基于影像组学方法构建弥漫性低级别胶质瘤癫痫发生及远期生存预测模型
DOI:
作者:
作者单位:

南京医科大学附属肿瘤医院

作者简介:

通讯作者:

中图分类号:

基金项目:

江苏省预防医学会课题(Y2018091);江苏省卫健委面上项目(M2021093)


A predictive model of epilepsy and long-term survival of diffuse low-grade glioma based on radiomics
Author:
Affiliation:

Affiliated Tumor Hospital of Nanjing Medical University

Fund Project:

Jiangsu Preventive Medicine Association Project(Y2018091);Health and Health Commission of Jiangsu Province Face Project(M2021093)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    目的:构建预测弥漫性低级别胶质瘤患者癫痫发作风险的影像组学风险模型,并初步评估患者预后。材料与方法:利用TCGA和TCIA数据库提供的影像数据集及临床资料,通过影像组学方法构建癫痫风险评分模型,并结合临床指标建立列线图以预测癫痫发生概率及远期生存概率。受试者工作曲线及决策曲线分析被用于评价模型的预测效能。结果:本研究筛选出10个癫痫相关的影像特征建立风险评分模型,内部及外部验证的ROC曲线下的面积分别为0.900和0.636。高风险组和低风险组的癫痫阳性率分别为80.6%和27.3%。高风险组和低风险组患者的5年疾病总生存率存在明显统计学差异(P=0.029),临床决策分析曲线显示“影像-临床”癫痫预测模型的净获益率优于临床预测模型。校准曲线显示3年,5年生存列线图有良好的校准和区分能力,列线图中高低危组5年总生存有明显统计学差异(P=0.008)。结论:本研究建立了一个基于术前MRI的影像组学模型,用于无创预测弥漫性低级别胶质瘤患者癫痫发生风险及预后,为临床采取个体化治疗策略提供参考。

    Abstract:

    Objective To construct a radiomic risk model to predict the risk of epileptic seizures in patients with diffuse low-grade glioma (DLGG) and to preliminatively assess patient outcomes. Materials and Methods Using the imaging datasets and clinical data provided by TCGA and TCIA databases, an epilepsy risk score model was constructed by radiomics methods. Further nomograms were established based on the model combined with clinical indicators to predict the probability of epilepsy and the probability of long-term survival. Receiver operator characteristic curve and decision curve analysis were used to evaluate the predictive effectiveness of predictive models. Results In this study, 10 radiomic features were filtered out to establish the epilepsy risk scoring model. The AUC values of internal verification and external verification were 0.900 and 0.636, respectively. The positive rate of epilepsy in high-risk group and low-risk group were 80.6% and 27.3%, respectively. There was significant difference in 5-year OS between the high-risk and low-risk groups (P=0.029). DCA showed that the net benefit rate of the image-clinical model was higher than that of the clinical prediction model. The calibration curve showed that the 3-year, 5-year survival nomogram had good calibration and differentiation ability and the 5-year survival of the high risk group and low risk group in the nomogram had significant difference (P=0.008). Conclusions:In this study, a radiomic model based on preoperative multimodal MRI of DLGG was established for non-invasive prediction of the risk of associated epilepsy and prognosis, which provides a reference for the individualized treatment strategies.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-08-29
  • 最后修改日期:2023-02-02
  • 录用日期:2023-04-17
  • 在线发布日期:
  • 出版日期: