Document Type

Conference Paper

Publication Date

2020

DOI

10.1117/12.2550764

Publication Title

Proceedings of SPIE

Volume

11317

Pages

113170Y (1-7)

Conference Name

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 18-20 February 2020, Houston, Texas, U.S.A.

Abstract

An updated classification of diffuse lower-grade gliomas is established in the 2016 World Health Organization Classification of Tumors of the Central Nervous System based on their molecular mutations such as TP53 mutation. This study investigates machine learning methods for TP53 mutation status prediction and classification using radiomics and genomics features, respectively. Radiomics features represent patients' age and imaging features that are extracted from conventional MRI. Genomics feature is represented by patients’ gene expression using RNA sequencing. This study uses a total of 105 LGG patients, where the patient dataset is divided into a training set (80 patients) and testing set (25 patients). Three TP53 mutation prediction models are constructed based on the source of the training features; TP53-radiomics model, TP53-genomics model, and TP53-radiogenomics model, respectively. Radiomics feature selection is performed using recursive feature selection method. For genomics data, EdgeR method is utilized to select the differentially expressed genes between the mutated TP53 versus the non-mutated TP53 cases in the training set. The training classification model is constructed using Random Forest and cross-validated using repeated 10-fold cross validation. Finally, the predictive performance of the three models is assessed using the testing set. The three models, TP53-Radiomics, TP53-RadioGenomics, and TP53-Genomics, achieve a predictive accuracy of 0.84±0.04, 0.92±0.04, and 0.89±0.07, respectively. These results show promise of non-invasive MRI radiomics features and fusion of radiomics with genomics features for prediction of TP53.

Comments

SPIE grants to authors (and their employers) of papers, posters, and presentation recordings published in SPIE Proceedings or SPIE Journals on the SPIE Digital Library (hereinafter "publications") the right to post an author-prepared version or an official version (preferred version) of the publication on an internal or external server controlled exclusively by the author/employer or the entity funding the research, provided that (a) such posting is noncommercial in nature and the publication is made available to users without charge; (b) an appropriate copyright notice and citation appear with the publication; and (c) a link to SPIE's official online version of the publication is provided using the item's DOI.

© 2020 SPIE

Publisher's version available at: https://doi.org/10.1117/12.2550764

Original Publication Citation

Shboul, Z., & Iftekharuddin, K. (2020). Efficacy of radiomics and genomics in predicting TP53 mutations in diffuse lower grade glioma. In Krol, A. and Gimi, B.S. (Eds.), Medical Imaging 2020: Biomedical Applications in Molecular, Structural and Functional Imaging, Houston, Texas, U.S.A. 113170Y (1-7). SPIE. https://doi.org/10.1117/12.2550764

Download

Share

COinS