Baseline texture features extracted from dynamic contrast-enhanced magnetic resonance imaging for predicting the pathological response to chemoradiotherapy in rectal cancer

LIU Siye; WEN Lu; HOU Jing; NIE Shaolin; ZHOU Jumei; CAO Fang; LU Qiang; QIN Yuhui; YU Xiaoping

doi:10.19732/j.cnki.2096-6210.2020.02.013

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Baseline texture features extracted from dynamic contrast-enhanced magnetic resonance imaging for predicting the pathological response to chemoradiotherapy in rectal cancer

更新时间：2025-12-15

- Baseline texture features extracted from dynamic contrast-enhanced magnetic resonance imaging for predicting the pathological response to chemoradiotherapy in rectal cancer
- Vol. 29, Issue 2, Pages: 148-154(2020)
- 作者机构：
  
  1. 中南大学湘雅医学院附属肿瘤医院，湖南省肿瘤医院放射诊断科,湖南,长沙,410006
  2. 中南大学湘雅医学院附属肿瘤医院，湖南省肿瘤医院结直肠外科,湖南,长沙,410006
  3. 中南大学湘雅医学院附属肿瘤医院，湖南省肿瘤医院放疗科,湖南,长沙,410006
  4. 中南大学湘雅医学院附属肿瘤医院，湖南省肿瘤医院病理科,湖南,长沙,410006
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2020.02.013
  CLC：
- Published Online：28 April 2020，
  
  Published：28 April 2020
- 稿件说明：
移动端阅览
刘思野,文露,侯静,等. 基线动态对比增强磁共振成像纹理特征对直肠癌放化疗病理反应状态的预测价值[J]. 肿瘤影像学, 2020, 29(2): 148-154 https://doi.

org/10.19732/j.cnki.2096-6210.2020.02.013
刘思野,文露,侯静,等. 基线动态对比增强磁共振成像纹理特征对直肠癌放化疗病理反应状态的预测价值[J]. 肿瘤影像学, 2020, 29(2): 148-154 https://doi. DOI： 10.19732/j.cnki.2096-6210.2020.02.013.

org/10.19732/j.cnki.2096-6210.2020.02.013 DOI：

摘要

目的：

探讨基线动态对比增强磁共振成像（dynamic contrast-enhanced magnetic resonance imaging，DCE-MRI）参数图纹理特征在预测直肠癌原发灶新辅助放化疗病理反应方面的价值。

方法：

搜集38例局部进展期的直肠癌患者治疗前的DCE-MRI及术后病理学检查资料，采用受试者工作特征（receiver operating characteristic，ROC）曲线比较治疗前DCE-MRI参数［容量转运常数（volume transfer constant，K

trans

）、速率常数（rate constant，K

）、血浆容量分数（plasma volume fraction，fPV）、血管外细胞外间隙容量分数（extravascular extracellular volume fraction，V

）］及参数图纹理特征对原发灶病理学完全缓解（pathological complete response，pCR）的预测效能。

结果：

基线DCE-MRI参数值在pCR组与非pCR组间差异无统计学意义。与非pCR组相比，pCR组的偏度 V

（Skewness V

）、相关度 K

（Correlat K

）、差分方差

fPV

（difference variance of

fPV

，DifVarnc

fPV

）、差分方差 K

（difference variance of K

，DifVarnc K

）、差分熵

fPV

（difference entropy of

fPV

，DifEntrp

fPV

）、差分熵 V

（difference entropy of V

，DifEntrp V

）、熵

fPV

（Entropy

fPV

）、熵 Ve （Entropy

）、和平均数

fPV

（sum average of

fPV，SumAverg

fPV

）、和熵

fPV

（sum entropy of fPV，SumEntrp

fPV

）及和熵 V

（sum entropy of V

，SumEntrp

）值更高，均数 V

（Mean V

）、角二阶矩

fPV

（angular second moment of fPV，AngScMom

fPV

）、角二阶矩 Ve（angular second moment of V

，AngScMom

）、对比度

ktrans

（Contrast ktrans ）、逆差矩

fPV

（Inverse Difference Moment of fPV，InvDfMom

fPV

）、和平均数K

trans

（sum average of K

trans

，SumAverg

ktrans

）及和平均数 V

（sum average of V

，SumAverg

）值更低。单个纹理特征预测pCR的ROC曲线的曲线下面积（area under curve，AUC）值为0.571~0.817；在多变量分析中，基于二级纹理特征构建的logistic回归模型预测pCR的AUC高于基于一级纹理特征构建的logistic回归模型（

=0.028）。

结论：

基线DCE-MRI参数图纹理特征可能在预测直肠癌原发灶新辅助放化疗病理反应状态方面有潜在价值。

Abstract

Objective:

To evaluate the utility of baseline texture features based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC).

Methods:

Thirty-eight patients with LARC received DCE-MRI before neoadjuvant chemoradiotherapy. The data of DCE-MRI and the postoperative pathological data were collected. Receiver operating characteristic (ROC) curves were used to determine the efficiency of the DCE-MRI parameters (K

trans

fPV and V

) and texture features derived from the DCE-MRI maps (K

trans

fPV and V

) for identifying pCR.

Results:

The DCE-MRI parameters (K

trans

fPV and V

) showed no significant difference between the pCR group and the non-pCR group. Compared to the non-pCR group

the pCR group exhibited significantly higher values of Skewne

Correlat

Kep

difference variance of fPV (DifVarnc

fPV

)

difference variance of K

(DifVarnc

Kep

)

difference entropy of fPV (DifEntrp

fPV

)

difference entropy of V

(DifEntrp

)

Entropy

fPV

Entropy

sum average of fPV (SumAverg

fPV

)

sum entropy of fPV (SumEntrp

fPV

) and sum entropy of V

(SumEntrp

)

as well as obviously lower values of Mean

angular second moment of fPV (AngScMom

fPV

)

angular second moment of V

(AngScMom

)

Contrast

ktrans

inverse difference moment of fPV (InvDfMom

fPV

)

sum average of K

trans

(SumAverg

Ktrans

) and sum average of V

(SumAverg

). In univariate analysis

the area under the ROC curve (AUC) values for individual predictor to identify pCR ranged from 0.571~0.817. In multivariate analysis

the gray level co-occurrence matrix texture features-based (GLCM TFs-based) logistic regression model had significantly higher AUC value than that based on the first-order TFs (

=0.028) in the differentiation between pCR and non-pCR.

Conclusion:

TFs based on baseline DCE-MRI may be potential to predict the pathological response of LARC receiving neoadjuvant chemoradiotherapy.

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