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04-05-2024 | Crohn's Disease | Hollow Organ GI

A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn’s disease

Authors: Baolan Lu, Zengan Huang, Jinjiang Lin, Ruonan Zhang, Xiaodi Shen, Lili Huang, Xinyue Wang, Weitao He, Qiapeng Huang, Jiayu Fang, Ren Mao, Zhoulei Li, Bingsheng Huang, Shi-Ting Feng, Ziying Ye, Jian Zhang, Yangdi Wang

Published in: Abdominal Radiology

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Abstract

Objectives

Differentiating intestinal tuberculosis (ITB) from Crohn’s disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD.

Methods

Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong’s test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models.

Results

The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68–0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71–0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48–0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49–0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78–1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility.

Conclusions

Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

Graphical Abstract

Appendix
Available only for authorised users
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Metadata
Title
A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn’s disease
Authors
Baolan Lu
Zengan Huang
Jinjiang Lin
Ruonan Zhang
Xiaodi Shen
Lili Huang
Xinyue Wang
Weitao He
Qiapeng Huang
Jiayu Fang
Ren Mao
Zhoulei Li
Bingsheng Huang
Shi-Ting Feng
Ziying Ye
Jian Zhang
Yangdi Wang
Publication date
04-05-2024
Publisher
Springer US
Published in
Abdominal Radiology
Print ISSN: 2366-004X
Electronic ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-024-04307-7
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