Lee Grace Yoojin, Won Jongjun, Kim Sunwoo, Jo Sungyang, Lee Jihyun, Lee Sangjin, Kim Jae Seung, Sung Changhwan, Oh Jungsu S, Kim Jihwan, Kim Namkug, Chung Sun Ju
Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
NPJ Parkinsons Dis. 2025 May 12;11(1):125. doi: 10.1038/s41531-025-00982-5.
We aimed to develop a convolutional neural network (CNN) model with multi-task learning to predict the onset of levodopa-induced dyskinesia (LID) in patients with Parkinson's disease (PD) using baseline [F]FP-CIT PET images. In this retrospective, single-center study, 402 patients were classified based on whether they developed LID within 5 years after starting levodopa (within 5 years: n = 134; beyond 5 years or none: n = 268). The proposed CNN model achieved a mean AUROC ± SD of 0.666 ± 0.036. Model-derived probabilities were also incorporated into a Cox regression model, yielding a mean concordance index (C-index ± SD) of 0.643 ± 0.046, significantly outperforming the model based on specific/nonspecific binding ratios of striatal subregions (C-index = 0.392 ± 0.036) in four of five test configurations. These results suggest that model-extracted features from [F]FP-CIT PET carry prognostic value for LID, although further performance improvements are needed for clinical application.
我们旨在开发一种具有多任务学习能力的卷积神经网络（CNN）模型，利用基线[F]FP-CIT PET图像预测帕金森病（PD）患者左旋多巴诱导的异动症（LID）的发生。在这项回顾性单中心研究中，402例患者根据在开始左旋多巴治疗后5年内是否发生LID进行分类（5年内发生：n = 134；5年后发生或未发生：n = 268）。所提出的CNN模型的平均受试者工作特征曲线下面积（AUROC）±标准差为0.666±0.036。模型得出的概率也被纳入Cox回归模型，得出的平均一致性指数（C指数±标准差）为0.643±0.046，在五种测试配置中的四种中，显著优于基于纹状体亚区域特异性/非特异性结合率的模型（C指数 = 0.392±0.036）。这些结果表明，从[F]FP-CIT PET中提取的模型特征对LID具有预后价值，尽管临床应用还需要进一步提高性能。
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