目的:生长抑素受体(SSTR)影像学特征可预测接受肽受体放射性核素治疗(PRRT)的神经内分泌肿瘤(NET)患者的治疗结果。然而,全面(所有转移性病变),纵向(时间变化),从未探索过病变水平的测量特征。这样的特征允许捕获疾病对治疗的反应中的异质性。此外,缺乏结合这些特征的模型。在这项工作中,我们评估了综合的预测能力,纵向,病变水平68GA-SSTR-PET特征结合多元线性回归(MLR)模型。
方法:这项回顾性研究纳入了接受[177Lu]Lu-DOTA-TATE治疗的NET患者,并在基线和治疗后使用[68Ga]Ga-DOTA-TATE进行成像。所有病变都被分割,解剖学标记,纵向匹配。测量病变水平的摄取和摄取的变化。设计患者水平的特征并选择用于无进展生存期(PFS)的建模。通过一致性指数对模型进行了验证,患者分类(ROC分析),和生存分析(Kaplan-Meier和Cox比例风险)。MLR以单特征预测为基准。
结果:纳入了36例NET患者,并将其分为不良反应者和良好反应者(PFS≥25个月)。选择了四个患者级别的特征,MLR一致性指数为0.826,AUC为0.88(0.85特异性,0.81灵敏度)。生存分析导致显著的患者分层(p<.001)和风险比(310-5)。最后,在一项基准研究中,MLR建模方法优于所有单个特征预测因子。
结论:综合,病变水平,纵向68GA-SSTR-PET分析,结合MLR建模,对NET患者的PRRT结果有很好的预测,表现优于非全面,患者级别,和单时间点特征预测。
结论:神经内分泌肿瘤,肽受体放射性核素治疗,生长抑素受体成像,结果预测,治疗反应评估。
OBJECTIVE: Somatostatin receptor (SSTR) imaging features are predictive of treatment outcome for neuroendocrine tumor (NET) patients receiving peptide receptor radionuclide therapy (PRRT). However, comprehensive (all metastatic lesions), longitudinal (temporal variation), and lesion-level measured features have never been explored. Such features allow for capturing the heterogeneity in disease response to treatment. Furthermore, models combining these features are lacking. In this work we evaluated the predictive power of comprehensive, longitudinal, lesion-level 68GA-SSTR-PET features combined with a multivariate linear regression (MLR) model.
METHODS: This retrospective study enrolled NET patients treated with [177Lu]Lu-DOTA-TATE and imaged with [68Ga]Ga-DOTA-TATE at baseline and post-therapy. All lesions were segmented, anatomically labeled, and longitudinally matched. Lesion-level uptake and variation in uptake were measured. Patient-level features were engineered and selected for modeling of progression-free survival (PFS). The model was validated via concordance index, patient classification (ROC analysis), and survival analysis (Kaplan-Meier and Cox proportional hazards). The MLR was benchmarked against single feature predictions.
RESULTS: Thirty-six NET patients were enrolled and stratified into poor and good responders (PFS ≥ 25 months). Four patient-level features were selected, the MLR concordance index was 0.826, and the AUC was 0.88 (0.85 specificity, 0.81 sensitivity). Survival analysis led to significant patient stratification (p<.001) and hazard ratio (3⨯10-5). Lastly, in a benchmark study, the MLR modeling approach outperformed all the single feature predictors.
CONCLUSIONS: Comprehensive, lesion-level, longitudinal 68GA-SSTR-PET analysis, combined with MLR modeling, leads to excellent predictions of PRRT outcome in NET patients, outperforming non-comprehensive, patient-level, and single time-point feature predictions.
CONCLUSIONS: Neuroendocrine tumor, peptide receptor radionuclide therapy, Somatostatin Receptor Imaging, Outcome Prediction, Treatment Response Assessment.