Predicting the expression of Ki-67 in hepatocellular carcinoma based on radiomics approach

DAI Meng; DONG Yi; HAN Hong; CAO Jiaying; YU Jinhua; WANG Wenping

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Predicting the expression of Ki-67 in hepatocellular carcinoma based on radiomics approach

更新时间：2025-12-15

- Predicting the expression of Ki-67 in hepatocellular carcinoma based on radiomics approach
- Vol. 27, Issue 1, Pages: 7-11(2018)
- 作者机构：
  
  1. 复旦大学附属中山医院超声科,上海,200032
  2. 复旦大学电子工程系,上海,200433
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：03 April 2018，
  
  Published：03 April 2018
- 稿件说明：
移动端阅览
戴猛,董怡,韩红,等. 原发性肝细胞肝癌Ki-67表达的影像组学预测[J]. 肿瘤影像学, 2018, 27(1): 7-11
DOI：

戴猛,董怡,韩红,等. 原发性肝细胞肝癌Ki-67表达的影像组学预测[J]. 肿瘤影像学, 2018, 27(1): 7-11 DOI：

摘要

目的：

基于影像组学方法，分析超声医学影像信息，评估影像组学方法预测原发性肝细胞癌（hepatocellular carcinoma，HCC）中Ki-67表达的可行性。

方法：

回顾性分析133例经术后病理证实的HCC患者的灰阶超声图像，通过图像分割、提取图像中肿瘤病灶的小波、纹理、形态特征，结合最大相关最小冗余（minimum-redundancy-maximum-relevance，mRMR）准则的遗传算法选出234个特征，之后再用稀疏表示（sparse representation coefficient

SRC）方法进一步筛选，得到最优特征子集并用于分类。

结果：

利用支持向量机（support vector machine，SVM）和留一法（leave-one-out cross validation，LOOCV）的预测模型对临床HCC超声图像进行分类和评价。结果显示，受试者操作特性曲线下面积（area under the receiver operating characteristic curve，AUC）达到了0.75。

结论：

基于HCC超声灰阶图像的影像组学分析和Ki-67表达存在相关性，可以为临床合理诊疗和预后预测提供有用的信息。

Abstract

Objective:

This retrospective study aims to evaluate the feasibility of gray scale ultrasound image based radiomics analysis in prediction of Ki-67 positive rate in histopathollogically proved hepatocellular carcinoma (HCC).

Methods:

Grayscale ultrasound images of 133 patients that underwent operation and histopathologically proved HCC lesions were analyzed. Ultrasound gray scale images (GS-US) were segmented to extract the wavelet

texture and morphological features of the tumor in the image. Afterwards

234 features were selected by genetic algor

ithm with minimum-redundancy-maximum-relevance (mRMR)

and were further screened by the sparse representation method. The best feature subsets were used for classification.

Results:

GS-US images of HCC lesions were classified and evaluated by the prediction model of support vector machine (SVM) with leave-one-of-cross validation (LOOCV). The area under the receiver operating characteristic curve (AUC) in the results reached 0.75.

Conclusion:

Imaging (based radiomics approach) analysis of GS-US images correlated to the Ki-67 expression positive rate in HCC lesions

which might be helpful in clinical management and prognosis prediction.

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