Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer

Wei Wei; Yu Rong; Zhenyu Liu; Bin Zhou; Zhenchao Tang; Shuo Wang; Di Dong; Yali Zang; Yingkun Guo; Jie Tian

doi:10.1109/EMBC.2018.8513351

Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer

Wei Wei^*
, Yu Rong
, Zhenyu Liu
, Bin Zhou
, Zhenchao Tang
, Shuo Wang
, Di Dong
, Yali Zang
, Yingkun Guo
, Jie Tian

^*Corresponding author for this work

Xidian University
West China Second University Hospital
CAS - Institute of Automation

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

19 Scopus citations

Abstract

In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8567 (95% CI: 0.7251-0.9498) and 0.8533 (95% CI: 0.7231-0.9671) were obtained in the training cohort and validation cohort with the logistic regression classification model respectively. Experimental results show that CT-based radiomics features were closely associated with the recurrence of advanced ovarian cancer. It is possible to prejudge the recurrence of ovarian cancer before surgery.

Original language	English
Title of host publication	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4130-4133
Number of pages	4
ISBN (Electronic)	9781538636466
DOIs	https://doi.org/10.1109/EMBC.2018.8513351
State	Published - 26 Oct 2018
Externally published	Yes
Event	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States Duration: 18 Jul 2018 → 21 Jul 2018

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2018-July
ISSN (Print)	1557-170X

Conference

Conference	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Country/Territory	United States
City	Honolulu
Period	18/07/18 → 21/07/18

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.2018.8513351

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Wei, W., Rong, Y., Liu, Z., Zhou, B., Tang, Z., Wang, S., Dong, D., Zang, Y., Guo, Y., & Tian, J. (2018). Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 4130-4133). Article 8513351 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8513351

Wei, Wei ; Rong, Yu ; Liu, Zhenyu et al. / Radiomics : A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4130-4133 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{a0126316d98140f6b77d5dfd23e1c511,

title = "Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer",

abstract = "In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8567 (95\% CI: 0.7251-0.9498) and 0.8533 (95\% CI: 0.7231-0.9671) were obtained in the training cohort and validation cohort with the logistic regression classification model respectively. Experimental results show that CT-based radiomics features were closely associated with the recurrence of advanced ovarian cancer. It is possible to prejudge the recurrence of ovarian cancer before surgery.",

author = "Wei Wei and Yu Rong and Zhenyu Liu and Bin Zhou and Zhenchao Tang and Shuo Wang and Di Dong and Yali Zang and Yingkun Guo and Jie Tian",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 ; Conference date: 18-07-2018 Through 21-07-2018",

year = "2018",

month = oct,

day = "26",

doi = "10.1109/EMBC.2018.8513351",

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 = "4130--4133",

booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",

address = "美国",

}

Wei, W, Rong, Y, Liu, Z, Zhou, B, Tang, Z , Wang, S, Dong, D, Zang, Y, Guo, Y & Tian, J 2018, Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018., 8513351, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 4130-4133, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 18/07/18. https://doi.org/10.1109/EMBC.2018.8513351

Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. / Wei, Wei; Rong, Yu; Liu, Zhenyu et al.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 4130-4133 8513351 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July).

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

TY - GEN

T1 - Radiomics

T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

AU - Wei, Wei

AU - Rong, Yu

AU - Liu, Zhenyu

AU - Zhou, Bin

AU - Tang, Zhenchao

AU - Wang, Shuo

AU - Dong, Di

AU - Zang, Yali

AU - Guo, Yingkun

AU - Tian, Jie

PY - 2018/10/26

Y1 - 2018/10/26

N2 - In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8567 (95% CI: 0.7251-0.9498) and 0.8533 (95% CI: 0.7231-0.9671) were obtained in the training cohort and validation cohort with the logistic regression classification model respectively. Experimental results show that CT-based radiomics features were closely associated with the recurrence of advanced ovarian cancer. It is possible to prejudge the recurrence of ovarian cancer before surgery.

AB - In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8567 (95% CI: 0.7251-0.9498) and 0.8533 (95% CI: 0.7231-0.9671) were obtained in the training cohort and validation cohort with the logistic regression classification model respectively. Experimental results show that CT-based radiomics features were closely associated with the recurrence of advanced ovarian cancer. It is possible to prejudge the recurrence of ovarian cancer before surgery.

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

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BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 July 2018 through 21 July 2018

ER -

Wei W, Rong Y, Liu Z, Zhou B, Tang Z , Wang S et al. Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4130-4133. 8513351. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2018.8513351

Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer

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