Prediction of Malignant and Benign of Lung Tumor using a Quantitative Radiomic Method
文献类型：会议论文
| 作者 | Wang, Jun1,2; Liu, Xia1; Dong, Di2 ; Song, Jiangdian3; Xu, Min2; Zang, Yali2; Tian, Jie2; Tian Jie
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| 出版日期 | 2016 |
| 会议日期 | 2016-8 |
| 会议地点 | Orlando, Florida USA |
| 关键词 | Radiomics |
| 英文摘要 | 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. |
| 会议录 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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| 源URL | [http://ir.ia.ac.cn/handle/173211/12479]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Liu, Xia; Zang, Yali; Tian Jie |
| 作者单位 | 1.Measurement-Control Technology and Communications Engineering School, Harbin University of Science and Technology 2.Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences 3.Sino-Dutch Biomedical and Information Engineering School, Northeastern University |
| 推荐引用方式 GB/T 7714 | Wang, Jun,Liu, Xia,Dong, Di,et al. Prediction of Malignant and Benign of Lung Tumor using a Quantitative Radiomic Method[C]. 见:. Orlando, Florida USA. 2016-8. |
入库方式： OAI收割
来源：自动化研究所
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