1. Introduction
Ovarian cancer is the fifth leading cause of cancer-related death in women [
1]. This disease cannot be diagnosed in the early stages and is called the silent killer [
2,
3,
4]. As such, most ovarian cancer cases are diagnosed at advanced stages [
5,
6,
7], and over 185,000 deaths due to this disease are reported annually worldwide [
8,
9]. Ovarian cancer is divided into epithelial, germ cell, and sex cord-stromal tumors, and, of these, epithelial ovarian cancer has the highest rate [
10,
11]. Epithelial ovarian cancer can be divided into two categories, designated as types 1 and 2 [
12,
13,
14], by molecular genetics and morphologic characteristics. Type 1 tumors show a stepwise progression (adenoma–carcinoma sequence), which comprises endometriosis-associated ovarian cancer (EAOC), such as clear cell carcinoma (CCC) and low-grade endometrioid carcinoma, as well as mucinous carcinoma and low-grade serous carcinoma [
15,
16]. Type 2 tumors range from the normal epithelium to precursor lesions, and, finally, to high-grade serous and endometrioid carcinoma, malignant mixed mesodermal tumors (carcinosarcomas), and undifferentiated carcinoma [
15,
17]. The former shows slow, and the latter fast, progression to advanced stages [
18,
19].
Ovarian endometriosis is defined as the presence of endometrial glands and stroma outside of the uterus, and it is most often detected in the pelvic peritoneum and ovaries [
20,
21,
22]. Repeated hemorrhages in the peritoneum or ovaries may contribute to several symptoms of dysmenorrhea [
23,
24,
25], chronic pelvic pain [
26,
27,
28,
29], and infertility [
30,
31,
32,
33], which negatively affect the patient’s quality of life [
34,
35,
36]. Epidemiologically, endometriosis has been reported to increase the risk of EAOC, such as EC, CCC, low-grade serous carcinoma, and seromucinous neoplasms [
37,
38,
39]. CCC and EC of the ovary are the two most common types of ovarian cancer, which arise from endometriosis [
38,
39].
In general, the presence of mural nodules and papillary projections is considered to constitute evidence of malignancy [
40]. These can be seen in either OE or EAOC, which can pose a challenging diagnostic dilemma to clinicians [
41,
42]. Therefore, we investigated how to discriminate EAOC from OE, and showed that total iron levels of cyst fluid can discriminate EAOC from ovarian endometrioma (OE), with a cut-off point of 64.8 mg/L (sensitivity, 85%; specificity, 98%) [
43]. Magnetic resonance (MR) relaxometry, which can noninvasively measure cyst fluid iron concentration, can discriminate with a cut-off point of 12.1 (sensitivity, 86%; specificity, 94%) [
44]. Moreover, we showed a novel predictive tool in the R2 predictive index, which requires tumor diameter and serum CEA level. This model had good efficacy to detect the malignant transformation of endometrioma (i.e., EAOC) without MRI, with good accuracy (sensitivity, 82%; specificity, 68%), and is useful in following up outpatients [
45,
46]. MR relaxometry has exhibited a limitation in discriminating malignancy for preoperative assessment [i.e., false positive (FP) or false negative (FN)].
The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate both a more powerful and non-invasive tool to discriminate EAOC from OE.
2. Materials and Methods
2.1. Patients
A list of patients with primary, previously untreated, histologically-confirmed ovarian tumors, who were treated at Nara Medical University Hospital between December, 2012, and May, 2022, was generated from our institutional registry. We retrospectively included in this study the following cases of OE as benign ovarian tumors and EAOC cases as malignant tumors. Patients who were over 20 years old at the time of surgery and who consented to, and received, magnetic resonance imaging (MR imaging) after hospitalization were included in the current cohort. Patients who were under 20 years old, contraindicated for MR imaging, prone to claustrophobia, or who refused to undergo MR imaging after hospitalization were excluded. All of the OE and EAOC cases were histologically confirmed. Written consent for the use of the patients’ clinical data for research was obtained at the first hospitalization, and, after approval by the Ethics Review Committee of the Nara Medical Hospital, the opt-out form was provided through our institutional homepage. A total of 150 patients were included in the current cohort. One hundred and eight patients were benign OE cases and forty-two patients were malignant cases. No patients had undergone chemotherapy or radiotherapy for the ovarian tumors prior to treatment. Patients with OE mainly received laparoscopic surgery, and the patients suspected of harboring malignant tumors underwent laparotomy. The following factors were collected through a chart review of the patients’ medical records: age, body mass index (BMI), parity, postoperative diagnosis, including FIGO (The International Federation of Gynecology and Obstetrics) stage, tumor diameter, menopausal status, and pre-treatment blood test results, including carbohydrate antigen125 (CA125), carbohydrate antigen 19-9 (CA 19-9), and carcinoembryonic antigen (CEA) as a tumor marker.
2.2. Tumor Imaging and Diagnoses
All patients first visited the outpatient clinic and underwent internal examination, including ultrasound, followed by routine MR imaging using T1W and T2W sequences. Tumor diameter was recorded as the largest diameter among axial, sagittal, and coronal imaging. Patients were largely diagnosed with OE or EAOC by MRI, and this was confirmed by histological examination, using surgically removed tissue, by at least two pathologists who were blinded to the study. The R2 values were obtained by a 3T system (Magnetom Verio or Skyla, Siemens Healthcare, Erlangen, Germany). After the routine clinical MR imaging, the registered patients underwent MR relaxometry using the single-voxel acquisition mode sequence at multiple echo times and by fitting an exponential decay to the echo amplitude at different multiple echo times [
47]. A parameter R2 value (s-1) was calculated using a high-speed T2 *-corrected multi-echo MR sequence (HISTO) by the 3T–MR system in vivo and ex vivo, which has been previously described [
48,
49]. The HISTO sequence was based on the single voxel steam sequences that could be used for relative fat quantification in the liver [
50]. This sequence allows estimation of liver iron deposition, since the T2 of water changes with iron concentration. The pulse sequence design and programming were done with an imaging platform (Siemens Medical Systems, Erlangen, Germany) and applied to the 3T system. The sequence had a fixed number of five measurements with different TEs, which were as follows: 12, 24, 36, 48, and 72 ms. The typical protocol was performed in breath-hold, with a total acquisition time of 15 sec. The repetition time (TR) was fixed to 3000 ms, which proved to be enough to compensate for the effects of signal saturation, while maintaining an acceptable acquisition time. A 15 × 15 × 15-mm spectroscopy voxel (VOI) was placed to select a region encompassing the liquid portion, but not the solid portion, of the cyst lumen. The fluid from the largest cyst was measured if there were any patients who had more than one cyst. The VOI was located in the center of the OE or EAOC cyst by a radiologist who specializes in female pelvic MR imaging.
2.3. Statistical Analysis
Analyses were performed using SPSS version 25.0 (IBM SPSS, Armonk, NY, USA). The differences of each factor, including the CPH index, the ROMA index, and the R2 predictive index, among groups were compared using a Mann–Whitney U test or Kruskal–Wallis one-way ANOVA test. A receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off value for predicting malignant ovarian tumors. The cut-off value was based on the highest Youden index (i.e., sensitivity + specificity − 1). We next used a logistic regression analysis to assess the risk factors for malignant ovarian tumors (i.e., EAOC). A two-sided p < 0.05 was considered as indicating a statistically significant difference.
4. Discussion
We previously reported that MR relaxometry could be a noninvasive preoperative prediction tool and showed a favorable predictive accuracy for malignant transformations, with sensitivity and specificity of 86% and 94%, respectively [
44]. In the current study, MR relaxometry showed a similar sensitivity to (85.7%), but lower specificity (80.6%) than, the previous reports. This result could have been influenced by the accumulative effect of the cases. MR relaxometry has diagnostic limitations in clinical use in the case of FP or FN. The e-NARA index improved the specificity (87.0%).
Ovarian tumors are diagnosed mainly as benign or malignant by transvaginal ultrasound because of its low cost and easily operable characteristics. However, this device has yielded to enhanced MR imaging, because of its poor subjectivity. To improve its weak point, the International Ovarian Tumor Analysis (IOTA) Group developed a system of standardization in the characterization of adnexal masses [
51]. Lee Cohen Ben-Meir et al. investigated this method in OE, or its associated malignant tumor, EAOC, and reported that this method could discriminate malignant tumors with high sensitivity [
52]. Since our study showed relatively high specificity, the IOTA system is recommended to evaluate ovarian tumors for screening, and the e-NARA to validate.
Among previously reported indexes, the risk of ovarian malignancy algorithm (ROMA) index and the Copenhagen (CPH) index were the two major predictive tools that use serum markers and age, or menopausal status, in discriminating malignant ovarian tumor from benign [
53,
54]. Our reports also showed tumor diameter and serum CEA level could discriminate EAOC from OE without MRI [
45,
46]. Similar to these indexes, the e-NARA index included one of these factors, such as age, and tumor diameter improved the diagnostic accuracy. In discriminating malignant from benign tumors, relying on only one indicator (i.e., MR relaxometry) could be inadequate, and indexes using multilateral indicators, as above, should be required.
CEA is reported as an independent predictor for identifying epithelial ovarian cancer and ovarian metastases [
55]. Further studies found that the cut-off value of CEA in the differential diagnosis of primary ovarian tumors and metastatic ovarian cancer was 2.33 μg/L [
56]. In this cohort, CEA showed good diagnostic efficacy in univariate analysis. However, it did not achieve significant differentiation in multivariate analysis. The serum CEA level could exert its ability in predicting the R2 value, rather than in discriminating EAOC from OE with the real R2 value.
In recent years, inflammatory reactions in the tumor microenvironment have been shown to play an important role in tumor development and progression [
57,
58]. Peripheral leukocytes, neutrophils, lymphocytes, platelets, and acute-phase proteins contribute to the inflammatory response and can be detected easily. A number of studies have demonstrated that inflammatory response factors are related to the survival of patients with cancer who have been surgically treated [
59,
60,
61,
62,
63]. In the current study, inflammatory factors, such as elevated monocytes, platelets, and CRP, and decreased lymphocytes and albumin, showed good diagnostic efficacy at univariate analysis, which was comparable to previous reports [
64,
65,
66,
67,
68,
69]. Similar to the above malignant tumors, OE also induces severe inflammatory responses [
70,
71,
72]. The inflammatory response between OE and EAOC should be different.
Finally, the current study supports a scenario of the 2-step malignant transformation model which Hiroshi Kobayashi et al. hypothesized [
73]. In the first step, excess hemoglobin and iron species, produced by autoxidation and the Fenton reaction cause oxidative damage, which results in DNA damage and mutations. In the second step, reduced iron content and increased antioxidant protection could help in cell survival and the tumorigenic effect of endometriotic cells. In the current study, the sub-group analysis showed that the R2 value, which reflects the iron concentration of the cyst fluid, reduced stepwise in the order of benign OE to malignant tumor. The iron species could play a key role in causing malignant transformation of OE, and further investigation into the balance of iron species and antioxidant protection is required.
This study had some limitations. The first limitation was possible selection bias, due to the nature of retrospective study. To overcome this bias, a multi-center prospective cohort study is now proceeding (UMIN000034969). Second, the newly reported tumor marker of tissue factor pathway inhibitor 2 (TFPI2) was not assessed [
74,
75]. Finally, we did not compare diagnostic efficacy among the IOTA classification, ROMA index, the CPH index, and the e-NARA index. To assess the efficacy of the above tumor marker and these indexes, a prospective study is needed.