Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features

Asma Shaheen, Stefano Burigat, Ulas Bagci, Hassan Mohy-ud-Din*

*Corresponding author for this work

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

Abstract

In this paper, we explored predictive performance of region-specific radiomic models for overall survival classification task in BraTS 2019 dataset. We independently trained three radiomic models: single-region model which included radiomic features from whole tumor (WT) region only, 3-subregions model which included radiomic features from non-enhancing tumor (NET), enhancing tumor (ET), and edema (ED) subregions, and 6-subregions model which included features from the left and right cerebral cortex, the left and right cerebral white matter, and the left and right lateral ventricle subregions. A 3-subregions radiomics model relied on a physiology-based subdivision of WT for each subject. A 6-subregions radiomics model relied on an anatomy-based segmentation of tumor-affected regions for each subject which is obtained by a diffeomorphic registration with the Harvard-Oxford subcortical atlas. For each radiomics model, a subset of most predictive features was selected by ElasticNetCV and used to train a Random Forest classifier. Our results showed that a 6-subregions radiomics model outperformed the 3-subregions and WT radiomic models on the BraTS 2019 training and validation datasets. A 6-subregions radiomics model achieved a classification accuracy of 47.1% on the training dataset and a classification accuracy of 55.2% on the validation dataset. Among the single subregion models, Edema radiomics model and Left Lateral Ventricle radiomics model yielded the highest classification accuracy on the training and validation datasets.

Original languageEnglish (US)
Title of host publicationMachine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsSeyed Mostafa Kia, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers, Saima Rathore, Madhura Ingalhalikar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages259-267
Number of pages9
ISBN (Print)9783030668426
DOIs
StatePublished - 2020
Event3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/8/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features'. Together they form a unique fingerprint.

Cite this