Can trophectoderm morphology act as a predictor for euploidy?

Yoshida, Ivan Henrique; Santos, Monise; Berton, Caroline Z; Chiarella, Caroline L; Tanada, Michelli Suemi; BCordts, Emerson; Carvalho, Waldemar Pereira de; Barbosa, Caio Parente

doi:10.5935/1518-0557.20180036

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…Despite the existence of clear and detailed guidelines for morphology-based classifications, blastocyst morphology assessment remains an artisanal technique, which lacks objectivity and reproducibility, limiting its overall predictive value. Furthermore, current scoring systems for human blastocysts focus on only three parameters (i.e., degree of expansion, quality of inner cell mass, and quality of trophectoderm), while other features that may have an impact on implantation potential are mostly disregarded 1,2 .…”

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confidence: 99%

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Chávez-Badiola¹,

Farías²,

Mendizabal-Ruiz

et al. 2020

Sci Rep

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Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221). We created a system consisting of different classifiers that is feed with novel morphometric features extracted from the digital micrographs, along with other non-morphometric data to predict pregnancy. It was evaluated using five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cross validation to assess the model's generalization capabilities. In the database A, the SVM classifier achieved an F1 score of 0.74, and AUC of 0.77. In the database B the RF classifier obtained a F1 score of 0.71, and AUC of 0.75. Our results suggest that the system is able to predict a positive pregnancy test from a single digital image, offering a novel approach with the advantages of using a small database, being highly adaptable to different laboratory settings, and easy integration into clinical practice.

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confidence: 99%

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Chávez-Badiola¹,

Farías²,

Mendizabal-Ruiz

et al. 2020

Sci Rep

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“…In addition, blastocyst grade plays an important role in pregnancy outcomes. Blastocysts with trophectoderm grades A, B, and C were found to have euploidy rates of 71.43, 60.00 and 19.67%, respectively (P<0.05) (17). Yang et al (11) reported that high-quality D6 blastocysts in vitrified-warmed cycles had similar developmental potential and pregnancy outcomes compared to those of high-quality D5 blastocysts, while Irani et al (18) observed that embryos reaching good-quality blastocysts on day 5 yielded significantly higher implantation rate (77.7% vs. 58.7%) compared with those reaching similar quality blastocysts on day 6.…”

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confidence: 96%

Frozen blastocysts: Assessing the importance of day 5/day 6 blastocysts or blastocyst quality

et al. 2022

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The aim of the present study was to analyze the high-quality blastocyst (HB) rate in all embryo frozen cycles and investigate the pregnancy outcomes for day 5/day 6 (D5/D6) blastocysts with respect to the blastocyst quality in programmed single vitrified-warmed blastocyst transfer (SVBT). We performed a retrospective study comparing D5/D6 HBs in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) for all blastocyst frozen cycles. Patients were <35 years at the oocyte collection in their first fresh cycle without fresh transfer. A total of 1,560 IVF/ICSI cycles and 5,328 blastocysts were analyzed. The IVF HB rate was higher than that of ICSI (52.7% vs. 42.6%; P<0.05). The D5 HB rate was much higher than the D6 HB rate (61.6% vs. 29.4%; P<0.05). There were 22.4% (349/1,560) cycles that only had D6 blastocysts, of which IVF cycles were lower than ICSI (19.8% vs. 28.5%; P<0.05). The clinical pregnancy rate and implantation rate in the D5 group were significantly higher than these rates in the D6 group (57.4% vs. 46.2%, 58.9% vs. 47.3%; P<0.05). However, the clinical pregnancy rate and implantation rate of the D5 HBs were not significantly different from those of the D6 HBs (60% vs. 54.5%, 62% vs. 56.3%; P>0.05). In conclusion, the fertilization method (IVF/ICSI) directly influences the HB rate and blastocyst development rates. When we controlled for patient age, transfer frequency, and endometrium on day 5, it was not the development stage (D5/D6), rather the transfer blastocyst quality that played an important role in pregnancy outcomes.

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“…Various other studies have investigated the positive correlation between TE morphology and an increase in the pregnancy rates ( Chen et al, 2014 ; Ahlström et al, 2011 ; Honnma et al, 2012 ) but the importance of the quality of ICM was not underestimated. Yoshida et al (2018) studied the influence of TE morphology on ploidy status but suggested that the quality of TE was not the single indicator of ploidy of blastocysts.…”

Section: Discussionmentioning

confidence: 99%

Morphology of inner cell mass: a better predictive biomarker of blastocyst viability

Sivanantham

Saravanan

Sharma

et al. 2022

PeerJ

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Background Transfer of embryos at the blastocyst stage is one of the best approaches for achieving a higher success rate in In vitro fertilization (IVF) treatment as it demonstrates an improved uterine and embryonic synchrony at implantation. Despite novel biochemical and genetic markers proposed for the prediction of embryo viability in recent years, the conventional morphological grading of blastocysts remains the classical way of selection in routine practice. This study aims to investigate the association between the morphological features of blastocysts and pregnancy outcomes. Methods This prospective study included women undergoing single or double frozen blastocyst transfers following their autologous cycles in a period between October 2020 and September 2021. The morphological grades (A—good, B—average, and C—poor) of inner cell mass (ICM) and trophectoderm (TE) of blastocysts with known implantation were compared to assess their predictive potential of pregnancy outcome. It was further explored by measuring the relationship between the two variables using logistic regression and receiver operating characteristic (ROC) analysis. Results A total of 1,972 women underwent frozen embryo transfer (FET) cycles with a total of 3,786 blastocysts. Known implantation data (KID) from 2,060 blastocysts of 1,153 patients were subjected to statistical analysis, the rest were excluded. Implantation rates (IR) from transfer of ICM/TE grades AA, AB, BA, BB were observed as 48.5%, 39.4%, 23.4% and 25% respectively. There was a significantly higher IR observed in blastocysts with ICM grade A (p < 0.001) than those with B irrespective of their TE scores. The analysis of the interaction between the two characteristics confirmed the superiority of ICM over TE as a predictor of the outcome. The rank biserial correlation value for ICM was also greater compared to that of TE (0.11 vs 0.05). Conclusion This study confirms that the morphology of ICM of the blastocyst is a stronger predictor of implantation and clinical pregnancy than that of TE and can be utilized as a biomarker of viability.

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Can trophectoderm morphology act as a predictor for euploidy?

Cited by 7 publications

References 15 publications

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Frozen blastocysts: Assessing the importance of day 5/day 6 blastocysts or blastocyst quality

Morphology of inner cell mass: a better predictive biomarker of blastocyst viability

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