ArticleCancer medicine2026
Construction and Validation of a Risk Prediction Model for Lower Limb Lymphedema After Cervical Cancer Surgery: A Prospective Cohort Study.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo analyze various risk factors for lower limb lymphedema (LLL) after cervical cancer surgery, to build a risk prediction model and to verify the model's predictive effect.
methodsClinical data were collected from 544 patients who underwent cervical cancer surgery in our hospital from January 2020 to December 2022. LASSO regression was used to screen risk factors for postoperative LLL. Then we established a Cox proportional hazard regression model including several parameters screened by LASSO regression. The final prediction model was visualized, and the nomogram for postoperative LLL risk prediction was finally plotted.
resultsA total of 544 patients undergoing cervical cancer surgery were randomly divided into training and validation sets at a 6:4 ratio. LASSO regression screened out 9 candidate variables. Multivariate Cox regression identified 3 independent risk factors and 1 independent protective factor for postoperative LLL. Statistically significant risk factors included prolonged preoperative static posture (HR = 4.675, p < 0.001), pelvic lymph node dissection (HR = 2.261, p = 0.008), and radiotherapy within 3 months after the surgery (HR = 1.929, p = 0.029). Preoperative labor intensity served as an independent protective factor (HR = 0.536, 95% CI: 0.364-0.790, p = 0.002). The other 5 screened variables had no significant correlation with LLL (all p > 0.05). A nomogram prediction model was constructed and internally validated. The C-index reached 0.835 in the training set and 0.810 in the validation set. The time-dependent AUC values ranged from 0.882 to 0.887 in the training set and 0.882 to 0.890 in the validation set at 12, 24, and 36 months. Calibration curves showed moderate consistency between predicted and observed LLL risks. Decision curve analysis verified that the model yielded satisfactory clinical net benefit within a wide threshold probability range.
conclusionOur risk prediction model has a good prediction effect and can provide a valid reference for clinical medical staff to evaluate the risk of LLL after cervical cancer surgery.
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