Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. (May 2023)

• Record Type: Journal Article
• Authors: Zhang, Zhe; Wei, Xiawei
• Is Part Of: Seminars in cancer biology. Volume 90(2023)
• Page Start: 57
• Page End: 72
• Publication Date: 2023-05
• Subjects: AI artificial intelligence -- AUC area under the curve -- BRATS Brain Tumor Image Segmentation Benchmark -- CNN convolutional neural network -- CCLE Cancer Cell Line Encyclopedia -- CLL chronic lymphocytic leukemia -- CNVs copy number variations -- CTLA-4 cytotoxic T lymphocyte-associated antigen 4 -- DSC dynamic susceptibility contrast -- DoseNet dose prediction neural network -- dMMR mismatch repair deficiency -- DRD2 dopamine receptor D2 -- EGFR epidermal growth factor receptor -- FDA Food and Drug Administration -- FCN fully-convolutional neural network -- GCNNs graph convolutional neural networks -- GEO Gene Expression Omnibus -- GENIE Genomics Evidence Neoplasia Information Exchange -- GAN generative adversarial network -- HCMDB Human Cancer Metastasis Database -- HNSCC head and neck cancer -- HLA human leukocyte antigen -- H&E hematoxylin and eosin -- IDH isocitrate dehydrogenase -- INSDC International Nucleotide Sequence Database Collaboration -- JAK2 Janus protein kinase 2 -- LARC locally advanced rectal cancer -- MAPK mitogen-activated protein kinase -- MLP multilayer perceptron -- MSI microsatellite instability -- MHC major histocompatibility complex -- ML machine learning -- NGS next-generation sequencing -- NCT National Center for Tumor Diseases -- NSCLC non-small cell lung cancer -- PCA principal component analysis -- PC1 first principal component -- PDX patient-derived xenograft -- PDCs patient-derived cancer cells -- PD-L1 programmed death-ligand 1 -- PI3K phospatidylinositol-3 kinase -- PLK1 Polo-like kinase 1 -- P-gp P-glycoprotein -- QPOP quadratic phenotype optimization platform -- RNN recurrent neural network -- SVMs support vector machines -- SQCLC squamous cell lung cancer -- SRDFM Siamese Response Deep Decomposition Machine -- SBRT stereotactic body radiotherapy -- SNVs single-nucleotide variations -- SResCNN small-residual-convolutional-network -- SaMD Software as a Medical Device -- TGP targeted-gene panel -- t-SNE t-distributed stochastic neighbor embedding -- TCGA The Cancer Genome Atlas -- TCIA The Cancer Imaging Archive -- TKI tyrosine kinase inhibitor -- TNBC triple-negative breast cancer -- TILs tumor-infiltrating lymphocytes -- TPS tumor proportion score -- UMAP uniform manifold approximation and projection; Artificial intelligence -- Clinical oncology -- Targeted cancer therapy -- Conventional cancer therapy -- Cancer immunotherapy; Cancer -- Periodicals; Neoplasms -- Periodicals; Review Literature; Cancer -- Périodiques; Electronic journals; 616.994
• DOI: 10.1016/j.semcancer.2023.02.005
• Languages: English
• ISSNs: 1044-579X

Abstract

Abstract: The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.