Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level

Ovarian cancer is the most lethal disease in women’s gynecological malignancies [1]. In China, the latest epidemiology survey disclosed that there were an estimated 52,100 newly diagnosed cases and 22,500 deaths in 2015, and the mortality has still been increasing over the past decade [2]. Ovarian epithelial cancer (OEC) accounts for more than 70% of all ovarian malignancies. Based on the histology and gene research, OEC is classified into two subtypes: Type I and Type II ovarian cancer. Type I cancer includes low-grade endometrioid, mucinous, clear cell, and serous carcinomas. Type II cancer mainly comprises of high-grade serous, endometrioid, undifferentiated carcinomas, and malignant mixed mesodermal tumors [3, 4]. Both Type I and Type II OEC subgroups have unique biological behaviors and treatment responses [5]. Therefore, how to trigger patients into the right category before invasive procedure will help clinicians to design individual therapy. Sonography is the first modality of choice in evaluating adnexal masses. However, for most of complicated cases, MR imaging is usually needed to determine tumor characteristics in most of tertiary medical centers [6]. In gynecological malignancies, an ADC value derived from diffusion weighted imaging (DWI) is generally lower than benign conditions [7‐10]. Currently, CA-125 is considered an important biomarker for early detection and a prognostic tool for prediction of ovarian disease recurrence after treatment [11, 12]. Ki-67 antigen is a marker of cell proliferation and a higher Ki-67 expression is associated with more aggressive disease and worse survival rates in OEC patients [13, 14]. Owing to the advancement of imaging techniques, imaging biomarkers have gradually gained attention for their potential application [15]. It has been reported that MRI parameters could be used to predict survival in cancer patients [16]. Nakamura et al. reported that the ADC value was an independent prognostic factor for disease-free survival after radical hysterectomy in patients with cervical cancer [17]. Until now, there has been limited literature reported for categorization and survival analysis correlated with MRI features, especially with ADC measurements, for patients with both OEC subtypes. The purpose of this study was two folds: First, to clarify whether OEC MRI characteristics coupled with clinical markers (Ki-67 expression and serum CA-125 level) could be used as an index for differentiating between Type I and Type II OEC and second, to determine that some MRI features could be used as prognostic factors for predicting overall survival for OEC patients.

Ovarian cancer is the most malignant cancer in women’s reproductive diseases, accounting for more than 90% of deaths from ovarian cancers [18]. Type I and Type II OEC resemble two distinct ovarian cancer subtypes based on molecular characteristics [3]. The significance of distinguishing the two OEC subtypes is that Type I has different biological behaviors, treatment responses, and gene mutations from Type II [5], and Type II ovarian cancer (most being high-grade serous ovarian cancer) account for more than 70% of all ovarian cancer deaths [5, 18‐20]. Considering the recent treatment strategy advancements [21‐23], a minimally invasive approach and fertility-sparing approaches are necessary for patients with BOT tumors and patients of reproductive age with gynecological cancers [24, 25]. From this point of view, accurate preoperative evaluation of ovarian tumors play an important role in setting treatment plans [26].

Our findings indicated that both clinical and MRI features can distinguish between Type I and Type II OEC cancer to some extent. The mean ADC value in Type II was much lower than that in Type I. The old age, an advanced FIGO stage, and a solid component predicted poorer prognosis for this OEC patients group. To the best of our knowledge, no previous studies on the role of MRI features in differentiating Type I and Type II OEC cancers by using a large cohort has been reported.

Our finding that Type I cancer occurred more often in younger age groups than Type II cancer is in accordance with reported literature [19]. This may be explained by the fact that over a half of our patients with Type I cancer had borderline tumors (74/142), which usually occur in young patients. Both Ki-67 expression and CA-125 level were higher in Type II than in the Type I cancer group, because the patients with Type II cancer were hospitalized with a more advanced FIGO stage than those in the Type I group. Similar findings have also been reported in recently published literature [20].

On MRI, tumors with either vegetation or irregularly thickened septa or a wall of more than 3 mm is often suggestive of malignancies [27, 28]. Our study showed that both a thickened septa and high signal on T1WI in the lesion were more often observed in patients with Type I OEC than those with Type II OEC. The possible reason may be that Type II cancer comprised of only one pathological subtype, while there were four subtypes identified in Type I OEC patients resulting in much heterogeneity in the lesion. Also, previous research found that hyperintense cystic components were often detected on T1WI in clear cell and endometroid ovarian cancer than in other subtypes [29, 30]. These findings are supported by our study. Our results showed that the mainly solid tumors only constituted a small part of the total sample in both Type I (21/145) and Type II (15/105) groups. This is different from the study sample of Liu et al. where a solid or predominantly solid mass was detected on MRI in 44 patients with Type II cancer (38.6%) [20].

