Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas

WANG Hanwei; ZENG Linlan; ZHAO Mimi; LI Xuan; XIE Huan; LI Xiaoguang; YI Liang; WU Yi; WANG Shunan

doi:10.16016/j.2097-0927.202501030

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Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas

Monographic Reports | 更新时间：2025-07-25

- Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas
- Journal of Army Medical University Vol. 47, Issue 14, Pages: 1577-1586(2025)
- 作者机构：
  
  1.陆军特色医学中心（第三军医大学大坪医院）：，放射科，重庆市影像医学与核医学临床研究中心，重庆
  2.陆军特色医学中心（第三军医大学大坪医院）：，神经外科，重庆
  3.陆军军医大学（第三军医大学）生物医学工程与影像医学系数字医学教研室，重庆
- 作者简介：
  
  WU Yi， E-mail： wuy1979@tmmu.edu.cn；
  WANG Shunan， E-mail： wangshunan@tmmu.edu.cn
- 基金信息：
  
  the Joint Medical Research Project of Chongqing Science and Technology Commission and Chongqing Health Commission(2025QNXM014);the Clinical Medical Research Project of Army Medical University(2023XLC15);the Project of Science and Technology Plan of Chongqing Clinical Research Centre of Imaging and Nuclear Medicine(CSTC2015YFPT-gcjsyjzx0175)
- DOI：10.16016/j.2097-0927.202501030
  CLC： R181.23;R445.2;R730.264
- Received：11 January 2025，
  
  Revised：11 March 2025，
  
  Published：30 July 2025
- 稿件说明：
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‍王瀚苇，曾琳岚，赵咪咪，等.联合瘤内及瘤周异质性的MRI影像组学预测高级别胶质瘤原位复发空间模式的价值［J］.陆军军医大学学报，2025，47（14）：1577-1586.DOI：10.16016/j.2097-0927.202501030.

WANG H W，ZENG L L，ZHAO M M，et al.Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas［J］.J Army Med Univ，2025，47（14）：1577-1586.DOI：10.16016/j.2097-0927.202501030.
‍王瀚苇，曾琳岚，赵咪咪，等.联合瘤内及瘤周异质性的MRI影像组学预测高级别胶质瘤原位复发空间模式的价值［J］.陆军军医大学学报，2025，47（14）：1577-1586.DOI：10.16016/j.2097-0927.202501030. DOI：

WANG H W，ZENG L L，ZHAO M M，et al.Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas［J］.J Army Med Univ，2025，47（14）：1577-1586.DOI：10.16016/j.2097-0927.202501030. DOI：

摘要

目的

建立并验证基于瘤内及瘤周异质性的多模态MRI影像组学模型，预测高级别胶质瘤（high-grade gliomas， HGGs）原位复发空间模式。

方法

回顾性分析陆军特色医学中心2012-2021年采用最大范围安全切除并术后放疗联合替莫唑胺治疗后原位复发的HGGs患者临床及影像资料。2名放射科医师通过连续性随访MRI资料独立评估HGGs原位复发空间模式，分为切除腔内复发和切除腔外复发。采用完全随机法将患者按7∶3的比例分为训练集与验证集。在训练集中采用Pearson或Spearman相关性分析以及最小绝对收缩与选择算子分析，对瘤内及瘤周影像组学特征进行筛选及影像组学评分计算。采用Logistic回归分析建立影像组学模型，利用校准图、Hosmer-Lemeshow检验和受试者操作特征曲线下面积（AUC）评估模型效能，并在验证集中进行验证。

结果

本研究共纳入121例原位复发HGGs患者，其中切除腔内复发组54例，切除腔外复发组67例，按7∶3的比例分为训练集（

=84）和验证集（

=37）。在训练集中，切除腔外复发组的影像组学评分

为0.424（0.278，0.573），高于切除腔内复发组［-0.030（-0.226，0.248），

0.001］。同时，验证集中切除腔外复发组的影像组学评分［0.369（0.258，0.487）］高于切除腔内复发组［0.277（0.103，0.322），

=0.033］。通过Logistic回归分析建立的影像组学模型具有良好的校准效果，在预测原位复发空间模式方面表现良好，训练集AUC值为0.844（95%

：0.749~0.914），验证集AUC值为0.706（95%

：0.534~0.844）。

结论

联合瘤内及瘤周异质性的多模态影像组学模型可预测HGGs原位复发的空间模式，为HGGs的个体化治疗提供依据。

Abstract

Objective‍　‍To establish and validate a multimodal MRI radiomics model based on intratumoral and peritumoral heterogeneity for prediction of spatial pattern of locally recurrent high-grade gliomas (HGGs). Methods‍　‍A retrospective analysis was conducted on the clinical and imaging data of all HGGs patients who underwent maximum safe resection followed by postoperative radiotherapy combined with temozolomide treatment and experienced in local recurrence in Army Medical Center of PLA from 2012 to 2021. Two radiologists independently assessed the spatial patterns of locally recurrence HGGs through continuous follow-up MRI data

and primarily categorized the pattern into intra-resection cavity recurrence and extra-resection cavity recurrence. The subjected patients were randomly divided into a training set and a validation set in a 7∶3 ratio. In the training set

Pearson or Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) analysis were employed to screen radiomic features within the intratumoral and peritumoral regions

as well as to calculate radiomic scores. A radiomics model was established using logistic regression analysis. The performance of the model was assessed using calibration curves

Hosmer-Lemeshow goodness-of-fit test

and the area under the receiver operating characteristic curve (AUC). Validation of the model was performed in the validation set. Results‍　‍A total of 121 patients with locally recurrent HGGs were enrolled in this study

including 54 in intra-resection cavity r

ecurrence group and 67 in extra-resection cavity recurrence group. Among them

84 were assigned into the training set and 37 into the validation set. In the training set

the radiomics score for the extra-resection cavity recurrence group was 0.424 (0.278

0.573)

which was higher than that for the intra-resection cavity recurrence group [-0.030 (-0.226

0.248)

0.001

]

. In the validation set

the radiomics score for the extra-resection cavity recurrence group was 0.369 (0.258

0.487)

which was higher than that for the intra-resection cavity recurrence group [0.277 (0.103

0.322)

=0.033

]

. The established radiomics model exhibited good calibration and performed well in predicting spatial recurrence patterns

with an AUC value of 0.844 (95%

: 0.749~0.914) in the training set and 0.706 (95%

:0.534~0.844) in the validation set. Conclusion Our multimodal radiomics model combined with intratumoral and peritumoral heterogeneity can predict the spatial pattern of locally recurrent HGGs

providing a basis for individualized treatment of HGGs.

关键词

Keywords

references

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