Evidence map›Paper›PMID 31113971›Full record

ArticleScientific reports2019

Evaluation of Aminolevulinic Acid-Derived Tumor Fluorescence Yields Disparate Results in Murine and Spontaneous Large Animal Models of Lung Cancer.

Jarrod D Predina, Jeffrey Runge, Andrew Newton, Michael Mison, Leilei Xia, Christopher Corbett, Michael Shin, Lydia Frenzel Sulyok, Amy Durham, Shuming Nie and 2 more

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
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

12 authors at 2 institutions in 1 country.

Jarrod D PredinaCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA. jpredina@partners.org.
Jeffrey RungeDepartment of Clinical Studies, University of Pennsylvania School of Veterinary Medicine, Philadelphia, USA.
Andrew NewtonCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.ORCID http://orcid.org/0000-0003-4061-1538
Michael MisonDepartment of Clinical Studies, University of Pennsylvania School of Veterinary Medicine, Philadelphia, USA.
Leilei XiaCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
Christopher CorbettCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
Michael ShinCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
Lydia Frenzel SulyokCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
Amy DurhamDepartment of Pathobiology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, USA.
Shuming NieDepartments of Biomedical Engineering and Chemistry, Emory University, Atlanta, Georgia.
Sunil SinghalCenter for Precision Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
David HoltDepartment of Clinical Studies, University of Pennsylvania School of Veterinary Medicine, Philadelphia, USA.
University of Pennsylvania · USEmory University · US

Funding

UNIVERSITY OF PENNSYLVANIA CAN CTR SUPPORT GRANTP30CA016520 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Robert H. Vonderheide · 1985 to 2026
$222.3M
Near infrared intraoperative molecular imaging of lung adenocarcinomaR01CA193556 · NCI · UNIVERSITY OF PENNSYLVANIA · PI LOW, PHILIP STEWART, SINGHAL, SUNIL · 2016 to 2020
$3.3M
Optimizing Immunotherapy Strategies to Eliminate Recurrences after Cancer SurgeryF32CA210409 · NCI · UNIVERSITY OF PENNSYLVANIA · PI PREDINA, JARROD D · 2016 to 2017
$132k
NCI NIH HHS F32 CA210409NCI NIH HHS P30 CA016520NCI NIH HHS R01 CA193556
6 · The paper itself

Abstract

Fluorescence guided surgery is an emerging technology that may improve accuracy of pulmonary resection for non-small cell lung cancer (NSCLC). Herein we explore optical imaging for NSCLC surgery using the well-studied protoporphyrin IX (PPIX)/5-aminiolevulinic acid (5-ALA) system. More specifically, we evaluate fluorescent patterns observed when using (1) commonly utilized in vitro and murine NSCLC models and with (2) spontaneous canine NSCLCs, which closely mimic human disease. Using flow cytometry and fluorescent microscopy, we confirmed that NSCLC models fluoresce after exposure to 5-ALA in vitro. High levels of fluorescence were similarly observed in murine tumors within 2 hours of systemic 5-ALA delivery. When evaluating this approach in spontaneous canine NSCLC, tumor fluorescence was observed in 6 of 7 canines. Tumor fluorescence, however, was heterogenous owing to intratumoral variations in cellularity and necrosis. Margin and lymph node detection was inaccurate. These data demonstrate the importance of incorporating reliable cancer models into preclinical evaluations of optical agents. Utilization of spontaneous large animal models of cancer may further provide an important intermediate in the path to human translation of optical contrast agents.

Indexed as

Disease Models, AnimalAminolevulinic AcidAnimalsCarcinoma, Non-Small-Cell LungCell LineCell Line, TumorDogsFluorescenceHumansLung NeoplasmsMargins of ExcisionMiceMice, Inbred C57BLNecrosisOptical ImagingPhotosensitizing AgentsAminolevulinic AcidPhotosensitizing Agents

Identifiers

PMID31113971
PMCPMC6529469
OpenAlexW2944989254

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.