论著摘要 |【CT】使用定量影像组学方法预测肺肿瘤的良恶性(双语版)

2017-12-18 16:28:19 admin 6

Prediction of malignant and benign of lung tumor using a quantitative radiomic method.

发表日期: 2016.08.16   来源Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016:1272-1275.

作者

Jun Wang1, Xia Liu2, Di Dong3 , Jiangdian Song4, Min Xu3, Yali Zang3, Jie Tian3

作者介绍: 

1. Measurement-Control Technology and Communications Engineering School, Harbin University of Science and Technology, Harbin, 150080, China.

2. Harbin University of Science and Technology, Harbin, 150080, China.

3. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

4. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110819, China.

摘要

肺癌是全球癌症死亡的主要原因,肺癌的早期诊断在治疗方案选择中起着非常重要的作用。然而,肺癌在空间和时间上的异质性限制了侵入性活检的使用。但是通过应用大量定量图像特征来综合定量肿瘤表型的影像组学可以非侵入性方式捕获肿瘤内异质性。在这里,我们进行了150个特征的影像组学分析,量化肺肿瘤图像强度,形状和纹理。这些特征是从肺图像数据库联盟图像数据库资源计划(LIDC-IDRI)数据集中的593例计算机断层扫描(CT)数据中提取的。通过使用支持向量机,我们发现大量的定量影像组学特征具有诊断力。肺组织恶性肿瘤预测准确度训练组为86%,测试组为76.1%。由于肺肿瘤的CT成像被广泛应用于常规临床实践中,我们的影像组学分类器将成为有助于临床医生诊断肺癌的重要工具。

Abstract

Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture. These features are extracted from 593 patients computed tomography (CT) data on Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI) dataset. By using support vector machine, we find that a large number of quantitative radiomic features have diagnosis power. The accuracy of prediction of malignant of lung tumor is 86% in training set and 76.1% in testing set. As CT imaging of lung tumor is widely used in routine clinical practice, our radiomic classifier will be a valuable tool which can help clinical doctor diagnose the lung cancer.


关键词:

癌症,肺部,肿瘤,支持向量机,特征提取,图像分割,计算机断层扫描

Keywords:

Cancer, Lungs, Tumors, Support vector machines, Feature extraction, Image segmentation, Computed tomography

阅读原文:PMID: 28268557  DOI: 10.1109/EMBC.2016.7590938

 

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