Ovarian cancer is the leading cause of death from gynecologic malignancies in the United States with an annual incidence of 22,000 cases and an annual mortality rate of approximately 14,000.1 The optimal treatment for ovarian cancer includes cytoreductive surgery followed by adjuvant chemotherapy with significantly better prognostic outcomes if the initial surgery and treatment are performed by surgeons and at centers experienced in the management of ovarian cancer.2–6
At the time of the initial diagnosis, women with ovarian cancer symptoms and adnexal masses present primarily to gynecologists, primary care physicians, or general surgeons. Triage guidelines put forth by the American College of Obstetricians and Gynecologists and the Society of Gynecologic Oncologists recommend referral of women with a pelvic mass at high risk for ovarian cancer to a gynecologic oncologist.7,8 An important dilemma is faced by the physicians who initially see these patients as to which patients are appropriate to refer to a gynecologic oncologist. The triage of women at high risk for an ovarian malignancy is of vital importance because recent studies indicate that patients with ovarian cancer managed by gynecologic oncologists and at high-volume institutions are more likely to undergo complete surgical staging and optimal cytoreductive surgery with fewer complications and better survival rates than patients treated by surgeons less familiar with the management of ovarian cancer.2,3,6,9–12 To improve the triage of patients with pelvic masses, we performed a pilot study that measured seven biomarkers in sera drawn from women with a pelvic mass before surgery. We found that when adding an additional biomarker to CA 125, only HE4 was able to increase the sensitivity compared with using CA 125 alone.13 Data from this pilot study and a second pilot study were combined to develop the Risk of Ovarian Malignancy Algorithm, which combines serum levels of the biomarkers CA 125 and HE4 along with menopausal status in a logistic regression model to classify patients with a pelvic mass into high-risk or low-risk groups for having epithelial ovarian cancer. The clinical performance of the Risk of Ovarian Malignancy Algorithm was first validated in a prospective, double-blind clinical study on patients with an adnexal mass recruited at tertiary care centers in patients who presented to gynecologic oncologists and thus were considered a high-risk cohort.14 At a set specificity of 75%, the Risk of Ovarian Malignancy Algorithm displayed a sensitivity of 94% for distinguishing benign status from epithelial ovarian cancer and 85% sensitivity to identify early stage I and II disease. Additionally, the Risk of Ovarian Malignancy Algorithm outperformed the Risk of Malignancy Index, an algorithm that uses ultrasound, menopausal status, and CA 125 for differentiating benign from malignant disease.15
The purpose of the current study was to validate the use of the Risk of Ovarian Malignancy Algorithm in a low-risk population of women presenting to a generalist with an adnexal mass and evaluating the clinical use of the Risk of Ovarian Malignancy Algorithm in aiding the triage of women to gynecologic oncologists.
MATERIALS AND METHODS
This study was a prospective, multicenter, double-blind clinical trial conducted at 13 clinical study sites (seven general and six specialty hospitals) across the United States between October 2009 and August 2010. The study protocol was approved by institutional review boards at each site and was registered on the ClinicalTrials.gov web site (accession number NCT00987649). Written informed consent was obtained from each patient before entry into the trial and the collection of blood.
The study included premenopausal and postmenopausal women 18 years of age or older presenting to a generalist (defined as a general gynecologist, internist, family practitioner gastroenterologist, or general surgeon) with an ovarian cyst or an adnexal mass and subsequently scheduled to undergo surgery. An adnexal mass was defined as a simple, complex, or a solid ovarian cyst or any mass in the pelvis as determined by imaging (ie, ultrasonography, computed tomography scan, or magnetic resonance imaging). Menopausal status was determined by the physicians through history and physical examination. If the menopausal status was not known or reported, the following criteria were used: women 48 years or younger were considered premenopausal and women 55 years and older were considered postmenopausal. If the patient's age was between 49 and 55 years and the last menstrual cycle was unknown, the follicle-stimulating hormone levels were analyzed. Women with follicle-stimulating hormone levels greater than 22 milli-international units/mL were considered postmenopausal and lower than 22 were considered premenopausal as recommended in the U.S. Food and Drug Administration package insert for the Abbott ARCHITECT i2000 platform. Women with a history of ovarian cancer, bilateral oophorectomy, currently known to be pregnant, or unable to provide informed consent were excluded from the study.
