Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan Shandong, China; Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan Shandong, China.
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan Shandong, China.
Eur J Radiol. 2023 Aug;165:110933. doi: 10.1016/j.ejrad.2023.110933. Epub 2023 Jun 20.
To establish F-FDG PET/CT radiomics model for predicting brain metastasis in non-small cell lung cancer (NSCLC) patients.
This research comprised 203 NSCLC patients who had received surgical therapy at two institutions. To identify independent predictive factors of brain metastasis, metabolic indicators, CT features, and clinical features were investigated. A prediction model was established by incorporating radiomics signature and clinicopathological risk variables. The suggested model's performance was assessed from the perspective of discrimination, calibration, and clinical application.
The C-indices of the PET/CT radiomics model in the training, internal validation, and external validation cohorts were 0.911, 0.825 and 0.800, respectively. According to the multivariate analysis, neuron-specific enolase (NSE) and air bronchogram were independent risk factors for brain metastasis (BM). Furthermore, the combined model integrating radiomics and clinicopathological characteristics related to brain metastasis performed better in terms of prediction, with C-indices of 0.927, 0.861, and 0.860 in the training, internal validation, and external validation cohorts, respectively. The decision curve analysis (DCA) suggested that the PET/CT nomogram was clinically beneficial.
A predictive algorithm based on PET/CT imaging information and clinicopathological features may accurately predict the probability of brain metastasis in NSCLC patients following surgery. This presented doctors with a unique technique for screening NSCLC patients at high risk of brain metastasis.
建立 F-FDG PET/CT 影像组学模型，以预测非小细胞肺癌（NSCLC）患者的脑转移。
本研究纳入了在两个机构接受手术治疗的 203 例 NSCLC 患者。为了确定脑转移的独立预测因素，我们研究了代谢指标、CT 特征和临床特征。通过纳入影像组学特征和临床病理风险变量，建立了预测模型。从判别、校准和临床应用的角度评估了所建议模型的性能。
在训练、内部验证和外部验证队列中，PET/CT 影像组学模型的 C 指数分别为 0.911、0.825 和 0.800。通过多变量分析，神经元特异性烯醇化酶（NSE）和空气支气管征是非小细胞肺癌脑转移的独立危险因素。此外，整合与脑转移相关的影像组学和临床病理特征的联合模型在预测方面表现更优，其在训练、内部验证和外部验证队列中的 C 指数分别为 0.927、0.861 和 0.860。决策曲线分析（DCA）表明，PET/CT 列线图具有临床获益。
基于 PET/CT 成像信息和临床病理特征的预测算法可以准确预测 NSCLC 患者手术后发生脑转移的概率。这为医生提供了一种筛查 NSCLC 患者脑转移高危人群的独特技术。
