1. Introduction
Endometrial cancer (EC) ranks as the sixth most prevalent global cancer in women, comprising 4.8% of all female malignancies [1]. Its incidence rate in Europe and North America averages 13–19 cases per 100,000 women annually [2]. Only 2.8–6.5% of new EC diagnoses occur in women under 45, as reported by national statistics from the US [3], the UK [4], and Sweden [5]. Among premenopausal women with abnormal uterine bleeding, the EC risk stands at a mere 0.33% [6], contrasting with a substantially higher 9% (95% confidence interval: 8–11%) risk observed in postmenopausal bleeding cases [7]. Established risk factors encompass obesity, metabolic syndrome [8], early menarche, and late menopause [9]. In premenopausal women, obesity [10,11] and nulliparity [10,12] have emerged as confirmed risk factors. Younger patients typically present with earlier-stage, lower-grade disease and have significantly better disease-specific survival rates [13,14].
Expert ultrasound or MRI evaluations are recommended for assessing local tumor extension and identifying women at intermediate/high risk [15]. The combination of imaging and pre-operative biopsy histological grading achieves approximately 80% diagnostic accuracy in identifying high-risk cases [16]. Sonographic characteristics correlate with histological risk groups, underscoring an important role in presurgical triage [17]. However, despite the importance of menopausal status in shaping histological risk features and potentially sonographic findings, a noteworthy gap in the literature exists regarding high-quality studies comparing sonographic characteristics between pre- and postmenopausal EC patients.
To address this gap, we conducted an analysis utilizing data from the prospective International Endometrial Tumor Analysis (IETA) 4 cohort, comprising 1538 women, exploring multiple sonographic, demographic, and sonographic characteristics. While previous publications have presented results from this cohort [16,17,18,19], none have explored these findings while stratifying for menopausal status. Since these data have been analyzed previously, our study is post hoc and exploratory in nature. Therefore, our study (1) seeks to explore disparities in demographic, anthropometric, lifestyle, clinical, and (2) sonographic characteristics between pre- and postmenopausal women with EC and (3) assess if sonographic and anthropometric characteristics relate to risk classification differentially between pre- and postmenopausal women, to generate hypotheses for future confirmatory investigations.
2. Materials and Methods
2.1. Patients
In our investigation, we used the prospective IETA4 multicenter cohort, which encompassed 1538 women diagnosed with biopsy-confirmed endometrial cancer. Women were recruited from 17 gynecologic oncology centers consecutively between 1 January 2011 and 31 December 2015. Expert ultrasound examiners used high-end ultrasound systems and adhered to the IETA4 study protocol for scanning [17]. The examination procedure involved transvaginal scans, conducted with an empty bladder in the lithotomy position, complemented by transabdominal scans when necessary. The categorization of endometrial grayscale morphology and power Doppler findings followed the established IETA terminology [20]. Data are extensive, encompassing medical, reproductive, and gynecological history, family history, presenting symptoms, lifestyle factors, demographic characteristics, anthropometric measurements, and sonographic findings, and were recorded in an online database software designed for the IETA collaboration (the Clinical Data Miner, v. 0.0.1 at the start of data entry, v. 1.0 at the time of data withdrawal;
2.2. Variables
Participants in this study were categorized by menopausal status, with postmenopausal defined as a minimum of 12 consecutive months of amenorrhea. Comprehensive demographic and clinical data were collected through self-reported questionnaires. These data encompassed age, parity, the presence of abnormal bleeding as a presenting symptom (distinguished as ‘yes’ or ‘no’), the duration of abnormal bleeding if applicable (measured in months), family history of cancer (including ovarian, breast, colon, or other cancers among first-degree relatives), co-morbidities (specifically, diabetes mellitus type 2 and hypertension), use of hormonal treatment (for any indication), smoking status (‘never’, ‘former’, ‘present’), levels of physical activity (classified into five categories ranging from ‘never’ to ‘strenuous activity ≥ 2 times/week’), alcohol consumption (divided into four levels from ‘never’ to ‘>12 units/week’), and bra cup size (US system, from AA to I). Lynch syndrome data were not included in the dataset.
Anthropometric measurements, such as weight (in kilograms), height (in centimeters), body constitution (subjectively categorized as ‘lean’, ‘abdominal adiposity’, or ‘female adiposity’), and waist circumference (in centimeters), were directly assessed by the examining physician or an assisting nurse. Body mass index (BMI) was calculated from weight and height data and dichotomized using a threshold of ≥30 kg/m², while waist circumference was dichotomized at ≥88 cm [24].
Histopathological measures relied on hysterectomy findings and included FIGO surgical stage (‘IA’ or ‘≥IB’), histological type (‘endometrioid grade 1 + 2’ or ‘endometrioid grade 3 + non-endometrioid’). Due to the absence of data on lymphovascular space invasion, a modified postoperative risk grouping, aligned with the ESMO-ESGO-ESTRO consensus, was applied, dividing cases into ‘low risk’ and ‘intermediate/high risk’. Specifically, ‘low risk’ encompassed endometrioid histology of grade 1 or 2 with FIGO stage IA, while all other cases, including grade 3 endometrioid, non-endometrioid histology, and FIGO stage ≥IB, were categorized as ‘intermediate/high risk.’ Consequently, the ‘intermediate’ and ‘high’ risk groups were combined for the purposes of our analysis. Sonographic characteristics were classified in accordance with the IETA consensus criteria [20] and grouped based on the initial findings of the IETA4 study [17]. These characteristics included endometrial measurability, tumor definition, presence of fibroids, suspicion of adenomyosis, appearance of the endometrial–myometrial border, endometrial echogenicity and uniformity, presence of a bright edge sign, assessment of the endometrial midline, color score, vascular pattern, endometrial thickness, and tumor volume.
2.3. Statistical Analyses
Continuous variables are presented with medians and interquartile ranges and categorical variables by frequencies and percentages. Comparisons between groups were conducted using the Mann–Whitney U test for continuous variables and Fisher’s exact test for categorical variables. With multiple hypotheses testing in an exploratory approach, the risk of falsely rejecting a null hypothesis is high. Therefore, we used the Benjamini–Yekutieli procedure with its modification for arbitrary dependency between p-values [25] on all hypothesis testing. Stata 16.1/IC (StataCorp, College Station, TX, USA) was used for all calculations, with the user-written command multproc for the Benjamini–Yekutieli procedure [26].
