Journal Article

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

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

2025

Overview

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 (Infertility after Cancer Predictor) 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.

• Publisher: MDPI AG
• Subject: Amenorrhea / Breast cancer / Cancer / Cancer therapies / Chemotherapy / Clinical medicine / cross imputation / Datasets / Decision making / Feature selection / Fertility / Infertility / Learning algorithms / Machine learning / Medical research / Medicine, Experimental / Menopause / Menstruation / Missing data / Ovarian cancer / Ovaries / Prediction models / Premenopause / Quality of life / Reproductive status / Risk / risk prediction model / Sensitivity / treatment-related amenorrhea / Variables / Womens health