# Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

## TL;DR

This paper introduces a novel tractographic feature derived from stroke lesions and connectome data to improve the prediction of clinical outcomes in stroke patients, outperforming traditional volume-based features.

## Contribution

The study proposes a new tractographic feature that captures damaged brain regions, enhancing outcome prediction accuracy over existing methods.

## Key findings

- Higher accuracy in predicting mRS grades
- Lower average absolute error compared to stroke volume
- Effective on public stroke benchmark dataset

## Abstract

The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the tractographic feature to capture these potentially damaged regions and predict the modified Rankin Scale (mRS), which is a widely used outcome measure in stroke clinical trials. The tractographic feature is built from the stroke lesion and average connectome information from a group of normal subjects. The tractographic feature takes into account different functional regions that may be affected by the stroke, thus complementing the commonly used stroke volume features. The proposed tractographic feature is tested on a public stroke benchmark Ischemic Stroke Lesion Segmentation 2017 and achieves higher accuracy than the stroke volume and the state-of-the-art feature on predicting the mRS grades of stroke patients. In addition, the tractographic feature also yields a lower average absolute error than the commonly used stroke volume feature.