Scholarlink

Background <p>Lymphovascular space invasion (LVSI), a prognostic indicator closely associated with tumour invasiveness, lymph node metastasis risk, and recurrence rate, is crucial in endometrial cancer (EC) staging; however, LVSI is currently diagnosed via postoperative pathology, highlighting the need for non-invasive diagnostic methods. This study aimed to investigate the predictive value of intratumoural and peritumoral magnetic resonance imaging (MRI) multiparametric radiomics combined with clinical indicators of LVSI in EC.</p> Methods <p>This retrospective analysis included 310 patients with EC who underwent preoperative MRI examinations at the Affiliated Hospital of Shandong Second Medical University (Centre A) and the First Clinical Medical College of Shandong Second Medical University (Centre B). The patients were divided into training (Centre A) and validation (Centre B) sets. Clinically independent risk factors and intratumoural and peritumoural radiomic characteristics were screened. Five models were constructed: clinical, peritumoural radiomics, intratumoural radiomics, combined intratumoural and peritumoural radiomics, and combined clinical, intratumoural, and peritumoural radiomics. A nomogram was constructed based on the optimal model. The diagnostic efficacy of the five models was evaluated using area under the curve. The accuracy of the model was evaluated using calibration curves, and the clinical value of the model was analysed using decision curve analysis.</p> Results <p>Logistic regression analysis identified CA125 and tumour length as independent risk factors for LVSI in EC. Among the five models, the combined clinical + intratumoural + peritumoural radiomics model performed slightly better than the other four models, with area under the curve values of 0.870 (95% CI: 0.821–0.919) for the training set and 0.818 (95% CI: 0.731–0.905) for the validation set. The calibration curve showed good consistency, and decision curve analysis suggested that the model had good clinical benefits.</p> Conclusion <p>The combined clinical + intratumoural + peritumoural radiomics model based on clinical indicators and intratumoural and peritumoural multi-parametric MRI radiomics features demonstrated good diagnostic efficacy. This model provides a theoretical basis for preoperative evaluation of LVSI in EC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predictive value of models based on MRI radiomics and clinical indicators for lymphovascular space invasion in endometrial cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月28日

查看全文
  • Wenwen Ma,
  • Weijing Meng,
  • Jinfeng Yin,
  • Jie Liang,
  • Xizhen Wang,
  • Jingang Liu,
  • Fuyan Shi

摘要

Background

Lymphovascular space invasion (LVSI), a prognostic indicator closely associated with tumour invasiveness, lymph node metastasis risk, and recurrence rate, is crucial in endometrial cancer (EC) staging; however, LVSI is currently diagnosed via postoperative pathology, highlighting the need for non-invasive diagnostic methods. This study aimed to investigate the predictive value of intratumoural and peritumoral magnetic resonance imaging (MRI) multiparametric radiomics combined with clinical indicators of LVSI in EC.

Methods

This retrospective analysis included 310 patients with EC who underwent preoperative MRI examinations at the Affiliated Hospital of Shandong Second Medical University (Centre A) and the First Clinical Medical College of Shandong Second Medical University (Centre B). The patients were divided into training (Centre A) and validation (Centre B) sets. Clinically independent risk factors and intratumoural and peritumoural radiomic characteristics were screened. Five models were constructed: clinical, peritumoural radiomics, intratumoural radiomics, combined intratumoural and peritumoural radiomics, and combined clinical, intratumoural, and peritumoural radiomics. A nomogram was constructed based on the optimal model. The diagnostic efficacy of the five models was evaluated using area under the curve. The accuracy of the model was evaluated using calibration curves, and the clinical value of the model was analysed using decision curve analysis.

Results

Logistic regression analysis identified CA125 and tumour length as independent risk factors for LVSI in EC. Among the five models, the combined clinical + intratumoural + peritumoural radiomics model performed slightly better than the other four models, with area under the curve values of 0.870 (95% CI: 0.821–0.919) for the training set and 0.818 (95% CI: 0.731–0.905) for the validation set. The calibration curve showed good consistency, and decision curve analysis suggested that the model had good clinical benefits.

Conclusion

The combined clinical + intratumoural + peritumoural radiomics model based on clinical indicators and intratumoural and peritumoural multi-parametric MRI radiomics features demonstrated good diagnostic efficacy. This model provides a theoretical basis for preoperative evaluation of LVSI in EC.