Abstract：
OBJECTIVE: 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.
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.
摘要：
目的：区分肠结核（ITB）和克罗恩病（CD）仍然是一个诊断难题。误诊具有潜在的严重影响。我们的目标是使用机器学习方法建立基于多学科的模型，以区分ITB和CD。
方法：回顾性招募82例患者，其中包括25例ITB患者和57例CD患者（54例在训练队列，28例在测试队列）。在磁共振小肠造影（MRE）和结肠镜检查图像上描绘了病变的感兴趣区域（ROI）。通过最小绝对收缩和选择算子回归来提取放射学特征。采用深度学习方法自动提取病理特征。通过logistic回归分析筛选临床特征。通过受试者工作特征（ROC）曲线和决策曲线分析（DCA）评估诊断性能。Delong的测试用于比较基于多学科的模型与其他四个基于单学科的模型之间的效率。
结果：基于MRE特征的放射组学模型在测试数据集上产生的AUC为0.87（95％置信区间[CI]0.68-0.96），这与临床模型相似(AUC，0.90[95%CI0.71-0.98])，高于结肠镜检查影像组学模型(AUC，0.68[95%CI0.48-0.84])和病理学深度学习模型(AUC，0.70[95%CI0.49-0.85]）。多学科模型,整合3个临床，21MRE放射学，5结肠镜检查，和4个病理学深度学习特征，基于单学科的模型可以显着提高诊断性能（AUC为0.94，95％CI0.78-1.00）。DCA证实了临床实用性。
结论：基于多学科的模型整合临床，MRE,结肠镜检查,病理学特征有助于区分ITB和CD。
方法：回顾性招募82例患者，其中包括25例ITB患者和57例CD患者（54例在训练队列，28例在测试队列）。在磁共振小肠造影（MRE）和结肠镜检查图像上描绘了病变的感兴趣区域（ROI）。通过最小绝对收缩和选择算子回归来提取放射学特征。采用深度学习方法自动提取病理特征。通过logistic回归分析筛选临床特征。通过受试者工作特征（ROC）曲线和决策曲线分析（DCA）评估诊断性能。Delong的测试用于比较基于多学科的模型与其他四个基于单学科的模型之间的效率。
结果：基于MRE特征的放射组学模型在测试数据集上产生的AUC为0.87（95％置信区间[CI]0.68-0.96），这与临床模型相似(AUC，0.90[95%CI0.71-0.98])，高于结肠镜检查影像组学模型(AUC，0.68[95%CI0.48-0.84])和病理学深度学习模型(AUC，0.70[95%CI0.49-0.85]）。多学科模型,整合3个临床，21MRE放射学，5结肠镜检查，和4个病理学深度学习特征，基于单学科的模型可以显着提高诊断性能（AUC为0.94，95％CI0.78-1.00）。DCA证实了临床实用性。
结论：基于多学科的模型整合临床，MRE,结肠镜检查,病理学特征有助于区分ITB和CD。
