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.
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。