School of Science, China Pharmaceutical University, Nanjing, 211198, China.
Zhejiang Lab, #1818 Wenyi West Road, Yuhang District, Hangzhou, 311100, Zhengjiang province, China.
Talanta. 2023 Oct 1;263:124727. doi: 10.1016/j.talanta.2023.124727. Epub 2023 May 25.
Mass spectrometry imaging (MSI) is widely used for unlabeled molecular co-localization in biological samples and is also commonly used for screening cancer biomarkers. The main issues affecting the screening of cancer biomarkers are: 1) low-resolution MSI and pathological slices cannot be accurately matched; 2) a large amount of MSI data cannot be directly analyzed without manual annotation. This paper proposes a self-supervised cluster analysis method for colorectal cancer biomarkers based on multi-scale whole slide images (WSI) and MSI fusion images without manual annotation, which can accurately determine the correlation between molecules and lesion areas. This paper uses the combination of WSI multi-scale high-resolution and MSI high-dimensional data to obtain high-resolution fusion images. This method can observe the spatial distribution of molecules in pathological slices and use this method as an evaluation index for self-supervised screening of cancer biomarkers. The experimental results show that the method proposed in this chapter can train the image fusion model with a small amount of MSI and WSI data, and the mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) evaluation metrics of the fused images can reach 0.9587 and 0.8745. And self-supervised clustering using MSI features and fused image features can obtain good classification results, and the precision, recall, and F1-score values of the self-supervised model reach 0.9074, 0.9065, and 0.9069, respectively. This method effectively combines the advantages of WSI and MSI, which will significantly expand the application scenarios of MSI and facilitate the screening of disease markers.
质谱成像（MSI）广泛用于生物样本中未标记分子的共定位，也常用于筛选癌症生物标志物。影响癌症生物标志物筛选的主要问题有：1）低分辨率 MSI 与病理切片无法准确匹配；2）大量 MSI 数据未经人工注释，无法直接分析。本文提出了一种基于多尺度全切片图像（WSI）和 MSI 融合图像的无人工注释的结直肠癌生物标志物的自监督聚类分析方法，可以准确地确定分子与病变区域之间的相关性。本文使用 WSI 多尺度高分辨率和 MSI 高维数据的组合来获得高分辨率融合图像。这种方法可以观察分子在病理切片中的空间分布，并将该方法作为自监督筛选癌症生物标志物的评价指标。实验结果表明，本文提出的方法可以用少量的 MSI 和 WSI 数据训练图像融合模型，融合图像的平均像素准确率（mPA）和平均交并比（mIoU）评价指标可以达到 0.9587 和 0.8745。并且使用 MSI 特征和融合图像特征进行自监督聚类可以获得良好的分类结果，自监督模型的精度、召回率和 F1 得分分别达到 0.9074、0.9065 和 0.9069。该方法有效地结合了 WSI 和 MSI 的优势，将显著扩展 MSI 的应用场景，有助于疾病标志物的筛选。
