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      Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

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          Abstract

          Background

          The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re‐)activate the patient's immune system and direct it against the individual cancer in the most effective way.

          Recent Findings

          Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune‐oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune‐cancer‐network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer‐assisted development and clinical validation of such digital biomarker.

          Conclusions

          The successful implementation of AI‐supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into “precision pathology” delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a “precision oncology”.

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          Signatures of mutational processes in human cancer

          Ludmil Alexandrov, Serena Nik-Zainal, David Wedge … (2015)
          All cancers are caused by somatic mutations. However, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here, we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, kataegis, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer with potential implications for understanding of cancer etiology, prevention and therapy.
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            SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

            Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla (2017)
            We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet.
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              Rethinking the Inception Architecture for Computer Vision

              Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe … (2016)
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                Author and article information

                Contributors
                Ralf Huss:
                ORCID: https://orcid.org/0000-0002-6447-9300
                ralf.huss@uk-augsburg.de
                Journal
                Journal ID (nlm-ta): Cancer Rep (Hoboken)
                Journal ID (iso-abbrev): Cancer Rep (Hoboken)
                Journal ID (doi): 10.1002/(ISSN)2573-8348
                Journal ID (publisher-id): CNR2
                Title: Cancer Reports
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 2573-8348
                Publication date (Electronic): 22 February 2023
                Publication date Collection: July 2023
                Volume: 6
                Issue: 7 ( doiID: 10.1002/cnr2.v6.7 )
                Electronic Location Identifier: e1796
                Affiliations
                [ 1 ] Medical Faculty University Augsburg Augsburg Germany
                [ 2 ] Institute for Digital Medicine University Hospital Augsburg Augsburg Germany
                Author notes
                [*] [* ] Correspondence

                Ralf Huss, Institute for Digital Medicine, University Hospital Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany.

                Email: ralf.huss@ 123456uk-augsburg.de

                Author information
                https://orcid.org/0000-0002-6447-9300
                Article
                Publisher ID: CNR21796
                DOI: 10.1002/cnr2.1796
                PMC ID: 10363837
                PubMed ID: 36813293
                SO-VID: 37ad1edf-545d-42fb-85b0-af3bacdd3607
                Copyright © © 2023 The Authors. Cancer Reports published by Wiley Periodicals LLC.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Date revision received : 15 January 2023
                Date received : 29 August 2022
                Date accepted : 09 February 2023
                Page count
                Figures: 1, Tables: 2, Pages: 9, Words: 8460
                Categories
                Subject: Review
                Subject: Review
                Custom metadata
                source-schema-version-number 2.0
                cover-date July 2023
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.2 mode:remove_FC converted:24.07.2023

                Keywords: artificial intelligence,decision support,digital biomarker,immune oncology,precision pathology
                Data availability:
                Keywords: artificial intelligence, decision support, digital biomarker, immune oncology, precision pathology

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