Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

ABSTRACT:

Background

Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis.

Objective

To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP.

Methods

We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step

SUBMITTER: Zhang M 

PROVIDER: S-EPMC8764569 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature