Evidence map›Paper›PMID 34887674›Full record

ArticleJournal of inflammation research2021

A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis.

Chao Zhu, Yongmei Yu, Shihui Wang, Xia Wang, Yankun Gao, Cuiping Li, Jianying Li, Yaqiong Ge, Xingwang Wu

Abstract read
In one paragraph

Article in Journal of inflammation research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Urinary lipoarabinomannan for gastrointestinal tuberculosis: Another tool in the kit.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026
    Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. The Role of Radiomics in Fibrosis Crohn's Disease: A Review.Diagnostics (Basel, Switzerland) · 2023
    Review
  14. Article
  15. 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

9 authors.

Chao ZhuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China.
Yongmei YuDepartment of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People's Republic of China.
Shihui WangDepartment of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People's Republic of China.
Xia WangDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China.
Yankun GaoDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China.
Cuiping LiDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China.
Jianying LiGE Healthcare China, Shanghai, 210000, People's Republic of China.
Yaqiong GeGE Healthcare China, Shanghai, 210000, People's Republic of China.
Xingwang WuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo establish a clinical radiomics nomogram to differentiate Crohn's disease (CD) from intestinal tuberculosis (ITB). PATIENTS AND

methodsNinety-three patients with CD and 67 patients with ITB were recruited (111 in training cohort and 49 in test cohort). The region of interest (ROI) for the lesions in the ileocecal region was delineated on computed tomography enterography and radiomics features extracted. Radiomics features were filtered by the gradient boosting decision tree (GBDT), and a radiomics score was calculated by using the radiomics signature-based formula. We constructed a clinical radiomics model and nomogram combining clinical factors and radiomics score through multivariate logistic regression analysis, and the internal validation was undertaken by ten-fold cross validation. Analyses of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to evaluate the performance of the clinical, radiomics and combined model.

resultsThe clinical radiomics nomogram, which was based on the 9 radiomics signature and two clinical factors, indicated that the clinical radiomics model had an area under the ROC curve (AUC) value of 0.96 (95% confidence interval [CI]: 0.93-0.99) in the training cohort and 0.93 (95% CI: 0.86-1.00) in validation cohort. The clinical radiomics model was superior to the clinical model and radiomics model, and the difference was significant (

conclusionCTE-based radiomics model has a good performance in distinguishing CD from ITB. A nomogram constructed by combining radiomics and clinical factors can help clinicians accurately diagnose and select appropriate treatment strategies between CD and ITB.

Indexed as

computed tomographyCrohn’s diseaseintestinal tuberculosisnomogramradiomics

Identifiers

PMID34887674
PMCPMC8651213

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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.