Evidence map›Paper›PMID 39032243›Full record

ArticleComputers in biology and medicine2024

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

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

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01906385 (A Dual Phase 1/2, Investigator Initiated Study to Determine the Maximum Tolerated Dose, Safety, and Efficacy of 186Rhenium Nanoliposomes), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01906385 phase1 / phase2recruitingnot on this map

A Dual Phase 1/2, Investigator Initiated Study to Determine the Maximum Tolerated Dose, Safety, and Efficacy of 186Rhenium Nanoliposomes (186RNL) in Recurrent Glioma (CTRC# 12-02)

TypeinterventionalSponsorPlus TherapeuticsRan2015 to 2025Enrolled55ConditionsGliomaArmsRhenium Liposome Treatment
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Advancements in Drug Delivery Systems in Glioblastoma Therapy.International journal of molecular sciences · 2026
    Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Chengyue WuOden 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: CWu19@mdanderson.org.
David A HormuthOden 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.
Chase D ChristensonDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
Ryan T WoodallDivision of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center, 1500 East Duarte Rd, Duarte, CA, 91010, USA.
Michael R A AbdelmalikOden 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.
William T PhillipsDepartment of Radiology, UT Health San Antonio, San Antonio, TX, 78229, USA.
Thomas J R HughesOden 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.
Andrew J BrennerMays Cancer Center, UT Health San Antonio, San Antonio, TX, 78229, USA.
Thomas E YankeelovOden 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.

Funding

Imaging-based tumor forecasting to predict brain tumor progression and response to therapyR01CA260003 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI QUARLES, CHRISTOPHER CHAD, YANKEELOV, THOMAS E · 2022 to 2025
$3.2M
Clinical Development of Rhenium Nanoliposomes (RNL186) for GlioblastomaR01CA235800 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI BRENNER, ANDREW JACOB · 2019 to 2023
$3.0M
Using label-free Raman microscopy to predict therapeutic resistance of TNBC cellsU01CA253540 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI BROCK, AMY, YANKEELOV, THOMAS E · 2020 to 2024
$2.1M
NCI NIH HHS R01 CA235800NCI NIH HHS R01 CA260003NCI NIH HHS U01 CA253540
6 · The paper itself

Abstract

backgroundProper 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 (

methodsThe 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.

resultsFifteen 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.

conclusionsOur image-guided model achieved high accuracy for predicting patient-specific RNL distributions and indicates value for optimizing catheter placement for CED of radiolabeled liposomes.

Indexed as

GlioblastomaRheniumBrain NeoplasmsCathetersConvectionDrug Delivery SystemsFemaleHumansLiposomesMagnetic Resonance ImagingMaleMiddle AgedNanoparticlesNeoplasm Recurrence, LocalTomography, Emission-Computed, Single-PhotonLiposomesRheniumComputational fluid dynamicsImage-guide modelingMRIRadioactive nanoparticleSPECT/CT

Identifiers

PMID39032243
PMCPMC12307134

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