EXPEDITION: an Exploratory deep learning method to quantitatively predict hematoma progression after intracerebral hemorrhage.

Abstract

Objects: This study aims to develop an Exploratory deep learning method to quantitatively predict hematoma progression (EXPEDITION in short) after intracerebral hemorrhage (ICH).

Methods: Patients with primary ICH in the basal ganglia or thalamus were retrospectively enrolled, and their baseline non-contrast CT (NCCT) image, CT perfusion (CTP) images, and subsequent re-examining NCCT images from the 2nd to the 8th day after baseline CTP were collected. The subjects who had received three or more re-examining scans were categorized into the test data set, and others were assigned to the training data set. Hematoma volume was estimated by manually outlining the lesion shown on each NCCT scan. Cerebral venous hemodynamic feature was extracted from CTP images. Then, EXPEDITION was trained. The Bland-Altman analysis was used to assess the prediction performance.

Results: A total of 126 patients were enrolled initially, and 73 patients were included in the final analysis. They were then categorized into the training data set (58 patients with 93 scans) and the test data set (15 patients with 50 scans). For the test set, the mean difference [mean ±1.96SD] of hematoma volume between the EXPEDITION prediction and the reference is -0.96 [-9.64, +7.71] mL. Specifically, in the test set, the consistency between the true and the predicted volume values was compared, indicating that the EXPEDITION achieved the needed accuracy for quantitative prediction of hematoma progression.

Conclusions: An Exploratory deep learning method, EXPEDITION, was proposed to quantitatively predict hematoma progression after primary ICH in basal ganglia or thalamus.