Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.

ABSTRACT:

Background

Stereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort.

Methods

Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T₁ post-contrast (T₁c) and T₂ fluid-attenuated inversion recovery (T₂-FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort (N = 95) and valida

SUBMITTER: Chen X 

PROVIDER: S-EPMC8661085 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature