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Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in
Chengyue Wu1, David A Hormuth2, Chase D Christenson3
1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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.
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