Evidence map›Paper›PMID 40386358›Full record

ArticleTranslational pediatrics2025

Evaluation of radiomics as an assistant tool for radiologists in the diagnosis of necrotizing enterocolitis.

Chaogang Lu, Mingshu Yang, Yinghao Zhu, Yaqin Xia, Siqi Luo, Guang Yang, Mei Bai, Zhongwei Qiao

Abstract read
In one paragraph

Article in Translational pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

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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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026
    Review
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Chaogang LuShanghai Institute of Medical Imaging, Fudan University, Shanghai, China.ORCID https://orcid.org/0009-0009-2389-8072
Mingshu YangDepartment of Radiology, Children's Hospital of Fudan University, Shanghai, China.
Yinghao ZhuInstitute of Artificial Intelligence, Beihang University, Beijing, China.
Yaqin XiaDepartment of Radiology, Children's Hospital of Fudan University, Shanghai, China.
Siqi LuoDepartment of Radiology, Children's Hospital of Fudan University, Shanghai, China.
Guang YangShanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
Mei BaiDepartment of Radiology, Children's Hospital of Fudan University, Shanghai, China.
Zhongwei QiaoDepartment of Radiology, Children's Hospital of Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Necrotizing enterocolitis (NEC) is a severe gastrointestinal condition which is mainly diagnosed by abdominal radiographs. Early diagnosis of NEC remains challenging due to its nonspecific clinical symptoms and the variability in radiographic findings. Radiomics can enhance diagnostic accuracy by extracting quantitative features from medical images. This study aimed to evaluate the value of radiomics as an assistant tool in cases of missed diagnosis by radiologists. Methods: In this retrospective study, abdominal radiographs from 484 patients were collected, comprising 262 NEC patients and 222 non-NEC patients from January 2016 to December 2022 in Children's Hospital of Fudan University. The dataset was divided into a training set (n=246), test set (n=105), and a temporal validation set (n=133). Feature selection was performed consecutively using the minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms. A radiomics diagnostic model was constructed using logistic regression. Model performance was evaluated using the area under the curve (AUC). In the temporal validation set, we conducted a parallel test diagnosis using radiomics and the diagnostic results of radiologists, and performed a Chi-squared test against the diagnosis of radiologists. Results: The radiomics diagnostic model which has included 18 features achieved AUCs of 0.82, 0.74, and 0.71 for the training set, test set, and temporal validation set, respectively. In the temporal validation set, the diagnostic results of the parallel test were more sensitive than those of the radiologists (P=0.003). Conclusions: The radiomics model showed certain diagnostic value and offers a unique perspective compared to radiologists, focusing on quantitative features that can assist in early diagnosis and treatment of NEC. This demonstrates the potential of the model in recognizing challenging cases that might be overlooked by naked eyes of radiologists.

Indexed as

diagnosisNecrotizing enterocolitis (NEC)radiomics

Identifiers

PMID40386358
PMCPMC12079682

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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.