TY  - JOUR
AU  - Bachelot, Guillaume
AU  - Dhombres, Ferdinand
AU  - Sermondade, Nathalie
AU  - Haj Hamid, Rahaf
AU  - Berthaut, Isabelle
AU  - Frydman, Valentine
AU  - Prades, Marie
AU  - Kolanska, Kamila
AU  - Selleret, Lise
AU  - Mathieu-D’Argent, Emmanuelle
AU  - Rivet-Danon, Diane
AU  - Levy, Rachel
AU  - Lamazière, Antonin
AU  - Dupont, Charlotte
PY  - 2023
DA  - 2023/6/21
TI  - A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study
JO  - J Med Internet Res
SP  - e44047
VL  - 25
KW  - machine learning
KW  - azoospermia
KW  - prediction model
KW  - biomedical informatics
KW  - model
KW  - predict
KW  - sperm
KW  - men's health
KW  - infertility
KW  - infertile
AB  - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e44047
UR  - https://doi.org/10.2196/44047
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37342078
DO  - 10.2196/44047
ID  - info:doi/10.2196/44047
ER  -