Evidence map›Paper›PMID 39857998›Full record

ArticleCancers2025

CT-Derived Quantitative Image Features Predict Neoadjuvant Treatment Response in Adenocarcinoma of the Gastroesophageal Junction with High Accuracy.

Markus Graf, Sebastian Ziegelmayer, Stefan Reischl, Yannick Teumer, Florian T Gassert, Alexander W Marka, Philipp Raffler, Jeannine Bachmann, Marcus Makowski, Daniel Reim and 3 more

Abstract read
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Markus GrafDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.
Sebastian ZiegelmayerDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.
Stefan ReischlDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0001-7341-4296
Yannick TeumerDepartment of Medicine II, Ulm University Medical Center, 89081 Ulm, Germany.
Florian T GassertDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0003-4126-462X
Alexander W MarkaDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0002-2111-8177
Philipp RafflerDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.ORCID 0009-0006-7269-4998
Jeannine BachmannDepartment of Surgery, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.
Marcus MakowskiDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.
Daniel ReimDepartment of Surgery, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.ORCID 0000-0001-9736-3307
Fabian LohöferDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.
Egon BurianDiagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8006 Zurich, Switzerland.
Rickmer BrarenDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany.

Funding

Federal Ministry of Education and Research 01KD2418A, 01ZZ2315B AND 01KX2121
6 · The paper itself

Abstract

backgroundThe purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ).

methodsA total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection. Histopathological parameters and tumor regression grading according to Becker et al. were collected in 93 patients. Line profiles of the primary tumor area in baseline and preoperative CE-CT were generated using ImageJ. Maximum tumor density and tumor-to-wall density delta were calculated and correlated with the histopathological tumor response. In addition, tumor response was assessed according to standard RECIST measurements in all patients and by endoscopy in 72 patients.

resultsBaseline and change in baseline to preoperative CE-CT parameters showed no significant differences between responders (Becker grade 1a, 1b) and non-responders (Becker grade 2, 3). After neoadjuvant therapy, responders and non-responders showed significant differences in maximum density and tumor-to-wall density delta values. Line profile measurements showed excellent inter-rater agreement. In comparison, neither RECIST nor endoscopy showed significant differences between these groups.

conclusionsPosttreatment CE-CT can predict histopathological therapy response to neoadjuvant treatment in adenocarcinoma of GEJ patients with high accuracy and thus may improve patient management.

Indexed as

adenocarcinoma of the GEJcancercomputed tomographyneoadjuvant therapy

Identifiers

PMID39857998
PMCPMC11763438

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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.