Radiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.

Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.

Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets. Radiomic features were extracted from multiparametric magnetic resonance imaging to generate radiomic scores using a support vector machine algorithm. Three predictive models were constructed: clinical (Model), radiomics-only (Model), and fusion (Model). The models' performances were evaluated by analyzing their receiver operating characteristic curves, and pairwise comparisons of the models' areas under the curves were conducted using DeLong's test and adjusted using the Bonferroni correction. Decision curve analysis with integrated discrimination improvement was used for net benefit comparison.

Results: This study enrolled 283 women (training set: n = 198; test set: n = 85). The lymphovascular space invasion groups (substantial and no/focal) had significantly different radiomic scores (P < 0.05). Model achieved an area under the curve of 0.818 (95% confidence interval: 0.757-0.869) and 0.746 (95% confidence interval: 0.640-0.835) for the training and test sets, respectively, demonstrating a good fit according to the Hosmer-Lemeshow test (P > 0.05). The DeLong test with Bonferroni correction indicated that Model demonstrated better diagnostic efficiency than Model in predicting substantial lymphovascular space invasion in the two sets (adjusted P < 0.05). In addition, decision curve analysis demonstrated a higher net benefit for Model, with integrated discrimination improvements of 0.043 and 0.732 (P < 0.01) in the training and test sets, respectively.

Conclusion: The multiparametric magnetic resonance imaging-based radiomics machine learning nomogram showed moderate diagnostic performance for substantial lymphovascular space invasion in patients with endometrial cancer.