Application of radiomics approach for decoding normal liver features and predicting chemotherapyassociated liver injury: A preliminary study

夏　凡; 胡盼盼; 王佳舟

doi:10.19401/j.cnki.1007-3639.2016.06.007

您当前的位置：

首页 >

文章列表页 >

Application of radiomics approach for decoding normal liver features and predicting chemotherapyassociated liver injury: A preliminary study

更新时间：2025-12-31

- Application of radiomics approach for decoding normal liver features and predicting chemotherapyassociated liver injury: A preliminary study
- China Oncology Vol. 26, Issue 6, Pages: 521-526(2016)
- 作者机构：
  
  复旦大学附属肿瘤医院放疗科，复旦大学上海医学院肿瘤学系,上海,200032
- 作者简介：
- 基金信息：
- DOI：10.19401/j.cnki.1007-3639.2016.06.007
  CLC：
- Published Online：28 July 2016，
  
  Published：28 July 2016
- 稿件说明：
移动端阅览
夏　凡,胡盼盼,王佳舟,等. 影像组学应用于肝脏特征分析及其预测治疗反应的初步研究[J]. 中国癌症杂志, 2016, 26(6): 521-526.

夏　凡, 胡盼盼, 王佳舟. Application of radiomics approach for decoding normal liver features and predicting chemotherapyassociated liver injury: A preliminary study[J]. China Oncology, 2016, 26(6): 521-526.
夏　凡,胡盼盼,王佳舟,等. 影像组学应用于肝脏特征分析及其预测治疗反应的初步研究[J]. 中国癌症杂志, 2016, 26(6): 521-526. DOI： 10.19401/j.cnki.1007-3639.2016.06.007.

夏　凡, 胡盼盼, 王佳舟. Application of radiomics approach for decoding normal liver features and predicting chemotherapyassociated liver injury: A preliminary study[J]. China Oncology, 2016, 26(6): 521-526. DOI： 10.19401/j.cnki.1007-3639.2016.06.007.

摘要

背景与目的：影像组学通过挖掘影像特征数据对组织特性进行判断，预测组织对治疗的反应和患者的预后。该研究旨在探索运用影像组学方法评估肝脏组织特征，以及预测化疗后肝功能异常的可行性。方法：回顾性收集胃癌患者化疗前腹部CT平扫图像，勾画全部肝脏，利用影像组学方法提取图像特征值并进行聚类分析。采用Pearson χ

检验分析聚类结果与患者临床特征的相关性，以及其与化疗后肝功能异常发生的相关性。结果：73例患者根据其图像特征聚类为两组，两组间性别构成比差异有统计学意义(P=0.004)。两组化疗后肝功能异常的发生率为48.7%和67.6%，相差为18.9%。临床参数如年龄、性别、化疗给药途径、化疗周期数、HBV感染史、化疗前血清HBsAg状态与化疗后肝功能异常发生率均无显著相关性。影像组学聚类分析预测，化疗后肝功能异常的准确性为0.59。结论：影像组学分析结果反应了不同性别人群肝脏影像学特征的差异，并可能有助于预测化疗后肝功能异常。影像组学用于评估肝脏组织特征以及预测治疗相关不良反应是可行的。

Abstract

Background and purpose: Radiomics refers to the comprehensive quantification of human tissues through assessing a large number of quantitative image features. Radiomics approach is used to decode tumor phenotypes and predict treatment outcomes. Here we present a study investigating radiomic analysis to assess normal liver features and predict chemotherapy-associated liver injury. Methods: Gastric cancer patients treated with surgery and adjuvant chemotherapy were enrolled in this study retrospectively. CT images were obtained before chemotherapy. The whole liver organ was delineated by radiation oncologists. Images were extracted and filtered by radiomic approach to extract radiomic features. Clustering was performed to reveal clusters of patients with similar radiomic expression patterns. Chi-squared tests were used to assess the association of radiomic data with clinical data and chemotherapy-related liver injury. Results: Radiomic features of 73 patients were clustered into two clusters. A significant association with gender (P=0.004

chi-squared test) was observed

where in male showed a higher presence in cluster Ⅰ. Incidence of abnormal liver function after chemotherapy was 48.7% in cluster I and 67.6% in cluster Ⅱ

respectively (Δ=18.9%). Clinical data including age

gender

chemotherapy modality

number of chemotherapy cycles

HBV infection history

HBs-antigen presence were not associated with liver function abnormalities after chemotherapy. Accuracy of radiomic analysis to predict liver injury is 0.59. Conclusion: Radiomic approach revealed different imaging features of liver between men and women. It could help to predict chemotherapy-associated liver injury. It is feasible to use radiomics approach to decode normal liver features and predict treatment-associated liver injury.

关键词

Keywords

references

Views

2552

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research progress and prospects of MRI in predicting lymph node status in breast cancer

MRI-based radiomics and deep learning model construction: non-invasive differentiation of molecular subtypes in primary intracranial diffuse large B-cell lymphoma

A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation

Research progress on the correlation between imaging features and the molecular subtype, histopathology, clinical prognosis of ductal carcinoma in situ of the breast

Research progress in predicting the risk of lymphatic or hematologic metastasis based on chest CT in early non-small cell lung cancer

Related Author

Sheng CHEN

Zihan ZHAI

Yanwei ZENG

Zhijian XU

Xin CAO

Kun LÜ

Huiming LI

Min GAO

Related Institution

Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University

Department of Radiology, Second Xiangya Hospital, Central South University

Department of Radiology, Zhongda Hospital, Southeast University

Academy for Engineering and Technology, Fudan University

Institute of Functional and Molecular Medical Imaging, Fudan University

AI问答

⁰