Evidence map›Paper›PMID 33922981›Full record

ArticleCancers2021

Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

Friederike Jungmann, Georgios A Kaissis, Sebastian Ziegelmayer, Felix Harder, Clara Schilling, Hsi-Yu Yen, Katja Steiger, Wilko Weichert, Rebekka Schirren, Ishan Ekin Demir and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

10 citing papers in PubMed, 14 citations in OpenAlex.

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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 3 institutions in 2 countries.

Friederike JungmannInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Georgios A KaissisInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0001-8382-8062
Sebastian ZiegelmayerInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Felix HarderInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0002-3182-5924
Clara SchillingInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Hsi-Yu YenInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0003-2644-2782
Katja SteigerInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0002-7269-5433
Wilko WeichertInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Rebekka SchirrenSurgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Ishan Ekin DemirSurgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Helmut FriessSurgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Markus R MakowskiInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Rickmer F BrarenInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Fabian K LohöferInstitute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Technical University of Munich · DEDeutschen Konsortium für Translationale Krebsforschung · DEImperial College London · GB

Funding

Deutsche Forschungsgemeinschaft SFB824, C6 and SPP2177, P4
6 · The paper itself

Abstract

backgroundPDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task.

methodsDiscrete cellularity regions of PDAC resection specimen (

resultsA statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding.

conclusionIn this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

Indexed as

computed tomographypancreatic ductal adenocarcinomaPDACtumor cellularity

Identifiers

PMID33922981
PMCPMC8123300
OpenAlexW3158539647

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.