3. Results
3.1. Demographic and Anthropometric Characteristics
Among the 1538 patients in our study, 161 (10%) were premenopausal, while 1377 (90%) were postmenopausal. Demographic characteristics, lifestyle factors, anthropometric measurements, and clinical data comparing the two groups are detailed in Table 1. Notably, premenopausal women exhibited lower parity (median 1, IQR 0–2 vs. median 2, IQR 1–2, p < 0.001) and a higher incidence of familial colon cancer history (16% vs. 7%, p = 0.001). Although approximately two-fifths of both pre- and postmenopausal women were obese, premenopausal individuals had a smaller waist circumference (median 92, IQR 82–108 vs. median 98, IQR 87–112, p = 0.002). Abnormal uterine bleeding was as common in both groups, with premenopausal women reporting a longer duration of symptoms (median 5 months, IQR 3–12 vs. median 3 months, IQR 2–6, p < 0.001).
3.2. Histopathological Characteristics
Table 2 outlines the distribution of FIGO surgical stage, histology, and ESMO-ESGO-ESTRO risk groups among pre- and postmenopausal women. Notably, premenopausal women more frequently presented with FIGO stage IA tumors (84% vs. 58%, p < 0.001), endometrioid histology (98% vs. 85%, p < 0.001), lower-grade tumors (p = 0.0012, Mann–Whitney U test for difference in rank), and a higher prevalence of ‘low’ ESMO-ESGO-ESTRO risk group cases (78% vs. 46%, p < 0.001) compared to postmenopausal women. Due to the low frequency of non-endometrioid tumors, differences in histologic subtypes among these tumors could not be statistically assessed.
3.3. Sonographic Characteristics
Sonographic findings, as presented in Table 3, reveal notable distinctions between pre- and postmenopausal women. Premenopausal women had a tumor that was defined on ultrasound less often (73% vs. 84%, p = 0.001), a higher suspicion of adenomyosis (16% vs. 7%, p = 0.001), more frequently a regular endometrial–myometrial border (39% vs. 23%, p < 0.001), and more often a visible endometrial midline (23% vs. 11%, p < 0.001) compared to their postmenopausal counterparts. However, no significant differences emerged in endometrial measurability, fibroid prevalence, endometrial echogenicity, bright edge sign, color score, vascular pattern, endometrial thickness, or tumor volume between the two groups.
3.4. Endometrial Thickness in Relation to Anthropometric Characteristics
In premenopausal women, endometrial thickness did not significantly differ between those with BMI < 30 and BMI ≥ 30 (median 14 mm, IQR 10–23 vs. median 15 mm, IQR 10–22, p = 0.605) or between those with waist circumference <88 cm and ≥88 cm (median 14 mm, IQR 9–20 vs. median 15 mm, IQR 10–23.5, p = 0.219). In postmenopausal women, a subtle difference in endometrial thickness emerged between those with BMI < 30 and BMI ≥ 30 (median 16 mm, IQR 9–26 vs. median 18 mm, IQR 11–26, p = 0.036) and between those with waist circumference <88 cm and ≥88 cm (median 15 mm, IQR 8–24 vs. median 17 mm, IQR 10–26, p = 0.005). However, it is noteworthy that these differences, while statistically significant at the 0.05 level, did not maintain significance following correction by using the Benjamini–Yekutieli procedure (Figure 1 and Figure 2)
3.5. Sonographic and Anthropometric Characteristics in Relation to Risk Group
Table 4 provides a comprehensive view of the distribution of sonographic and anthropometric characteristics by ESMO-ESGO-ESTRO risk groups, stratified by menopausal status. Among premenopausal women, the sole sonographic characteristic associated with ‘intermediate/high’ risk was a non-regular endometrial–myometrial border (91% among ‘intermediate/high’ risk vs. 54% among ‘low’ risk, p < 0.001). In contrast, among postmenopausal women, several sonographic features, including a defined tumor, a non-regular endometrial–myometrial border, a non-uniform echogenicity, a ‘moderate-abundant’ color score, and a multiple, multifocal vascular pattern, all correlated with ‘intermediate/high’ risk. Both BMI ≥ 30 and waist circumference ≥88 cm were associated with the ‘low’ ESMO-ESGO-ESTRO risk group among postmenopausal women.
3.6. Results after Controlling the False Discovery Rate
Overall, our study entailed 53 hypothesis tests, resulting in the initial rejection of the null hypothesis in 24 tests at a significance level of p < 0.05. However, following the application of the Benjamini–Yekutieli procedure, the corrected overall critical p-value was adjusted to 0.0041. Under this more conservative threshold, 20 hypotheses were validated as true positive findings, while four were identified as false rejections of the null hypothesis, denoting false positives. These four false positives pertained to body constitution, bright edge sign, and the differences in endometrial thickness between the BMI and waist circumference groups among postmenopausal women (Supplementary Table S1).
4. Discussion
Our study is exploratory in nature and thus only ever hypothesis-generating [27]. Still, some valuable insights into the differences in sonographic, anthropometric, and clinical features between pre- and postmenopausal women with endometrial cancer can be found. Most notably, we saw a differential association between ultrasound findings and risk group designation. In premenopausal women, we observed a less frequent definition of tumors and a more regular endometrial–myometrial border, with only the latter being associated with risk group. Conversely, in postmenopausal women, several sonographic features, including defined tumors, irregular endometrial–myometrial borders, non-uniform echogenicity, elevated color scores, and a multiple, multifocal vessel pattern, correlated with ‘intermediate/high’ risk. Furthermore, a higher BMI and larger waist circumference were linked to low-risk disease in postmenopausal women but without such association in premenopausal women.