Our previous studies together with other research declared that ovarian cancer has much lower ADC value than benign disease [7, 8, 31, 32]. However, there is limited literature reporting on the usefulness of ADC measurements in discriminating OEC subtypes. Our study disclosed that the mean ADC value was lower in Type II cancer than in Type I cancer with statistical significance regardless of lesion components. Similar results were reported by Feng et al., who used multiple MRI diffusion models among 32 patients [33]. Another of our findings that a larger tumor was more likely to be found in patients with Type I cancer was also supported by Alcázar et al. and Liu D et al.’s studies [19, 20]. The reason is, as we mentioned above, that BOT was the most common pathological type in the Type I group, usually displaying a relatively large tumor when detected on MRI. However, in this study, when leaving BOTs out, the mean ADC value difference was still observed between Type I and Type II group. In one study with 43 BOTs and 43 OECs, the authors used multiparametric MRI to differentiate BOTs from FIGO stage I OEC by applying logistic regression analysis [34]. They found that mixed components and predominantly solid components, as well as a thickened irregular septa, were more frequently seen in OECs.

Our study revealed significant differences in ADC measurements between clinical and MRI subtype groups. However, no difference was noted in tumor size and treatment response subgroups. That means that tumor size could not influence the ADC value by itself. For the treatment response group, such results need to be confirmed in large cohort samples. Our findings also disclosed that ADC value was inversely related with Ki-67 expression in ovarian cancer tumor tissue, which is in accordance with the previous literature [35, 36]. This character reflects the ovarian tumor cell proliferation to some extent. Notably, this correlation was only observed in the patients with Type I cancer, but not in those with Type II cancer. Lindgren et al. reported that lower ADC values were significantly associated with high Ki-67 expression among 40 patients with primary and metastatic ovarian cancers on a 3.0 T MRI unit [37]. However, they did not describe the details in pathological types as well as OEC subtypes.

In 2015, a comprehensive study with near 2700 OEC patients reported that a significantly increased overall survival for those with Type I tumors was observed compared to those with Type II cancer after 730 days of follow-up [18]. In our study, FIGO stage was an independent prognostic factor that an advanced stage was always associated with poorer prognosis [18]. Our finding that old age predicting a poor prognosis for OEC is in line with another study’s results [38]. However, we did not find any difference in survival time between Type I and Type II OEC group. Clear cell ovarian cancer has the poorest prognosis compared with others and HGSC is the next OEC subtype that has a poor survival outcome. In a recent article, researchers reported that survival for patients with serous and endometrioid ovarian cancer was improved during the past decade; meanwhile, no improvement was seen for those with mucinous and clear cell carcinoma subtypes [38]. Our findings validate that finding to some extent. Chen et al. analyzed 410 OEC patients who had achieved a complete clinical remission [39]. They found that nadir CA-125 level and histotype were independent predictors of progress-free survival (PFS) and overall survival (OS) duration. The PFS and OS durations for patients with Type I OEC were longer than those with Type II OEC [39].

In this study, a solid mass was associated with poor survival in both Type I and Type II cancers. However, the real mechanism is unclear. We also found that a lower ADC value was not associated with worse survival at the end follow-up time point, which differs from Lindgren’s study [37]. The possible explanation is that their study also included metastatic ovarian cancer. We believe the inclusion of metastatic ovarian cancer could significantly affect the results of survival analysis. Future studies with more sample size is needed to testify our result.

This study had several limitations. First, our study was a retrospective research. Some missing data was found during the data collection period, especially patients’ follow-up information. All data was collected in our single institute so that it could partly balance out this bias. Secondly, the ADC value was manually measured on the selected area based on individual habits. Standardization in measurement may influence the final results. In this study, all the ADC measurements were performed by one person in order to minimize such a difference. Thirdly, in these studied samples, in the Type I group, BOTs comprised of more than 50% of the cohort samples (74/145) and future studies may be needed to clarify the true ADC measurement differences between Type II and Type I groups, without including borderline tumors. Besides, sixty-six patients with advanced ovarian cancer in the Type II group often had peritoneal implants and pelvic fluid extension, making primary lesion identification difficult. All of these conditions may eventually effect the ADC measurements in both groups.

MR features combined with an ADC value are helpful in categorizing OEC subtypes. ADC value can reflect tumor proliferative ability in Type I cancer. Solid mass component on MR imaging could predict poor prognosis for OEC patients.