Construction of a nomogram for personalized prediction of lower limb lymphedema risk after cervical cancer surgery.

Objective: This study aims to construct and evaluate an individualized nomogram for predicting the risk of lower limb lymphedema after cervical cancer surgery. Healthcare professionals can utilize line chart models to predict the probability of postoperative lower limb lymphedema in different patients, allowing for the early identification of high-risk patients and facilitating early prevention and treatment.

Methods: A retrospective study was conducted among 411 cervical cancer patients treated at our hospital from May 2021 to December 2023. The patients were randomly divided into a modeling group (313 cases) and a validation group (98 cases) according to an approximate 3:1 ratio. The modeling group was further divided into a lower limb lymphedema group (61 cases) and a non-lower limb lymphedema group (252 cases) based on the presence of postoperative lower limb lymphedema. Multiple factors Logistic regression was used to identify risk factors, and a nomogram was constructed using R software version 4.0.2, with internal and external validation performed.

Results: Risk factors for lower limb lymphedema following cervical cancer surgery include age 60 years or older, a Body Mass Index (BMI) of 24 kg/m² or higher, hypertension, the removal of 30 or more lymph nodes, adjuvant radiotherapy and chemotherapy, and prolonged standing for six hours or more (P < 0.05). Internal and external validation results demonstrated that the calibration curve closely aligned with the ideal curve. The Area Under Curve(AUC) of the Receiver Operating Characteristic(ROC) curve was 0.890 (95% CI: 0.844 ∼ 0.936) and 0.876 (95% CI: 0.821 ∼ 0.930), indicating high model calibration and discrimination. Decision Curve Analysis(DCA) curve revealed that the Logistic model had good net returns and high clinical practicality when the probability range of the high-risk threshold was 0.11 ∼ 0.98.

Conclusion: The nomogram, developed using factors such as age, BMI, hypertension, number of lymph nodes dissected, adjuvant radiotherapy and chemotherapy, and duration of standing, has strong predictive value and offers significant clinical benefits, making it a valuable tool for clinical decision-making.