Evidence map›Paper›PMID 40113648›Full record

ArticleInsights into imaging2025

Intestinal fibrosis assessment in Crohn's disease patient using unenhanced spectral CT combined with 3D-printing technique.

Qiapeng Huang, Zhihui Chen, Ruonan Zhang, Huasong Cai, Xufeng Yang, Xiaodi Shen, Lili Huang, Xinyue Wang, Qingzhu Zheng, Mingzhe Li and 6 more

Abstract read
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

16 authors.

Qiapeng Huang *Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Zhihui Chen *Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Ruonan Zhang *Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Huasong CaiDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Xufeng YangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Xiaodi ShenDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Lili HuangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Xinyue WangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Qingzhu ZhengDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Mingzhe LiCenter of Digestive Disease, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, People's Republic of China.
Ziyin YeDepartment of Pathology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Xubin LiuDepartment of Pathology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Ren MaoDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Yangdi WangDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China. wangyd83@mail.sysu.edu.cn.
Jinjiang LinDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China. linjinj@mail.sysu.edu.cn.
Zhoulei LiDepartment of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China. lizhlei3@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-9426-0836

Funding

2023 SKY Imaging Research Fund of the Chinese International Medical Foundation Z-2014-07-2301Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515010388Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515011097Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515011304Basic and Applied Basic Research Foundation of Guangdong Province 2023B1515020070Fundamental Research Funds for the Central Universities, Sun Yat-sen University 24ykqb003National Natural Science Foundation of China 82070680National Natural Science Foundation of China 82072002National Natural Science Foundation of China 82270693National Natural Science Foundation of China 82271958National Natural Science Foundation of China 82471948Shenzhen Medical Research Fund B2302008
6 · The paper itself

Abstract

objectivesTo integrate multiple parameters derived from unenhanced spectral CT with 3D-printing technique to accurately evaluate intestinal lesions in patients with Crohn's disease (CD).

methodsPatients with proven CD who underwent preoperative spectral CT and surgery were included. The spectral CT images and histopathological specimens were achieved by employing 3D-printing technique. Diagnostic models were developed utilizing Z-Effective, Electron Density (ED), and Hounsfield unit (HU) values derived from spectral CT, along with spectral curve slopes λ

resultsThe ED and HU at MonoE 50 keV of the spectral CT were determined to exhibit the highest correlation with the fibrosis degree of the diseased intestine. The training dataset yielded an AUC of 0.828 (95% CI: 0.705-0.951). The sensitivity and specificity were calculated to be 77.3% and 82.6%, respectively. The AUC of the validation set was 0.812 (95% CI: 0.676-0.948) with a sensitivity of 63.6% and specificity of 89.7%. Moreover, our model demonstrated enhanced diagnostic accuracy for detecting fibrosis with an AUC value of 0.933 (95% CI: 0.856-1.000), sensitivity of 90.9%, and specificity of 87.0%, after regulating the influence of inflammation.

conclusionThe integration of unenhanced multi-parametric spectral CT and 3D-printing technique seems to be able to assess the intestinal fibrosis. Our diagnostic model remains effective in assessing the severity of fibrosis under presence of inflammation. CRITICAL RELEVANCE STATEMENT: Our diagnostic model accurately assessed the degree of intestinal wall fibrosis in Crohn's disease patients by using unenhanced spectral CT and 3D-printing technique, which could facilitate individualized treatment. KEY POINTS: Evaluating the extent of Crohn's disease-related fibrosis is important. The combination of 3D-printing technique and multi-parametric spectral CT enhances diagnostic accuracy. The developed model using spectral CT allows for the assessment of intestinal fibrosis using multi-parameters.

Indexed as

3D printingComputed tomography enterographyCrohn’s diseaseFibrosis

Identifiers

PMID40113648
PMCPMC11926292

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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.