Our data revealed that more premenopausal women exhibited no evidence of disease at hysterectomy (6.2% vs. 1.4%), potentially explaining the lower prevalence of defined tumors on ultrasound, as these tumors were likely resected in toto during hysteroscopic resection. This observation aligns with the higher occurrence of FIGO stage IA cancers among premenopausal women, which typically entail only superficial myometrial invasion. Given the rarity of endometrial cancer among premenopausal women with abnormal uterine bleeding (about 0.33% [6]), this study could not have been performed on patients prior to endometrial sampling.
While certain variations in specific sonographic characteristics, such as a regular endometrial–myometrial border and a visible endometrial midline, are expected due to the higher prevalence of low-risk cancer among premenopausal women, our findings go beyond this expectation. We discovered that sonographic features were not solely dependent on ESMO-ESGO-ESTRO risk groups, as one might anticipate. Had that been the case, we would have observed a higher prevalence of uniform echogenicity, lower color scores, a thinner endometrium, and a lower prevalence of multiple, multifocal vessel patterns in premenopausal women, as these features are common among low-risk cases [17]. In premenopausal women, physiological secretory changes related to fertility can influence endometrial vascularity and thickness, potentially leading to misinterpretation as high-risk sonographic features.
In asymptomatic postmenopausal women, the mean endometrial thickness is around 3 mm [28,29], while in premenopausal women, it ranges from approximately 5 mm at its thinnest to about 11 mm at its thickest, depending on the menstrual cycle’s timing [30]. Although we noted a non-significant difference in median endometrial thickness between the groups (14 mm vs. 17 mm, p = 0.13), it is important to highlight that postmenopausal women exhibited more deviation from the normal range (six times thicker vs. two times thicker). Surprisingly, endometrial thickness did not show significant associations with BMI or waist circumference in either group after applying the Benjamini–Yekutieli correction, as would be expected with higher endogenous estrogen levels due to obesity.
Our study findings are in line with previous research indicating that obesity is linked to lower-stage, lower-grade, and thus overall low-risk endometrial cancer [31,32,33]. Yet, disease-specific survival is unaffected by BMI [34]. Our study further revealed that a higher BMI (≥30) and a greater waist circumference (≥88 cm) were specifically associated with low-risk disease among postmenopausal women. In our premenopausal group, 66.5% were overweight or obese, compared to 30.9% among 18- to 44-year-olds in the European Union according to official statistics [35].
While it is expected that premenopausal women report longer symptom durations due to the commonality of abnormal uterine bleeding in fertile women and the rarity of EC among them, our study confirms that these women still often present with low-risk tumors. This suggests that even subnormal levels of circulating progesterone [36] in premenopausal women may offer some protective effects against high-risk EC or that genetic random mutations driving high-risk EC have not had time to accumulate in younger women. Thus, our findings underscore that non-endometrioid pathologies are almost exclusively found in postmenopausal women.
This study is one of the largest multicenter prospective cohort studies, enhancing the generalizability of our results. The dedicated data capture software employed likely improved data quality. It is important to note that our power analysis was not initially designed for comparing pre- and postmenopausal women, and this study is exploratory in nature. While our study included only 161 premenopausal women, making it a potential limitation, it remains one of the largest prospective studies integrating detailed sonographic, demographic, and anthropometric data. Since only 35 premenopausal women had intermediate/high-risk EC, it was not possible to perform multivariable regression analysis to adjust for confounding factors between age, menopausal status, risk classification, demographic factors, and sonographic findings. Collinearity between these factors is likely. The overrepresentation of premenopausal cases in our database is attributed to the tertiary nature of the participating centers, where some low-risk cases may have been treated at secondary centers. Importantly, this sampling bias does not skew estimates in group comparisons.
Addressing concerns about multiple exploratory comparisons and the risk of false-positive findings, we mitigated this by implementing the Benjamini–Yekutieli procedure. This approach adjusts the p-value threshold to necessitate larger differences for statistical significance, reducing the likelihood of spurious findings [37]. Nonetheless, it is essential to acknowledge that the absence of detected differences does not definitively rule out the existence of differences, and conversely, detected differences can sometimes be spurious. Our findings, where 20 of 24 rejected null hypotheses were retained after correction to a p-value threshold of 0.0041, indicate that pre- and postmenopausal EC are distinct entities. We acknowledge that the use of p-values, or rather, null hypothesis significance testing (NHST), in an exploratory setting is heavily debated within the statistical community [38,39,40,41] and therefore chose a middle ground by conducting NHST but then controlling the false discovery rate with the use of the Benjamini–Yekutieli procedure.
While our study offers hypothesis-generating insights, these findings must be replicated in future studies before their incorporation into clinical practice. To validate our most clinically relevant discoveries regarding sonographic findings in association with risk group among premenopausal women, a study with as few as 36 low- and intermediate/high-risk premenopausal cases (for a 99% confidence level and 90% power) is required to verify a different proportion of regular to non-regular endometrial–myometrial border. However, a sample of at least 229 premenopausal women is necessary to detect differences in color score and vascular pattern between ‘low’ and ‘intermediate/high’ risk cases at a 97.5% confidence level if a Bonferroni adjustment is applied.
5. Conclusions
In premenopausal women with endometrial cancer, only an irregular endometrial–myometrial border is associated with ‘intermediate/high risk’ while vascularity and endometrial echogenicity should be interpreted cautiously since these features likely depend on physiological processes. Finding high-risk features among premenopausal women is difficult considering almost all premenopausal women have low-risk disease, despite reporting longer duration of abnormal uterine bleeding prior to diagnosis.
Conceptualization, R.W.G., D.F. (Daniela Fisherová), A.C.T., D.F. (Dorella Franchi), F.F., P.G.L., A.d.L., D.C., R.F., L.A.H., G.O., A.M.V.U., D.T., T.B., T.v.d.B. and E.E.; Data curation, R.W.G.; Formal analysis, R.W.G. and E.E.; Funding acquisition, E.E.; Investigation, R.W.G. and E.E.; Methodology, R.W.G., D.F. (Daniela Fisherová), A.C.T., D.F. (Dorella Franchi), F.F., P.G.L., A.d.L., D.C., R.F., L.A.H., G.O., A.M.V.U., D.T., T.B., T.v.d.B. and E.E.; Project administration, R.W.G.; Resources, D.F. (Daniela Fisherová), A.C.T., D.F. (Dorella Franchi), F.F., A.d.L., R.F., L.A.H., G.O., A.M.V.U., T.v.d.B. and E.E.; Software, R.W.G.; Supervision, E.E.; Visualization, R.W.G.; Writing—original draft, R.W.G., P.G.L. and E.E.; Writing—review and editing, P.G.L., L.A.H., D.T., T.v.d.B. and E.E. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and Ethics approval was obtained from the Leuven ethics committee (S52897/ML7087, with amendment AMEND-id 0001, approved 5 April 2011 and 21 October 2019 respectively) as well as local ethics committees or review boards of each participating center.
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to a lack of ethical approval for the public release of personal research data.
Sebastian Döhler (Darmstadt University of Applied Sciences, Department of Mathematics and Science) and Roger B. Newson (Imperial College London, Department of Primary Care and Public Health) are acknowledged for their helpful correspondence regarding the proper use of false discovery rate methods.
Bourne reports grants, personal fees, and non-financial support from Samsung Medison and non-financial support from Roche Diagnostics, outside of submitted work. All other authors have no conflict of interest to declare.
Footnotes
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Enlarge this image.Figure 1. Endometrial thickness among normal/overweight and obese women, stratified by menopausal status.
Enlarge this image.Figure 2. Endometrial thickness among women with smaller and larger waist circumference, stratified by menopausal status.
Demographic, family history, lifestyle, anthropometric, and clinical characteristics in pre- and postmenopausal women with endometrial cancer.
| Characteristic | Premenopausal Women | Postmenopausal Women | ||||
|---|---|---|---|---|---|---|
| Background | Median | IQR | Median | IQR | Uncorrected p-value | Significance after Benjamini–Yekutieli procedure |
| Age | 48 | 43–52 | 66 | 61–72 | NA | |
| Parity | N | % | N | % | <0.001 | significant |
| 0 | 54 | 34% | 230 | 17% | ||
| 1 | 40 | 25% | 275 | 20% | ||
| 2 | 52 | 32% | 588 | 43% | ||
| 3+ | 15 | 9% | 284 | 21% | ||
| Family history of cancer | N | % | N | % | ||
| Endometrial | 7 | 4% | 94 | 7% | 0.311 | not significant |
| Ovarian | 1 | 1% | 20 | 1% | 0.717 | not significant |
| Breast | 16 | 10% | 149 | 11% | 0.893 | not significant |
| Colon | 26 | 16% | 103 | 7% | 0.001 | significant |
| Other | 30 | 19% | 278 | 20% | 0.679 | not significant |
| Lifestyle | ||||||
| Smoking | 0.398 | not significant | ||||
| Never | 122 | 76% | 1047 | 76% | ||
| Former | 19 | 12% | 198 | 14% | ||
| Present | 20 | 12% | 132 | 10% | ||
| Exercise | 0.243 | not significant | ||||
| Never | 53 | 33% | 515 | 37% | ||
| >20 min, 1–2/w. | 46 | 29% | 436 | 32% | ||
| >20 min, ≥3/w. | 44 | 27% | 328 | 24% | ||
| Strenuous, 1/w. | 10 | 6% | 54 | 4% | ||
| Strenuous, ≥2/w. | 8 | 5% | 44 | 3% | ||
| Alcohol use | 0.193 | not significant | ||||
| Never | 61 | 38% | 596 | 43% | ||
| 0–6 units/w. | 89 | 55% | 691 | 50% | ||
| 7–12 units/w. | 9 | 6% | 85 | 6% | ||
| >12 units/w. | 2 | 1% | 5 | 0% | ||
| Anthropometric | ||||||
| Constitution | 0.024 | not significant | ||||
| Abdominal adiposity | 70 | 43% | 742 | 54% | ||
| Female adiposity | 32 | 20% | 259 | 19% | ||
| Lean | 59 | 37% | 376 | 27% | ||
| Median | IQR | Median | IQR | |||
| Weight (kg) | 77 | 65–93 | 75 | 65–89 | 0.671 | not significant |
| Height (cm) | 165 | 162–170 | 163 | 159–168 | NA | |
| BMI (kg/m2) | 27.6 | 23.1–34.6 | 28.4 | 24.4–33.2 | 0.290 | not significant |
| Waist circumference (cm) | 92 | 82–108 | 98 | 87–112 | 0.002 | significant |
| Bra cup size | N | % | N | % | 0.87 | not significant |
| AA-B | 55 | 34% | 443 | 32% | ||
| C-D | 87 | 54% | 756 | 55% | ||
| E-I | 19 | 12% | 178 | 13% | ||
| Co-morbidity | ||||||
| Current use of hormonal treatment | 18 | 11% | 160 | 12% | 1 | not significant |
| Type 2 diabetes | 11 | 7% | 235 | 17% | NA | |
| Hypertension | 35 | 22% | 730 | 53% | NA | |
| Bleeding history | ||||||
| Abnormal uterine bleeding | 140 | 87% | 1226 | 89% | 0.428 | not significant |
| Median | IQR | Median | IQR | |||
| Duration of abnormal uterine bleeding (months) | 5 | 3–12 | 3 | 2–6 | <0.001 | significant |
FIGO surgical stage, histology, and risk group in pre- and postmenopausal women with endometrial cancer.
