Wang Huaizhen, Li Jizhen, Chen Jinming, Li Meilin, Liu Jiahao, Wei Lingzhen, Zeng Qingshi
The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
J Imaging Inform Med. 2025 Feb;38(1):134-147. doi: 10.1007/s10278-024-01130-w. Epub 2024 Jul 17.
Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and validate a machine learning (ML) model for the non-invasive assessment of STA-MCA bypass surgery efficacy in MMD. This study enrolled 118 MMD patients undergoing STA-MCA bypass surgery. Clinical features were screened to construct a clinical model. MRI features were extracted from the middle cerebral artery supply area using 3D Slicer and employed to build five ML models using logistic regression algorithm. The combined model was developed by integrating the radiomics score (Rad-score) with the clinical features. Model performance validation was conducted using ROC curves. Platelet count (PLT) was identified as a significant clinical feature for constructing the clinical model. A total of 3404 features (851 × 4) were extracted, and 15 optimal features were selected from each MRI sequence as predictive factors. Multivariable logistic regression identified PLT and Rad-score as independent parameters used for constructing the combined model. In the testing set, the AUC of the T1WI ML model [0.84 (95% CI, 0.70-0.97)] was higher than that of the clinical model [0.66 (95% CI, 0.46-0.86)] and the combined model [0.80 (95% CI, 0.66-0.95)]. The T1WI ML model can be used to assess the postoperative efficacy of STA-MCA bypass surgery for MMD.
颞浅动脉-大脑中动脉（STA-MCA）搭桥手术是烟雾病（MMD）的主要治疗方法，其疗效取决于侧支血管的发育情况。本研究旨在开发并验证一种用于无创评估STA-MCA搭桥手术治疗MMD疗效的机器学习（ML）模型。本研究纳入了118例接受STA-MCA搭桥手术的MMD患者。筛选临床特征以构建临床模型。使用3D Slicer从大脑中动脉供血区域提取MRI特征，并使用逻辑回归算法构建五个ML模型。通过将影像组学评分（Rad分数）与临床特征相结合来开发联合模型。使用ROC曲线进行模型性能验证。血小板计数（PLT）被确定为构建临床模型的重要临床特征。共提取了3404个特征（851×4），并从每个MRI序列中选择15个最佳特征作为预测因子。多变量逻辑回归确定PLT和Rad分数为用于构建联合模型的独立参数。在测试集中，T1WI ML模型的AUC [0.84（95%CI，0.70-0.97）]高于临床模型[0.66（95%CI，0.46-0.86）]和联合模型[0.80（95%CI，0.66-0.95）]。T1WI ML模型可用于评估STA-MCA搭桥手术治疗MMD的术后疗效。
