Evidence map›Paper›PMID 36508089›Full record

ArticleCellular oncology (Dordrecht, Netherlands)2023

OrBITS: label-free and time-lapse monitoring of patient derived organoids for advanced drug screening.

Christophe Deben, Edgar Cardenas De La Hoz, Maxim Le Compte, Paul Van Schil, Jeroen M H Hendriks, Patrick Lauwers, Suresh Krishan Yogeswaran, Filip Lardon, Patrick Pauwels, Steven Van Laere and 4 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cellular oncology (Dordrecht, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
6.8field-weighted citation impact, top 2% 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

48 citing papers in PubMed, 73 citations in OpenAlex.

  1. Review
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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

14 authors at 2 institutions in 1 country.

Christophe DebenCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium. christophe.deben@uantwerpen.be.ORCID http://orcid.org/0000-0001-8085-2040
Edgar Cardenas De La HozIndustrial Vision Lab, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.
Maxim Le CompteCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Paul Van SchilDepartment of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium.
Jeroen M H HendriksDepartment of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium.
Patrick LauwersDepartment of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium.
Suresh Krishan YogeswaranDepartment of Thoracic and Vascular Surgery, Antwerp University Hospital, Edegem, Belgium.
Filip LardonCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Patrick PauwelsCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Steven Van LaereCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Annemie BogaertsPlasma Lab for Applications in Sustainability and Medicine ANTwerp (PLASMANT), University of Antwerp, 2610, Wilrijk, Belgium.
Evelien SmitsCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Steve VanlanduitIndustrial Vision Lab, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.
Abraham LinCenter for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.ORCID http://orcid.org/0000-0003-0097-3323
University of Antwerp · BEAntwerp University Hospital · BE

Funding

Fonds Wetenschappelijk Onderzoek 12S9218NFonds Wetenschappelijk Onderzoek 12S9221NFonds Wetenschappelijk Onderzoek G044420NUniversiteit Antwerpen PS ID 45151
6 · The paper itself

Abstract

backgroundPatient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications.

methodsHere, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach. The OrBITS deep learning analysis approach was validated against current standard assays for kinetic imaging and automated analysis of organoids. A drug screen of standard-of-care lung and pancreatic cancer treatments was also performed with the OrBITS platform and compared to the gold standard, CellTiter-Glo 3D assay. Finally, the optimal parameters and drug response metrics were identified to improve patient stratification.

resultsOrBITS allowed for the detection and tracking of organoids in routine extracellular matrix domes, advanced Gri3D

conclusionOur findings validate that OrBITS, as a scalable, automated live-cell image analysis software, would facilitate the use of patient-derived organoids for drug development and therapy screening. The developed wet-lab workflow and software also has broad application potential, from providing a launching point for further brightfield-based assay development to be used for fundamental research, to guiding clinical decisions for personalized medicine.

Indexed as

Pancreatic NeoplasmsDrug Evaluation, PreclinicalHumansOrganoidsPrecision MedicineTime-Lapse ImagingDrug screeningHigh-throughputImage-based analysisPatient-derived organoids

Identifiers

PMID36508089
PMCPMC10060271
OpenAlexW4311201479

What OpenQuestion holds

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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.