Evidence map›Paper›PMID 41097790›Full record

ArticleCancers2025

From CT to Microscopy: Radiological-Histopathological Correlation for Understanding Abdominal Lymphomas.

Ante Luetić, Martina Luetić, Benjamin Benzon, Danijela Budimir Mršić

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
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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

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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

4 authors.

Ante LuetićDepartment of Diagnostic and Interventional Radiology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia.
Martina LuetićDepartment of Pathology, Forensic Medicine and Cytology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia.
Benjamin BenzonSchool of Medicine, University of Split, Šoltanska 2, 21000 Split, Croatia.ORCID 0000-0003-3681-8499
Danijela Budimir MršićDepartment of Diagnostic and Interventional Radiology, University Hospital Split, Spinčićeva 1, 21000 Split, Croatia.ORCID 0000-0003-1362-2901

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNon-Hodgkin lymphomas (NHLs) are a heterogeneous group of indolent or aggressive lymphoproliferative neoplasms arising from lymph nodes or in extranodal locations. Computed tomography (CT) is the imaging modality of choice, while the definitive diagnosis is confirmed by analyzing tissue samples. The aim of this study was to determine the correlation between CT characteristics and histopathological types of abdominal lymphomas.

methodsA retrospective cross-sectional study included 119 patients with histopathologically confirmed abdominal lymphomas who underwent CT of the abdomen and pelvis prior to treatment. The following CT parameters were extracted: morphological presentation (enlarged lymph nodes/conglomerates, solid mass/masses, gastrointestinal wall thickening, abdominal organ involvement, intra- and extraperitoneal infiltrates), location, two-dimensional size, propagation if present, and postcontrast enhancement.

resultsEnlarged lymph nodes were a slightly more common CT morphological appearance in the indolent B NHL group, while gastrointestinal (GI) wall thickening, solid masses, and infiltrates were more frequent in the aggressive B NHL group (

conclusionsObserved differences in abdominal lymphomas' histopathological and imaging characteristics including location/origin, CT morphological appearance, and postcontrast enhancement revealed that extranodal lymphomas were more often of the aggressive B-cell type, aggressive B-cell types were larger, and GI tract lymphomas showed the most prominent enhancement. These findings can help in the diagnostic process and enable better management of lymphomas.

Indexed as

aggressiveCT characteristicsindolentintra-abdominal lymphomanon-Hodgkin lymphoma

Identifiers

PMID41097790
PMCPMC12523887

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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.