Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

ABSTRACT:

Background

Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO).

Methods

Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" M

SUBMITTER: Chang K 

PROVIDER: S-EPMC6827825 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature