Andrology · 2025
Machine learning-based personalized prediction of sperm retrieval in patients with non-obstructive azoospermia prior to microdissection testicular sperm extraction: A multi-center cohort study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Xi Yu, Zhang Bailing, Zhang Yun, Zhao Lianming, Liu Defeng, Mao Jiaming, Tang Wenhao, Zhang Haitao, Lin Haocheng, Wang Xiaoyan, Ren Pengcheng, Tang Yanlin, Yang Yuzhuo, Hong Kai, Guo Jingtao, Zhang Zhe, Jiang Hui
Аннотация
BACKGROUND: Non-obstructive azoospermia represents the most severe form of male infertility. The heterogeneous nature of focal spermatogenesis within the testes of non-obstructive azoospermia patients poses significant challenges for accurately predicting sperm retrieval rates.
OBJECTIVES: To develop a machine learning-based predictive model for estimating sperm retrieval rates in patients with non-obstructive azoospermia.
MATERIALS AND METHODS: This multi-center study included more than 2800 men with non-obstructive azoospermia who underwent microdissection testicular sperm extraction. Preoperative clinical variables were used to train, test, and validate multiple machine learning models. The predictive performance of eight models was assessed with several metrics, including area under the receiver operating characteristic curve, overall accuracy, etc. RESULTS: Of the eight models evaluated, Extreme Gradient Boosting, Random Forest, and Light Gradient Boosting Machine consistently outperformed the others. Extreme Gradient Boosting, which achieved the highest mean area under the receiver operating characteristic curve (0.9183), was selected to power SpermFinder-an online calculator for sperm retrieval rates prediction. The model maintained strong discriminatory ability in both validation sets, with an area under the receiver operating characteristic curve of 0.8469 in the internal cohort and 0.8301 in the external cohort.
DISCUSSION AND CONCLUSION: By leveraging routine clinical features and machine learning-powered models, we developed a web-based platform that reliably predicts sperm retrieval outcomes in men with non-obstructive azoospermia. The predictive tool could provide valuable insights for preoperative assessments, and patients with a lower probability of success could gain the opportunity to make informed decisions.
OBJECTIVES: To develop a machine learning-based predictive model for estimating sperm retrieval rates in patients with non-obstructive azoospermia.
MATERIALS AND METHODS: This multi-center study included more than 2800 men with non-obstructive azoospermia who underwent microdissection testicular sperm extraction. Preoperative clinical variables were used to train, test, and validate multiple machine learning models. The predictive performance of eight models was assessed with several metrics, including area under the receiver operating characteristic curve, overall accuracy, etc. RESULTS: Of the eight models evaluated, Extreme Gradient Boosting, Random Forest, and Light Gradient Boosting Machine consistently outperformed the others. Extreme Gradient Boosting, which achieved the highest mean area under the receiver operating characteristic curve (0.9183), was selected to power SpermFinder-an online calculator for sperm retrieval rates prediction. The model maintained strong discriminatory ability in both validation sets, with an area under the receiver operating characteristic curve of 0.8469 in the internal cohort and 0.8301 in the external cohort.
DISCUSSION AND CONCLUSION: By leveraging routine clinical features and machine learning-powered models, we developed a web-based platform that reliably predicts sperm retrieval outcomes in men with non-obstructive azoospermia. The predictive tool could provide valuable insights for preoperative assessments, and patients with a lower probability of success could gain the opportunity to make informed decisions.