Automatic and observable spinal cord compression grading system for lung cancer spinal metastasis.

ABSTRACT:

Background

Epidural spinal cord compression (ESCC) assessment is a critical step in surgical intervention for lung cancer spinal metastasis. However, clinicians can only assess compression grades through manual observation, which is subjective and inefficient. The metastatic cancer features on magnetic resonance imaging (MRI) slices are complex and difficult to identify, and there are no publicly labeled datasets currently. The objective of this study was to develop an automatic and efficient system for assessing spinal cord compression grading based on multi-tissue and feature segmentation.

Methods

We constructed a dataset containing 577 lung cancer spinal metastasis MRI slices with multi-tissue semantic segmentation masks and ESCC grading. We then proposed a progressive ap

SUBMITTER: Wang G 

PROVIDER: S-EPMC12591789 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature