PDF(Pubmed)
Abstract：
BACKGROUND: Crohn\'s disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed.
OBJECTIVE: To develop a method to identify CD and ITB with high accuracy, specificity, and speed.
METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.
RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB.
CONCLUSIONS: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.
OBJECTIVE: To develop a method to identify CD and ITB with high accuracy, specificity, and speed.
METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.
RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB.
CONCLUSIONS: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.
摘要：
背景：克罗恩病（CD）常被误诊为肠结核（ITB）。然而,这两种疾病的治疗和预后有很大的不同。因此,开发一种高精度识别CD和ITB的方法非常重要，特异性，和速度。
目的：开发一种高精度鉴别CD和ITB的方法，特异性，和速度。
方法：共72个石蜡包埋组织切片经病理和临床诊断为CD或ITB。将石蜡包埋的组织切片附着在金属涂层上，并使用中红外波长的衰减全反射傅里叶变换红外光谱与XGBoost结合进行鉴别诊断进行测量。
结果：结果表明，石蜡包埋的CD和ITB标本在1074cm-1和1234cm-1波段的光谱信号显着不同，基于光谱特征与机器学习相结合的鉴别诊断模型具有较高的准确性,特异性，灵敏度为91.84%，92.59%,和90.90%，分别,用于CD和ITB的鉴别诊断。
结论：中红外区域的信息可以在分子水平上揭示CD和ITB的不同组织学成分，频谱分析结合机器学习建立诊断模型有望成为CD和ITB鉴别诊断的新方法。
目的：开发一种高精度鉴别CD和ITB的方法，特异性，和速度。
方法：共72个石蜡包埋组织切片经病理和临床诊断为CD或ITB。将石蜡包埋的组织切片附着在金属涂层上，并使用中红外波长的衰减全反射傅里叶变换红外光谱与XGBoost结合进行鉴别诊断进行测量。
结果：结果表明，石蜡包埋的CD和ITB标本在1074cm-1和1234cm-1波段的光谱信号显着不同，基于光谱特征与机器学习相结合的鉴别诊断模型具有较高的准确性,特异性，灵敏度为91.84%，92.59%,和90.90%，分别,用于CD和ITB的鉴别诊断。
结论：中红外区域的信息可以在分子水平上揭示CD和ITB的不同组织学成分，频谱分析结合机器学习建立诊断模型有望成为CD和ITB鉴别诊断的新方法。
