifications & phase II: arterial-phase CT limited to the tumor; slice thickness 1.2 mm & phase III: equilibrium-phase CT of the abdomen and pelvis; slice thickness 2.5 mm The reformats in volume rendering (VR) and maximum intensity projection (MIP) algorithms were acquired in all patients. The following features of malignancy were taken into account in the analysis of tumor vascularity: 1. the number of vessels in papillary projections, solidtissue component, and septa (2 vs 92); 2. serpentine and chaotic course of vessels; 3. presence of microaneurysms, defined as tiny focal dilatations of arteries observed in the arterial phase; and 4. presence of arteriovenous microfistulas, defined as an early venous outflow observed in the arterial phase. The vessels meeting the previously mentioned criteria were classified as pathologic. The analysis was performed by 2 independent radiologists blinded to the results at surgery and histology. Depending on the features of tumor vascularity, patients were divided into 2 groups: * 2013 IGCS and ESGO




Ovarian Cancer & Borderline Ovarian Tumor

1. Group A with features suggesting ovarian cancer: more than 2 vessels in papillary projections, solid-tissue component, and septa with microaneurysms, and fistulas. Minimum 3 of 4 features of malignancy should have been present. 2. Group B with features suggesting borderline tumor: sparse vessels in papillary projections (maximum, 2), solid-tissue component, and septa without microaneurysms and fistulas. Radiological findings were compared with results of the postoperative pathologic findings. Statistical analysis was performed using Analyse-it (Leeds, UK) software. The J statistic was used to evaluate the interobserver variability in the assessment of each of 4 features of pathologic vascularity. The sensitivity, specificity, and positive and negative predictive values of the tested method were calculated for each reader.

RESULTS Pathologic vessels were found in all 56 patients. Thirtytwo patients were included in group A and 24 in group B. Characteristics of vascular abnormalities in groups A and B are shown in Table 1 and Figures 1Y5. Aside from the vascular abnormalities, morphologic features of tumors in groups A and B were indistinctive. All histology examinations were performed in the academic referential center and were assessed by 2 pathologists by agreement. The results of histopathology were as follows: & in group A: 31 ovarian cancers and 1 benign cystadenoma & in group B: 22 borderline ovarian tumors, 1 benign cystadenoma, and 1 ovarian cancer. From the 32 ovarian cancers, 8 were stage I disease, 6 were stage II disease, and 18 were stage III disease (only microscopic infiltration in omentum). From the borderline tumors, 21 were stage I disease, and 1 was stage III disease. There were 10 cases of discrepancies in the assessment of tumor vasculature between 2 readers: 2 regarding the number of vessels (J coefficient of 0.93), 3 regarding the serpentine course of the vessels (J = 0.89), 3 regarding the presence of

FIGURE 1. A 39-year old woman with ovarian cancer. MultiYdetector row CT arterial phase. The reformat in MIP algorithm. Serpentine and chaotic course of vessels in solid parts of ovarian mass (arrows). microaneurysms (J = 0.89), and 2 regarding the presence of microfistulas (J = 0.92). Those differences did not influence the classification of tumors by the readers either as ovarian carcinoma (group A, J = 1,0) or borderline tumor (group B, J = 1,0). Two tumors were misclassified by both readers: & one tumor showing more than 2 vessels with a serpentine course and microaneurysms (3 of 4 features, group A) proved to be a borderline tumor at histology & one tumor with 1 feeding vessel showing a serpentine course and microaneurysms (2 of 4 features, group B) proved to be an ovarian carcinoma at histology. The distribution of radiological and histopathologic diagnoses in groups A and B is presented in Table 2. Sensitivity and specificity for the differentiation between ovarian carcinomas and borderline tumors are shown in Table 3.

TABLE 1. Characteristics of groups Group A Type of Vascular Abnormalities No. vessels in papillary projections, solidtissue component, and septa 92 No. vessels in papillary projections, solidtissue component, and septa G2 Serpentine and chaotic course Presence of microaneurysms Presence of microfistulas

Group B

No. No. Patients Patients 32

0

0

24

30 28 21

2 1 0

DISCUSSION Borderline ovarian tumors are characterized by a far better prognosis than ovarian carcinoma. However, the differentiation of borderline ovarian tumors from carcinomas and other cystic tumors with imaging is often difficult. Transvaginal sonography is the most widely used imaging modality in the evaluation of ovarian tumors and is believed to be suggestive of malignancy when intracystic papillae and septa are observed.8 However, when a further attempt at the differentiation between the ovarian carcinoma and borderline ovarian tumor is made, a significant rate of errors is to be expected. Several systems of morphologic evaluation of ovarian tumors have been proposed for sonography. Although, high sensitivity can be achieved with those methods, specificity

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FIGURE 2. A 39-year old woman with ovarian cancer. The reformat in MIP algorithm. Early venous outflow as an indirect sign of arteriovenous shunting in solid parts of the tumor (arrows).


