Evidence map›Paper›PMID 31549269›Full record

ArticleMagma (New York, N.Y.)2020

Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer.

Jiyeong Lee, Chan Kyo Kim, Sung Yoon Park

Abstract read
PubMed Publisher
In one paragraph

Article in Magma (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

7 citing papers in PubMed, 16 citations in OpenAlex.

  1. Frontiers in oncology · 2024
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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

3 authors at 1 institution in 1 country.

Jiyeong LeeDepartment of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Chan Kyo KimDepartment of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. chankyokim@skku.edu.ORCID http://orcid.org/0000-0003-0482-1140
Sung Yoon ParkDepartment of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Samsung Medical Center · KR

Funding

National Research Foundation of Korea NRF-2017R1A2B4006020
6 · The paper itself

Abstract

objectiveTo investigate the value of apparent diffusion coefficient (ADC) histogram analysis in predicting pelvic lymph node (LN) metastasis in patients with cervical cancer undergoing surgery. MATERIALS AND

methodsA total of 162 cervical cancer patients who underwent radical abdominal hysterectomy with pelvic LN dissection performed with pelvic 3 T-MRI including diffusion-weighted imaging were enrolled in this study. The ADC histogram variables (minimum, mean, median, 97.5th percentile [ADC

resultsPelvic LN metastasis was identified histopathologically in 50 patients (30.9%). In patients with LN metastasis, all ADC histogram variables were significantly different from those without LN metastasis (all p < 0.01). Univariate analysis demonstrated that long- and short-axis diameter of LN, MRI T-stage, squamous cell carcinoma antigen, tumor size, and the ADC DISCUSSION: The ADC

Indexed as

Diffusion Magnetic Resonance ImagingAdultAgedAged, 80 and overFemaleHumansHysterectomyImage Interpretation, Computer-AssistedLymphatic MetastasisMiddle AgedObserver VariationRegression AnalysisRetrospective StudiesROC CurveUterine Cervical NeoplasmsCervical cancerDiffusion-weighted MRILymphatic metastasisMagnetic resonance imaging

Identifiers

PMID31549269
OpenAlexW2977084477

What OpenQuestion holds

Textmetadata
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.