预处理吸收剂量的预测对于患者选择和基于剂量计导航的放射性药物治疗的个体化至关重要。我们的目标是利用术前68Ga-DOTATATE PET摄取数据和其他基线临床因素/生物标志物建立回归模型，以预测177Lu-DOTATATE肽受体核素治疗（177Lu-PRRT）释放到肾脏的吸收剂量，用于神经内分泌肿瘤。我们探索了生物标志物和68Ga PET摄取指标的结合，假设这将比单变量回归提高预测力。对25名患者（50个肾脏）的术前68Ga-DOTATATE PET/CT进行了分析，这些患者还进行了大约在第1个周期后4、24、96和168小时进行的定量177Lu SPECT/CT成像。使用经过验证的基于深度学习的工具在PET/CT和SPECT/CT的CT上对肾脏进行了轮廓标记。通过将多时间点SPECT/CT图像与内部Monte Carlo代码相耦合进行剂量测量。使用单变量和双变量模型研究术前肾PET SUV指标、注射后活性浓度（Bq/mL/MBq）和其他基线临床因素/生物标志物作为预测177Lu SPECT/CT派生的平均吸收剂量的预测因素注入肾脏。使用留一法交叉验证（LOOCV）来估计模型表现，包括所预测的肾脏吸收剂量的均方根误差和绝对百分比误差，包括平均绝对百分比误差（MAPE）和相应的标准偏差（SD）。中位数治疗提供的肾脏剂量为0.5Gy/GBq（范围为0.2-1.0Gy/GBq）。在单变量模型的LOOCV中，PET摄取（Bq/mL/MBq）表现最佳，MAPE为18.0%（SD = 13.3%），而估算肾小球滤过率（eGFR）给出28.5%（SD = 19.2%）的MAPE。同时使用PET摄取和eGFR的双变量回归模型在LOOCV中的MAPE为17.3%（SD = 11.8%），表明与单变量模型相比提高很少。预处理的68Ga-DOTATATE PET肾摄取可以精确预测177Lu-PRRT SPECT派生的肾脏吸收剂量，平均精度在18％内。与仅使用PET摄取相比，将eGFR包含在同一模型中以考虑患者特异性动力学并没有提高预测能力。在独立队列中进一步验证这些初步发现后，可以在临床上使用肾PET摄取来选择患者并在启动PRRT的第一周期之前个体化治疗。版权所有 ©2023 Wolters Kluwer Health，Inc。保留所有权利。

Pretreatment predictions of absorbed doses can be especially valuable for patient selection and dosimetry-guided individualization of radiopharmaceutical therapy. Our goal was to build regression models using pretherapy 68Ga-DOTATATE PET uptake data and other baseline clinical factors/biomarkers to predict renal absorbed dose delivered by 177Lu-DOTATATE peptide receptor radionuclide therapy (177Lu-PRRT) for neuroendocrine tumors. We explore the combination of biomarkers and 68Ga PET uptake metrics, hypothesizing that they will improve predictive power over univariable regression.Pretherapy 68Ga-DOTATATE PET/CTs were analyzed for 25 patients (50 kidneys) who also underwent quantitative 177Lu SPECT/CT imaging at approximately 4, 24, 96, and 168 hours after cycle 1 of 177Lu-PRRT. Kidneys were contoured on the CT of the PET/CT and SPECT/CT using validated deep learning-based tools. Dosimetry was performed by coupling the multi-time point SPECT/CT images with an in-house Monte Carlo code. Pretherapy renal PET SUV metrics, activity concentration per injected activity (Bq/mL/MBq), and other baseline clinical factors/biomarkers were investigated as predictors of the 177Lu SPECT/CT-derived mean absorbed dose per injected activity to the kidneys using univariable and bivariable models. Leave-one-out cross-validation (LOOCV) was used to estimate model performance using root mean squared error and absolute percent error in predicted renal absorbed dose including mean absolute percent error (MAPE) and associated standard deviation (SD).The median therapy-delivered renal dose was 0.5 Gy/GBq (range, 0.2-1.0 Gy/GBq). In LOOCV of univariable models, PET uptake (Bq/mL/MBq) performs best with MAPE of 18.0% (SD = 13.3%), and estimated glomerular filtration rate (eGFR) gives an MAPE of 28.5% (SD = 19.2%). Bivariable regression with both PET uptake and eGFR gives LOOCV MAPE of 17.3% (SD = 11.8%), indicating minimal improvement over univariable models.Pretherapy 68Ga-DOTATATE PET renal uptake can be used to predict post-177Lu-PRRT SPECT-derived mean absorbed dose to the kidneys with accuracy within 18%, on average. Compared with PET uptake alone, including eGFR in the same model to account for patient-specific kinetics did not improve predictive power. Following further validation of these preliminary findings in an independent cohort, predictions using renal PET uptake can be used in the clinic for patient selection and individualization of treatment before initiating the first cycle of PRRT.Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.