ArticleJournal of assisted reproduction and genetics2021
Development of a fertility risk calculator to predict individualized chance of ovarian failure after chemotherapy.
Article in Journal of assisted reproduction and genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Algorithmic Prognostication in Female Oncofertility Counseling: Ethical Challenges of Bias, Autonomy, and Predictive Uncertainty.Healthcare (Basel, Switzerland) · 2026Review
- Machine learning-based endometrial ultrasound radiomics habitat analysis for predicting pregnancy outcomes after embryo transfer.Journal of ovarian research · 2026Article
- Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.Journal of assisted reproduction and genetics · 2025Review
- Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Prostate Cancer Treatments and Their Effects on Male Fertility: Mechanisms and Mitigation Strategies.Journal of personalized medicine · 2025Review
- Changing Etiological Spectrum of Premature Ovarian Insufficiency over the Past Decades: A Comparative Analysis of Two Cohorts from a Single Center.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Multi-omics and machine learning for the prevention and management of female reproductive health.Frontiers in endocrinology · 2023Review
- Oocyte cryopreservation versus ovarian tissue cryopreservation for adult female oncofertility patients: a cost-effectiveness study.Journal of assisted reproduction and genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop an innovative machine learning (ML) model that predicts personalized risk of primary ovarian insufficiency (POI) after chemotherapy for reproductive-aged women. Currently, individualized prediction of a patient's risk of POI is challenging.
methodsAuthors of published studies examining POI after gonadotoxic therapy were contacted, and six authors shared their de-identified data (N = 435). A composite outcome for POI was determined for each patient and validated by 3 authors. The primary dataset was partitioned into training and test sets; random forest binary classifiers were trained, and mean prediction scores were computed. Institutional data collected from a cross-sectional survey of cancer survivors (N = 117) was used as another independent validation set.
resultsOur model predicted individualized risk of POI with an accuracy of 88% (area under the ROC 0.87, 95% CI: 0.77-0.96; p < 0.001). Mean prediction scores for patients who developed POI and who did not were 0.60 and 0.38 (t-test p < 0.001), respectively. Highly weighted variables included age, chemotherapy dose, prior treatment, smoking, and baseline diminished ovarian reserve.
conclusionWe developed an ML-based model to estimate personalized risk of POI after chemotherapy. Our web-based calculator will be a user-friendly decision aid for individualizing risk prediction in oncofertility consultations.
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