使用全面的、基于病变的、纵向的[Ga]Ga-DOTA-TATE PET 衍生特征的模型可导致用[Lu]Lu-DOTA-TATE 治疗的神经内分泌肿瘤患者的预后预测更优。
Models using comprehensive, lesion-level, longitudinal [Ga]Ga-DOTA-TATE PET-derived features lead to superior outcome prediction in neuroendocrine tumor patients treated with [Lu]Lu-DOTA-TATE.
机构信息
Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
Section of Nuclear Medicine and Molecular Imaging, Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
出版信息
Eur J Nucl Med Mol Imaging. 2024 Sep;51(11):3428-3439. doi: 10.1007/s00259-024-06767-x. Epub 2024 May 25.
PURPOSE
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 GA-SSTR-PET features combined with a multivariate linear regression (MLR) model.
METHODS
This retrospective study enrolled NET patients treated with [Lu]Lu-DOTA-TATE and imaged with [Ga]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). Lastly, in a benchmark study, the MLR modeling approach outperformed all the single feature predictors.
CONCLUSION
Comprehensive, lesion-level, longitudinal GA-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.
MESSAGE
Neuroendocrine tumor, peptide receptor radionuclide therapy, Somatostatin Receptor Imaging, Outcome Prediction, Treatment Response Assessment.
目的
生长抑素受体（SSTR）成像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，综合（所有转移病灶）、纵向（时间变化）和病灶水平测量的特征从未被探索过。这些特征可以捕捉到疾病对治疗反应的异质性。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、纵向、病灶水平 GA-SSTR-PET 特征与多元线性回归（MLR）模型相结合的预测能力。
方法
这项回顾性研究纳入了接受[Lu]Lu-DOTA-TATE 治疗并在基线和治疗后进行[Ga]Ga-DOTA-TATE 成像的 NET 患者。对所有病灶进行分割、解剖标记和纵向匹配。测量病灶水平摄取和摄取变化。对患者水平特征进行工程设计，并选择用于无进展生存期（PFS）建模。通过一致性指数、患者分类（ROC 分析）和生存分析（Kaplan-Meier 和 Cox 比例风险）对模型进行验证。MLR 与单一特征预测进行了基准测试。
结果
纳入 36 名 NET 患者，并分为预后不良和预后良好（PFS≥25 个月）两组。选择了四个患者水平特征，MLR 一致性指数为 0.826，AUC 为 0.88（特异性为 0.85，敏感性为 0.81）。生存分析导致显著的患者分层（p<.001）和危险比（3×10）。最后，在基准研究中，MLR 建模方法优于所有单一特征预测因子。
结论
综合、病灶水平、纵向 GA-SSTR-PET 分析与 MLR 建模相结合，可对 NET 患者的 PRRT 结果进行出色预测，优于非综合、患者水平和单一时点特征预测。
信息
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，预后预测，治疗反应评估。
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