Fetal Fraction and Uterine Leiomyoma Volume: New Insights From Interpretable Modeling

ABSTRACT Objective This study aims to develop a predictive model to estimate the likelihood of achieving a sufficient fetal fraction (FF) for non‐invasive prenatal testing (NIPT) based on maternal characteristics such as age, body mass index (BMI), gestational age, gravida, parity, and uterine leiomyoma volume, if present. Method This retrospective study include singleton pregnancies with normal NIPT results and complete data from a tertiary hospital. Maternal and clinical variables are analyzed. Machine learning models (Decision Tree, Random Forest, XGBoost) are trained to classify FF as sufficient (≥ 4%) or insufficient. The best‐performing model (XGBoost) is interpreted using SHAP values. Additionally, the impact of uterine leiomyoma volume on FF is demonstrated through scenario‐based analyses derived from the model. Results XGBoost achieves the highest prediction accuracy (0.89). SHAP analysis shows that age, BMI, and gestational age are most influential, followed by uterine leiomyoma volume. Scenario‐based simulations on 24 patients with both uterine leiomyoma and insufficient FF demonstrate that reducing uterine leiomyoma volume often led to a predicted improvement in FF. Conclusion Uterine leiomyoma volume is identified as a significant factor influencing FF levels in NIPT. This predictive modeling has the potential to support clinical decision‐making in cases where low FF poses challenges to effective patient management.