Evidence map›Paper›PMID 29056576›Full record

ArticleUrology2018

Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses.

Durgesh Kumar Dwivedi, Yonatan Chatzinoff, Yue Zhang, Qing Yuan, Michael Fulkerson, Rajiv Chopra, James Brugarolas, Jeffrey A Cadeddu, Payal Kapur, Ivan Pedrosa

Abstract read
In one paragraph

Article in Urology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  15. Deciphering Intratumoral Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma with a Radiogenomics Platform.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021
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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

10 authors.

Durgesh Kumar DwivediDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX.
Yonatan ChatzinoffDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX.
Yue ZhangDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX.
Qing YuanDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX.
Michael FulkersonDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX.
Rajiv ChopraDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX; Department of Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX.
James BrugarolasDepartment of Internal Medicine, UT Southwestern Medical Center, Dallas, TX; Department of Developmental Biology, UT Southwestern Medical Center, Dallas, TX.
Jeffrey A CadedduDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX; Department of Urology, UT Southwestern Medical Center, Dallas, TX.
Payal KapurDepartment of Urology, UT Southwestern Medical Center, Dallas, TX; Department of Pathology, UT Southwestern Medical Center, Dallas, TX.
Ivan PedrosaDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX; Department of Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX; Department of Urology, UT Southwestern Medical Center, Dallas, TX. Electronic address: ivan.pedrosa@UTSouthwestern.edu.

Funding

University of Texas Southwestern Medical Center SPORE in Kidney CancerP50CA196516 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI James Brugarolas · 2016 to 2026
$24.7M
NON-INVASIVE PHYSIOLOGIC PREDICTORS OF AGGRESSIVENESS IN RENAL CELL CARCINOMAR01CA154475 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI PEDROSA, IVAN · 2011 to 2024
$4.3M
NCI NIH HHS P50 CA196516NCI NIH HHS R01 CA154475
6 · The paper itself

Abstract

objectiveTo implement a platform for colocalization of in vivo quantitative multiparametric magnetic resonance imaging features with ex vivo surgical specimens of patients with renal masses using patient-specific 3-dimensional (3D)-printed tumor molds, which may aid in targeted tissue procurement and radiomics and radiogenomic analyses. MATERIALS AND

methodsVolumetric segmentation of 6 renal masses was performed with 3D Slicer (http://www.slicer.org) to create a 3D tumor model. A slicing guide template was created with specialized software, which included notches corresponding to the anatomic locations of the magnetic resonance images. The tumor model was subtracted from the slicing guide to create a depression in the slicing guide corresponding to the exact size and shape of the tumor. A customized, tumor-specific, slicing guide was then printed using a 3D printer. After partial nephrectomy, the surgical specimen was bivalved through the preselected magnetic resonance imaging (MRI) plane. A thick slab of the tumor was obtained, fixed, and processed as a whole-mount slide and was correlated to multiparametric MRI findings.

resultsAll patients successfully underwent partial nephrectomy and adequate fitting of the tumor specimens within the 3D mold was achieved in all tumors. Distinct in vivo MRI features corresponded to unique pathologic characteristics in the same tumor. The average cost of printing each mold was US$160.7 ± 111.1 (range: US$20.9-$350.7).

conclusionMRI-based preoperative 3D printing of tumor-specific molds allow for accurate sectioning of the tumor after surgical resection and colocalization of in vivo imaging features with tissue-based analysis in radiomics and radiogenomic studies.

Indexed as

Magnetic Resonance ImagingPrinting, Three-DimensionalAlgorithmsFemaleHumansImaging, Three-DimensionalKidney NeoplasmsMaleMiddle AgedModels, AnatomicNephrectomyTissue and Organ Procurement

Identifiers

PMID29056576
PMCPMC5885749

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.