Establish and validation of a clinical-radiomics model for preoperative prediction of the differentiation degree of pancreatic cancer before operation

LIU Min; ZHOU Qian; WU Yawei; ZANG Xiu; FEI Xiaojing; HOU Jinlu; YE Dehua

doi:10.19732/j.cnki.2096-6210.2022.01.011

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Establish and validation of a clinical-radiomics model for preoperative prediction of the differentiation degree of pancreatic cancer before operation

更新时间：2025-12-15

- Establish and validation of a clinical-radiomics model for preoperative prediction of the differentiation degree of pancreatic cancer before operation
- Vol. 31, Issue 1, Pages: 64-68(2022)
- 作者机构：
  
  1. 盱眙县人民医院影像科,江苏,淮安,223000
  2. 无锡市锡山人民医院鹅湖分院消化内科,江苏,无锡,214000
  3. 盐城市第一人民医院影像科,江苏,盐城,224000
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2022.01.011
  CLC：
- Published Online：28 February 2022，
  
  Published：28 February 2022
- 稿件说明：
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刘敏,周倩,吴雅蔚,等. 术前预测胰腺癌分化程度的临床-影像组学模型的建立和验证[J]. 肿瘤影像学, 2022, 31(1): 64-68 https://doi.

org/10.19732/j.cnki.2096-6210.2022.01.011
刘敏,周倩,吴雅蔚,等. 术前预测胰腺癌分化程度的临床-影像组学模型的建立和验证[J]. 肿瘤影像学, 2022, 31(1): 64-68 https://doi. DOI： 10.19732/j.cnki.2096-6210.2022.01.011.

org/10.19732/j.cnki.2096-6210.2022.01.011 DOI：

摘要

目的：

建立预测胰腺癌分化程度的临床-影像组学模型，并在独立队列中进行验证。

方法：

收集56例经病理学检查确诊为胰腺癌的患者。基于MaZda将整个病灶作为感兴趣区（region of interest，ROI），采用软件自带的3种提取方式提取共计30个影像组学特征，并去除重复的特征。然后对影像组学特征进行检测，剔除高共线性特征。依据正态性检验，分别行独立样本t检验和Mann-Whitney U检验。再联合临床指标糖类抗原（carbohydrate antigen，CA）19-9，构建临床-影像组学预测模型。

结果：

特征Teta2和S（1，0）Entropy在高、中低分化组胰腺癌中存在显著差异，曲线下面积（area under curve，AUC）分别为0.68和0.70。两者联合得到的AUC为0.74。联合肿瘤标志物CA19-9建立的临床-影像组学模型的AUC为0.82，该临床-影像组学模型在验证组中同样获得了较好的诊断效力（AUC为0.78）。

结论：

联合影像组学特征和临床指标构建的临床-影像组学预测模型可辅助评判胰腺癌分化程度。

Abstract

Objective:

To establish a clinical-radiomics prediction model for the differentiation degree of pancreatic cancer

and verify its performance in an independent cohort.

Methods:

Fifty-six patients diagnosed with pancreatic cancer by pathological examination were collected. Based on MaZda

whole-lesion region of interest (ROI) was placed to extract radiomics features

and a total of 30 radiomics features were extracted using three extraction methods

and dupli

cate radiomics features were removed. Then the high-collinearity features were removed from the radiomics feature detection. According to the normality test

independent sample t test and Mann-Whitney U test were performed

respectively. Then

combined with the clinical indicator carbohydrate antigen (CA)19-9

a clinical-image prediction model was constructed.

Results:

Teta2 and S(1

0) Entropy differed significantly between highly and moderately-poorly differentiated groups

and area under curve (AUC) was 0.68 and 0.70

respectively. The AUC obtained by the combination of Teta2 and S(1

0) Entropy was 0.74. The AUC of the clinical-radiomics mode which integrated CA19-9 was 0.815. In the validation group

the clinical-radiomics model also achieved good diagnostic performance (AUC=0.78).

Conclusion:

The clinical-radiomics prediction model based on texture features and clinical classic indicators might be helpful for predicting the differentiation degree of pancreatic cancer.

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