Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma

Chengyue Wu; David A Hormuth 2nd; Chase D Christenson; Ryan T Woodall; Michael R A Abdelmalik; William T Phillips; Thomas J R Hughes; Andrew J Brenner; Thomas E Yankeelov

doi:10.1016/j.compbiomed.2024.108889

Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma

Comput Biol Med. 2024 Sep:179:108889. doi: 10.1016/j.compbiomed.2024.108889. Epub 2024 Jul 19.

Authors

Chengyue Wu¹, David A Hormuth 2nd², Chase D Christenson³, Ryan T Woodall⁴, Michael R A Abdelmalik⁵, William T Phillips⁶, Thomas J R Hughes⁷, Andrew J Brenner⁸, Thomas E Yankeelov⁹

Affiliations

¹ Oden 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. Electronic address: [email protected].
² Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA.
³ Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
⁴ Division of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Rd, Duarte, CA, 91010, USA.
⁵ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA; Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
⁶ Department of Radiology, UT Health San Antonio, San Antonio, TX, 78229, USA.
⁷ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA; Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX, 78712, USA.
⁸ Mays Cancer Center, UT Health San Antonio, San Antonio, TX, 78229, USA.
⁹ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA; Livestrong Cancer Institutes, 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.

PMID: 39032243
DOI: 10.1016/j.compbiomed.2024.108889

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.

Keywords: Computational fluid dynamics; Image-guide modeling; MRI; Radioactive nanoparticle; SPECT/CT.

MeSH terms

Brain Neoplasms / diagnostic imaging
Catheters
Convection
Drug Delivery Systems / methods
Female
Glioblastoma* / diagnostic imaging
Humans
Liposomes / chemistry
Magnetic Resonance Imaging / methods
Male
Middle Aged
Nanoparticles / chemistry
Neoplasm Recurrence, Local / diagnostic imaging
Rhenium* / therapeutic use
Tomography, Emission-Computed, Single-Photon / methods

Substances

Rhenium
Liposomes