| Characteristic | Premenopausal Women | Postmenopausal Women | ||||
|---|---|---|---|---|---|---|
| N | % | N | % | Uncorrected p-value | Significance after Benjamini–Yekutieli procedure | |
| FIGO surgical stage | <0.001 ϕ | significant | ||||
| IA | 135 | 84% | 801 | 58% | ||
| IB | 9 | 6% | 315 | 23% | ||
| II | 7 | 4% | 79 | 6% | ||
| III | 10 | 6% | 159 | 12% | ||
| IV | 0 | 0% | 23 | 2% | ||
| Histologic subtype | ||||||
| Endometrioid | 158 | 98% | 1172 | 85% | <0.001 Ω | significant |
| Grade 1 | 91 | 58% | 512 | 44% | 0.0012 Ψ | significant |
| Grade 2 | 49 | 31% | 463 | 40% | ||
| Grade 3 | 18 | 11% | 197 | 17% | ||
| Non-endometrioid | 3 | 2% | 205 | 15% | NA | |
| Serous | 0 | 0% | 91 | 44% | ||
| Carcinosarcoma | 2 | 67% | 39 | 19% | ||
| Clear cell carcinoma | 0 | 0% | 33 | 16% | ||
| Mixed cell carcinoma | 1 | 33% | 35 | 17% | ||
| Undifferentiated | 0 | 0% | 7 | 3% | ||
| ESMO-ESGO-ESTRO risk group ε | <0.001 | significant | ||||
| Low | 126 | 78% | 636 | 46% | ||
| Intermediate/High | 35 | 22% | 741 | 54% | ||
Fisher’s exact test was used for categorical data and Mann–Whitney U test for continuous data. ϕ Comparison of FIGO stage IA and ≥IB. Ω Comparison of endometrioid and non-endometrioid. Ψ Rank of grade among endometrioid. ε ESMO risk group: Low (grade 1 + 2, FIGO stage IA), Intermediate/High (all other). Based on hysterectomy if available, otherwise on pre-operative biopsy.
Sonographic findings in pre- and postmenopausal women with endometrial cancer.
| Finding | Premenopausal Women | Postmenopausal Women | ||||
|---|---|---|---|---|---|---|
| Sonographic findings, all women | N | % | N | % | Uncorrected p-value | Significance after Benjamini–Yekutieli procedure |
| Endometrium | 0.356 | not significant | ||||
| Measurable | 154 | 96% | 1274 | 93% | ||
| Not measurable | 4 | 2% | 45 | 3% | ||
| Not visible | 3 | 2% | 58 | 4% | ||
| Tumor | 0.001 | significant | ||||
| Defined | 118 | 73% | 1157 | 84% | ||
| Not defined | 43 | 27% | 220 | 16% | ||
| Myometrium | ||||||
| Fibroid present | 48 | 30% | 503 | 37% | 0.099 | not significant |
| Adenomyosis suspected | 25 | 16% | 96 | 7% | 0.001 | significant |
| Sonographic findings, in women w. visible endometrium only | ||||||
| Endometrial–myometrial border | <0.001 | significant | ||||
| Regular | 61 | 39% | 298 | 23% | ||
| Endometrial echogenicity | ||||||
| Uniform | 65 | 41% | 496 | 38% | 0.387 ϕ | not significant |
| Hyperechoic | 49 | 75% | 377 | 76% | ||
| Hypo-/iso-/three layered | 16 | 25% | 119 | 24% | ||
| Non-uniform | 93 | 59% | 823 | 62% | ||
| Homogenous w. cysts | 9 | 10% | 70 | 9% | ||
| Heterogenous without cysts | 77 | 83% | 661 | 80% | ||
| Heterogenous w. cysts | 7 | 8% | 92 | 11% | ||
| Bright edge sign | 0.007 | not significant | ||||
| Yes | 11 | 7% | 191 | 14% | ||
| Endometrial midline | <0.001 | significant | ||||
| Seen | 37 | 23% | 147 | 11% | ||
| Undefined/not seen | 121 | 77% | 1172 | 89% | ||
| Color score | 0.545 Ω | not significant | ||||
| No flow | 25 | 16% | 267 | 20% | ||
| Minimal flow | 32 | 20% | 246 | 19% | ||
| Moderate flow | 65 | 41% | 436 | 33% | ||
| Abundant flow | 36 | 23% | 370 | 28% | ||
| Vascular pattern | 0.439 Ψ | not significant | ||||
| No flow | 25 | 16% | 267 | 20% | ||
| Single +/− branching | 21 | 13% | 150 | 11% | ||
| Multiple vessels, focal | 26 | 16% | 214 | 16% | ||
| Multiple vessels, multifocal | 57 | 36% | 519 | 39% | ||
| Scattered vessels | 28 | 18% | 168 | 13% | ||
| Circular | 1 | 1% | 1 | 0% | ||
| Endometrial thickness (mm) | 14 | 10–23 | 17 | 10–26 | 0.132 | not significant |
| Tumor volume (cm3) | 7.3 | 2.4–23.3 | 7.9 | 2.7–21.1 | 0.786 ε | not significant |
Fisher’s exact test was used for categorical data and Mann–Whitney U test for continuous data. ϕ Comparison of uniform vs. non-uniform. Ω Comparison of no flow + minimal flow vs. moderate flow + abundant flow. Ψ Comparison of multiple vessels, multifocal vs. all others. ε On those with a defined tumor only.
Association of sonographic features and anthropometric factors to ESMO-ESGO-ESTRO risk groups, stratified by menopausal status.
| Finding | Premenopausal Women | Postmenopausal Women | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Low risk | Intermediate/high risk | Low risk | Intermediate/high risk | |||||||||
| Sonographic findings, all women | N | % | N | % | Uncorrected p-value | Significance after Benjamini–Yekutieli procedure | N | % | N | % | Uncorrected p-value | Significance after Benjamini–Yekutieli procedure |
| Tumor | 0.196 | not significant | <0.001 | significant | ||||||||
| Defined | 89 | 71% | 29 | 83% | 509 | 80% | 648 | 87% | ||||
| Not defined | 37 | 29% | 6 | 17% | 127 | 20% | 93 | 93% | ||||
| BMI | 0.696 | not significant | <0.001 | significant | ||||||||
| <30 | 77 | 61% | 23 | 66% | 338 | 53% | 473 | 64% | ||||
| ≥30 | 49 | 39% | 12 | 34% | 298 | 47% | 268 | 36% | ||||
| Waist group | 1.000 | not significant | 0.001 | significant | ||||||||
| <80 cm | 49 | 39% | 13 | 37% | 146 | 23% | 229 | 31% | ||||
| ≥80 cm | 77 | 61% | 22 | 63% | 490 | 77% | 512 | 69% | ||||
| Sonographic findings, in women w. visible endometrium only | ||||||||||||
| Endometrial–myometrial border | <0.001 | significant | <0.001 | significant | ||||||||
| Regular | 58 | 46% | 3 | 9% | 207 | 33% | 91 | 13% | ||||
| Non-regular | 68 | 54% | 29 | 91% | 415 | 67% | 606 | 87% | ||||
| Endometrial echogenicity | 0.232 | not significant | <0.001 | significant | ||||||||
| Uniform | 55 | 44% | 10 | 31% | 266 | 43% | 230 | 33% | ||||
| Non-uniform | 71 | 56% | 22 | 69% | 356 | 57% | 467 | 67% | ||||
| Endometrial midline | 0.818 | not significant | 0.431 | not significant | ||||||||
| Seen | 29 | 23% | 8 | 25% | 74 | 12% | 73 | 10% | ||||
| Undefined/not seen | 97 | 77% | 24 | 75% | 548 | 88% | 624 | 90% | ||||
| Color score | 0.156 | not significant | <0.001 | significant | ||||||||
| No flow–minimal flow | 49 | 39% | 8 | 25% | 324 | 52% | 189 | 27% | ||||
| Moderate flow–abundant flow | 77 | 61% | 24 | 75% | 298 | 48% | 508 | 73% | ||||
| Vascular pattern | 0.216 | not significant | <0.001 | significant | ||||||||
| All others | 84% | 67% | 17 | 53% | 449 | 72% | 351 | 50% | ||||
| Multiple vessels, multifocal | 42 | 33% | 15 | 47% | 173 | 28% | 346 | 50% | ||||
Supplementary Materials
The following supporting information can be downloaded at
References
1. Amant, F.; Mirza, M.R.; Koskas, M.; Creutzberg, C.L. Cancer of the Corpus Uteri. Int. J. Gynecol. Obstet.; 2015; 131, pp. S96-S104. [DOI: https://dx.doi.org/10.1016/j.ijgo.2015.06.005]
2. Ferlay, J.; Soerjomataram, I.; Dikshit, R.; Eser, S.; Mathers, C.; Rebelo, M.; Parkin, D.M.; Forman, D.; Bray, F. Cancer Incidence and Mortality Worldwide: Sources, Methods and Major Patterns in GLOBOCAN 2012. Int. J. Cancer; 2015; 136, pp. E359-E386. [DOI: https://dx.doi.org/10.1002/ijc.29210] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25220842]
3. National Cancer Institute (NIH). Cancer Stat Facts: Endometrial Cancer. Available online: https://seer.cancer.gov/statfacts/html/corp.html (accessed on 30 October 2019).
4. Cancer Research UK. Available online: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/uterine-cancer/incidence#heading-One (accessed on 30 October 2019).
5. Socialstyrelsen, Statistikdatabas för Cancer. Available online: https://sdb.socialstyrelsen.se/if_can/val.aspx (accessed on 30 October 2019).
6. Pennant, M.E.; Mehta, R.; Moody, P.; Hackett, G.; Prentice, A.; Sharp, S.J.; Lakshman, R. Premenopausal Abnormal Uterine Bleeding and Risk of Endometrial Cancer. BJOG; 2017; 124, pp. 404-411. [DOI: https://dx.doi.org/10.1111/1471-0528.14385] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27766759]
7. Clarke, M.A.; Long, B.J.; Del Mar Morillo, A.; Arbyn, M.; Bakkum-Gamez, J.N.; Wentzensen, N. Association of Endometrial Cancer Risk with Postmenopausal Bleeding in Women a Systematic Review and Meta-Analysis. JAMA Intern. Med.; 2018; 178, pp. 1201-1208. [DOI: https://dx.doi.org/10.1001/jamainternmed.2018.2820] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30083701]
8. Hernandez, A.V.; Pasupuleti, V.; Benites-Zapata, V.A.; Thota, P.; Deshpande, A.; Perez-Lopez, F.R. Insulin Resistance and Endometrial Cancer Risk: A Systematic Review and Meta-Analysis. Eur. J. Cancer; 2015; 51, pp. 2747-2758. [DOI: https://dx.doi.org/10.1016/j.ejca.2015.08.031] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26597445]
9. McPherson, C.P.; Sellers, T.A.; Potter, J.D.; Bostick, R.M.; Folsom, A.R. Reproductive Factors and Risk of Endometrial Cancer. The Iowa Women’s Health Study. Am. J. Epidemiol.; 1996; 143, pp. 1195-1202. [DOI: https://dx.doi.org/10.1093/oxfordjournals.aje.a008707] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/8651218]
10. Soliman, P.T.; Oh, J.C.; Schmeler, K.M.; Sun, C.C.; Slomovitz, B.M.; Gershenson, D.M.; Burke, T.W.; Lu, K.H. Risk Factors for Young Premenopausal Women With Endometrial Cancer. Obstet. Gynecol.; 2005; 105, pp. 575-580. [DOI: https://dx.doi.org/10.1097/01.AOG.0000154151.14516.f7]
11. Wise, M.R.; Jordan, V.; Lagas, A.; Showell, M.; Wong, N.; Lensen, S.; Farquhar, C.M. Obesity and Endometrial Hyperplasia and Cancer in Premenopausal Women: A Systematic Review. Am. J. Obstet. Gynecol.; 2016; 214, pp. 689.e1-689.e17. [DOI: https://dx.doi.org/10.1016/j.ajog.2016.01.175]
12. Schmeler, K.M.; Soliman, P.T.; Sun, C.C.; Slomovitz, B.M.; Gershenson, D.M.; Lu, K.H. Endometrial Cancer in Young, Normal-Weight Women. Gynecol. Oncol.; 2005; 99, pp. 388-392. [DOI: https://dx.doi.org/10.1016/j.ygyno.2005.06.029]
13. Lee, N.K.; Cheung, M.K.; Shin, J.Y.; Husain, A.; Teng, N.N.; Berek, J.S.; Kapp, D.S.; Osann, K.; Chan, J.K. Prognostic Factors for Uterine Cancer in Reproductive-Aged Women. Obstet. Gynecol.; 2007; 109, pp. 655-662. [DOI: https://dx.doi.org/10.1097/01.AOG.0000255980.88205.15]
14. Pellerin, G.P.; Finan, M.A. Endometrial Cancer in Women 45 Years of Age or Younger: A Clinicopathological Analysis. Am. J. Obstet. Gynecol.; 2005; 193, pp. 1640-1644. [DOI: https://dx.doi.org/10.1016/j.ajog.2005.05.003]
15. Colombo, N.; Creutzberg, C.; Amant, F.; Bosse, T.; González-Martín, A.; Ledermann, J.; Marth, C.; Nout, R.; Querleu, D.; Mirza, M.R. et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: Diagnosis, Treatment and Follow-Up. Ann. Oncol.; 2016; 27, pp. 16-41. [DOI: https://dx.doi.org/10.1093/annonc/mdv484]
16. Verbakel, J.Y.; Mascilini, F.; Wynants, L.; Fischerova, D.; Testa, A.C.; Franchi, D.; Frühauf, F.; Cibula, D.; Lindqvist, P.G.; Fruscio, R. et al. Validation of Ultrasound Strategies to Assess Tumor Extension and to Predict High-Risk Endometrial Cancer in Women from the Prospective IETA (International Endometrial Tumor Analysis)-4 Cohort. Ultrasound Obstet. Gynecol.; 2020; 55, pp. 115-124. [DOI: https://dx.doi.org/10.1002/uog.20374] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31225683]
17. Epstein, E.; Fischerova, D.; Valentin, L.; Testa, A.C.; Franchi, D.; Sladkevicius, P.; Frühauf, F.; Lindqvist, P.G.; Mascilini, F.; Fruscio, R. et al. Ultrasound Characteristics of Endometrial Cancer as Defined by International Endometrial Tumor Analysis (IETA) Consensus Nomenclature: Prospective Multicenter Study. Ultrasound Obstet. Gynecol.; 2018; 51, pp. 818-828. [DOI: https://dx.doi.org/10.1002/uog.18909] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28944985]
18. Eriksson, L.S.E.; Epstein, E.; Testa, A.C.; Fischerova, D.; Valentin, L.; Sladkevicius, P.; Franchi, D.; Frühauf, F.; Fruscio, R.; Haak, L.A. et al. Ultrasound-Based Risk Model for Preoperative Prediction of Lymph-Node Metastases in Women with Endometrial Cancer: Model-Development Study. Ultrasound Obstet. Gynecol.; 2020; 56, pp. 443-452. [DOI: https://dx.doi.org/10.1002/uog.21950] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31840873]
19. Green, R.W.; Valentin, L.; Alcazar, J.L.; Chiappa, V.; Erdodi, B.; Franchi, D.; Frühauf, F.; Fruscio, R.; Guerriero, S.; Graupera, B. et al. Endometrial Cancer Off-Line Staging Using Two-Dimensional Transvaginal Ultrasound and Three-Dimensional Volume Contrast Imaging: Intermethod Agreement, Interrater Reliability and Diagnostic Accuracy. Gynecol. Oncol.; 2018; 150, pp. 438-445. [DOI: https://dx.doi.org/10.1016/j.ygyno.2018.06.027] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30029961]
20. Leone, F.P.G.; Timmerman, D.; Bourne, T.; Valentin, L.; Epstein, E.; Goldstein, S.R.; Marret, H.; Parsons, A.K.; Gull, B.; Istre, O. et al. Terms, Definitions and Measurements to Describe the Sonographic Features of the Endometrium and Intrauterine Lesions: A Consensus Opinion from the International Endometrial Tumor Analysis (IETA) Group. Ultrasound Obstet. Gynecol.; 2010; 35, pp. 103-112. [DOI: https://dx.doi.org/10.1002/uog.7487] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20014360]
21. Installé, A.J.F.; Van Den Bosch, T.; De Moor, B.; Timmerman, D. Clinical Data Miner: An Electronic Case Report Form System with Integrated Data Preprocessing and Machine-Learning Libraries Supporting Clinical Diagnostic Model Research. J. Med. Internet Res.; 2014; 16, e28. [DOI: https://dx.doi.org/10.2196/medinform.3251] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25600863]
22. Pecorelli, S. Revised FIGO Staging for Carcinoma of the Vulva, Cervix, and Endometrium. Int. J. Gynecol. Obstet.; 2009; 105, pp. 103-104. [DOI: https://dx.doi.org/10.1016/j.ijgo.2009.02.012]
23. Kurman, R.J.; Carcangiu, M.L.; Herrington, S.C.; Young, R.H. WHO Classification of Tumours of Female Reproductive Organs; 4th ed. International Agency for Research on Cancer: Lyon, France, 2014; ISBN 978-92-832-4487-5
24. Alberti, K.G.M.M.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.T.; Loria, C.M.; Smith, S.C. Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International. Circulation; 2009; 120, pp. 1640-1645. [DOI: https://dx.doi.org/10.1161/CIRCULATIONAHA.109.192644]
25. Benjamini, Y.; Yekutieli, D. The Control of the False Discovery Rate in Multiple Testing under Dependency. Ann. Stat.; 2001; 29, pp. 1165-1188. [DOI: https://dx.doi.org/10.1214/aos/1013699998]
26. Newson, R. Multiple—Test Procedures and Smile Plots. Stata J. Promot. Commun. Stat. Stata; 2003; 3, pp. 109-132. [DOI: https://dx.doi.org/10.1177/1536867X0300300202]
27. Gaus, W. Interpretation of Statistical Significance-Exploratory Versus Confirmative Testing in Clinical Trials, Epidemiological Studies, Meta-Analyses and Toxicological Screening (Using Ginkgo Biloba as an Example). Clin. Exp. Pharmacol.; 2015; 5, 1000182. [DOI: https://dx.doi.org/10.4172/2161-1459.1000182]
28. Breijer, M.C.; Peeters, J.A.H.; Opmeer, B.C.; Clark, T.J.; Verheijen, R.H.M.; Mol, B.W.J.; Timmermans, A. Capacity of Endometrial Thickness Measurement to Diagnose Endometrial Carcinoma in Asymptomatic Postmenopausal Women: A Systematic Review and Meta-Analysis. Ultrasound Obstet. Gynecol.; 2012; 40, pp. 621-629. [DOI: https://dx.doi.org/10.1002/uog.12306] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23001905]
