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Related Experiment Video

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A review on deep learning applications in highly multiplexed tissue imaging data analysis.

Mohammed Zidane1, Ahmad Makky1, Matthias Bruhns2,3

  • 1Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.

Frontiers in Bioinformatics
|August 11, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) combined with spatial omics enhances oncology research by analyzing complex biological data. This integration improves cancer prognostication and therapy prediction, offering deeper mechanistic insights than traditional methods.

Keywords:
artificial intelligencebiomarkercancerdeep learninghighly multiplexed tissue imagingpredictionreviewspatial transcriptomics

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Deep learning (DL) has significantly impacted oncology, aiding clinical discoveries and biomarker predictions using diverse biological data.
  • DL algorithms analyze genomics, proteomics, and imaging data, predicting genetic variant effects and protein structures.
  • Spatial omics technologies, like spatial transcriptomics and proteomics, provide mechanistic biological insights.

Purpose of the Study:

  • To review the impact of artificial intelligence (AI), specifically DL, combined with spatial omics technologies in oncology.
  • To focus on DL applications in biomedical image analysis for oncology, including cell segmentation, phenotype identification, prognostication, and therapy prediction.
  • To highlight the advantages of highly multiplexed images over conventional histopathological images for deep mechanistic insights.

Main Methods:

  • Review of DL applications in analyzing spatial omics data in oncology.
  • Comparison of highly multiplexed images (spatial proteomics) with conventional histopathological images.
  • Evaluation of DL-based pipelines for preprocessing highly multiplexed images (cell segmentation, cell type annotation).

Main Results:

  • DL combined with spatial omics provides deep mechanistic insights unobtainable with conventional imaging, even with explainable AI.
  • DL-based pipelines offer advantages and disadvantages for preprocessing highly multiplexed images, aiding in pipeline selection.
  • DL is crucial for discovering novel biological mechanisms using highly multiplexed tissue imaging data.

Conclusions:

  • DL is an essential tool for discovering biological mechanisms when integrated with spatial omics technologies.
  • DL's role in clinical routine is growing, supporting oncology diagnosis, prognosis, and decision-making.
  • The integration of DL with advanced imaging techniques promises to improve patient care quality in oncology.