基于计算机断层扫描的放射组学与机器学习相结合,用于预测硬膜外血肿的发病时间。

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL International Journal of Legal Medicine Pub Date : 2024-11-18 DOI:10.1007/s00414-024-03374-1
Mingzhe Wu, Pengfei Wang, Hao Cheng, Ziyuan Chen, Ning Wang, Ziwei Wang, Chen Li, Linlin Wang, Dawei Guan, Hongzan Sun, Rui Zhao
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引用次数: 0

摘要

硬膜外血肿(EDH)的年龄估计是临床法医学中的一项挑战,这一问题尚未得到最终解决。计算机断层扫描(CT)成像具有客观性和非侵入性的优点,是诊断活体硬膜外血肿的潜在方法。最近,从医学影像中提取隐藏信息的放射组学已成为构建预测模型的一种有前途的方法。本研究旨在探索基于 CT 的放射组学在预测幸存受害者 EDH 损伤时间方面的可行性和适用性。研究选取了 95 例有明确受伤时间(受伤后 12 小时内)的 EDH 病例。记录了临床特征(年龄、性别、受伤时间、出血部位、出血量和骨折情况)。数据集被随机分为训练组和测试组。使用 LIFEx 软件分割 CT 中的血肿区域并提取放射学特征。机器学习算法用于特征选择和模型建立。我们选择了 23 个特征来计算 Radscore,这是我们分析中的一个关键指标。利用 Radscore 和受伤后的时间,我们构建了一个普通最小二乘法(OLS)模型。我们的验证研究表明,测试队列的平均绝对误差(MAE)为 2.42 h,这表明预测具有很高的准确性。为了提高预测的准确性,我们将数据集分为受伤后 5 小时内的不稳定期和稳定期。随机森林算法在两个阶段之间的预测性能存在显著差异,曲线下面积(AUC)为 0.79,准确率为 75.86%。回归模型的 MAE 在不稳定期为 1.05 小时,在稳定期为 1.23 小时。我们的研究结果凸显了基于 CT 的放射组学提供一种新颖、便捷、高效的 EDH 测定方法的潜力,有望为医学诊断领域开辟新的道路。
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Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.

Estimation of the age of epidural hematoma (EDH) is a challenge in clinical forensic medicine, and this issue has yet to be conclusively resolved. The advantages of objectivity and non-invasiveness make computing tomography (CT) imaging an potential diagnostic method for EDH in living individuals. Recently, radiomics, the extraction hidden information from medical images, has emerged as a promising method for constructing predictive models. The aim of this study is to explore the feasibility and applicability of CT-based radiomics in predicting the timing of EDH injuries in surviving victims. A cohort of 95 EDH cases with definite injured time (within 12 h since injury) was selected. Clinical characteristics (age, gender, injury time, bleeding location, bleeding volume, and fracture) were recorded. The datasets were divided randomly into training and test cohorts. LIFEx software was used to segment the hematoma area in the CT and extract radiomic features. Machine learning algorithms were applied for features selection and model building. Twenty-three features were selected to calculate the Radscore, a key metric in our analysis. Utilizing this Radscore in conjunction with the time since injury, we constructed an Ordinary Least Squared (OLS) model. Our validation study has shown that mean absolute error (MAE) of the test cohort was 2.42 h, indicating a high degree of accuracy. In order to enhance the accuracy of prediction, the dataset was divided into unstable phase, occurring within the first 5 h post injury, and the stable phases. The Random Forest algorithm presented a significant divergence in predictive performance between the two phases, achieving an area under the curve (AUC) of 0.79, with an accuracy of 75.86%. The MAE of the regression model was 1.05 h for the unstable phase, and 1.23 h for the stable phase. Our findings underscore the potential of CT-based radiomics to offer a novel, convenient, and efficient approach to dating EDH, promising to illuminate new avenues in the field of medical diagnostics.

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来源期刊
CiteScore
5.80
自引率
9.50%
发文量
165
审稿时长
1 months
期刊介绍: The International Journal of Legal Medicine aims to improve the scientific resources used in the elucidation of crime and related forensic applications at a high level of evidential proof. The journal offers review articles tracing development in specific areas, with up-to-date analysis; original articles discussing significant recent research results; case reports describing interesting and exceptional examples; population data; letters to the editors; and technical notes, which appear in a section originally created for rapid publication of data in the dynamic field of DNA analysis.
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