Prediction of sperm extraction in non-obstructive azoospermia patients: a machine-learning perspective

• Published in: Human reproduction (Oxford) Vol. 35; no. 7; pp. 1505 - 1514
• Main Authors: Zeadna, A, Khateeb, N, Rokach, L, Lior, Y, Har-Vardi, I, Harlev, A, Huleihel, M, Lunenfeld, E, Levitas, E
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-07-2020
• Subjects: Azoospermia - therapy; Female; Humans; Machine Learning; Male; Pregnancy; Prospective Studies; Retrospective Studies; Sperm Retrieval; Spermatozoa; Testis; prediction; NOA; machine-learning; non-obstructive azoospermia; TESE; male infertility
• ISSN: 0268-1161; 1460-2350
• DOI: 10.1093/humrep/deaa109

Abstract
STUDY QUESTION
Can a machine-learning-based model trained in clinical and biological variables support the prediction of the presence or absence of sperm in testicular biopsy in non-obstructive azoospermia (NOA) patients?
SUMMARY ANSWER
Our machine-learning model was able to accurately predict (AUC of 0.8) the presence or absence of spermatozoa in patients with NOA.
WHAT IS KNOWN ALREADY
Patients with NOA can conceive with their own biological gametes using ICSI in combination with successful testicular sperm extraction (TESE). Testicular sperm retrieval is successful in up to 50% of men with NOA. However, to the best of our knowledge, there is no existing model that can accurately predict the success of sperm retrieval in TESE. Moreover, machine-learning has never been used for this purpose.
STUDY DESIGN, SIZE, DURATION
A retrospective cohort study of 119 patients who underwent TESE in a single IVF unit between 1995 and 2017 was conducted. All patients with NOA who underwent TESE during their fertility treatments were included. The development of gradient-boosted trees (GBTs) aimed to predict the presence or absence of spermatozoa in patients with NOA. The accuracy of these GBTs was then compared to a similar multivariate logistic regression model (MvLRM).
PARTICIPANTS/MATERIALS, SETTING, METHODS
We employed univariate and multivariate binary logistic regression models to predict the probability of successful TESE using a dataset from a retrospective cohort. In addition, we examined various ensemble machine-learning models (GBT and random forest) and evaluated their predictive performance using the leave-one-out cross-validation procedure. A cutoff value for successful/unsuccessful TESE was calculated with receiver operating characteristic (ROC) curve analysis.
MAIN RESULTS AND THE ROLE OF CHANCE
ROC analysis resulted in an AUC of 0.807 ± 0.032 (95% CI 0.743–0.871) for the proposed GBTs and 0.75 ± 0.052 (95% CI 0.65–0.85) for the MvLRM for the prediction of presence or absence of spermatozoa in patients with NOA. The GBT approach and the MvLRM yielded a sensitivity of 91% vs. 97%, respectively, but the GBT approach has a specificity of 51% compared with 25% for the MvLRM. A total of 78 (65.3%) men with NOA experienced successful TESE. FSH, LH, testosterone, semen volume, age, BMI, ethnicity and testicular size on clinical evaluation were included in these models.
LIMITATIONS, REASONS FOR CAUTION
This study is a retrospective cohort study, with all the associated inherent biases of such studies. This model was used only for TESE, since micro-TESE is not performed at our center.
WIDER IMPLICATIONS OF THE FINDINGS
Machine-learning models may lay the foundation for a decision support system for clinicians together with their NOA patients concerning TESE. The findings of this study should be confirmed with further larger and prospective studies.
STUDY FUNDING/COMPETING INTEREST(S)
The study was funded by the Division of Obstetrics and Gynecology, Soroka University Medical Center, there are no potential conflicts of interest for all authors.