Blood samples were collected from all patientss within 30 days before their surgical procedures, and all samples were drawn before the induction of anesthesia. Blood was collected into Serum Separator Tubes and was allowed to clot for at least 30 minutes before centrifugation. The blood samples were centrifuged for 10 minutes at 1,100–1,300 g, serum and plasma were separated, transferred to cryovials, and frozen at −20°C until testing. Appropriate volumes of serum were tested to quantitate the concentrations of CA 125 and HE4. Serum HE4 levels were determined using an enzyme-linked immunoabsorbent assay kit and serum CA 125 levels were determined using an ARCHITECT CA125II assays on the ARCHITECT Instrument. All the samples were tested in duplicates. Coefficient of variation for all the tests did not exceed 15.0%.
The primary end point of the current clinical study was to determine the effectiveness of the Risk of Ovarian Malignancy Algorithm for predicting epithelial ovarian cancer in patients presenting to a generalist with a pelvic mass. A detailed description of the development of the Risk of Ovarian Malignancy Algorithm and statistical approach has been described in a prior publication by Moore et al.14
To calculate the Risk of Ovarian Malignancy Algorithm score, a predictive index was calculated using the serum HE4 and serum CA 125 II levels and one of the following equations, depending on the patient's menopausal status:
1. Premenopausal: predictive index (PI)=−12.0+ 2.38*LN[HE4]+0.0626*LN[CA 125]
2. Postmenopausal: predictive index (PI)=−8.09+ 1.04*LN[HE4]+0.732*LN[CA 125]
The following equation used the predictive index for each patient to calculate a Risk of Ovarian Malignancy score:
ROMA score (%)=exp(PI)/[1+exp(PI)]*100
The Risk of Ovarian Malignancy Algorithm was used to stratify women into high-risk or low-risk groups for having a pelvic mass that is malignant or benign respectively. For both statistical and medical reasons, cut points were chosen that provided a set specificity of 75% for the HE4 EIA and ARCHITECT CA 125 II assay panel.14 In the original pilot trial and prior validation trial, the Risk of Ovarian Malignancy Algorithm score thresholds of 13.1% or greater and 27.7% for premenopausal and postmenopausal women, respectively, achieved a set specificity of 75% as desired.14,16 Thus, for premenopausal patients, a Risk of Ovarian Malignancy Algorithm score of 13.1% or greater is considered high risk for malignancy and for postmenopausal patients, a Risk of Ovarian Malignancy Algorithm score of 27.7% or greater is considered high risk for malignancy.
Preoperative Risk of Ovarian Malignancy Algorithm scores were calculated and the sensitivity, specificity, positive predictive value, and negative predictive value were determined for all groups and subgroups. The Wilcoxon rank-sum test was used for the comparison of benign compared with malignant groups and between menopausal status when examining serum levels of HE4 and CA 125. A P value of <.05 was considered as statistically significant. The P values were not adjusted for multiple evaluations.
Thirteen diverse sites from across the United Stated enrolled 512 women with a pelvic mass of which 472 (92.2%) were evaluable and are the focus of this report. The demographics for this cohort and pathologic classification are presented in Tables 1 and 2, respectively. There were 255 premenopausal patients and 217 postmenopausal patients. Plasma follicle-stimulating hormone levels were used to determine menopausal status in 36 women, 22 of which had at least a remaining ovary after a prior hysterectomy. The mean age of all women entered on the trial was 50.3 years (range 18–89 years). The mean age for the subgroup of premenopausal women was 39.7 years (range 18–56 years) and for the subgroup of postmenopausal women 62.8 years (range 44–89 years). Women diagnosed with benign disease made up 81.1% (383) of the cohort (150 postmenopausal and 233 premenopausal), and women diagnosed with a malignancy or a low malignant potential tumor made up 18.9% (89) of the cohort (67 postmenopausal and 22 premenopausal). The histologic classification of the benign pathology is provided in Table 1 and malignant disease along with the primary site is displayed in Table 2.
