Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (In<b>f</b>e<b>r</b>tility aft<b>e</b>r <b>Ca</b>ncer Predic<b>t</b>or) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817-0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666-0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives-patients incorrectly classified as likely to regain menses-could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

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Journal
Bioengineering
Published
2025-10-28
DOI
https://doi.org/10.3390/bioengineering12111171
Primary Topic
Reproductive Biology and Fertility
Type
article
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article

Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer

Anne‐Sophie Hamy, Uwe Aickelin, Fabien Reyal, Barbara Pistilli et al.
Bioengineering
Reproductive Biology and Fertility
article

Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer

Anne‐Sophie Hamy, Uwe Aickelin, Fabien Reyal, Barbara Pistilli, Lesley Stafford, Fabio Puglisi, Martha Hickey, Alexandra Gorelik, Hadi Akbarzadeh Khorshidi, Scott McGill Nelson, Isabelle Demeestere, Kathryn J. Ruddy, Richard A. Anderson, H. Irene Su, Mary Macheras‐Magias, Wanda Cui, Antoinette Catherine Anazodo, Shanton Chang, Christobel Saunders, Michelle Peate, Michail Ignatiadis, Tuong L. Nguyen, Elizabeth Sullivan, Laura Chin‐Lenn, M. Condorelli, Matteo Lambertini, Kate Stern, Zobaida Edib, Ann Partridge, Janet E. Olson, Yasmin Jayasinghe, Alessandro Marco Minisini, Patrick Cheong‐Iao Pang, Long Song, Fergus J. Couch, Franca Agresta, Paniti Sukumvanich
article en

Abstract

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (In<b>f</b>e<b>r</b>tility aft<b>e</b>r <b>Ca</b>ncer Predic<b>t</b>or) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817-0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666-0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives-patients incorrectly classified as likely to regain menses-could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

BioengineeringVol. 12(11)
University of Udine (IT), The Royal Melbourne Hospital (AU), Université Libre de Bruxelles (BE), Royal Children's Hospital (AU), Royal Women's Hospital (AU), The University of Melbourne (AU), University of Pittsburgh (US), Peter MacCallum Cancer Centre (AU), Breast Cancer Network Australia (AU), Institut Jules Bordet (BE), Cancer Institute of New South Wales (AU), Erasmus Hospital (BE), WinnMed (US), Myer Foundation (AU), University of California San Diego (US), Children's Cancer Institute Australia (AU), MRC Centre for Reproductive Health (GB), Dana-Farber Cancer Institute (US), Monash Health (AU), Sydney Children's Hospital (AU), Ospedale Santa Maria della Misericordia di Udine (IT), UniCancer Group (FR), Ospedale Policlinico San Martino (IT), Mayo Clinic in Florida (US), Murdoch Children's Research Institute (AU), Melbourne Health (AU), Macao Polytechnic University (MO), Monash University (AU), University of Newcastle Australia (AU), University of Glasgow (GB), Institut Curie (FR), University of Genoa (IT), University of Edinburgh (GB)
Openalex Percentile: Top 59%
Reproductive Biology and Fertility
DOIOpen Access PDFOpenAlex
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