Development and validation of a multi-modal MRI-based deep learning framework for differentiation of intraspinal tumors (ISMF-Net).

ABSTRACT:

Background

Intraspinal tumors (ISTs) pose diagnostic challenges due to their complex anatomy and reliance on subjective clinical imaging interpretation, while artificial intelligence-based methods offer significant potential for non-invasive ISTs diagnosis. We aimed to develop and validate an MRI-based deep learning model for IST differentiation.

Methods

We conducted a retrospective study across three hospitals in China between January 2010 and July 2025. This retrospective study included 1004 patients diagnosed with ISTs, categorized into Schwannoma (SCN), Meningioma (MNG), Astrocytoma (AST), Ependymoma (EPN), and Metastasis (MET). Patients from Center 1 (n = 723) formed the internal dataset, which was randomly divided into training, validation and internal test sets at a 7

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC12663647 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature