Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype

Zhenyu Tang; Yuyun Xu; Zhicheng Jiao; Junfeng Lu; Lei Jin; Abudumijiti Aibaidula; Jinsong Wu; Qian Wang; Han Zhang; Dinggang Shen

doi:10.1007/978-3-030-32239-7_46

Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype

Zhenyu Tang, Yuyun Xu, Zhicheng Jiao, Junfeng Lu, Lei Jin, Abudumijiti Aibaidula, Jinsong Wu, Qian Wang, Han Zhang, Dinggang Shen

Department of Brain and Cognitive Engineering

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

8 Citations (Scopus)

Abstract

Glioblastoma (GBM) is the most common and deadly malignant brain tumor with short yet varied overall survival (OS) time. Per request of personalized treatment, accurate pre-operative prognosis for GBM patients is highly desired. Currently, many machine learning-based studies have been conducted to predict OS time based on pre-operative multimodal MR images of brain tumor patients. However, tumor genotype, such as MGMT and IDH, which has been proven to have strong relationship with OS, is completely not considered in pre-operative prognosis as the genotype information is unavailable until craniotomy. In this paper, we propose a new deep learning based method for OS time prediction. It can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction. Particularly, we propose a multi-task convolutional neural network (CNN) to accomplish tumor genotype and OS time prediction tasks. As the network can benefit from learning genotype related features toward genotype prediction, we verify upon a dataset of 120 GBM patients and conclude that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	415-422
Number of pages	8
ISBN (Print)	9783030322380
DOIs	https://doi.org/10.1007/978-3-030-32239-7_46
Publication status	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11764 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/13 → 19/10/17

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Genetic information
Glioblastoma
Overall survival time prediction

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-32239-7_46

Cite this

Tang, Z., Xu, Y., Jiao, Z., Lu, J., Jin, L., Aibaidula, A., Wu, J., Wang, Q., Zhang, H., & Shen, D. (2019). Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 415-422). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11764 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32239-7_46

Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype. / Tang, Zhenyu; Xu, Yuyun; Jiao, Zhicheng et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 415-422 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11764 LNCS).

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

Tang, Z, Xu, Y, Jiao, Z, Lu, J, Jin, L, Aibaidula, A, Wu, J, Wang, Q, Zhang, H & Shen, D 2019, Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11764 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 415-422, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32239-7_46

Tang Z, Xu Y, Jiao Z, Lu J, Jin L, Aibaidula A et al. Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 415-422. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32239-7_46

Tang, Zhenyu ; Xu, Yuyun ; Jiao, Zhicheng et al. / Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 415-422 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype",

abstract = "Glioblastoma (GBM) is the most common and deadly malignant brain tumor with short yet varied overall survival (OS) time. Per request of personalized treatment, accurate pre-operative prognosis for GBM patients is highly desired. Currently, many machine learning-based studies have been conducted to predict OS time based on pre-operative multimodal MR images of brain tumor patients. However, tumor genotype, such as MGMT and IDH, which has been proven to have strong relationship with OS, is completely not considered in pre-operative prognosis as the genotype information is unavailable until craniotomy. In this paper, we propose a new deep learning based method for OS time prediction. It can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction. Particularly, we propose a multi-task convolutional neural network (CNN) to accomplish tumor genotype and OS time prediction tasks. As the network can benefit from learning genotype related features toward genotype prediction, we verify upon a dataset of 120 GBM patients and conclude that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.",

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AU - Zhang, Han

AU - Shen, Dinggang

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Pre-operative overall survival time prediction for glioblastoma patients using deep learning on both imaging phenotype and genotype

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this