Prediction of pulmonary nodule malignancy based on radiomics

YANG Chunran; GUO Yi; WANG Yuanyuan

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Prediction of pulmonary nodule malignancy based on radiomics

更新时间：2025-12-15

- Prediction of pulmonary nodule malignancy based on radiomics
- Vol. 26, Issue 2, Pages: 97-101(2017)
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- Published Online：02 June 2017，
  
  Published：02 June 2017
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杨春然,郭翌,汪源源. 基于影像组学的肺结节恶性程度预测[J]. 肿瘤影像学, 2017, 26(2): 97-101
DOI：

杨春然,郭翌,汪源源. 基于影像组学的肺结节恶性程度预测[J]. 肿瘤影像学, 2017, 26(2): 97-101 DOI：

摘要

目的：

探讨基于影像组学的肺结节恶性程度预测。

方法：

对肺部图像影像数据库(The Lung Image Database Consortium，LIDC)-IDRI (Image Database Resource Initiative)中604例肺结节患者的CT图像进行分析，其中含肺结节的CT图像共2 803幅，医师手工勾画肺结节轮廓。根据肺结节诊断标准，共提取96个灰度、形态和纹理高通量特征，输入基于随机森林的多类分类器进行恶性程度预测。恶性程度分为5级，以数字1~5表示。随机选取1 000幅CT图像作为训练样本，剩余的1 803幅CT图像作为测试样本，实验重复10次。

结果：

对于单个肺结节，5类恶性程度的平均预测准确率为77.85%。对于每一类预测，曲线下面积(area under curve，AUC)均在0.94以上。对于每例患者，肺结节恶性程度的预测准确率为75.16%。

结论：

该研究提出的基于影像组学的方法对肺结节恶性程度的预测性能良好，可为临床诊断提供可靠的辅助信息，以利于早期发现病灶。

Abstract

Objective:

To investigate the method of malignancy prediction of pulmonary nodules based on radiomics.

Methods:

A total of 2 803 computed tomography (CT) images containing pulmonary nodules were extracted from 604 scans in the publicly available dataset of The Lung Image Database Consortium (LIDC)-Image Database Resource Initiative (IDRI). Each contour of nodules was labelled by the clinical doctor. Totally 96 high throughput features including gray level features

shape features and texture features were extracted according to the pulmonary nodule diagnosis criteria and put into the multi-class classifier based on the random forest to predict the malign

ancy. The degree of malignancy was classified into 1 to 5 levels. Among all images

1 000 of them were randomly chosen as the training set and the rest were used as the testing set. The experiment was repeated 10 times.

Results:

For a single nodule

the average prediction accuracy of five levels was 77.85%. The area under curve (AUC) of each category reached over 0.94. For each patient

the malignancy prediction accuracy of pulmonary nodules was 75.16%.

Conclusion:

The method of malignancy prediction of pulmonary nodules based on radiomics has a good performance. The results can provide a reliable basis for clinical diagnosis and help to detect the disease in the early stage.

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