RADT-14. TOWARDS IMAGE-GUIDED MODELING OF PATIENT-SPECIFIC RHENIUM-186 NANOLIPOSOME DISTRIBUTION VIA CONVECTION-ENHANCED DELIVERY FOR GLIOBLASTOMA MULTIFORME

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi44-vi44
Author(s):  
Chengyue Wu ◽  
David Hormuth ◽  
Chase Christenson ◽  
Michael Abdelmalik ◽  
William Phillips ◽  
...  
Keyword(s):  
Model Calibration ◽  
Specific Model ◽  
Catheter Placement ◽  
Patient Specific ◽  
Central Component ◽  
Fluid Loss ◽  
Convection Enhanced Delivery ◽  
Advection Diffusion Equation ◽  
Specific Tissue ◽  
Tissue Geometry

Abstract Convection-enhanced delivery (CED) of Rhenium-186 nanoliposomes (RNL) is a promising approach to provide precise delivery of large, localized doses of radiation with the goal of extending overall survival for patients with recurrent GBM. A central component of successful CED, is achieving optimal catheter placement for delivery of the therapy. While surgical planning software exists for this purpose, current approaches are designed for small molecules and therefore are not appropriate for larger particles like RNL. To address this concern, we have developed a mathematical model to predict the distribution of RNL via CED on a patient-specific basis. The model is defined on the 3D brain domain which consists of 1) pressure and flow fields generated by accounting for catheter infusion, flow through brain, and fluid loss into capillaries, and 2) the transport of RNL governed by an advection-diffusion equation. We utilize pre-operative MRI to assign patient-specific tissue geometry and properties (e.g., diffusivity, conductivity), and calibrate the model with SPECT measurements within 24 h post the RNL delivery. This model is implemented on one patient enrolled in NCT01906385. The accuracy of model calibration and prediction is evaluated by the Dice score and concordance correlation coefficient (CCC) between modeled and measured distributions of RNL. Our model calibration achieves Dice scores of 0.80, 0.81, 0.69 and CCC of 0.92, 0.93, 0.73 for RNL distributions at the mid-delivery, end of delivery, and 24 h after the delivery, respectively. Long-term model prediction achieves Dice scores of 0.69 and 0.52 at 144 h and 196 h after the delivery, respectively, and CCC of 0.57 and 0.31. Preliminary results demonstrate a proof-of-concept for a patient-specific model to predict the spatiotemporally-resolved distribution of nanoparticles. Ongoing efforts focus on improving our model by accounting backflow and angle of catheter placement, and applying to more patients. Funding: NIH R01CA235800, CPRIT RR160005.

scholarly journals Optimization of catheter placement for convection-enhanced delivery to brain tumors

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 18
Author(s):  
Lisa H. Antoine ◽  
Roy P. Koomullil ◽  
Timothy M. Wick ◽  
Arie Nakhmani
Keyword(s):  
Brain Tumors ◽  
The United States ◽  
Catheter Placement ◽  
Patient Specific ◽  
Computer Simulation Model ◽  
Study Group ◽  
Volume Distribution ◽  
Central Nervous System Tumor ◽  
Convection Enhanced Delivery ◽  
Volume Percentage

Background: Recent trends suggest that physicians will diagnose thousands of children in the United States with a brain or central nervous system tumor in 2020. Malignant brain tumors are difficult to treat, with low life expectancy rates in children and adults. Convection-enhanced delivery (CED) shows promise for the treatment of brain tumors, yet remains in clinical trials despite being developed more than 20 years ago. To advance CED to standard of care status and help improve survival rates, this study group developed a quantitative computer simulation model to determine and optimize therapy distribution in brain tumors based on the catheter infusion locations for CED. Methods: The simulations resulted in the identification of four infusion reference locations, which were used to conduct an optimization study to identify the optimal locations for CED. Patient-specific T1-weighted images and diffusion-weighted images provided information regarding tumor shape and size and the approximate rate at which therapy distributes at spatial locations within the tumor. Using the images, the researchers in this study developed a model which allowed the calculation of therapy distribution within the tumor while considering its permeability, porosity, and interstitial fluid pressure characteristics. We divided the tumor into regions and calculated distribution for four infusion locations per region. Using the location from each region with the highest volume distribution allowed our study group to conduct the response surface optimization. Results: Twelve optimal locations emerged from the optimization with volume percentage distributions ranging from 7.92% to 9.09%, compared to 2.87% to 6.32% coverage for non-optimal locations. This optimization method improved distribution from 27.80% to 45.95%, which may improve therapeutic value. Conclusions: Catheter placement appears to influence volume therapy distribution percentages. The selection of the highest percentages per region may provide optimal therapy for the entire tumor region.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

Use of human aortic extracellular matrix as a scaffold for construction of a patient-specific tissue engineered vascular patch

2017 ◽  
Vol 12 (6) ◽  
pp. 065006 ◽  
Author(s):  
Li-Ping Gao ◽  
Ming-Jun Du ◽  
Jing-Jing Lv ◽  
Sebastian Schmull ◽  
Ri-Tai Huang ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Patient Specific ◽  
Specific Tissue

New directions in bioabsorbable technology

2002 ◽  
Vol 97 (4) ◽  
pp. 481-489 ◽  
Author(s):  
Stephen M. Warren ◽  
Marc H. Hedrick ◽  
Karl Sylvester ◽  
Michael T. Longaker ◽  
Constance M. Chen
Keyword(s):  
Interdisciplinary Approach ◽  
Patient Specific ◽  
Therapeutic Gene ◽  
Content Type ◽  
Tissue Constructs ◽  
New Directions ◽  
Material Sciences ◽  
Specific Tissue ◽  
Tissue Restoration ◽  
Fine Print

✓ Generating replacement tissues requires an interdisciplinary approach that combines developmental, cell, and molecular biology with biochemistry, immunology, engineering, medicine, and the material sciences. Because basic cues for tissue engineering may be derived from endogenous models, investigators are learning how to imitate nature. Endogenous models may provide the biological blueprints for tissue restoration, but there is still much to learn. Interdisciplinary barriers must be overcome to create composite, vascularized, patient-specific tissue constructs for replacement and repair. Although multistep, multicomponent tissue fabrication requires an amalgamation of ideas, the following review is limited to the new directions in bioabsorbable technology. The review highlights novel bioabsorbable design and therapeutic (gene, protein, and cell-based) strategies currently being developed to solve common spine-related problems.

scholarly journals Determination of dominant loop in dual loop atrial tachycardia with direct graph mapping: an alternative for entrainment mapping?

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
L Lowie ◽  
E Van Nieuwenhuyse ◽  
J Sanchez ◽  
A Panfilov ◽  
S Knecht ◽  
...  
Keyword(s):  
Simulated Data ◽  
Anterior Wall ◽  
Specific Model ◽  
Patient Specific ◽  
Double Loop ◽  
Direct Graph ◽  
Funding Sources ◽  
Graph Mapping ◽  
Activation Times ◽  
Clinical Cases

Abstract Funding Acknowledgements Type of funding sources: None. Background Entrainment mapping (EM) is an important tool to determine the mechanism of complex reentrant atrial tachycardias (ATs), mostly to distinguish dominant from bystander reentrant loops. However, entrainment maneuvers are challenging, time consuming and risk to end the tachycardia.  Purpose Recently, we developed a novel method Directed Graph Mapping (DGM), using concepts of network theory, allowing to automatically determine AT reentry loops from the local activation times (LAT) of any clinical mapping system. DGM showed good performance: it correctly finds ablation target (100 % success rate) on simple AT cases and could automatically determine reentry loops confirmed by the expert electrophysiologist with EM in complex AT cases. Out of 32 single loop cases, 62.5 % was identified correctly with automated DGM and out of 6 true double loop cases, 83.3 %. Lower performance for single reentry complex cases compared to EM was mainly because DGM could not distinguish the dominant loop from additional bystander loops found by DGM. Hence, the purpose of this work was to develop additional algorithms which in case of multiple found DGM loops could automatically find the dominant loop and compare it with the results of EM. Methods We performed multiple  simulations of various types of double loop reentry on a patient specific model of the left atrium. Based on a clinical case, double loops were simulated around a scar at the anterior wall (localized reentry) and the mitral valve (MV). LAT maps were determined similar as in the clinic. By varying the size of the scar in multiple steps, we obtained a transition from a regime of a dominant loop around the scar (small scar), to a true double loop and further to a regime of a dominant loop around the MV (large scar). We developed a novel DGM algorithm to determine the dominant loop from the region of collision (ROC) found from the vector field of the wavefront graph.   The developed method was also tested on 8 clinical cases of double loop ATs with EM measurements. Results Our algorithm found the location of the ROC and determined the correct dominant loop in 100% of the simulated data.  We tested this on 8 clinical cases of AT, and accuracy of the method was 75 %. Conclusions Determining the ROC in case of multiple loops in AT could correctly determine the dominant versus bystander loop, leading to the correct ablation target, without the need for further EM.

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