Evidence map›Paper›PMID 38201380›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone.

Eric K van Staalduinen, Robert Matthews, Adam Khan, Isha Punn, Renee F Cattell, Haifang Li, Ana Franceschi, Ghassan J Samara, Lukasz Czerwonka, Lev Bangiyev and 1 more

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

11 authors.

Eric K van StaalduinenAlbert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA.
Robert MatthewsStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Adam KhanStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Isha PunnStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Renee F CattellStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Haifang LiStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Ana FranceschiStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Ghassan J SamaraStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Lukasz CzerwonkaStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Lev BangiyevStony Brook Medicine, Department of Radiology, Stony Brook, NY 11794, USA.
Tim Q DuongAlbert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, NY 10467, USA.ORCID 0000-0001-6403-2827

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (

Indexed as

cervical lymphadenopathymachine learningPET-MRIsquamous cell carcinomatexture analysis

Identifiers

PMID38201380
PMCPMC10802850

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.