MRI-based Surgical Planning for Lumbar Spinal Stenosis

TL;DR

This paper presents an automated MRI analysis algorithm to accurately localize lumbar spinal stenosis lesions, aiming to standardize and improve surgical planning for elderly patients.

Contribution

It introduces a novel automated pipeline that predicts lesion locations in MRI scans using 22 features across five spinal levels, enhancing objectivity in surgical decisions.

Findings

Successful prediction of stenosis location in MRI scans.

Quantitative validation of radiological features' importance.

Automated analysis applicable to large patient datasets.

Abstract

The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis(LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon shows large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans.

Figures

Full text

See pages 1-last of mri-based-surgical-prediction-jmlr.pdf