Development and validation of a nomogram-based predictive model for recurrence risk of uterine leiomyoma after myomectomy.

Uterine fibroids are common among women of reproductive age and often recur after treatment. Accurate recurrence prediction is essential for guiding clinical decisions, yet existing models remain inadequate. This study aimed to develop a nomogram based on Least Absolute Shrinkage and Selection Operator (LASSO) regression to estimate recurrence risk after myomectomy. We retrospectively analyzed data from 678 patients who underwent myomectomy, randomly dividing them into training and validation cohorts (7:3 ratio). LASSO regression was used to select relevant predictors, and a nomogram was constructed. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. Six key predictors were identified: leiomyoma subclassification, fibroid diameter ≤ 4 cm, postoperative residual fibroids, postoperative pregnancy or childbirth, family history, and the number of fibroids detected via transvaginal ultrasound. The nomogram demonstrated strong discrimination, calibration, and clinical utility. The proposed nomogram provides a reliable and practical tool for predicting fibroid recurrence, supporting personalized postoperative management and follow-up planning.