Predictors of pathological grade in patients with brain stem glioma and its relationship with APT imaging

TANG Yilin; LIN Li; HUANG Li; LIN Huiqing

doi:10.19732/j.cnki.2096-6210.2024.02.008

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Predictors of pathological grade in patients with brain stem glioma and its relationship with APT imaging

更新时间：2025-12-15

- Predictors of pathological grade in patients with brain stem glioma and its relationship with APT imaging
- Vol. 33, Issue 2, Pages: 150-155(2024)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2024.02.008
  CLC：
- Published Online：11 May 2024，
  
  Published：11 May 2024
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汤奕林,林砺,黄丽,等. 脑干胶质瘤病理学分级预测因素及与APT成像的关系研究[J]. 肿瘤影像学, 2024, 33(2): 150-155 https://doi.

org/10.19732/j.cnki.2096-6210.2024.02.008
汤奕林,林砺,黄丽,等. 脑干胶质瘤病理学分级预测因素及与APT成像的关系研究[J]. 肿瘤影像学, 2024, 33(2): 150-155 https://doi. DOI： 10.19732/j.cnki.2096-6210.2024.02.008.

org/10.19732/j.cnki.2096-6210.2024.02.008 DOI：

摘要

目的：

探讨脑干胶质瘤病理学分级的预测因素及与酰胺质子转移（amide proton transfer，APT）成像的关系，旨在为高级别胶质瘤患者早期诊断识别及后续预测模型构建提供借鉴。

方法：

回顾并纳入2019年1月—2023年1月于第九〇九医院行手术治疗并接受APT成像检查的脑干胶质瘤患者79例，根据病理学分级标准分为高级别组（38例）和低级别组（41例）；采用单因素分析和多因素logistic回归分析评价脑干胶质瘤病理学分级的独立预测因素，并分析脑干胶质瘤病理学分级预测因素的预测效能。

结果：

单因素分析结果显示，囊性病变、病灶最大径、APT成像信号强度平均值及最大值均可能与脑干胶质瘤病理学分级相关（

＜0.05）；多因素logistic分析结果显示，囊性病变、病灶最大径≥2 cm、APT成像信号强度平均值及最大值均是脑干胶质瘤病理学分级的独立预测因素（

＜0.05）。利用囊性病变、病灶最大径≥2 cm、APT成像信号强度平均值、APT成像信号强度最大值及logistic模型预测概率对脑干胶质瘤病理学分级情况进行预测，最佳截断值分别为0.50、0.50、2.95%、4.11%、37.85%，约登指数分别为38.19%、44.42%、51.73%、42.17%、65.28%。

结论：

囊性病变、病灶最大径与APT成像信号强度可作为脑干胶质瘤病理学分级的独立预测因素，且APT成像信号强度平均值具有更佳的预测效能。

Abstract

Objective:

To explore the predictive factors of pathological grade of brain stem glioma and its relationship with amide proton transfer (APT) imaging

in order to provide reference for early diagnosis and recognition of high-grade glioma patients and subsequent prediction model construction.

Methods:

A total of 79 brain stem glioma patients under

went surgical treatment and APT imaging were retrospectively chosen in the period from January 2019 to January 2023 in the 909th Hospital and grouped according to histopathological grading into high-grade group (38 cases) and low-grade group (41 cases). The independent predictors of pathological grade of brain stem glioma were evaluated by univariate factor and multivariate factor method. Predictive efficacies of prognostic factors for pathological grading of brainstem gliomas were analyzed.

Results:

The results of univariate analysis showed that cystic lesions

maximum lesion diameter

mean and maximum APT imaging signal intensity may be related to the pathological grade of brain stem glioma (

＜0.05). The results of multi-factor analysis showed that cystic lesions

maximum lesion diameter ≥2 cm

average and maximum APT imaging signal intensity were independent predictors of the pathological grade of brain stem glioma (

＜0.05). Cystic lesions

maximum lesion diameter ≥2 cm

mean value of APT imaging signal intensity

maximum value of APT imaging signal intensity and logistic model prediction probability were used to predict the pathological grade of brain stem glioma. The optimal cut- off values were 0.50

0.50

2.95%

4.11% and 37.85%

respectively. The Yoden index was 38.19%

44.42%

51.73%

42.17% and 65.28%

respectively.

Conclusion:

Cystic lesion

maximum lesion diameter and APT imaging signal intensity can be independent predictors of brain stem glioma pathological grade

and mean APT imaging signal intensity has better predictive efficacy.

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