Evidence map›Paper›PMID 38538907›Full record

ArticleClinical rheumatology2024

The value of MRI-based radiomics for evaluating early parotid gland injury in primary Sjögren's syndrome.

Lu Lu, Tiantian Dai, Yi Zhao, Hang Qu, Qi An Sun, Hongyi Xia, Wei Wang, Guoqing Li

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In one paragraph

Article in Clinical rheumatology, 2024. 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
1.6field-weighted citation impact, top 18% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Review
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 at 1 institution in 1 country.

Lu LuMedical College, Yangzhou University, Yangzhou, 255000, China.
Tiantian DaiMedical College, Yangzhou University, Yangzhou, 255000, China.
Yi ZhaoDepartment of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 255000, China.
Hang QuDepartment of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 255000, China.
Qi An SunDepartment of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 255000, China.
Hongyi XiaDepartment of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 255000, China.
Wei WangMedical College, Yangzhou University, Yangzhou, 255000, China. waywang@126.com.ORCID http://orcid.org/0000-0002-4345-1608
Guoqing LiMedical College, Yangzhou University, Yangzhou, 255000, China.
Yangzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the value of machine learning models (ML) based on MRI radiomics in diagnosing early parotid gland injury in primary Sjögren's syndrome (pSS).

methodsA total of 164 patients (114 in the training cohort and 50 in the testing cohort) with pSS (n=82) or healthy controls (HC) (n=82) were enrolled. Itksnap software was used to perform two-dimensional segmentation of the bilateral parotid glands on T1-weighted (T1WI) and fat-suppressed T2-weighted imaging (fs-T2WI) images. A total of 1548 texture features of the parotid glands were extracted using radiomics software. A radiomics score (Radscore) was constructed and calculated. A t-test was used to compare the Radscore between the two groups. Finally, five machine learning models were trained and tested to identify early pSS parotid injury, and the performance of the machine learning models was evaluated by calculating the acceptance operating curve (ROC) and other parameters.

resultsThe Radscores between the pSS and HC groups showed significant statistical differences (p<0.001). Among the five machine learning models, the Extra Trees Classifier (ETC) model performed high predictive efficacy in identifying early pSS parotid injury, with an AUC of 0.87 in the testing set.

conclusionMRI radiomics-based machine learning models can effectively diagnose early parotid gland injury in primary Sjögren's syndrome.

Indexed as

Parotid GlandSjogren's SyndromeHumansMachine LearningMagnetic Resonance ImagingRadiomicsRetrospective StudiesMachine learningMagnetic resonance imagingPrimary Sjögren’s syndromeRadiomics

Identifiers

PMID38538907
OpenAlexW4393218032

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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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.