ArticleAnnals of translational medicine2021
Assessing the risk of rapid fibroid growth in patients with asymptomatic solitary uterine myoma using a multivariate prediction model.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- An AI-Driven Clinical Decision Support Framework Utilizing Female Sex Hormone Parameters for Surgical Decision Guidance in Uterine Fibroid Management.Medicina (Kaunas, Lithuania) · 2025Article
- The Causal Role of Uterine Fibroid in Keloid and Hypertrophic Scar: A Bidirectional Mendelian Randomization Study on European Populations.Current pharmaceutical biotechnology · 2025Article
- Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Multiparametric quantitative magnetic resonance imaging of uterine fibroids for prediction of growth rate-a pilot study.Quantitative imaging in medicine and surgery · 2024Article
- Exploring myometrial microenvironment changes at the single-cell level from nonpregnant to term pregnant states.Physiological genomics · 2024Article
- Preoperative Differentiation of Uterine Leiomyomas and Leiomyosarcomas: Current Possibilities and Future Directions.Cancers · 2022Review
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLong-term conservative approaches are effective management options for asymptomatic uterine fibroids, but not for uterine myomas with excessive growth. In this investigation, a regression model was constructed to evaluate the clinical characteristics related to uterine fibroids' growth.
methodsIn this retrospective study, 19,840 patients with ultrasound-diagnosed uterine fibroids were identified from six centers between 2013 and 2019. In total, 739 patients were followed up for more than 1 year with B-ultrasound test results and clinical test results and had no acute events or surgical treatments. The endpoint was changed in the size of the uterine fibroids. Multivariate stepwise logistic regression analysis was used to identify predictors of uterine fibroid growth, and these were used to build a prediction model. The prediction model's discrimination, calibration, and clinical efficacy were assessed using the area under the curve (AUC)/index of concordance (C-index), calibration plot, decision curve analysis, and clinical impact curve. Internal validation was performed using bootstrapping validation. A linear regression model was constructed to predict uterine fibroids' growth rate without the occurrence of acute events.
resultsA total of 513 patients presented with significant growth of uterine fibroids, with an average follow-up time of 927 days, and 267 patients showed negative growth, with an average follow-up time of 960 days. Age, follicle-stimulating hormone (FSH), low-density lipoprotein (LDL), luteinizing hormone (LH), total cholesterol (TCHO), and neutrophil to lymphocyte ratio (NLR) were the main influential factors that predicted the uterine fibroid growth state, and these were used to develop a nomogram with predictive accuracy (AUC: 0.825). A linear regression prediction model was built based on the following factors: FSH, high-density lipoprotein (HDL), LH, triglyceride (TRIG), TCHO, and lymphocyte to monocyte ratio (LMR). The mean square error (MSE) was 0.32.
conclusionsThis study directly measured the growth rate of uterine fibroids. A prediction model assessing the growth rate of asymptomatic uterine fibroids was established. This model is useful for the early detection of potentially rapidly growing uterine fibroids in patients.
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