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DTSTART;TZID=America/Denver:20231112T161500
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UID:submissions.supercomputing.org_SC23_sess430_ws_cafcw109@linklings.com
SUMMARY:Optimized Patient-Specific Catheter Placement for Convection-Enhan
 ced Nanoparticle Delivery in Recurrent Glioblastoma
DESCRIPTION:Chengyue Wu (University of Texas, Oden Institute)\n\nIntroduct
 ion: Glioblastoma multiforme (GBM) is the most common and deadliest of all
  primary brain cancers. One promising treatment strategy for patients with
  recurrent GBM is convection-enhanced delivery (CED) of Rhenium-186 (186Re
 )-nanoliposomes (RNL) to provide delivery of large, localized doses of rad
 iation. The success of treatment by CED relies on proper catheter placemen
 t for therapy delivery to maximize tumor coverage and minimize the leakage
  to healthy tissue. In this project, we are developing an image-guided phy
 sics-based model to optimize catheter placement for RNL delivery on a pati
 ent-specific basis. \n\nMethods: The mathematical model consists of 1) the
  steady-state flow field generated via the catheter infusion and the Darcy
  flow through the 3D brain domain, 2) the transport of RNL governed by an 
 advection-diffusion equation, and 3) the point-spread function to transfor
 m the RNL distribution into the SPECT signal. Pre-delivery MRIs were used 
 to assign patient-specific tissue geometries. Two scenarios were performed
  to personalize the model parameters: a) patient-specific calibration with
  longitudinal SPECT images monitoring RNL distributions, and b) population
 -based assignment with the leave-one-out cross-validation (LOOCV). The acc
 uracy of model predictions was evaluated by the concordance correlation co
 efficients (CCC) between predicted and measured voxel-wise SPECT signals. 
 Furthermore, in each patient, we used the image-guided model—with either c
 alibrated or assigned parameters—to simulate RNL distributions for all pos
 sible locations of catheter tip(s), resulting in a ratio of the cumulative
  dose of RNL outside the tumor to that within the tumor, termed as “off-ta
 rget ratio” (OTR). We minimized the OTR to optimize the placement of cathe
 ter(s), and compared OTRs obtained by the optimized and the original place
 ments. \n\nResults: Fifteen patients with recurrent GBM from a Phase I/II 
 clinical trial of RNL were included in the study. For scenario a) with the
  patient-specific calibrated parameters, our model achieved median CCCs of
  0.91, 0.87, and 0.82 for predicting RNL distributions at the mid-delivery
 , end-of-delivery, and 24 h post-delivery, respectively. For scenario b) w
 ith the LOOCV assigned parameters, our model achieved median CCCs of 0.89,
  0.84, and 0.79 for predicting RNL distributions at the mid-delivery, end-
 of-delivery, and 24 h post-delivery, respectively. Compared to the origina
 l catheter placements, the optimized placements with the patient-specifica
 lly calibrated model achieved a median (range) of 34.56% (14.70% – 61.12%)
  reduction on OTR at the 24h post-delivery. Similarly, the optimized place
 ments with the LOOCV assigned model achieved a 34.56% (13.30% – 56.62%) re
 duction on OTR at the 24h post-delivery. Furthermore, the optimization pro
 vides insights into whether a patient is a proper candidate for CED of RNL
 , and whether a reduction of catheter number is possible for the patient. 
 \n\nConclusion: Our image-guided model, with either patient-specific calib
 rated parameters or LOOCV assigned parameters, achieved high accuracy for 
 predicting RNL distributions up to 24 h after the RNL delivery. The placem
 ent of catheter(s) optimized via our modeling substantially reduced the of
 f-target ratio of RNL delivery. These results proved the potential of our 
 image-guided modeling to guide patient-specific optimization of catheter p
 lacement for convection-enhanced delivery of radiolabeled liposomes. \n\nA
 cknowledgments: NCI R01CA235800, U01CA253540, and R01CA260003. CPRIT RR160
 005.\n\nTag: Applications, State of the Practice\n\nRegistration Category:
  Workshop Reg Pass\n\nSession Chairs: Lynn Borkon (Frederick National Labo
 ratory for Cancer Research); Sally Ellingson (University of Kentucky); Sea
 n Hanlon (National Institutes of Health (NIH), National Cancer Institute (
 NCI)); Patricia Kovatch (Icahn School of Medicine at Mount Sinai); and Eri
 c Stahlberg (MD Anderson Cancer Center, University of Texas)\n\n
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