背景:睾丸精子提取(TESE)是治疗男性不育的重要治疗工具。然而,这是一种侵入性手术,成功率高达50%。迄今为止,没有基于临床和实验室参数的模型足够强大,可以准确预测TESE中精子回收的成功.
目的:本研究的目的是比较广泛的预测模型在相似条件下非梗阻性无精子症(NOA)患者的TESE结局,以确定正确的数学方法。最合适的研究规模,和输入生物标志物的相关性。
方法:我们分析了在Tenon医院接受TESE的201例患者(援助巴黎公共医院,索邦大学,巴黎),分布在175例患者的回顾性训练队列(2012年1月至2021年4月)和26例患者的前瞻性测试队列(2021年5月至2021年12月).术前数据(根据法国男性不育标准探索,16个变量),包括泌尿生殖道病史,荷尔蒙数据,遗传数据,收集TESE结果(代表目标变量)。如果我们获得了足够的精子用于胞浆内精子注射,则TESE被认为是阳性的。预处理原始数据后,在回顾性训练队列数据集上训练和优化8个机器学习(ML)模型:通过随机搜索执行超参数调整。最后,前瞻性测试队列数据集用于模型评估.用于评估和比较模型的指标如下:灵敏度,特异性,受试者工作特征曲线下面积(AUC-ROC),和准确性。使用置换特征重要性技术评估模型中每个变量的重要性,使用学习曲线评估纳入研究的最佳患者数量.
结果:合奏模型,基于决策树,表现出最好的表现,特别是随机森林模型,结果如下:AUC=0.90,灵敏度=100%,特异性=69.2%。此外,120名患者的研究规模似乎足以在建模过程中正确利用术前数据,因为在模型训练期间增加超过120例的患者并没有带来任何表现改善。此外,抑制素B和精索静脉曲张病史表现出最高的预测能力。
结论:基于适当方法的ML算法可以预测接受TESE的NOA男性的精子回收成功,有希望的表现。然而,尽管这项研究与这一过程的第一步是一致的,在任何临床应用之前,应进行后续正式的前瞻性多中心验证研究.作为未来的工作,我们考虑使用最近和临床相关的数据集(包括精浆生物标志物,尤其是非编码RNA,作为NOA患者残余精子发生的标志物),以进一步改善我们的结果。
Testicular sperm extraction (TESE) is an essential therapeutic tool for the management of male infertility. However, it is an invasive procedure with a success rate up to 50%. To date, no model based on clinical and laboratory parameters is sufficiently powerful to accurately predict the success of sperm retrieval in TESE.
The aim of this
study is to compare a wide range of predictive models under similar conditions for TESE outcomes in patients with nonobstructive azoospermia (NOA) to identify the correct mathematical approach to apply, most appropriate
study size, and relevance of the input biomarkers.
We analyzed 201 patients who underwent TESE at Tenon Hospital (Assistance Publique-Hôpitaux de Paris, Sorbonne University, Paris), distributed in a retrospective training cohort of 175 patients (January 2012 to April 2021) and a prospective testing cohort (May 2021 to December 2021) of 26 patients. Preoperative data (according to the French standard exploration of male infertility, 16 variables) including urogenital history, hormonal data, genetic data, and TESE outcomes (representing the target variable) were collected. A TESE was considered positive if we obtained sufficient spermatozoa for intracytoplasmic sperm injection. After preprocessing the raw data, 8 machine learning (ML) models were trained and optimized on the retrospective training cohort data set: The hyperparameter tuning was performed by random search. Finally, the prospective testing cohort data set was used for the model evaluation. The metrics used to evaluate and compare the models were the following: sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and accuracy. The importance of each variable in the model was assessed using the permutation feature importance technique, and the optimal number of patients to include in the
study was assessed using the learning curve.
The ensemble models, based on decision trees, showed the best performance, especially the random forest model, which yielded the following results: AUC=0.90, sensitivity=100%, and specificity=69.2%. Furthermore, a
study size of 120 patients seemed sufficient to properly exploit the preoperative data in the modeling process, since increasing the number of patients beyond 120 during model training did not bring any performance improvement. Furthermore, inhibin B and a history of varicoceles exhibited the highest predictive capacity.
An ML algorithm based on an appropriate approach can predict successful sperm retrieval in men with NOA undergoing TESE, with promising performance. However, although this study is consistent with the first step of this process, a subsequent formal prospective multicentric validation
study should be undertaken before any clinical applications. As future work, we consider the use of recent and clinically relevant data sets (including seminal plasma biomarkers, especially noncoding RNAs, as markers of residual spermatogenesis in NOA patients) to improve our results even more.