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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Pages415-422
Number of pages8
ISBN (Print)9783030322380
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11764 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period19/10/1319/10/17

Keywords

  • Genetic information
  • Glioblastoma
  • Overall survival time prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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