Department of Chemistry, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Republic of Korea.
Division of Convergence Technology, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10408, Republic of Korea.
Surg Endosc. 2023 Aug;37(8):5825-5835. doi: 10.1007/s00464-023-10053-6. Epub 2023 Apr 13.
Determination of stomach tumor location and invasion depth requires delineation of gastric histological structure, which has hitherto been widely accomplished by histochemical staining. In recent years, alternative histochemical evaluation methods have been pursued to accelerate intraoperative diagnosis, often by bypassing the time-consuming step of dyeing. Owing to strong endogenous signals from coenzymes, metabolites, and proteins, autofluorescence spectroscopy is a favorable candidate technique to achieve this aim.
We investigated stomach tissue slices and block specimens using a fast fluorescence imaging scanner. To obtain histological information from broad and structureless fluorescence spectra, we analyzed tens of thousands of spectra with multiple machine-learning algorithms and built a tissue classification model trained with dissected gastric tissues.
A machine-learning-based spectro-histological model was built based on the autofluorescence spectra measured from stomach tissue samples with delineated and validated histological structures. The scores from a principal components analysis were employed as input features, and prediction accuracy was confirmed to be 92.0%, 90.1%, and 91.4% for mucosa, submucosa, and muscularis propria, respectively. We investigated the tissue samples in both sliced and block forms using a fast fluorescence imaging scanner.
We successfully demonstrated differentiation of multiple tissue layers of well-defined specimens with the guidance of a histologist. Our spectro-histology classification model is applicable to histological prediction for both tissue blocks and slices, even though only sliced samples were trained.
确定胃部肿瘤的位置和浸润深度需要对胃组织结构进行描绘，这一过程迄今主要依赖组织化学染色来完成。近年来，人们一直在探索替代的组织化学评估方法，以加速术中诊断，通常可以绕过染色这一耗时步骤。由于辅酶、代谢物和蛋白质会产生强烈的内源性信号，因此荧光光谱自动分析法是一种很有前途的技术手段。
我们使用快速荧光成像扫描仪对胃组织切片和组织块进行了研究。为了从广泛且无结构的荧光光谱中获取组织学信息，我们使用多种机器学习算法对数万条光谱进行了分析，并构建了一个使用解剖胃组织进行训练的组织分类模型。
基于从具有明确组织学结构的胃组织样本中测量得到的自发荧光光谱，构建了一个基于机器学习的光谱组织学模型。主成分分析的得分被用作输入特征，黏膜、黏膜下层和固有肌层的预测准确率分别达到了 92.0%、90.1%和 91.4%。我们使用快速荧光成像扫描仪对切片和组织块形式的组织样本进行了研究。
在组织学家的指导下，我们成功地对具有明确界限的样本的多个组织层进行了区分。我们的光谱组织学分类模型适用于组织块和切片的组织学预测，尽管仅对切片样本进行了训练。
