Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods:18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.
背景：治疗后预后评估与监测对确定胶质母细胞瘤患者挽救性治疗时机至关重要，但由于传统影像难以区分肿瘤进展与治疗效应，这一直是临床面临的挑战。本探索性研究旨在通过机器学习和降维分析，建立氨基酸示踪剂18F-DOPA PET时序影像的放射组学特征与临床结局的相关性。方法：收集接受剂量递增放疗的IDH野生型且MGMT未甲基化患者队列的18F-DOPA PET影像。从放疗前及随访18F-DOPA PET图像中高摄取区域（T/N > 2.0）提取定量特征。采用定制化工作流程进行预测特征预选，继而进行流形学习。运用机器学习算法建立影像特征与剩余生存期（定义为随访扫描至死亡时间间隔）的关联模型。结果：在独立测试数据集上，机器学习模型预测剩余生存期的ROC曲线下面积达81-83%。研究提出通过系列18F-DOPA PET扫描构建剩余生存期图谱以监测肿瘤演变，该图谱较RANO标准展现出更优的敏感性及与生存期更好的相关性。结论：本研究证明基于随访18F-DOPA PET影像的机器学习模型能有效预测接受剂量递增放疗的胶质母细胞瘤患者远期生存结局。通过影像学动态评估肿瘤演变的能力，不仅有助于区分治疗效应与真实肿瘤进展，还可为患者分层及挽救性治疗方案选择提供决策支持。
肿瘤(癌症)患者之家