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（所有转移性病灶）、纵向（时间变化）和病灶水平的测量特征从未被探索过。这些特征可以捕捉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、病变水平 68GA-SSTR-PET 特征与多元线性回归 (MLR) 模型相结合的预测能力。这项回顾性研究纳入了接受 [177Lu]Lu-DOTA-TATE 治疗的 NET 患者，在基线和治疗后使用 [68Ga]Ga-DOTA-TATE 进行成像。所有病变均被分割、解剖标记并纵向匹配。测量了病变水平的摄取和摄取的变化。设计和选择患者级别的特征用于无进展生存期 (PFS) 建模。该模型通过一致性指数、患者分类（ROC 分析）和生存分析（Kaplan-Meier 和 Cox 比例风险）进行验证。 MLR 以单一特征预测为基准。36 名 NET 患者被纳入并分层为反应不佳和良好的患者（PFS ≥ 25 个月）。选择了四个患者级别特征，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）。生存分析得出显着的患者分层 (p<.001) 和风险比 (3⨯10-5)。最后，在一项基准研究中，MLR 建模方法优于所有单一特征预测因子。综合、病变级别、纵向 68GA-SSTR-PET 分析与 MLR 建模相结合，可以对 NET 患者的 PRRT 结果进行出色的预测，优于非-全面、患者水平和单时间点特征预测。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。© 2024。作者，获得 Springer-Verlag 独家许可GmbH 德国，隶属于施普林格自然集团。

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.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.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.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.Neuroendocrine tumor, peptide receptor radionuclide therapy, Somatostatin Receptor Imaging, Outcome Prediction, Treatment Response Assessment.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.