A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study
BackgroundTesticular 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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
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JMIR Publications
2023-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e44047 |
| _version_ | 1797733965696598016 |
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| author | Guillaume Bachelot Ferdinand Dhombres Nathalie Sermondade Rahaf Haj Hamid Isabelle Berthaut Valentine Frydman Marie Prades Kamila Kolanska Lise Selleret Emmanuelle Mathieu-D’Argent Diane Rivet-Danon Rachel Levy Antonin Lamazière Charlotte Dupont |
| author_facet | Guillaume Bachelot Ferdinand Dhombres Nathalie Sermondade Rahaf Haj Hamid Isabelle Berthaut Valentine Frydman Marie Prades Kamila Kolanska Lise Selleret Emmanuelle Mathieu-D’Argent Diane Rivet-Danon Rachel Levy Antonin Lamazière Charlotte Dupont |
| author_sort | Guillaume Bachelot |
| collection | DOAJ |
| description |
BackgroundTesticular 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.
ObjectiveThe 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.
MethodsWe 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.
ResultsThe 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.
ConclusionsAn 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. |
| first_indexed | 2024-03-12T12:37:20Z |
| format | Article |
| id | doaj.art-69d987fa9e4b411c9cdd9d590b629cc0 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:37:20Z |
| publishDate | 2023-06-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj.art-69d987fa9e4b411c9cdd9d590b629cc02023-08-29T00:05:18ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-06-0125e4404710.2196/44047A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation StudyGuillaume Bachelothttps://orcid.org/0000-0002-4175-4816Ferdinand Dhombreshttps://orcid.org/0000-0003-3246-8727Nathalie Sermondadehttps://orcid.org/0000-0002-7044-6935Rahaf Haj Hamidhttps://orcid.org/0000-0001-7045-1454Isabelle Berthauthttps://orcid.org/0000-0002-3617-3769Valentine Frydmanhttps://orcid.org/0009-0008-4844-5064Marie Pradeshttps://orcid.org/0000-0003-1627-7840Kamila Kolanskahttps://orcid.org/0000-0002-7445-5345Lise Sellerethttps://orcid.org/0000-0003-0205-6828Emmanuelle Mathieu-D’Argenthttps://orcid.org/0000-0002-7613-1619Diane Rivet-Danonhttps://orcid.org/0000-0003-1005-7070Rachel Levyhttps://orcid.org/0000-0003-2544-5711Antonin Lamazièrehttps://orcid.org/0000-0003-0813-7370Charlotte Duponthttps://orcid.org/0000-0003-0402-4280 BackgroundTesticular 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. ObjectiveThe 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. MethodsWe 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. ResultsThe 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. ConclusionsAn 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.https://www.jmir.org/2023/1/e44047 |
| spellingShingle | Guillaume Bachelot Ferdinand Dhombres Nathalie Sermondade Rahaf Haj Hamid Isabelle Berthaut Valentine Frydman Marie Prades Kamila Kolanska Lise Selleret Emmanuelle Mathieu-D’Argent Diane Rivet-Danon Rachel Levy Antonin Lamazière Charlotte Dupont A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study Journal of Medical Internet Research |
| title | A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study |
| title_full | A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study |
| title_fullStr | A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study |
| title_full_unstemmed | A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study |
| title_short | A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study |
| title_sort | machine learning approach for the prediction of testicular sperm extraction in nonobstructive azoospermia algorithm development and validation study |
| url | https://www.jmir.org/2023/1/e44047 |
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