Establishment of a nomogram prediction model for severe primary lower limb lymphedema

Background The International Society of Lymphology (ISL) guidelines have established grading criteria for primary lower limb lymphedema (PLEL), but there is a lack of model on a unified standard for assessing the severity of the disease. Purpose The aim of this study was to establish and validate a predictive model for evaluating severe PLEL. Methods and Materials: This retrospective study included 226 patients with unilateral PLEL from 2018 to 2023, who were divided into non-severe (143 cases) and severe (83 cases) groups according to the ISL grading criteria. The two groups of patients had a total of 26 MRI and 15 clinical features recorded. One-way ANOVA was performed first, followed by multi-factor ANOVA, and logistic regression was used to construct a nomogram prediction model. The model’s performance was evaluated via the area under the receiver operating characteristic (ROC) curve (AUC), decision curve analysis, and internal validation. Results The predictive model identified six independent risk factors associated with the severity of PLEL, including the parallel line sign, crescent sign, longitudinal range, band sign thickness, fat area, and fat diameter. The nomogram model established based on the above six factors predicts a training set AUC of 0.908 (95% CI: 0.868–0.947) for severe PLEL, with a sensitivity of 0.868, specificity of 0.832, accuracy of 0.845, precision of 0.75. The AUC of the validation set was 0.891 (95% CI: 0.847 ~ 0.935), the sensitivity was 0.831, the specificity was 0.825, the accuracy was 0.827, the precision was 0.734. In decision curve analysis, more net benefit can be achieved when the threshold probability is between 1% and 90%. Conclusions The severity risk prediction model based on MRI and clinical practice has good discriminatory power and accuracy in evaluating the severity of PLEL which can provide a reference for individualized clinical prediction of PLEL.