Forty-eight women (39 postmenopausal and nine premenopausal) were diagnosed with a primary ovarian, fallopian tube, or primary peritoneal cancer all categorized under epithelial ovarian cancer. The stage of these cancers was as follows: eight stage I, four stage II, 32 stage III, two stage IV, and two unstaged. Low malignant potential tumors were diagnosed in 19 patients (12 postmenopausal and seven premenopausal), two patients were diagnosed with nonepithelial ovarian cancer, 11 patients with other gynecologic cancers, and nine patients with nongynecologic cancers (Table 2).
Evaluation of serum levels of HE4 and CA 125 was carried out for all patients and for separate menopausal groups (Table 3). Significant differences for serum HE4 and CA 125 levels were detected when comparing benign cases with all malignant cases or with all epithelial ovarian cancers and low malignant potential tumors together (all P<.001).
The Risk of Ovarian Malignancy Algorithm assignment of women into high-risk and low-risk categories for harboring a malignancy based on the menopausal cut points is illustrated in Table 4. The sensitivity, specificity, positive predictive value, and negative predictive values of the Risk of Ovarian Malignancy Algorithm for discrimination of benign disease from epithelial ovarian cancer and low malignant potential tumors are displayed in Table 5.
Examination of all women with benign disease (n=383) or with epithelial ovarian cancer and low malignant potential tumors (n=67) revealed that the Risk of Ovarian Malignancy Algorithm classified 59 of the 67 women with epithelial ovarian cancer or low malignant potential tumors into the high-risk group and 287 of the 383 women with benign tumors into the low-risk group providing a sensitivity of 88.1% (95% confidence interval [CI] 77.8–94.7%), a specificity of 74.9% (95% CI 70.3–79.2%), and a negative predictive value of 97.3% (95% CI 94.7–98.8%) as illustrated in Table 5. The algorithm incorrectly classified five of 19 patients with low malignant potential tumors and only three of 48 with epithelial ovarian cancer (all three stage I–II) to the low-risk group, providing a sensitivity for epithelial ovarian cancer of 93.8% (95% CI 82.8–98.7%) at a specificity of 74.9% (95% CI 70.3–79.2%) and a negative predictive value of 99.0% (95% CI 97.0–99.8%) (Table 6).
Examination of postmenopausal women with benign disease (n=150) or with epithelial ovarian cancer and low malignant potential tumors (n=51) revealed that the Risk of Ovarian Malignancy Algorithm classified 46 of the 51 women with epithelial ovarian cancer or low malignant potential tumors into the high-risk group and 114 of the 150 women with benign tumors into the low-risk group providing a sensitivity of 90.2% (95% CI 78.6–96.7%), a specificity of 76.0% (95% CI 68.4–82.6%), and a negative predictive value of 95.8% (95% CI 90.5–98.6%) as illustrated in Table 5. The algorithm incorrectly classified two of 12 patients with low malignant potential tumors and only three of 39 with epithelial ovarian cancer (all three stage I–II) to the low-risk group, providing a sensitivity for epithelial ovarian cancer of 92.3% (95% CI 79.1–98.4%) at a specificity of 76.0% (95% CI 68.4–82.6%) and a negative predictive value of 97.4% (95% CI 92.7–99.5%) (Table 6).
Examination of premenopausal women with benign disease (n=233) or with epithelial ovarian cancer and low malignant potential tumors (n=16) revealed that the Risk of Ovarian Malignancy Algorithm classified 13 of the 16 women with epithelial ovarian cancer or low malignant potential into the high-risk group and 173 of the 233 women with benign tumors into the low-risk group providing a sensitivity of 81.3% (95% CI 54.4–96.0%), a specificity of 74.2% (95% CI 68.1–79.7%), and an negative predictive value of 98.3% (95% CI 95.1–99.6%) as illustrated in Table 5. The algorithm incorrectly classified three of seven patients with low malignant potential tumors. All patients with epithelial ovarian cancer were correctly classified into the high-risk group. Thus, of the nine premenopausal patients with epithelial ovarian cancer, all were correctly classified providing a sensitivity for epithelial ovarian cancer of 100% (95% CI 66.4–100%) at a specificity of 74.2% (95% CI 68.1–79.7%) and a negative predictive value of 100% (95% CI 97.9–100%) (Table 6).
