A novel MRI-based deep learning-radiomics framework for evaluating cerebrospinal fluid signal in central nervous system infection.

ABSTRACT:

Introduction

Accurate and timely diagnosis of central nervous system infections (CNSIs) is critical, yet current gold-standard techniques like lumbar puncture (LP) remain invasive and prone to delay. This study proposes a novel noninvasive framework integrating handcrafted radiomic features and deep learning (DL) to identify cerebrospinal fluid (CSF) alterations on magnetic resonance imaging (MRI) in patients with acute CNSI.

Methods

Fifty-two patients diagnosed with acute CNSI who underwent LP and brain MRI within 48 h of hospital admission were retrospectively analyzed alongside 52 control subjects with normal neurological findings. CSF-related signals were segmented from the ventricular system and sub-lentiform nucleus parenchyma, including perivascular spaces (PVSs), usi

SUBMITTER: Cuce F 

PROVIDER: S-EPMC12405214 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature