Cell-free nucleic acids as non-invasive biomarkers of gynecological cancers, ovarian, endometrial and obstetric disorders and fetal aneuploidy

BACKGROUND

Proper folliculogenesis is fundamental to obtain a competent oocyte that, once fertilized, can support the acquisition of embryo developmental competence and pregnancy. MicroRNAs (miRNAs) are crucial regulators of folliculogenesis, which are expressed in the cumulus-oocyte complex and in granulosa cells and some can also be found in the bloodstream. These circulating miRNAs are intensively studied and used as diagnostic/prognostic markers of many diseases, including gynecological and pregnancy disorders. In addition, serum contains small amounts of cell-free DNA (cfDNA), presumably resulting from the release of genetic material from apoptotic/necrotic cells. The quantification of nucleic acids in serum samples could be used as a diagnostic tool for female infertility.

METHODS

An overview of the published literature on miRNAs, and particularly on the use of circulating miRNAs and cfDNA as non-invasive biomarkers of gynecological diseases, was performed (up to January 2014).

RESULTS

In the past decade, cell-free nucleic acids have been studied for potential use as biomarkers in many diseases, particularly in gynecological cancers, ovarian and endometrial disorders, as well as in pregnancy-related pathologies and fetal aneuploidy. The data strongly suggest that the concentration of cell-free nucleic acids in serum from IVF patients or in embryo culture medium could be related to the ovarian hormone status and embryo quality, respectively, and be used as a non-invasive biomarker of IVF outcome.

CONCLUSIONS

The profiling of circulating nucleic acids, such as miRNAs and cfDNA, opens new perspectives for the diagnosis/prognosis of ovarian disorders and for the prediction of IVF outcomes, namely (embryo quality and pregnancy).

Male infertility: an obstacle to sexuality?

Interactions between infertility and sexuality are numerous and complex. Infertile men may suffer from sexual dysfunction (SD) when undergoing an assisted reproductive technology programme. We undertook a review both in French and English of the available data on male SD when being diagnosed with a fertility problem with a specific focus on azoospermic men. The review was performed over a 30-year time period using PubMed/Medline. The sexual concerns and needs of infertile/sterile men for whom potential parenting can be compromised were evaluated. When diagnosed with infertility, men usually go through a crisis that can have a deleterious effect on their sexuality with sometimes a feeling of sexual inadequacy. Infertile men will feel stigmatized because they are perceived as being deficient in a specific component of their masculinity. Hence, subsequent SD may occur that can impact the couple sexuality and the infertility management. However, little is known on how the announcement of azoospermia may affect male on a sexual and psychological point of view. The present review suggests that a global management through a healthcare network (biologist, andrologist, sexologist and psychologist) is required which will allow to consider infertility and its subsequent sexual disorders as a whole and not as dichotomized issues.

A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems

STUDY QUESTION

Can artificial intelligence (AI) algorithms developed to assist embryologists in evaluating embryo morphokinetics be enriched with multi-centric clinical data to better predict clinical pregnancy outcome?

SUMMARY ANSWER

Training algorithms on multi-centric clinical data significantly increased AUC compared to algorithms that only analyzed the time-lapse system (TLS) videos.

WHAT IS KNOWN ALREADY

Several AI-based algorithms have been developed to predict pregnancy, most of them based only on analysis of the time-lapse recording of embryo development. It remains unclear, however, whether considering numerous clinical features can improve the predictive performances of time-lapse based embryo evaluation.

STUDY DESIGN, SIZE, DURATION

A dataset of 9986 embryos (95.60% known clinical pregnancy outcome, 32.47% frozen transfers) from 5226 patients from 14 European fertility centers (in two countries) recorded with three different TLS was used to train and validate the algorithms. A total of 31 clinical factors were collected. A separate test set (447 videos) was used to compare performances between embryologists and the algorithm.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Clinical pregnancy (defined as a pregnancy leading to a fetal heartbeat) outcome was first predicted using a 3D convolutional neural network that analyzed videos of the embryonic development up to 2 or 3 days of development (33% of the database) or up to 5 or 6 days of development (67% of the database). The output video score was then fed as input alongside clinical features to a gradient boosting algorithm that generated a second score corresponding to the hybrid model. AUC was computed across 7-fold of the validation dataset for both models. These predictions were compared to those of 13 senior embryologists made on the test dataset.

MAIN RESULTS AND THE ROLE OF CHANCE

The average AUC of the hybrid model across all 7-fold was significantly higher than that of the video model (0.727 versus 0.684, respectively, P = 0.015; Wilcoxon test). A SHapley Additive exPlanations (SHAP) analysis of the hybrid model showed that the six first most important features to predict pregnancy were morphokinetics of the embryo (video score), oocyte age, total gonadotrophin dose intake, number of embryos generated, number of oocytes retrieved, and endometrium thickness. The hybrid model was shown to be superior to embryologists with respect to different metrics, including the balanced accuracy (P ≤ 0.003; Wilcoxon test). The likelihood of pregnancy was linearly linked to the hybrid score, with increasing odds ratio (maximum P-value = 0.001), demonstrating the ranking capacity of the model. Training individual hybrid models did not improve predictive performance. A clinic hold-out experiment was conducted and resulted in AUCs ranging between 0.63 and 0.73. Performance of the hybrid model did not vary between TLS or between subgroups of embryos transferred at different days of embryonic development. The hybrid model did fare better for patients older than 35 years (P < 0.001; Mann-Whitney test), and for fresh transfers (P < 0.001; Mann-Whitney test).

LIMITATIONS, REASONS FOR CAUTION

Participant centers were located in two countries, thus limiting the generalization of our conclusion to wider subpopulations of patients. Not all clinical features were available for all embryos, thus limiting the performances of the hybrid model in some instances.

WIDER IMPLICATIONS OF THE FINDINGS

Our study suggests that considering clinical data improves pregnancy predictive performances and that there is no need to retrain algorithms at the clinic level unless they follow strikingly different practices. This study characterizes a versatile AI algorithm with similar performance on different time-lapse microscopes and on embryos transferred at different development stages. It can also help with patients of different ages and protocols used but with varying performances, presumably because the task of predicting fetal heartbeat becomes more or less hard depending on the clinical context. This AI model can be made widely available and can help embryologists in a wide range of clinical scenarios to standardize their practices.

STUDY FUNDING/COMPETING INTEREST(S)

Funding for the study was provided by ImVitro with grant funding received in part from BPIFrance (Bourse French Tech Emergence (DOS0106572/00), Paris Innovation Amorçage (DOS0132841/00), and Aide au Développement DeepTech (DOS0152872/00)). A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. and F.D.M. hold a patent for 'Devices and processes for machine learning prediction of in vitro fertilization' (EP20305914.2). A.D., N.D., M.M.F., and F.D.M. are or have been employees of ImVitro and have been granted stock options. X.P.-V. has been paid as a consultant to ImVitro and has been granted stocks options of ImVitro. L.C.-D. and C.G.-S. have undertaken paid consultancy for ImVitro SAS. The remaining authors have no conflicts to declare.

TRIAL REGISTRATION NUMBER

N/A.

[Comparison of frozen embryo transfer outcomes at blastocyst stage according to freezing method and type of endometrial preparation]

OBJECTIVE

This study intended to compare frozen embryo transfer (FET) outcomes at blastocyst stage according to freezing methods, slow freezing versus vitrification and according to the type of endometrial preparation.

PATIENTS AND METHODS

A total of 172 FET at blastocyst stage (day 5 or 6) were included retrospectively from April, 2007 to December, 2012. The FET outcomes from slow freezing (group 1, n=86) were compared with those from vitrification (group 2, n=86). More particularly, the survival rate after thawing, as well as implantation and pregnancy rates (clinical and ongoing pregnancy rates) were compared respectively between these two groups, after matching on women's age at freezing day, embryo number and embryo development stage for transfer. Furthermore, for each freezing method, FET outcomes were compared according to the type of endometrial preparation, i.e. natural cycle (group N) versus stimulated cycle (group S).

RESULTS

The survival rate as well as implantation and clinical pregnancy rates were significantly higher for FET after vitrification compared to FET after slow freezing (97% vs 85%, P<0.0001; 32% vs 20%, P=0.02; 43% vs 28%, P=0.04, respectively). By taking into account the number of transferred embryos for each group, the multiple pregnancy rate was three-fold higher in the group of FET after vitrification compared to the group of FET after slow freezing but not significantly (27.3% vs 8.3%, NS). However, FET outcomes were not affected significantly by the type of endometrial preparation whatever freezing methods. Nevertheless, the early spontaneous abortion (ESA) rate was lower in the case of embryos that were frozen by vitrification and transferred in natural cycle (group N2 vs group S2: 20% vs 47%, NS).

DISCUSSION AND CONCLUSION

Our study confirms that the survival rate after thawing at blastocyst stage (day 5 or 6) is significantly improved after freezing by vitrification compared to slow freezing method. Likewise, implantation and clinical pregnancy rates are significantly increased in the case of FET at blastocyst stage when these embryos were frozen by vitrification. The results obtained by vitrification are very satisfactory but are also associated with an increased multiple pregnancy rate. Moreover, FET associated with natural or stimulated cycle does not modify significantly the outcomes of attempts, whatever the freezing method. However, the risk of ESA is reduced in the case of FET with natural cycle and after embryo vitrification.

O-124 A new artificial intelligence (AI) system in the block: impact of clinical data on embryo selection using four different time-lapse incubators

Study question

Can AI algorithms assist embryologists in evaluating embryos from any time-lapse system (TLS) along with clinical data to better predict pregnancy outcomes and reduce time-to-pregnancy?

Summary answer

Our algorithm (Embryoly) significantly increases accuracy in predicting clinical pregnancy by 26.9% amongst embryos deemed of fair and good quality when clinical data is included.

What is known already

Embryologists routinely use defined morpho-kinetic criteria to decide which embryo to transfer, and yet, many embryos deemed of good quality fail to lead to a pregnancy. Thus, AI algorithms to assist embryologists in objectively selecting the most promising embryos are in demand. To date, several reports indicate that AI algorithms are capable of predicting pregnancy clinical outcomes but to the best of our knowledge they only consider visual data (or together with a small set of clinical features) from individual TLI systems to generate their predictions. 

Study design, size, duration

A dataset of 6790 embryos (97.82% known clinical pregnancy outcome, 31.47% frozen transfers) from 2519 patients from 11 European fertility centers recorded with 4 different TLS (GERI-Merck, Embryoscope & EmbryoscopePlus-Vitrolife and MIRI-Esco) was used to train and validate Embryoly. Nine out of 93 clinical factors were identified as being the most predictive, including woman age, woman and man BMI and AMH levels. Performances were evaluated on a separate test dataset (393 videos).

Participants/materials, setting, methods

Clinical pregnancy outcome was predicted using a 3D convolutional neural network that analyzed up to 5 days of embryo development. The output score was further analyzed considering the clinical features to generate a second clinical score. Both predictions were compared to those of 10 senior embryologists made on the same test dataset (with and without clinical features). Embryo quality was assessed as: poor, fair, good. Unless specified otherwise, McNemar test was used for statistical tests.

Main results and the role of chance

Overall accuracy of embryologists in predicting clinical pregnancy  based on videos alone was 57.25% (CI 95% : 52.34% - 62.16%) compared to 60.56% (CI 95% : 55.71% - 65.41%) for Embryoly (p = 0.35).

When videos were analyzed together with the clinical factors, overall accuracy of embryologists was significantly lower than Embryoly (60.05% [CI 95% : 55.19% - 64.91%] vs 68.19% [CI 95% : 63.57% - 72.82%], p-value=0.015, respectively). Clinical factors significantly increased our accuracy by 7.63% (p-value=0.030). More specifically, Embryoly algorithms fared better in terms of detecting false positives (31.30% vs 19.34%) compared to embryologists, with a specificity of 74.4% vs. 58.6%, respectively.

If we consider only embryos of fair and good quality (71.50% of our test dataset) Embryoly’s accuracy was 13.52% higher than that of embryologists. This translates into AI having an even better ability to detect false positives for embryos that could be seen as good candidates for transfer (20.28% false positives against 42.70% for the embryologists). Embryoly performs differently across selected TLS when analyzing videos alone, but not when clinical data was also considered (chi2 test, p &lt; 0.001 and 0.5, respectively). Further work will investigate these discrepancies across TLS.

Limitations, reasons for caution

As of today, Embryoly’s accuracy in predicting the outcome of poor-quality embryos is not different to that of embryologists (79.46% vs 84.96%; p-value=0.19). We are improving this by exposing Embryoly to more “poor quality” embryos, so as to also identify poor quality embryos with unexpected potential for implantation.

Wider implications of the findings

Our pioneering findings support the use of AI for a standardized and couple-centered care in clinical embryology, integrating male and female factors with embryo development analyses from multiple TLS. Our approach has the potential to cost-effectively reduce time to pregnancy and is another step toward a personalized embryo transfer strategy.

Trial registration number

Not applicable

P–756 Predictive factors influencing multiple live births in cumulative IVF cycles: retrospective analysis of over 265000 embryo transfer procedures from the national French registry

Study question

What are the factors that could predict the number of embryos to be transferred in order to diminish risk of multiple pregnancies?

Summary answer

Single embryo transfer (SET) is advisable for &lt;38 year-old women in fresh cycles and for &lt;35 year-old women in FET whatever the IVF number attempts.

What is known already

Multiple pregnancies are associated to increased maternal and perinatal complications. Risks associated to multiple implantations are significantly reduced with SET policy. However, while SET is more assertive with a lesser negative impact in younger patients (&lt;35 years), its feasibility is less evident for the older population, whom oocyte quality is likely compromised. A double embryo transfer (DET) could improve chances of implantation and shorten their time to pregnancy. Identification of risk factors for multiple pregnancies could help in decision making for a double or SET and reduce chances for multiple gestations without reducing the chances to achieve pregnancy.

Study design, size, duration

A retrospective study from the national French data registry provided and approved by the Agence de la Biomédecine was performed. A total of 196530 fresh and 68913 frozen cycles from women aged 18–43 year-old were included (2014–2017). Risk factors assessed included women’s age, number of attempts, number of oocytes, fertilization rate, embryo stage, number of embryos transferred, number of supernumerary embryos frozen. Secondary infertility, oocyte donor, oocyte freezing, PGT, freeze-all and IVM cycles were excluded.

Participants/materials, setting, methods

Cumulative cycles derived from 65% of ICSI, 32% of IVF and 3,2% IVF/ICSI. The distribution of patients age at oocyte retrieval was 60% &lt; 35, 21% &lt; 38, 11% &lt; 40, and 8% ≥ 40 years old. Multivariable logistic regression was conducted to calculate adjusted odds ratios with 95% confidence intervals for live birth chance and multiple live birth risk associated with each risk factor.

Main results and the role of chance

The chances of obtaining a cumulative live birth decreases with increased patients age (OR 0.71 for 35–38 years and 0.47 for 38–40 years, p &lt; 0.00001), with increased number of attempts (from OR 0.87 for attempt = 2 to OR 0.74 for attempt ≥ 4, p &lt; 0.00001), and for frozen embryos transferred (OR 0.14, p &lt; 0.00001). The chances of live birth increases with the increased number of oocytes (from OR 1.33 for 4–12 to OR 1.52 for &gt; 18, p &lt; 0.00001 in all cases), with a fertilisation rate &gt;40% (OR 1.29, p &lt; 0.00001), with blastocyst transfer (OR 1.29, p &lt; 0.00001), with the increase on the number of frozen embryos (OR 7.37 for &gt;1, OR 13.08 for &gt;2, and OR 16.92 for &gt;6, p &lt; 0.00001 in all cases) and number of embryos transferred (OR 1.42 for 2 embryos and OR 1.39 for &gt;2 embryos, p &lt; 0.00001 in all cases).

In case of live birth, the risks of multiple births when two embryos were transferred decreases in patients aged &gt;38 years (OR 0.50, p &lt; 0.00001) and for frozen embryos transferred (OR 0.65, p &lt; 0.00001). The risk increases with a fertilisation rate &gt;60% (OR 1.30, p &lt; 0.00001), with blastocysts transfer (OR 1.34, p &lt; 0.00001) and when at least one supernumerary embryo is frozen (OR &gt; 1.30, p &lt; 0.00001).

Limitations, reasons for caution

This study is limited in only providing a risk-benefit balance for multiples on the choice of transferring one or two embryos. Clinical data such as stimulation protocols and doses of gonadotropins were not considered in this evaluation.

Wider implications of the findings: This study provides help to develop a strategy for the medical staff in the decision making for the number of embryos to be transferred. It may also serve as a patient’s information aid and help to improve their chances of achieving a health singleton if pregnant.

Trial registration number

Not applicable