On consideration of women with early-stage disease alone (stage I and II), the Risk of Ovarian Malignancy Algorithm classified nine of 12 patients with epithelial ovarian cancer correctly into the high-risk group, thus achieving a sensitivity for early stage epithelial ovarian cancer of 75.0% (95% CI 42.8–94.5%), a specificity of 74.9% (95% CI 70.3–79.2%), and a negative predictive value of 99.0% (95% CI 97.0–99.8%). When including early-stage epithelial ovarian cancer and low malignant potential tumors, the Risk of Ovarian Malignancy Algorithm classified 23 of 31 patients with epithelial ovarian cancer or low malignant potential tumors correctly to the high-risk group, thus achieving a sensitivity of 74.2% (95% CI 55.4–88.1%), a specificity of 74.9% (95% CI 70.3–79.2%), and a negative predictive value of 97.3% (95% CI 94.7–98.8%).
Examination of all women with benign neoplasms (n=383) or with any malignancy including low malignant potential tumors (n=89), the Risk of Ovarian Malignancy Algorithm correctly classified 72 of 89 malignant or low malignant potential tumors achieving a sensitivity of 80.9% (95% CI 71.2–88.5%) at a specificity of 74.9% (95% CI 70.3–79.2%). In postmenopausal women only, the Risk of Ovarian Malignancy Algorithm correctly classified 56 of 67 malignant or low malignant potential tumors achieving a sensitivity of 83.6% (95% CI 72.5–91.5%) at a specificity of 76.0% (95% CI 68.4–82.6%) and in premenopausal women only, the Risk of Ovarian Malignancy Algorithm correctly classified 16 of 22 malignant or low malignant potential tumors achieving a sensitivity of 72.7% (95% CI 49.8–89.3%) at a specificity of 74.2% (95% CI 68.1–79.7%).
The serum biomarker HE4 is a novel marker recently cleared by the U.S. Food and Drug Administration for ovarian cancer monitoring. HE4 is a member of a family of protease inhibitors that function in protective immunity and consists of two whey acidic protein domains and a 4-disulfide core. The HE4 gene is expressed by epithelial ovarian tumors and can be detected through immunohistochemical staining of malignant ovarian tissue.17–20 The HE4 protein expressed by epithelial ovarian malignancies can also be detected in the serum of patients with ovarian cancer.21 Equally important, HE4 has been shown to be a sensitive marker for the differentiation of benign ovarian tumors from those that are malignant and a marker for adenocarcinomas of the endometrium.13,14,16,21 Using area under the receiver operator characteristic curves (AUC ROC) as a measure of test performance, a pilot study examining nine potential serum biomarkers for epithelial ovarian cancers found HE4 to be the most sensitive marker for ovarian cancer. More importantly, it was demonstrated that a combination of the serum biomarkers HE4 and CA 125 achieved a higher AUC ROC and therefore had increased sensitivities than either marker alone. These findings have since been independently validated. Huhtinen et al demonstrated the combination of serum HE4 and CA 125 can differentiate endometriosis from an ovarian malignancy with a sensitivity of 79% compared with CA 125 and HE4 alone with sensitivities of 64% and 71%, respectively. As well, when studying ovarian cancers compared with controls, the dual marker combination achieved a sensitivity of 93% compared with CA 125 or HE4, which each achieved a sensitivity of 79% alone.22 More recently, Nolen et al evaluated 65 ovarian cancer-related biomarkers in the circulation of women diagnosed with an adnexal mass and found that as individual markers, HE4 and CA 125 provided the greatest level of discrimination between benign and malignant cases. In addition, consistent with our prior trial, these researchers also demonstrated that the combination of the biomarkers HE4 and CA 125 provided a higher level of discriminatory power than either marker considered alone.23
The stratification of women into menopausal groups is critical because biomarker expression, both HE4 and CA 125, can vary depending on age and the presence of benign or malignant tumors in the two groups. Therefore, these variables must be taken into account when developing algorithms using biomarkers. The Risk of Ovarian Malignancy Algorithm was developed from the combination of two pilot studies and uses serum levels of HE4 and CA 125 along with menopausal status to stratify patients into high-risk and low-risk groups for epithelial ovarian cancer.13,14 The Risk of Ovarian Malignancy Algorithm was then subsequently validated in a multicenter trial assessing women who presented with a pelvic mass. The cohort of women in this trial was considered to be at increased risk for having a malignancy because all patients were initially evaluated and enrolled by gynecologic oncologists. The initial validation trial had an incidence of all cancers of 33% and 24% for epithelial ovarian cancer alone for the women enrolled onto the trial. With this in mind, the application of the Risk of Ovarian Malignancy Algorithm to the higher-risk cohort of women separated patients into high-risk and low-risk groups effectively. When assessing premenopausal and postmenopausal women together, the Risk of Ovarian Malignancy Algorithm stratified women with benign compared with epithelial ovarian cancer and low malignant potential tumors into high-risk and low-risk groups with a sensitivity of 89%, a specificity of 75%, and a 94% negative predictive value. When assessing postmenopausal women alone, the Risk of Ovarian Malignancy Algorithm achieved a sensitivity of 92% and a specificity of 75% with a negative predictive value of 93%. For premenopausal women alone, the Risk of Ovarian Malignancy Algorithm achieved a sensitivity of 77% and a specificity of 75% with a negative predictive value of 95%. More importantly, the Risk of Ovarian Malignancy Algorithm detected 94% of the women with an invasive epithelial ovarian cancer and 85% of the women with early-stage epithelial ovarian cancer in the initial validation trial. The findings of a decreased sensitivity for the premenopausal group can be accounted for by a higher incidence of low malignant potential tumors that do not overexpress CA 125 or HE4 and the increased incidence of benign tumors that overexpress CA 125 in the premenopausal giving rise to false-positive tests.
In the current trial reported here, all patients were initially evaluated by gynecologists, family practitioners, internists, gastroenterologists, or general surgeons. For this cohort, the incidence of all cancers was 15% and for epithelial ovarian cancer alone, it was 10%. These rates are significantly lower than in the previously reported validation trial and more in line with the population of women being assessed by generalists and primary care physicians. Despite the lower incidence of cancer in this study, the Risk of Ovarian Malignancy Algorithm stratified women into high-risk and low-risk groups with similar sensitivities and specificities as seen in the prior validation trial. The high sensitivity achieved through the use of the Risk of Ovarian Malignancy Algorithm allows for 94% of women with an epithelial ovarian cancer to be identified and triaged to a gynecologic oncologist, whereas 75% of the patients with a pelvic mass will be treated by their gynecologist in their community. Equally important is the high negative predictive value (97%) achieved by the Risk of Ovarian Malignancy Algorithm for women with epithelial ovarian cancer and low malignant potential tumors. This high negative predictive value provides a strong reassurance that a pelvic mass is benign. Accurate detection of women at low risk for malignancy would reduce unnecessary referrals to a gynecologic oncologist allowing the patient to stay in their community with their primary gynecologist and support network with a low risk of ultimately being diagnosed with a malignancy.
Traditionally, the biomarker CA 125 has been demonstrated to be elevated in only half of patients with early-stage ovarian cancer and elevated in up to 80–90% of all patients with ovarian cancer.24,25 Therefore, the use of biomarkers for the detection of early-stage disease has been limited. Examining patients with stage I and II disease, the Risk of Ovarian Malignancy Algorithm stratified the majority (75%) of patients with early-stage ovarian cancer correctly to the high-risk group, therefore identifying preoperatively patients who will benefit from full surgical staging, which will dictate subsequent management and the need for chemotherapy.
Recently, the Risk of Ovarian Malignancy Algorithm has been evaluated and validated in populations outside of the United States. Montagnana et al reported on their findings on the performance of HE4 and CA 125 for detecting ovarian cancer. Using an AUC ROC analysis, these researchers found that HE4 achieved higher AUC ROC compared with CA 125 alone (AUC, 0.77 compared with 0.64) for premenopausal women and (AUC ROC, 0.94 and 0.84) for postmenopausal women, similar to the finding described in our pilot trial.26 Likewise, when combining HE4 and CA 125 and using the Risk of Ovarian Malignancy Algorithm in premenopausal and postmenopausal women separately, the authors report that the Risk of Ovarian Malignancy Algorithm had an AUC ROC of 0.77 compared with an AUC ROC of 0.64 for CA 125 alone in the premenopausal group and a AUC ROC of 0.92 for the Risk of Ovarian Malignancy Algorithm and 0.84 for CA 125. Interestingly, the AUC ROC of HE4 compared with that of the Risk of Ovarian Malignancy Algorithm was near equal in both the premenopausal and postmenopausal groups.26 The size of this study was small compared with previously reported trials and may not have had the power to detect a difference between the use of CA 125 and HE4 over that of HE4 alone. Kim et al studied the use of the Risk of Ovarian Malignancy Algorithm in a Korean population and found that the Risk of Ovarian Malignancy Algorithm classified women into high-risk and low-risk groups with a sensitivity of 88% and a specificity of 94%, findings that are consistent with our two multicenter trials.27
The current trial reported in this issue validates the previously reported trial for the use of the Risk of Ovarian Malignancy Algorithm to stratify patients into risk groups. Despite a lower incidence of epithelial ovarian cancer in this study population, the Risk of Ovarian Malignancy Algorithm performed equally as well, attaining high sensitivities and specificities for the prediction of a women having ovarian cancer. There is an urgent need for tools to help physicians identify which women with an ovarian cyst or pelvic mass are at high risk for a malignancy to accurately triage patients to surgeons and institutions specializing in the care and management of ovarian cancer while retaining most of the women with benign disease for management by their gynecologist. The accumulating data support the use of HE4 and CA 125 along with the Risk of Ovarian Malignancy Algorithm as an accurate tool to assist in the triage of women with a pelvic mass.
1. American Cancer Society. Cancer facts & figures 2010. Atlanta (GA): American Cancer Society; 2010; p. 1–62.
2. Bristow RE, Palis BE, Chi DS, Cliby WA. The National Cancer Database report on advanced-stage epithelial ovarian cancer: impact of hospital surgical case volume on overall survival and surgical treatment paradigm. Gynecol Oncol 2010;118:262–7.
3. Earle CC, Schrag D, Neville BA, Yabroff KR, Topor M, Fahey A, et al. Effect of surgeon specialty on processes of care and outcomes for ovarian cancer patients. J Natl Cancer Inst 2006;98:172–80.
4. Giede KC, Kieser K, Dodge J, Rosen B. Who should operate on patients with ovarian cancer? An evidence-based review. Gynecol Oncol 2005;99:447–61.
5. Mercado C, Zingmond D, Karlan BY, Sekaris E, Gross J, Maggard-Gibbons M, et al. Quality of care in advanced ovarian cancer: the importance of provider specialty. Gynecol Oncol 2010;117:18–22.
6. Vernooij F, Heintz P, Witteveen E, van der Graaf Y. The outcomes of ovarian cancer treatment are better when provided by gynecologic oncologists and in specialized hospitals: a systematic review. Gynecol Oncol 2007;105:801–12.
7. Guidelines for referral to a gynecologic oncologist: rationale and benefits. The Society of Gynecologic Oncologists. Gynecol Oncol 2000;78:S1–13.
8. The role of the generalist obstetrician–gynecologist in the early detection of ovarian cancer. ACOG Committee Opinion No. 280. American College of Obstetricians and Gynecologists. Obstet Gynecol 2002;100:1413–6.
9. Engelen MJ, Kos HE, Willemse PH, Aalders JG, de Vries EG, Schaapveld M, et al. Surgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma. Cancer 2006 Feb 1;106:589–98.
10. Goff BA, Matthews BJ, Larson EH, Andrilla CH, Wynn M, Lishner DM, et al. Predictors of comprehensive surgical treatment in patients with ovarian cancer. Cancer 2007;109:2031–42.
11. Paulsen T, Kjaerheim K, Kaern J, Tretli S, Trope C. Improved short-term survival for advanced ovarian, tubal, and peritoneal cancer patients operated at teaching hospitals. Int J Gynecol Cancer 2006;16(suppl 1):11–7.
12. Vernooij F, Heintz AP, Coebergh JW, Massuger LF, Witteveen PO, van der Graaf Y. Specialized and high-volume care leads to better outcomes of ovarian cancer treatment in the Netherlands. Gynecol Oncol 2009;112:455–61.
13. Moore RG, Brown AK, Miller MC, Skates S, Allard WJ, Verch T, et al. The use of multiple novel tumor biomarkers for the detection of ovarian carcinoma in patients with a pelvic mass. Gynecol Oncol 2008;108:402–8.
14. Moore RG, McMeekin DS, Brown AK, Disilvestro P, Miller MC, Allard WJ, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol 2009;112:40–6.
15. Moore RG, Jabre-Raughley M, Brown AK, Robison KM, Miller MC, Allard WJ, et al. Comparison of a novel multiple marker assay vs the Risk of Malignancy Index for the prediction of epithelial ovarian cancer in patients with a pelvic mass. Am J Obstet Gynecol 2010;203:228.e1–6.
16. Moore RG, Brown AK, Miller MC, Badgwell D, Lu Z, Allard WJ, et al. Utility of a novel serum tumor biomarker HE4 in patients with endometrioid adenocarcinoma of the uterus. Gynecol Oncol 2008;110:196–201.
17. Drapkin R, von Horsten HH, Lin Y, Mok SC, Crum CP, Welch WR, et al. Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Res 2005;65:2162–9.
18. Galgano MT, Hampton GM, Frierson HF Jr. Comprehensive analysis of HE4 expression in normal and malignant human tissues. Mod Pathol 2006;19:847–53.
19. Hough CD, Sherman-Baust CA, Pizer ES, Montz FJ, Im DD, Rosenshein NB, et al. Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Res 2000;60:6281–7.
20. Welsh JB, Zarrinkar PP, Sapinoso LM, Kern SG, Behling CA, Monk BJ, et al. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl Acad Sci U S A 2001;98:1176–81.
21. Hellstrom I, Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M, et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res 2003;63:3695–700.
22. Huhtinen K, Suvitie P, Hiissa J, Junnila J, Huvila J, Kujari H, et al. Serum HE4 concentration differentiates malignant ovarian tumours from ovarian endometriotic cysts. Br J Cancer 2009;100:1315–9.
23. Nolen B, Velikokhatnaya L, Marrangoni A, De GK, Lomakin A, Bast RC Jr, et al. Serum biomarker panels for the discrimination of benign from malignant cases in patients with an adnexal mass. Gynecol Oncol 2010;117:440–5.
24. Bast RC Jr, Klug TL, St John E, Jenison E, Niloff JM, Lazarus H, et al. A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer. N Engl J Med 1983;309:883–7.
25. Woolas RP, Xu FJ, Jacobs IJ, Yu YH, Daly L, Berchuck A, et al. Elevation of multiple serum markers in patients with stage I ovarian cancer. J Natl Cancer Inst 1993;85:1748–51.
26. Montagnana M, Danese E, Ruzzenente O, Bresciani V, Nuzzo T, Gelati M, et al. The ROMA (The Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clin Chem Lab Med 2011;49:521–5.
© 2011 by The American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.
27. Kim YM, Whang DH, Park J, Kim SH, Lee SW, Park HA, et al. Evaluation of the accuracy of serum human epididymis protein 4 in combination with CA125 for detecting ovarian cancer: a prospective case-control study in a Korean population. Clin Chem Lab Med 2011;49:527–34.