Bai Chen, Leng Yilin, Xiao Haixing, Li Lei, Cui Wenju, Li Tan, Dong Yuefang, Zheng Jian, Cai Xiuying
School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Quant Imaging Med Surg. 2025 May 1;15(5):3964-3981. doi: 10.21037/qims-24-2010. Epub 2025 Apr 21.
Post-stroke cognitive impairment (PSCI) is a common and severe complication following acute lacunar stroke (ALS). Due to the limitations of current assessment tools and imaging methods, the early diagnosis of PSCI within 3 months of ALS remaining challenging. This study aimed to develop an effective, reliable, and accurate deep-learning method to predict PSCI within 3 months of ALS.
In total, 100 ALS patients were enrolled in the study, of whom 39 were diagnosed with PSCI and 61 were non-PSCI. First, we quantified three-dimensional (3D) gray-matter damage and white-matter tract disconnection, providing both regional damage (RD) and structural disconnection (SDC) higher-dimensional insights into brain network disruption. Second, we developed a novel deep-learning model based on ResNet18, integrating 3D RD, SDC, and diffusion-weighted imaging (DWI) to provide a comprehensive analysis of brain network integrity and predict PSCI. Finally, we compared the performance of our method with other methods, and visualized brain network damage associated with PSCI.
Our model showed strong predictive performance and had a mean accuracy (ACC) of 0.820±0.024, an area under the curve (AUC) of 0.795±0.068, a sensitivity (SEN) of 0.746±0.121, a specificity (SPE) of 0.869±0.044, and a F1-score (F1) of 0.760±0.050 in the five-fold cross-validation, outperforming existing models. In the PSCI patients, brain network damage significantly affected the salience, default mode, and somatic motor networks.
This study not only established a model that can accurately predict PSCI, it also identified potential targets for symptom-based treatments, offering new insights into PSCI.
卒中后认知障碍（PSCI）是急性腔隙性卒中（ALS）后常见且严重的并发症。由于当前评估工具和成像方法的局限性，在ALS发病3个月内对PSCI进行早期诊断仍具有挑战性。本研究旨在开发一种有效、可靠且准确的深度学习方法，以预测ALS发病3个月内的PSCI。
本研究共纳入100例ALS患者，其中39例被诊断为PSCI，61例为非PSCI。首先，我们对三维（3D）灰质损伤和白质纤维束中断进行量化，提供区域损伤（RD）和结构中断（SDC），以更深入地了解脑网络破坏情况。其次，我们基于ResNet18开发了一种新型深度学习模型，整合3D RD、SDC和扩散加权成像（DWI），以全面分析脑网络完整性并预测PSCI。最后，我们将我们的方法与其他方法的性能进行比较，并可视化与PSCI相关的脑网络损伤。
我们的模型显示出强大的预测性能，在五折交叉验证中，平均准确率（ACC）为0.820±0.024，曲线下面积（AUC）为0.795±0.068，灵敏度（SEN）为0.746±0.121，特异性（SPE）为0.869±0.044，F1分数（F1）为0.760±0.050，优于现有模型。在PSCI患者中，脑网络损伤显著影响突显网络、默认模式网络和躯体运动网络。
本研究不仅建立了一个能够准确预测PSCI的模型，还确定了基于症状治疗的潜在靶点，为PSCI提供了新的见解。