29. Su, D.; Li, L.; Zhong, M.; Xia, Y. Capacity of Endometrial Thickness Measurement to Diagnose Endometrial Carcinoma in Asymptomatic Postmenopausal Women: A Systematic Review and Meta-Analysis. Ann. Palliat. Med.; 2021; 10, pp. 10840-10848. [DOI: https://dx.doi.org/10.21037/apm-21-2516] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34763445]
30. Tsuda, H.; Ito, Y.M.; Todo, Y.; Iba, T.; Tasaka, K.; Sutou, Y.; Hirai, K.; Dozono, K.; Dobashi, Y.; Manabe, M. et al. Measurement of Endometrial Thickness in Premenopausal Women in Office Gynecology. Reprod. Med. Biol.; 2018; 17, pp. 29-35. [DOI: https://dx.doi.org/10.1002/rmb2.12062]
31. Gao, Y.; Dai, X.; Lee, A.C.; Wise, M.R.; Shen, F.; Chen, Q. Body Mass Index Is Negatively Associated with Endometrial Cancer Stage, Regardless of Subtype and Menopausal Status. J. Cancer; 2018; 9, pp. 4756-4761. [DOI: https://dx.doi.org/10.7150/jca.21137] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30588261]
32. Arem, H.; Irwin, M.L. Obesity and Endometrial Cancer Survival: A Systematic Review. Int. J. Obes.; 2013; 37, pp. 634-639. [DOI: https://dx.doi.org/10.1038/ijo.2012.94]
33. Nevadunsky, N.S.; Van Arsdale, A.; Strickler, H.D.; Moadel, A.; Kaur, G.; Levitt, J.; Girda, E.; Goldfinger, M.; Goldberg, G.L.; Einstein, M.H. Obesity and Age at Diagnosis of Endometrial Cancer. Obstet. Gynecol.; 2014; 124, pp. 300-306. [DOI: https://dx.doi.org/10.1097/AOG.0000000000000381]
34. Van Arsdale, A.; Miller, D.T.; Kuo, D.Y.; Isani, S.; Sanchez, L.; Nevadunsky, N.S. Association of Obesity with Survival in Patients with Endometrial Cancer. Gynecol. Oncol.; 2019; 154, pp. 156-162. [DOI: https://dx.doi.org/10.1016/j.ygyno.2019.03.258]
35. Eurostat. Available online: https://ec.europa.eu/eurostat/data/database (accessed on 13 December 2019).
36. Santoro, N.; Lasley, B.; McConnell, D.; Allsworth, J.; Crawford, S.; Gold, E.B.; Finkelstein, J.S.; Greendale, G.A.; Kelsey, J.; Korenman, S. et al. Body Size and Ethnicity Are Associated with Menstrual Cycle Alterations in Women in the Early Menopausal Transition: The Study of Women’s Health across the Nation (SWAN) Daily Hormone Study. J. Clin. Endocrinol. Metab.; 2004; 89, pp. 2622-2631. [DOI: https://dx.doi.org/10.1210/jc.2003-031578] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15181033]
37. Benjamin, D.J.; Berger, J.O.; Johannesson, M.; Nosek, B.A.; Wagenmakers, E.J.; Berk, R.; Bollen, K.A.; Brembs, B.; Brown, L.; Camerer, C. et al. Redefine Statistical Significance. Nat. Hum. Behav.; 2018; 2, pp. 6-10. [DOI: https://dx.doi.org/10.1038/s41562-017-0189-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30980045]
38. Benjamini, Y.; Zeevi, Y. It’s the Selection’s Fault—Not the p-Values’: A Comment on “The Role of p-Values in Judging the Strength of Evidence and Realistic Replication Expectations”. Stat. Biopharm. Res.; 2020; 13, pp. 22-25. [DOI: https://dx.doi.org/10.1080/19466315.2020.1845790]
39. Amrhein, V.; Greenland, S.; McShane, B. Scientists Rise up against Statistical Significance. Nature; 2019; 567, pp. 305-307. [DOI: https://dx.doi.org/10.1038/d41586-019-00857-9]
40. Dirnagl, U. The p Value Wars (Again). Eur. J. Nucl. Med. Mol. Imaging; 2019; 46, pp. 2421-2423. [DOI: https://dx.doi.org/10.1007/s00259-019-04467-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31375858]
41. Ioannidis, J.P.A. Retiring Statistical Significance Would Give Bias a Free Pass. Nature; 2019; 567, 461. [DOI: https://dx.doi.org/10.1038/d41586-019-00969-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30903096]
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Details
; Fischerová, Daniela 2
; Testa, Antonia C 3
; Franchi, Dorella 4 ; Frühauf, Filip 2 ; Lindqvist, Pelle G 5
; Alessia di Legge 6 ; Cibula, David 2
; Fruscio, Robert 7
; Haak, Lucia A 8
; Opolskiene, Gina 9
; Vidal Urbinati, Ailyn M 4
; Timmerman, Dirk 10
; Bourne, Tom 11 ; van den Bosch, Thierry 10
; Epstein, Elisabeth 5
1 Department of Clinical Science and Education, Karolinska Institute, Södersjukhuset, Sjukhusbacken 10, 118 83 Stockholm, Sweden;
2 Department of Gynaecology, Obstetrics and Neonatology, General University Hospital and First Faculty of Medicine, Charles University, Apolinářská 18, 128 51 Prague, Czech Republic;
3 Department of Women and Child Health, Division of Gynecologic Oncology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
4 Department of Gynecological Oncology, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy;
5 Department of Clinical Science and Education, Karolinska Institute, Södersjukhuset, Sjukhusbacken 10, 118 83 Stockholm, Sweden;
6 Department of Obstetrics and Gynecology, Catholic University of the Sacred Heart, 00168 Rome, Italy;
7 UO Gynecology, Department of Medicine and Surgery, IRCCS San Gerardo, University of Milan Bicocca, 20126 Milan, Italy;
8 Institute for the Care of Mother and Child, Prague and Third Faculty of Medicine, Charles University, 147 00 Prague, Czech Republic
9 Center of Obstetrics and Gynecology, Faculty of Medicine, Vilnius University Hospital, 08661 Vilnius, Lithuania;
10 Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium;
11 Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium;