Inci Öz

HbA1c as a Key Metabolic Marker in Predicting Myomectomy Requirement in Women with Uterine Fibroids: A Machine Learning Study

Background and Objectives: Uterine fibroids are common benign tumors that frequently require surgical management, particularly myomectomy, in women of reproductive age. Metabolic dysfunction and insulin resistance have been implicated in fibroid biology; however, the clinical relevance of glycated hemoglobin (HbA1c) in predicting myomectomy requirement remains unclear. This study aimed to evaluate the predictive role of HbA1c for myomectomy requirement in women with uterine fibroids using conventional statistical analyses and machine learning-based models under real-world clinical decision-making conditions. Materials and Methods: This study evaluated data from a retrospective multicenter cohort comprising 618 women with a diagnosis of uterine fibroids. Patients were stratified according to myomectomy status (performed vs. not performed). Comparative analyses, univariate and multivariate logistic regression, and machine learning modeling were conducted using demographic, laboratory, hormonal, and fibroid-related variables. A total of 155 machine learning models were trained, and the top 20 models with the highest accuracy were evaluated. Blinded concordance analysis was conducted on 50 independent, anonymized cases evaluated by a gynecologist who was blinded to the study data. Results: Patients undergoing myomectomy (38.5%) had significantly higher HbA1c levels than non-surgical patients (5.57 ± 0.32 vs. 5.03 ± 0.61, p < 0.001). HbA1c showed a strong association with myomectomy requirement in univariate analysis (OR 0.026, 95% CI 0.012–0.055) but lost significance in multivariate models, while ferritin remained independently associated. Machine learning models incorporating HbA1c, ferritin, hormonal, and fibroid parameters achieved accuracies between 0.99 and 1.00. Blinded concordance analysis demonstrated 94% concordance between model predictions and expert clinical judgment. Conclusions: HbA1c is a valuable integrative marker in predicting myomectomy requirement when evaluated within multidimensional machine learning frameworks, although its independent effect is confounded by iron-related parameters. These findings support the use of HbA1c as part of a comprehensive decision-support approach in uterine fibroid management.

An AI-Driven Clinical Decision Support Model Based on Anemia and Fibroid Parameters to Guide Surgical Decision-Making

Background and Objectives: This study aimed to identify the clinical factors associated with the need for surgical intervention in women with uterine fibroids (UFs) and develop a data-driven clinical decision helper algorithm. By comparing hematologic and fibroid characteristics and prospectively assessing clinical concordance with the model predictions, we sought to create an objective tool for surgical decision-making. Materials and Methods: This retrospective study enrolled 618 women with UFs who were evaluated at three participating hospitals. Of these, 238 (38.5%) underwent surgery. Comparative statistical analyses were conducted between patients who underwent myomectomy and those who did not. Machine learning (ML) models were trained to predict myomectomy necessity. A clinical concordance assessment was conducted using 50 cases that were evaluated in real time by a gynecologist blinded to both the clinical outcomes and the model outputs. Agreement between clinical assessment and algorithm-based predictions was subsequently evaluated. Results: Hemoglobin and ferritin concentrations were significantly reduced in the surgery group compared with the non-surgery group (p < 0.001). ML analyses integrating fibroid characteristics with anemia-related markers identified support vector ML models as the most accurate classifiers. Ferritin-based models achieved accuracies of 98–99% and near-perfect ROC–AUC values. ML models combining UF number or volume with ferritin demonstrated the highest precision, sensitivity, and F1-scores. Clinical concordance analysis showed 98% agreement with the blinded gynecologist, with only one borderline discordant case. Conclusions: This decision helper algorithm provides a highly accurate and objective tool for predicting surgical necessity in patients with UFs. Anemia status and fibroid characteristics were the strongest predictors. By reducing subjective variability and closely reflecting expert reasoning, the model offers a practical framework for integration into routine gynecologic decision-making.

Prediction of surgery type for uterine fibroids using machine learning algorithms and hormone values

This study aimed to develop and externally validate machine learning (ML)-based models to characterize surgical classification patterns between hysterectomy and myomectomy using fibroid characteristics and female sex hormone profiles. This multicenter study included 600 women with uterine fibroids (UFs) who presented to 3 hospitals. Of these, 362 (60.3%) underwent hysterectomy, while 238 (39.7%) underwent myomectomy. Statistical analyses and ML models were applied to both groups. ML model development was performed using individual and combined inputs of female sex hormones together with fibroid characteristics. Five ML classification algorithms were evaluated, including support vector machines, decision trees, random forests, k-nearest neighbors, and logistic regression. In total, 2555 model–input combinations were tested. The performance of the selected best-performing model was further evaluated using an independent, blinded external validation cohort comprising 30 cases. Women in the hysterectomy group had significantly higher mean age, follicle-stimulating hormone, luteinizing hormone, UF number, UF volume, uterine volume, disease duration, gravidity, parity, and prolactin (PRL) levels compared with the myomectomy group (all P  < .001). In contrast, estradiol and anti-Müllerian hormone levels were significantly lower in the hysterectomy group ( P  < .001). Across all modeling experiments, 2012 of 2555 model–input combinations achieved perfect classification performance (accuracy = 100%) when sex hormone profiles and UF characteristics were jointly used as inputs. Models using UF number alone also demonstrated high predictive performance, with accuracy reaching up to 96%. Agreement between algorithmic predictions and final surgical decisions was observed in 97% of cases, with one discordant case identified at a clinically borderline threshold. ML models trained on hormone profiles and fibroid characteristics were able to reproduce prevailing surgical classification patterns, largely reflecting strong baseline separability driven by age- and menopause-associated hormonal profiles, with consistent performance observed in an independent blinded validation cohort. These findings support the feasibility of quantitatively modeling routine decision structures, while highlighting the need for further validation in clinically heterogeneous and ambiguous cases.

An AI-Driven Clinical Decision Support Framework Utilizing Female Sex Hormone Parameters for Surgical Decision Guidance in Uterine Fibroid Management

Background and Objective: Changes in female sex hormone levels are closely linked to the development and progression of uterine fibroids (UFs). Clinical approaches to fibroid management vary according to guidelines and depend on patient symptoms, fibroid size, and clinician judgment. Despite available diagnostic tools, surgical decisions remain largely subjective. With the advancement of artificial intelligence (AI) and clinical decision support technologies, clinical experience can now be transferred into data-driven computational models trained with hormone-based parameters. To develop a clinical decision support algorithm that predicts surgical necessity for uterine fibroids by integrating fibroid characteristics and female sex hormone levels. Methods: This multicenter study included 618 women with UFs who presented to three hospitals; 238 underwent surgery. Statistical analyses and artificial intelligence-based modeling were performed to compare surgical and non-surgical groups. Training was conducted with each hormone—follicle-stimulating hormone (FSH), luteinizing hormone (LH), estrogen (E2), prolactin (PRL), and anti-Müllerian hormone (AMH)—and with 126 input combinations including hormonal and morphological variables. Five supervised learning algorithms—support vector machine, decision tree, random forest, and k-nearest neighbors—were applied, resulting in 630 trained models. In addition to this retrospective development phase, a prospective validation was conducted in which 20 independent clinical cases were evaluated in real time by a gynecologist blinded to both the model predictions and the surgical outcomes. Agreement between the clinician’s assessments and the model outputs was measured. Results: FSH, LH, and PRL levels were significantly lower in the surgery group (p < 0.001, 0.009, and <0.001, respectively), while E2 and AMH were higher (p = 0.012 and 0.001). Fibroid volume was also greater among surgical cases (90.8 cc vs. 73.1 cc, p < 0.001). The random forest model using LH, FSH, E2, and AMH achieved the highest accuracy of 91 percent. In the external validation phase, the model’s predictions matched the blinded gynecologist’s decisions in 18 of 20 cases, corresponding to a 90% concordance rate. The two discordant cases were later identified as borderline scenarios with clinically ambiguous surgical indications. Conclusions: The decision support algorithm integrating hormonal and fibroid parameters offers an objective and data-driven approach to predicting surgical necessity in women with UFs. Beyond its strong internal performance metrics, the model demonstrated a high level of clinical concordance during external validation, achieving a 90% agreement rate with an independent, blinded gynecologist. This alignment underscores the model’s practical reliability and its potential to reduce subjective variability in surgical decision-making. By providing a reproducible and clinically consistent framework, the proposed AI-based system represents a meaningful advancement toward the validated integration of computational decision tools into routine gynecological practice.