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Artificial Intelligence in Studies of Malignant Tumours

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Biomarkers of the Tumor Microenvironment

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Abstract

With the introduction of digital pathology and artificial intelligence (AI)-based methods, we may be facing a new era in cancer diagnostics and prognostication. AI can assist pathologists in labour-intensive tasks and potentially discover new features currently not detected and characterized in routine diagnostics. As entire digital histopathological sections can be included in the analysis, AI can be used both to study the epithelial component of a tumour and the microenvironment. Most state-of-the-art AI approaches used for image analysis utilize multi-step pipelines. AI-based methods have shown promising results in a wide range of clinically relevant tasks. It is, however, important to be aware of some challenges and limitations, such as the lack of generalizability of AI-based models, and the importance of understanding the reason behind a conclusion.

Digital Pathology

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Notes

  1. 1.

    Algorithm: A finite list of computer-implemented instructions and rules a computer needs to solve a specific task.

  2. 2.

    Convolution: A mathematical operation that performs filtering of input data, using a predefined filter function or kernel. An example could be to blur an image or extract edge information.

  3. 3.

    Patch: A subregion of an image. It is also commonly referred to as a tile.

  4. 4.

    Histogram: A method used to extract the frequency of different values.

  5. 5.

    Pixel: The smallest addressable region in an image. The size of a pixel is defined by the resolution.

  6. 6.

    Class: A predefined type of object or structure in a data sample. A set of images of cats and a set of images of dogs could be labelled with two different classes, one for each animal. Then a classifier may be trained to distinguish between images of the different classes/animals.

  7. 7.

    DBSCAN: A density-based algorithm for discovering clusters in large spatial databases with noise. An unsupervised machine learning method for performing clustering.

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Authors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    André Pedersen & Marit Valla

  2. SINTEF, Trondheim, Norway

    Ingerid Reinertsen

  3. Stavanger University Hospital/University of Stavanger, Stavanger, Norway

    Emiel A. M. Janssen

Authors
  1. André Pedersen
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  2. Ingerid Reinertsen
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  3. Emiel A. M. Janssen
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  4. Marit Valla
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Corresponding author

Correspondence to Marit Valla .

Editor information

Editors and Affiliations

  1. Centre for Cancer Biomarkers CCBIO Department of Clinical Medicine, University of Bergen, Bergen, Norway

    Lars A. Akslen

  2. Department of Surgery, Harvard Medical School, Vascular Biology Program, Boston Children’s Hospital, Boston, USA

    Randolph S. Watnick

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Pedersen, A., Reinertsen, I., Janssen, E.A.M., Valla, M. (2022). Artificial Intelligence in Studies of Malignant Tumours. In: Akslen, L.A., Watnick, R.S. (eds) Biomarkers of the Tumor Microenvironment. Springer, Cham. https://doi.org/10.1007/978-3-030-98950-7_21

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  • DOI: https://doi.org/10.1007/978-3-030-98950-7_21

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  • Online ISBN: 978-3-030-98950-7

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