Prenatal diagnosis · 2026
Fetal Fraction and Uterine Leiomyoma Volume: New Insights From Interpretable Modeling.
СтатьяНазвание и аннотация: Перевод в очереди
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Uçar İlker, Sarıyer Görkem, Yaprak Uçar Esra, Çağıran Rumeysa Nur, Demirel Emine
Аннотация
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.
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.