Nomogram visualization of peritumoral infiltration risk in glioblastoma using fused contrast-enhanced ultrasound and contrast-enhanced MRI

HU Xing; XIE Rong; ZHANG Xiandi; WANG Yong; ZHU Yingfeng; DING Hong

doi:10.19732/j.cnki.2096-6210.2026.01.011

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Nomogram visualization of peritumoral infiltration risk in glioblastoma using fused contrast-enhanced ultrasound and contrast-enhanced MRI

Article | 更新时间：2026-03-25

- Nomogram visualization of peritumoral infiltration risk in glioblastoma using fused contrast-enhanced ultrasound and contrast-enhanced MRI
- Oncoradiology Vol. 35, Issue 1, Pages: 84-94(2026)
- 作者机构：
  
  1.复旦大学附属华山医院超声医学科，上海 200040
  2.复旦大学附属华山医院神经外科，上海 200040
  3.复旦大学附属华山医院病理科，上海 200040
- 作者简介：
  
  DING Hong Email: dding_hong@fudan.edu.cn
- 基金信息：
  
  General Project of National Natural Science Foundation of China(82272017)
- DOI：10.19732/j.cnki.2096-6210.2026.01.011
  CLC： R739.41;R445.2
- Received：17 October 2025，
  
  Revised：2025-11-13，
  
  Published：28 February 2026
- 稿件说明：
移动端阅览
胡　星, 谢　嵘, 张显迪, 等. 基于超声造影与增强MRI图像融合列线图可视化胶质母细胞瘤的瘤周浸润风险［J］. 肿瘤影像学, 2026, 35(1): 84-94.

Citation:HU X, XIE R, ZHANG X D, et al. Nomogram visualization of peritumoral infiltration risk in glioblastoma using fused contrast-enhanced ultrasound and contrast-enhanced MRI［J］. Oncoradiology, 2026, 35(1): 84-94.
胡　星, 谢　嵘, 张显迪, 等. 基于超声造影与增强MRI图像融合列线图可视化胶质母细胞瘤的瘤周浸润风险［J］. 肿瘤影像学, 2026, 35(1): 84-94. DOI： 10.19732/j.cnki.2096-6210.2026.01.011.

Citation:HU X, XIE R, ZHANG X D, et al. Nomogram visualization of peritumoral infiltration risk in glioblastoma using fused contrast-enhanced ultrasound and contrast-enhanced MRI［J］. Oncoradiology, 2026, 35(1): 84-94. DOI： 10.19732/j.cnki.2096-6210.2026.01.011.

摘要

目的

旨在开发结合超声造影（contrast enhanced ultrasound，CEUS）和增强磁共振成像（magnetic resonance imaging，MRI）图像融合的列线图，评估胶质母细胞瘤（glioblastoma，GBM）瘤周浸润风险。

方法

回顾并收集2021年3月—2023年3月在复旦大学附属华山医院神经外科接受手术的GBM患者，术前借助灰阶超声和增强MRI图像融合描绘肿瘤核心，进一步通过CEUS定位浸润区并量化浸润区和邻近正常脑区血流动力学表现，并以对侧正常外观白质的测量值进行归一化处理。采用Mann-Whitney

或方差分析检验并进行Bonferroni校正比较各区的血流动力学参数，基于多元logistic回归分析构建列线图可视化瘤周浸润风险，并使用校准曲线、决策曲线和受试者工作特征曲线评估模型性能。

结果

共纳入经病理学检查证实的GBM患者16例，正常脑区和浸润区分别提取136个感兴趣区，从正常脑区到浸润区，峰值强度（

0.001）、时间-强度曲线曲线下面积（

0.013）和归一化峰值强度（

0.001）显著增加，而上升时间（

0.010）和归一化达峰时间（

0.010）显著降低。峰值强度（

0.001）、时间-强度曲线曲线下面积（

0.001）、归一化峰值强度（

0.001）和归一化达峰时间（

0.015）是瘤周浸润的独立危险因素，基于这4个参数的logistic回归模型表现出更好的预测性能，校准曲线的Hosmer-Lemeshow统计量为8.851（

0.355），表明预测结果与实际结果之间良好的一致性，决策曲线显示，当瘤周浸润发生率为8%~91%时能带来净收益，表明具有较强的临床适用性，受试者工作特征曲线的曲线下面积为0.806，表明具有较好的诊断性能。

结论

基于CEUS和增强MRI图像融合的列线图为量化GBM瘤周浸润提供了一种可靠的工具，有助于术中进行非侵入性预测。

Abstract

Objective

To develop a nomogram integrating contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance imaging (MRI) to evaluate the risk of peritumoral infiltration in glioblastoma (GBM).

Methods

Data from patients with GBM who underwent surgical tre

atment at the Department of Neurosurgery

Huashan Hospital

Fudan University

between March 2021 and March 2023 were retrospectively collected. Preoperatively

grayscale ultrasound and fused contrast-enhanced MRI were used to delineate the tumor core. CEUS was subsequently used to identify infiltrative regions and quantify the hemodynamic characteristics of both infiltrated and adjacent normal brain tissue. Measurements were normalized using contralateral normal-appearing white matter as the reference. Hemodynamic parameters across regions were compared using the Mann-Whitney

test or analysis of variance

with Bonferroni correction. A multivariate logistic regression model was developed to create a nomogram visualizing infiltration risk; its performance was evaluated using calibration curves

decision curve analysis

and receiver operating characteristic curves.

Results

A total of 16 patients with pathologically confirmed GBM were included

and 136 regions of interest were extracted from both normal and infiltrated brain tissue. Compared with normal tissue

infiltrated regions showed significantly higher peak intensity (

0.001)

area under the time-intensity curve (

0.013)

and normalized peak intensity (

0.001)

as well as lower rise time (

0.010) and normalized time to peak (

0.010). Multivariate analysis revealed that peak intensity (

0.001)

area under the curve (

0.001)

normalized peak intensity (

0.001)

and normalized time to peak (

0.015) were independent predictors of peritumoral infiltration. The logistic regression model incorporating these four variables demonstrated strong predictive performance. The Hosmer-Lemeshow test yielded a statistic of 8.851 (

0.355)

indicating good model calibration. Decision curve analysis demonstrated a net clinical benefit when t

he predicted peritumoral infiltration probability ranged from 8% to 91%. The model achieved an area under the receiver operating characteristic curve of 0.806

indicating good diagnostic accuracy.

Conclusion

The nomogram combining fused CEUS and contrast-enhanced MRI provides a reliable tool for quantifying peritumoral infiltration in GBM and supports non-invasive

intraoperative risk prediction.

关键词

Keywords

references

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