<sup>18 </sup>F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析

华涛; 周维燕; 周支瑞; 黄琪; 李明; 朱毓华; 管一晖

doi:10.19732/j.cnki.2096-6210.2024.02.005

您当前的位置：

首页 >

文章列表页 >

¹⁸F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析

专题论著 | 更新时间：2025-12-15

- ¹⁸F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析
- The non-invasive prediction analysis of radiomic features from ¹⁸ F-FET PET/CT dual imaging modality for tumor grading in untreated adult gliomas
- 肿瘤影像学 2024年33卷第2期页码：127-135
- 作者机构：
  
  1. 复旦大学附属华山医院核医学科 /PET 中心,上海,200235
  2. 复旦大学附属华山医院放射治疗中心,上海,200040
- 作者简介：
- 基金信息：
  
  国家自然科学基金（82302337）;上海周良辅医学发展基金会“脑科学与脑疾病青年创新计划”
- DOI：10.19732/j.cnki.2096-6210.2024.02.005
  中图分类号：
- 网络出版：2024-05-10，
  
  纸质出版：2024-05-10
- 稿件说明：
移动端阅览
华涛,周维燕,周支瑞,等. ¹⁸F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析[J]. 肿瘤影像学, 2024, 33(2): 127-135 https://doi.

org/10.19732/j.cnki.2096-6210.2024.02.005
华涛,周维燕,周支瑞,等. ¹⁸F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析[J]. 肿瘤影像学, 2024, 33(2): 127-135 https://doi. DOI： 10.19732/j.cnki.2096-6210.2024.02.005.

org/10.19732/j.cnki.2096-6210.2024.02.005 DOI：

摘要

目的：

基于

F-FET正电子发射体层成像（positron emission tomography，PET）/计算机体层成像（computed tomography，CT）双模态影像组学特征构建模型，并对成人胶质瘤的病理学分级进行非侵入性预测分析。

方法：

回顾并分析58例经组织病理学检查证实的未治疗成年胶质瘤患者的

F-FET PET/CT影像学数据，根据病理学分级将患者分成低级别胶质瘤组［世界卫生组织（World Health Organization，WHO）Ⅱ级，共计32例］和高级别胶质瘤组（WHO Ⅲ级13例、WHO Ⅳ级13例，共计26例）。在PET、CT影像模态中分别提取105个影像组学特征参数进行分析，采用基于R语言机器学习算法的5折交叉验证最小绝对收缩与选择算子（least absolute shrinkage and selection operator，LASSO）回归分析方法，构建成人胶质瘤病理学分级的3个独立的影像组学预测模型（PET-Rad模型、CT-Rad模型和PET/CT-Rad模型），然后再采用全子集回归对影像组学预测模型进行校正。采用受试者工作特征曲线的曲线下面积（area under curve，AUC）对预测模型进行评价。

结果：

基于4个

F-FET PET影像组学参数建立PET-Rad模型的AUC为0.845（95% CI 0.726~0.927）；基于3个CT影像组学参数构建的CT-Rad模型的AUC为0.802（95% CI 0.676~0.895）；而联合3个CT和2个PET影像组学特征的

F-FET PET/CT-Rad模型的AUC为0.901（95% CI 0.794~0.964），准确度为86.21%。DeLong检验结果显示PET/CT-Rad模型优于CT-Rad模型（

=0.032）。尽管PET/CT-Rad模型效能优于PET-Rad模型，但差异无统计学意义（

=0.146）。构建胶质瘤病理学级别预测的PET/CT-Rad模型中，3个CT影像组学参数分别为firstorder_10Percentile、glrlm_LowGrayLevelRunEmphasis、ngtdm_Busyness，其中glrlm_LowGrayL

evelRunEmphasis是最重要的预测变量，其相对重要性为30.97%；2个PET组学特征为firstorder_Maximum和ngtdm_Contrast，其相对重要性分别为21.99%、21.01%。

结论：

基于

F-FET PET与CT双模态影像组学特征相结合的预测模型能够有效地预测未经治疗成人胶质瘤的病理学分级，可为临床诊治决策提供依据和重要参考。

Abstract

Objective:

To build radiomics models based on the features from

F-FET positron emission tomography (PET)/computed tomography (CT) dual imaging modality

and investigate the predictive efficacy for tumor grading in untreated adult gliomas.

Methods:

The

F-FET PET/CT imaging data of 58 histopathologically confirmed untreated adult gliomas were retrospectively analyzed. Based on pathological grading

the patients were divided into low-grade glioma groups ［World Health Organization (WHO) Grade Ⅱ

32 cases in total］ and high-grade gliomas (13 cases for WHO grade Ⅲ

13 cases for WHO grade Ⅳ

26 cases in total). 105 radiomics features were extracted from PET and CT modalities respectively. Five-fold cross-validation least absolute shrinkage and selection operator (LASSO) regression analysis based on R-language machine learning algorithm and all- subset regression were adopted to filter and optimize the identify the optimal feature combinations with higher distinguishing power for glioma grading. Three independent radiomics prediction models (PET-Rad model

CT-Rad model and PET/CT-Rad model) for adult glioma pathological grading were constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the prediction model.

Results:

The AUC of PET-Rad model based on four

F-FET PET radiomics features was 0.845 (95% CI 0.726-0.927) and the AUC of CT-Rad model consisting of three CT radiomics features was 0.802 (95% CI 0.676-0.895). The AUC of the

F-FET PET/ CT-Rad model combined with three CT and two PET radiomics features was 0.901 (95% CI 0.794-0.964)

and the accuracy was 86.21%. DeLong test results

showed that PET/CT-Rad model was superior to CT-Rad model (

=0.032). The efficacy of PET/CT-Rad model was better than that of PET-Rad model

but there was no statistical difference (

=0.146). The three CT imaging parameters in PET/CT-Rad model were firstorder_10Percentile

glrlm_LowGrayLevelRunEmphasis

and ngtdm_Busyness

while Glrlm_LowGrayLevelRunEmphasis was the most important predictive variable with a relative importance of 30.97%. The relative importance of the other two selected PET radiomic features firstorder_Maximum and ngtdm_Contrast was 21.99% and 21.01%

respectively.

Conclusion:

The non-invasive prediction model based on the combination of integrated

F-FET PET and CT modality radiomic features can effectively help grading the untreated adult glioma

facilitating clinical decision-making for patients with varied glioma grades.

关键词

Keywords

references

浏览量

427

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

¹⁸ F-FDG PET/CT影像组学特征对肺腺癌患者EGFR基因突变的预测价值

基于多参数超声影像组学的浅表淋巴瘤预测模型研究

智能影像赋能胰腺肿瘤精准诊疗：应用与展望

基于增强CT多区域影像组学特征联合临床危险因素构建结肠癌术前脉管侵犯预测模型

利用¹⁸F-FDG PET显像评估不同治疗阶段前列腺癌的研究

18 F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析

The non-invasive prediction analysis of radiomic features from 18 F-FET PET/CT dual imaging modality for tumor grading in untreated adult gliomas

DOI：10.19732/j.cnki.2096-6210.2024.02.005

摘要

Abstract

关键词

Keywords

references

¹⁸F-FET PET/CT双模态影像组学特征对成人胶质瘤病理学分级的非侵入性预测分析

The non-invasive prediction analysis of radiomic features from ¹⁸ F-FET PET/CT dual imaging modality for tumor grading in untreated adult gliomas