MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

ABSTRACT:

Background

Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model.

Methods

We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model per

SUBMITTER: Tam LT 

PROVIDER: S-EPMC8095337 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature