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

Jun Wang; Xia Liu; Di Dong; Jiangdian Song; Min Xu; Yali Zang; Jie Tian

doi:10.1109/EMBC.2016.7590938

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

Jun Wang
, Xia Liu^*
, Di Dong
, Jiangdian Song
, Min Xu
, Yali Zang
, Jie Tian

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

58 Scopus citations

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.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1272-1275
Number of pages	4
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7590938
State	Published - 13 Oct 2016
Externally published	Yes
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	16/08/16 → 20/08/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1109/EMBC.2016.7590938

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Wang, J., Liu, X., Dong, D., Song, J., Xu, M., Zang, Y., & Tian, J. (2016). Prediction of malignant and benign of lung tumor using a quantitative radiomic method. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 1272-1275). Article 7590938 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7590938

Wang, Jun ; Liu, Xia ; Dong, Di et al. / Prediction of malignant and benign of lung tumor using a quantitative radiomic method. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1272-1275 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{2204290e979249b6b8d96b5d32b1103d,

title = "Prediction of malignant and benign of lung tumor using a quantitative radiomic method",

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.",

author = "Jun Wang and Xia Liu and Di Dong and Jiangdian Song and Min Xu and Yali Zang and Jie Tian",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",

year = "2016",

month = oct,

day = "13",

doi = "10.1109/EMBC.2016.7590938",

language = "英语",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1272--1275",

booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",

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}

Wang, J, Liu, X, Dong, D, Song, J, Xu, M, Zang, Y & Tian, J 2016, Prediction of malignant and benign of lung tumor using a quantitative radiomic method. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7590938, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, Institute of Electrical and Electronics Engineers Inc., pp. 1272-1275, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7590938

Prediction of malignant and benign of lung tumor using a quantitative radiomic method. / Wang, Jun; Liu, Xia; Dong, Di et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1272-1275 7590938 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of malignant and benign of lung tumor using a quantitative radiomic method

AU - Wang, Jun

AU - Liu, Xia

AU - Dong, Di

AU - Song, Jiangdian

AU - Xu, Min

AU - Zang, Yali

AU - Tian, Jie

PY - 2016/10/13

Y1 - 2016/10/13

N2 - 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.

AB - 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.

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M3 - 会议稿件

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T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 1272

EP - 1275

BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

Y2 - 16 August 2016 through 20 August 2016

ER -

Wang J, Liu X, Dong D, Song J, Xu M, Zang Y et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1272-1275. 7590938. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7590938

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

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