Abstract：
BACKGROUND: Proper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM).
METHODS: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient (CCC) was used to quantify the agreement between the predicted and measured SPECT signals. The model was then used to simulate RNL distributions from a range of catheter placements, resulting in a ratio of the cumulative RNL dose outside versus inside the tumor, the \"off-target ratio\" (OTR). Optimal catheter placement) was identified by minimizing OTR.
RESULTS: Fifteen patients with rGBM from a Phase I/II clinical trial (NCT01906385) were recruited to the study. Our model, with either individual-calibrated or population-assigned parameters, achieved high accuracy (CCC > 0.80) for predicting RNL distributions up to 24 h after delivery. The optimal catheter placements identified using this model achieved a median (range) of 34.56 % (14.70 %-61.12 %) reduction on OTR at the 24 h post-delivery in comparison to the original placements.
CONCLUSIONS: Our image-guided model achieved high accuracy for predicting patient-specific RNL distributions and indicates value for optimizing catheter placement for CED of radiolabeled liposomes.
METHODS: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient (CCC) was used to quantify the agreement between the predicted and measured SPECT signals. The model was then used to simulate RNL distributions from a range of catheter placements, resulting in a ratio of the cumulative RNL dose outside versus inside the tumor, the \"off-target ratio\" (OTR). Optimal catheter placement) was identified by minimizing OTR.
RESULTS: Fifteen patients with rGBM from a Phase I/II clinical trial (NCT01906385) were recruited to the study. Our model, with either individual-calibrated or population-assigned parameters, achieved high accuracy (CCC > 0.80) for predicting RNL distributions up to 24 h after delivery. The optimal catheter placements identified using this model achieved a median (range) of 34.56 % (14.70 %-61.12 %) reduction on OTR at the 24 h post-delivery in comparison to the original placements.
CONCLUSIONS: Our image-guided model achieved high accuracy for predicting patient-specific RNL distributions and indicates value for optimizing catheter placement for CED of radiolabeled liposomes.
摘要：
背景：需要正确放置用于对流增强递送（CED）的导管，以最大程度地提高肿瘤覆盖率并最大程度地减少对健康组织的暴露。我们开发了一种基于图像的模型，以患者专门优化renium-186（186Re）-纳米脂质体（RNL）递送的导管放置，以治疗复发性胶质母细胞瘤（rGBM）。
方法：该模型由1）通过导管输注产生的流场组成，2）RNL的动态运输，和3)将RNL浓度转化为SPECT信号。从递送前MRI分配患者特异性组织几何形状。模型参数通过以下方式进行个性化：1)使用纵向SPECT图像进行基于个体的校准，或2)通过留一交叉验证进行基于人口的分配。一致性相关系数（CCC）用于量化预测和测量的SPECT信号之间的一致性。然后使用该模型来模拟一系列导管放置的RNL分布，导致肿瘤外部与内部的累积RNL剂量之比，“脱靶比率”(OTR)。通过最小化OTR确定最佳导管放置）。
结果：从I/II期临床试验（NCT01906385）招募了15名rGBM患者。我们的模型，使用个体校准或群体分配的参数，在交货后24小时内预测RNL分布具有很高的准确性(CCC>0.80)。与原始放置相比，使用该模型确定的最佳导管放置在分娩后24小时的OTR中值（范围）降低34.56％（14.70％-61.12％）。
结论：我们的图像引导模型在预测患者特异性RNL分布方面取得了很高的准确性，并为优化放射性标记脂质体CED的导管放置指明了价值。
方法：该模型由1）通过导管输注产生的流场组成，2）RNL的动态运输，和3)将RNL浓度转化为SPECT信号。从递送前MRI分配患者特异性组织几何形状。模型参数通过以下方式进行个性化：1)使用纵向SPECT图像进行基于个体的校准，或2)通过留一交叉验证进行基于人口的分配。一致性相关系数（CCC）用于量化预测和测量的SPECT信号之间的一致性。然后使用该模型来模拟一系列导管放置的RNL分布，导致肿瘤外部与内部的累积RNL剂量之比，“脱靶比率”(OTR)。通过最小化OTR确定最佳导管放置）。
结果：从I/II期临床试验（NCT01906385）招募了15名rGBM患者。我们的模型，使用个体校准或群体分配的参数，在交货后24小时内预测RNL分布具有很高的准确性(CCC>0.80)。与原始放置相比，使用该模型确定的最佳导管放置在分娩后24小时的OTR中值（范围）降低34.56％（14.70％-61.12％）。
结论：我们的图像引导模型在预测患者特异性RNL分布方面取得了很高的准确性，并为优化放射性标记脂质体CED的导管放置指明了价值。
