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Deep Learning of Histopathology Images at the Single Cell Level
Kyubum Lee1, John H Lockhart2, Mengyu Xie1
1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
Insights
Machine learning analyzes digital histopathology images to understand the tumor immune microenvironment (TIME). This review focuses on cell-level analysis, integrating spatial and molecular data for better cancer characterization and drug response prediction.
Area of Science:
- Computational pathology
- Digital histopathology
- Machine learning in oncology
Background:
- The tumor immune microenvironment (TIME) comprises diverse cells crucial for cancer progression, drug response, and metastasis.
- Spatial organization and cellular crosstalk within TIME are key for predicting clinical outcomes.
- Deep learning and omics data integration are emerging tools for TIME characterization.
Purpose of the Study:
- To review machine learning (ML)-based digital histopathology image analysis for characterizing the tumor ecosystem.
- To focus on ML methods at whole slide image (WSI), region of interest (ROI), and cell levels, emphasizing cell-level analysis.
- To discuss data generation workflows, limitations, and future integration of omics data with histopathology.
Main Methods:
- Systematic review of ML methods applied to digital histopathology at WSI, ROI, and cell levels.
- Focus on cell-level analysis techniques and workflows for creating "weakly-labeled" training datasets using immunohistochemistry.
- Discussion of data preparation steps and inherent limitations in current ML approaches.
Main Results:
- Machine learning offers powerful tools for analyzing complex TIME at multiple scales.
- Cell-level analysis, supported by "weakly-labeled" data, is a promising approach for detailed TIME characterization.
- Integration of spatial histopathology with molecular omics data holds significant potential for advancing cancer research.
Conclusions:
- ML-based digital histopathology is pivotal for dissecting the tumor ecosystem and TIME.
- Cell-level analysis and innovative data generation strategies are advancing cancer diagnostics and prognostics.
- Future research should prioritize integrating multi-omics data with histopathology for a comprehensive understanding of TIME and improved therapeutic strategies.
Abstract:
The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to "weakly-label" the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.
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