Tan Jiahe, Xiao Mengjun, Wang Zhipeng, Wu Shuzhen, Han Kun, Wang Haiyan, Huang Yong
Computer Science, Graduate Studies, University of California, 1 Shields Ave, Davis, CA, 95616, USA.
Department of Radiology, Shandong Provincial Hospital, Shandong First Medical University, Jingwu Road No. 324, Jinan, Shandong, 250021, China.
BMC Med Imaging. 2025 Jul 9;25(1):277. doi: 10.1186/s12880-025-01822-x.
In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality for diagnosing acute ischemic stroke. However, due to the small density difference between the infarct and the surrounding normal brain tissue on NCCT images within the initial 6 h post-onset, it poses significant challenges in promptly and accurately positioning and quantifying the infarct at the early stage.
To investigate whether a radiomics-based model using NCCT could effectively assess the risk of acute ischemic stroke (AIS).
This study proposed a machine learning (ML) for infarct detection, enabling automated quantitative assessment of AIS lesions on NCCT images. In this retrospective study, NCCT images from 228 patients with AIS (< 6 h from onset) were included, and paired with MRI-diffusion-weighted imaging (DWI) images (attained within 1 to 7 days of onset). NCCT and DWI images were co-registered using the Elastix toolbox. The internal dataset (153 AIS patients) included 179 AIS VOIs and 153 non-AIS VOIs as the training and validation groups. Subsequent cases (75 patients) after 2021 served as the independent test set, comprising 94 AIS VOIs and 75 non-AIS VOIs.
The random forest (RF) model demonstrated robust diagnostic performance across the training, validation, and independent test sets. The areas under the receiver operating characteristic (ROC) curves were 0.858 (95% CI: 0.808-0.908), 0.829 (95% CI: 0.748-0.910), and 0.789 (95% CI: 0.717-0.860), respectively. Accuracies were 79.399%, 77.778%, and 73.965%, while sensitivities were 81.679%, 77.083%, and 68.085%. Specificities were 76.471%, 78.431%, and 81.333%, respectively.
NCCT-based radiomics combined with a machine learning model could discriminate between AIS and non-AIS patients within less than 6 h of onset. This approach holds promise for improving early stroke diagnosis and patient outcomes.
Not applicable.
在大多数医疗中心，尤其是基层医院，非增强计算机断层扫描（NCCT）是诊断急性缺血性卒中的主要影像学检查方法。然而，由于发病后最初6小时内NCCT图像上梗死灶与周围正常脑组织之间的密度差异较小，在早期快速、准确地定位和量化梗死灶面临重大挑战。
研究基于NCCT的放射组学模型能否有效评估急性缺血性卒中（AIS）风险。
本研究提出一种用于梗死灶检测的机器学习（ML）方法，能够对NCCT图像上的AIS病灶进行自动定量评估。在这项回顾性研究中，纳入了228例AIS患者（发病<6小时）的NCCT图像，并与磁共振扩散加权成像（DWI）图像（发病后1至7天内获取）配对。使用Elastix工具包对NCCT和DWI图像进行配准。内部数据集（153例AIS患者）包括179个AIS感兴趣区（VOI）和153个非AIS VOI，作为训练和验证组。2021年之后的后续病例（75例患者）作为独立测试集，包括94个AIS VOI和75个非AIS VOI。
随机森林（RF）模型在训练集、验证集和独立测试集上均表现出强大的诊断性能。受试者操作特征（ROC）曲线下面积分别为0.858（95%CI：0.808 - 0.908）、0.829（95%CI：0.748 - 0.910）和0.789（95%CI：0.717 - 0.860）。准确率分别为79.399%、77.778%和73.965%，灵敏度分别为81.679%、77.083%和68.085%。特异性分别为76.471%、78.431%和81.333%。
基于NCCT的放射组学结合机器学习模型能够在发病后不到6小时内区分AIS和非AIS患者。这种方法有望改善早期卒中诊断和患者预后。
不适用。
