Evidence map›Paper›PMID 39606126›Full record

ArticleCureus2024

Enhancing Precision in Rectal Cancer Care: Unravelling the Correlation Between MRI Locoregional Staging and Prognostic Factors, With Post-resection Pathology.

Manoj S Gowda, Bennett C Peter, Mesut E Enc, Sundaram V Gowtham, Sri Vishnu Thulasiraman, Mohamed Mansour, Trisha Jha, Madan Jha

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

8 authors.

Manoj S GowdaGeneral Surgery, Royal Victoria Infirmary, Newcastle, GBR.
Bennett C PeterGeneral Surgery, James Cook University Hospital, Middlesbrough, GBR.
Mesut E EncTrauma and Orthopaedics, Royal Victoria Infirmary, Newcastle, GBR.
Sundaram V GowthamGeneral Surgery, James Cook University Hospital, Middlesbrough, GBR.
Sri Vishnu ThulasiramanGeneral Surgery, Kovai Medical Center and Hospital, Coimbatore, IND.
Mohamed MansourSurgery, James Cook University Hospital, Middlesbrough, GBR.
Trisha JhaSurgery, Keele University School of Medicine, Newcastle, GBR.
Madan JhaGeneral Surgery, James Cook University Hospital, Middlesbrough, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Magnetic resonance imaging (MRI) serves as a pivotal tool in the preoperative assessment of rectal cancer. This study aims to evaluate the accuracy of preoperative MRI pelvis in rectal cancer for locoregional staging, circumferential margin (CRM+), and vascular invasion (V1) with postoperative histopathological findings. Methods All patients who underwent preoperative staging MRI pelvis scanning for histology-proven primary rectal adenocarcinoma between January 2020 and July 2022 were included in this study. Preoperative MRI assessment data, including tumor (T), nodal (N) staging, and other prognostic factors, including circumferential margin and vascular invasion, were compared with histopathological reports. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy were calculated. Agreement between MRI and pathology reports was evaluated using the weighted kappa statistic. Results The study included 143 patients. The accuracy of MRI is at its peak for T0 and T4 tumors (T0-97.9%, T4-93.37). The weighted kappa statistic for T staging is 0.401, and for N staging, it is 0.286. The standard kappa statistic for extramural vascular invasion (V) and circumferential resection margin (CRM) is 0.269 and 0.225, respectively. Conclusion MRI demonstrates fair agreement with pathology regarding T and N staging and exhibits high specificity for CRM and V1.

Indexed as

colonoscopycolorectal cancercomputed tomographymagnetic resonance imagingmesorectummri pelvisoncological outcomespreoperative assessmentprognostic toolrectal cancer

Identifiers

PMID39606126
PMCPMC11601879

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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.