FIGURE 4. A 42-year old woman with borderline ovarian tumor. Equilibrium-phase MDCT. MIP algorithm. Endophytic parts of the tumor (arrows).

remains relatively low. Doppler imaging is an important complimentary tool to 2- and 3-dimensional sonography. The most often used flow indices include resistivity index and pulsatility index, the decrease of which is observed in malignant tumors.9,10 The comparison of flow indices in benign, malignant, and borderline ovarian tumors showed no difference between the malignant and borderline tumors. In both groups, the average values of resistivity index and pulsatility index were significantly lower than those in benign tumors, being, respectively, 0.45 and 0.67 for borderline and 0.39 and 0.58 for malignant tumors. Color Doppler imaging has also proved to be of no value in discriminating between malignant and borderline

tumors.11 Transvaginal ultrasonography is the primary imaging for evaluating adnexal tumors with sensitivity of 88% to 100% and specificity of 39% to 87% in detection of ovarian cancer.12 With an aim of increasing diagnostic confidence, the attempts to identify specific pathomorphologic features of different groups of ovarian tumors in CT and magnetic resonance imaging (MRI) were undertaken.13 Especially, MRI was expected to provide substantial progress in the evaluation of ovarian tumors. Its capability to differentiate various types of internal cyst contents makes MRI a superb tool for diagnosis of dermoids and endometrial cysts.14 The diagnosis of coexistence of 2 different ovarian pathologies, for instance, dermoid and malignant or benign mucinous tumor, is also

FIGURE 3. A 39-year old woman with ovarian cancer. MultiYdetector row CT arterial phase. The reformat in VR algorithm. Microaneurysms and arteriovenous shunts in the solid parts of the tumor (arrows).

FIGURE 5. A 42-year old woman with borderline ovarian tumor. MultiYdetector row CT arterial phase. The reformat in VR algorithm. Scant tiny vessels in the endophytic part of the tumor (arrows).

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more easily made with MRI.15 MultiYdetector row CT proved accurate in the detection and differentiation between benign and malignant adnexal masses. Computed tomography diagnosis was based on the diameter of the tumor (94 cm), presence of masses bilaterally, cystic-solid mass, cystic lesion with thick, necrosis, irregular walls or septa, or papillary projection.16 MultiYdetector row CT is perceived as a very good tool for the preoperative staging to determination of the bulk of neoplasm.17 In preoperative prediction of optimal or suboptimal debulking, sensitivity and specificity of MDCT are estimated at 79% and 75%, respectively.18 Intracystic papillary projections are an important finding, as they may be the only diagnostic clue of malignancy. Those projections are usually absent in benign tumors, such as cystadenoma. On the other hand, they are often present in malignant and borderline tumors.11,13 Histologically, they are made of neoplastic epithelial proliferation over stromal core. They usually display low signal intensity from fibrous core and high signal intensity from edematous epithelium in T2-weighted images and are well visible after intravenous administration of contrast medium.9,19 In a recent study, in which dynamic MRI was used, during the arterial and delayed phases in cases of malignant tumors, a sharp and significant rise in signal intensity was observed, followed by a plateau. Benign tumors showed a gradual increase in enhancement without a peak. Borderline tumors had moderate initial enhancement with an early plateau.20 Until now, the differential diagnosis in CT was based on the measurements of density of solid components after intravenous administration of contrast medium.13 Some authors suggest that, aside from the evaluation of vascular pathologies in abdominal cavity, such as aneurysms, MDCT can be an excellent tool for evaluation of tumor vascularity.6,21 First attempts at the differentiation between borderline ovarian tumors and early-stage ovarian carcinoma have not brought unequivocal results. de Souza and colleagues22 found that borderline ovarian tumors have similar features in MDCT and MRI as stage I ovarian carcinoma. In some cases, carcinomas were characterized by thicker septa and larger volume of solid component; however, unambiguous distinction was not possible. In more advanced tumors with features of extra-adnexal involvement, MDCT showed excellent results in detecting the features of malignancy. A recent study performed with a 64-row scanner reported a diagnostic accuracy of 90.7% and 90.7% for detecting pelvic involvement, 95.35% and 92.3% for detecting lymph nodes distribution, and 89.2% and 89.2% TABLE 2. Distribution of radiological and histopathologic diagnoses in groups A and B CT Results

True Disease Status

Group A

Borderline tumors Ovarian carcinomas Total

1 31 32

Ovarian Cancer & Borderline Ovarian Tumor



Group B 23* 1 24

Total 24 32 56

*Twenty-two borderline ovarian tumors and one cystadenoma.

TABLE 3. Diagnostic performance of MDCT in differentiation between borderline tumors and ovarian carcinomas Prevalence

%

95% CI

Sensitivity Specificity Positive predictive value Negative predictive value

97 96 97 96

84Y100 79Y100 84Y100 79Y100

for peritoneal involvement when compared with surgical and histopathologic findings, respectively.23 However, when there are no features of extra-adnexal disease, distinction between borderline tumors and carcinomas based on aforementioned morphologic features may be impossible. To our knowledge, no reports of using MDCT angiography for the evaluation of the vascularity of ovarian tumors have been published yet. MultiYdetector row CT, owing to the possibility of using very thin collimation of 0.4 to 0.6 millimeters, allows a very precise 3-dimensional reconstruction of a tumor and its vascularity, not only within the capsule, but within the papillary projections as well. Contrary to previous studies dedicated to the evaluation of contrast enhancement of solid-tissue components, our study was focused on the possibility of differentiating ovarian tumors on the basis of specific characteristics of their vascularity. It was possible to isolate 2 groups of tumors with different vascularity. Malignant tumors, mostly ovarian carcinomas, are characterized by a dense, chaotic net of pathologic vessels with numerous microaneurysms and fistulas. On the other hand, the presence of a solitary vessel in the papillary solid-tissue component is typical for borderline ovarian tumors.

CONCLUSIONS 1. Morphological evaluation of tumor vascularity in MDCT seems to be an efficient method of differentiating between borderline ovarian tumors and ovarian carcinomas. Because of a small number of cases in the current study, a further research seems justified to confirm our results. 2. The comparison of histopathologic results and MDCTangiographic evaluation based on presented diagnostic criteria showed a high sensitivity (97%) and specificity (96%) in differentiation of borderline ovarian tumors and ovarian carcinomas. 3. The presented CT-angiographic criteria of malignancy showed an excellent interobserver agreement.

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