ABSTRACT
Background:
Diffuse midline
gliomas (DMG) are aggressive pediatric
brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select
radiomic features that predict
patient overall
survival (OS).
Methods:
We acquired diagnostic and post-
radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG
patients. We pretrained a
deep learning model on a public
adult brain tumor dataset, and finetuned it to automatically segment
tumor core (TC) and whole
tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two
machine learning models were trained on our internal cohort to predict
patient 1-year
survival from
diagnosis. One model used only diagnostic
tumor features and the other used both diagnostic and post-RT features.
Results:
For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at
time of
diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS.
Conclusions:
Machine learning analysis of MRI
radiomics has potential to accurately and non-invasively predict which pediatric
patients with DMG
will survive less than one year from the
time of
diagnosis to provide
patient stratification and guide
therapy.