A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-01-01 DOI:10.3233/XST-230119

Ting Fang, Naijia Liu, Shengdong Nie, Shouqiang Jia, Xiaodan Ye

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Abstract

Background: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments.

Objective: We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced by physicians and assist them to improve the accuracy and comprehensiveness of the ASPECTS.

Methods: Our study used a brain region segmentation method based on an improved encoding-decoding network. Through the deep convolutional neural network, 10 regions defined for ASPECTS will be obtained. Then, we used Pyradiomics to extract features associated with cerebral infarction and select those significantly associated with stroke to train machine learning classifiers to determine the presence of cerebral infarction in each scored brain region.

Results: The experimental results show that the Dice coefficient for brain region segmentation reaches 0.79. Three radioactive features are selected to identify cerebral infarction in brain regions, and the 5-fold cross-validation experiment proves that these 3 features are reliable. The classifier trained based on 3 features reaches prediction performance of AUC = 0.95. Moreover, the intraclass correlation coefficient of ASPECTS between those obtained by the automated ASPECTS method and physicians is 0.86 (95% confidence interval, 0.56-0.96).

Conclusions: This study demonstrates advantages of using a deep learning network to replace the traditional template registration for brain region segmentation, which can determine the shape and location of each brain region more precisely. In addition, a new brain region classifier based on radiomics features has potential to assist physicians in clinical stroke detection and improve the consistency of ASPECTS.

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基于深度学习和放射组学的阿尔伯塔卒中项目CTA早期CT评分方法评估急性缺血性卒中。

背景:Alberta卒中项目早期CT评分(ASPECTS)是一种用于评价急性缺血性卒中患者早期缺血性改变的半定量评价方法，可以指导医生进行治疗决策和预后判断。目的:提出一种将深度学习与放射组学相结合的方法，以缓解医生在aspect方面面临的观察者间差异大的问题，帮助医生提高aspect的准确性和全面性。方法:采用一种基于改进编解码网络的脑区分割方法。通过深度卷积神经网络，将得到10个为ASPECTS定义的区域。然后，我们使用Pyradiomics提取与脑梗死相关的特征，并选择与卒中显著相关的特征来训练机器学习分类器，以确定每个评分脑区域是否存在脑梗死。结果:实验结果表明，脑区分割的Dice系数达到0.79。选择3个放射性特征识别脑区脑梗死，5倍交叉验证实验证明这3个特征是可靠的。基于3个特征训练的分类器达到AUC = 0.95的预测性能。此外，自动化ASPECTS方法与医生的类内相关系数为0.86(95%置信区间为0.56 ~ 0.96)。结论:本研究证明了使用深度学习网络代替传统的模板配准进行脑区分割的优势，可以更精确地确定每个脑区的形状和位置。此外，一种新的基于放射组学特征的脑区域分类器有可能帮助医生进行临床脑卒中检测并提高ASPECTS的一致性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.