Liu Jianmo, Li Jingyi, Wu Yifan, Luo Haowen, Yu Pengfei, Cheng Rui, Wang Xiaoman, Xian Hongfei, Wu Bin, Chen Yongsen, Ke Jingyao, Yi Yingping
Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, China; School of Public Health, Nanchang University, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang, China.
Neuroscience. 2025 Jan 26;565:222-231. doi: 10.1016/j.neuroscience.2024.12.002. Epub 2024 Dec 2.
To explore the performance of deep learning-based segmentation of infarcted lesions in the brain magnetic resonance imaging (MRI) of patients with acute ischemic stroke (AIS) and the recurrence prediction value of radiomics within 1 year after discharge as well as to develop a model incorporating radiomics features and clinical factors to accurately predict AIS recurrence.
To generate a segmentation model of MRI lesions in AIS, the deep learning algorithm multiscale residual attention UNet (MRA-UNet) was employed. Furthermore, the risk factors for AIS recurrence within 1 year were explored using logistic regression (LR) analysis. In addition, to develop the prediction model for AIS recurrence within 1 year after discharge, four machine learning algorithms, namely, LR, RandomForest (RF), CatBoost, and XGBoost, were employed based on radiomics data, clinical data, and their combined data.
In the validation set, the Mean Dice (MDice) and Mean IOU (MIou) of the MRA-UNet segmentation model were 0.816 and 0.801, respectively. In multivariate LR analysis, age, renal insufficiency, C-reactive protein, triglyceride glucose index, prognostic nutritional index, and infarct volume were identified as the independent risk factors for AIS recurrence. Furthermore, in the validation set, combining radiomics data and clinical data, the AUC was 0.835 (95%CI:0.738, 0.932), 0.834 (95%CI:0.740, 0.928), 0.858 (95%CI:0.770, 0.946), and 0.842 (95%CI:0.752, 0.932) for the LR, RF, CatBoost, and XGBoost models, respectively.
The MRA-UNet model can effectively improve the segmentation accuracy of MRI. The model, which was established by combining radiomics features and clinical factors, held some value for predicting AIS recurrence within 1 year.
探讨基于深度学习的急性缺血性卒中（AIS）患者脑磁共振成像（MRI）梗死灶分割性能及出院后1年内放射组学的复发预测价值，并建立一个整合放射组学特征和临床因素的模型以准确预测AIS复发。
为生成AIS患者MRI病变的分割模型，采用了深度学习算法多尺度残差注意力UNet（MRA-UNet）。此外，使用逻辑回归（LR）分析探索AIS患者1年内复发的危险因素。另外，为建立出院后1年内AIS复发的预测模型，基于放射组学数据、临床数据及其组合数据，采用了四种机器学习算法，即LR、随机森林（RF）、CatBoost和XGBoost。
在验证集中，MRA-UNet分割模型的平均骰子系数（MDice）和平均交并比（MIou）分别为0.816和0.801。在多变量LR分析中，年龄、肾功能不全、C反应蛋白、甘油三酯葡萄糖指数、预后营养指数和梗死体积被确定为AIS复发的独立危险因素。此外，在验证集中，结合放射组学数据和临床数据，LR、RF、CatBoost和XGBoost模型的曲线下面积（AUC）分别为0.835（95%CI：0.738，0.932）、0.834（95%CI：0.740，0.928）、0.858（95%CI：0.770，0.946）和0.842（95%CI：0.752，0.932）。
MRA-UNet模型可有效提高MRI的分割准确性。通过结合放射组学特征和临床因素建立的模型对预测1年内AIS复发具有一定价值。
