Xu Qingqing, Zhu Yan, Zhang Xi, Kong Dan, Duan Shaofeng, Guo Lili, Yin Xindao, Jiang Liang, Liu Zaiyi, Yang Wanqun
Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, China.
GE HealthCare, Shanghai, China.
Front Neurosci. 2023 Feb 22;17:1063391. doi: 10.3389/fnins.2023.1063391. eCollection 2023.
We explored whether radiomics features extracted from diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images can predict the clinical outcome of patients with acute ischaemic stroke. This study was conducted to investigate and validate a radiomics nomogram for predicting acute ischaemic stroke prognosis.
A total of 257 patients with acute ischaemic stroke from three clinical centres were retrospectively assessed from February 2019 to July 2022. According to the modified Rankin scale (mRS) at 3 months, the patients were divided into a favourable outcome group (mRS of 0-2) and an unfavourable outcome group (mRS of 3-6). The high-throughput features from the regions of interest (ROIs) within the radiologist-drawn contour by AK software were extracted. We used two feature selection methods, minimum redundancy and maximum (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO), to select the features. Three radiomics models (DWI, FLAIR, and DWI-FLAIR) were established. A radiomics nomogram with patient characteristics and radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the test and validation sets. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram.
The FLAIR, DWI, and DWI-FLAIR radiomics model exhibited good prediction performance, with area under the curve (AUCs) of 0.922 (95% CI: 0.876-0.968), 0.875 (95% CI: 0.815-0.935), and 0.895 (95% CI: 0.840-0.950). The radiomics nomogram with clinical characteristics including the overall cerebral small vessel disease (CSVD) burden score, hemorrhagic transformation (HT) and admission National Institutes of Health Stroke Scale score (NIHSS) score and the FLAIR Radscore presented good discriminatory potential in the training set (AUC = 0.94; 95% CI: 0.90-0.98) and test set (AUC = 0.94; 95% CI: 0.87-1), which was validated in the validation set 1 (AUC = 0.95; 95% CI: 0.88-1) and validation set 2 (AUC = 0.90; 95% CI: 0.768-1). In addition, it demonstrated good calibration, and decision curve analysis confirmed the clinical value of this nomogram.
This non-invasive clinical-FLIAR radiomics nomogram shows good performance in predicting ischaemic stroke prognosis after thrombolysis.
我们探讨了从扩散加权成像（DWI）和液体衰减反转恢复（FLAIR）图像中提取的放射组学特征是否能够预测急性缺血性脑卒中患者的临床结局。本研究旨在调查并验证一种用于预测急性缺血性脑卒中预后的放射组学列线图。
回顾性评估了2019年2月至2022年7月来自三个临床中心的257例急性缺血性脑卒中患者。根据3个月时的改良Rankin量表（mRS），将患者分为预后良好组（mRS为0 - 2）和预后不良组（mRS为3 - 6）。通过AK软件从放射科医生绘制的轮廓内的感兴趣区域（ROI）中提取高通量特征。我们使用了两种特征选择方法，最小冗余最大相关（mRMR）和最小绝对收缩和选择算子算法（LASSO）来选择特征。建立了三个放射组学模型（DWI、FLAIR和DWI - FLAIR）。使用多变量逻辑回归模型构建了包含患者特征和放射组学特征的放射组学列线图。在测试集和验证集中评估列线图的性能。最终进行决策曲线分析以评估列线图的临床价值。
FLAIR、DWI和DWI - FLAIR放射组学模型表现出良好的预测性能，曲线下面积（AUC）分别为0.922（95%CI：0.876 - 0.968）、0.875（95%CI：0.815 - 0.935）和0.895（95%CI：0.840 - 0.950）。包含总体脑小血管病（CSVD）负担评分、出血性转化（HT）和入院时美国国立卫生研究院卒中量表评分（NIHSS）以及FLAIR放射学评分等临床特征的放射组学列线图在训练集（AUC = 0.94；95%CI：0.90 - 0.98）和测试集（AUC = 0.94；95%CI：0.87 - 1）中表现出良好的区分潜力，并在验证集1（AUC = 0.95；95%CI：0.88 - 1）和验证集2（AUC = 0.90；95%CI：0.768 - 1）中得到验证。此外，它显示出良好的校准，决策曲线分析证实了该列线图的临床价值。
这种非侵入性的临床 - FLAIR放射组学列线图在预测溶栓后缺血性脑卒中预后方面表现良好。
