Evidence map›Paper›PMID 39563889›Full record

ArticleiScience2024

Image-based deep learning model to predict stoma-site incisional hernia in patients with temporary ileostomy: A retrospective study.

Zhongyi Dong, Jianhua Cai, Haigang Geng, Bo Ni, Mengqing Yuan, Yeqian Zhang, Xiang Xia, Haoyu Zhang, Jie Zhang, Chunchao Zhu and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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
–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. Review
  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

17 authors.

Zhongyi DongDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Jianhua CaiDepartment of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai 200040, P.R. China.
Haigang GengDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Bo NiDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Mengqing YuanSchool of Science, The Hongkong University of Science and Technology, Hongkong 999077, P.R. China.
Yeqian ZhangDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Xiang XiaDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Haoyu ZhangDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Jie ZhangDepartment of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Chunchao ZhuDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Un Wai ChoiSchool of Medicine, Shanghai Jiaotong University, Shanghai 200025, P.R. China.
Aksara RegmiSchool of Medicine, Shanghai Jiaotong University, Shanghai 200025, P.R. China.
Cheok I ChanSchool of Medicine, Shanghai Jiaotong University, Shanghai 200025, P.R. China.
Cara Kou YanSchool of Medicine, Shanghai Jiaotong University, Shanghai 200025, P.R. China.
Yan GuDepartment of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai 200040, P.R. China.
Hui CaoDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.
Zizhen ZhangDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prophylactic implantation of biological mesh can effectively prevent the occurrence of stoma-site incisional hernia (SSIH) in patients undergoing stoma retraction. Therefore, our study prospectively established and validated a mixed model, which combined radiomics, stepwise regression, and deep learning for the prediction of SSIH in patients with temporary ileostomy. The mixed model showed good discrimination of the SSIH patients on all cohorts, which outperformed deep learning, radiomics, and clinical models alone (overall area under the curve [AUC]: 0.947 in the primary cohort, 0.876 in the external validation cohort 1, and 0.776 in the external validation cohort 2). Moreover, the sensitivity, specificity, and precision for predicting SSIH were improved in the mixed model. Thus, the mixed model can provide more information for SSIH precaution and clinical decision-making.

Indexed as

Computing methodologyHealth sciencesMedical imaging

Identifiers

PMID39563889
PMCPMC11574812

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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.