Artificial intelligence and immunotherapy

Jabbari, Parnian; Rezaei, Nima

doi:10.1080/1744666x.2019.1623670

Cited by 30 publications

(12 citation statements)

References 16 publications

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“…Adjuvant radiotherapy stands to be significantly benefited by more accurate tumour segmentation, as described previously [ 48 , 66 , 89 , 90 , 97 ]. Immunotherapy in CNS tumours remain in the early stages of trials, yet AI platforms may in the future predict response to immunotherapy, as well as optimise the dose and treatment regimen [ 190 ]. Furthermore, AI may enable a whole new range of therapeutics to be discovered [ 188 ].…”

Section: Post-operative Phasementioning

confidence: 99%

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Williams

Horsfall

Funnell

et al. 2021

Cancers

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Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

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Section: Post-operative Phasementioning

confidence: 99%

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Williams

Horsfall

Funnell

et al. 2021

Cancers

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“…In the following sections, we summarize some machine-learning facilitated neuroimmunological achievements from the field of MG genomics. Detailed resources and technical details on available machine learning tools and platforms have been reviewed elsewhere [2,[4][5][6][7][8]. This chapter is structured to illustrate genetic research in a top-down approach, from tissue-based to the single-cell or even subcellular level.…”

Section: Big Data-omics In Neuroimmunoinformaticsmentioning

confidence: 99%

“…The intermingling of machine learning applications with wet lab and clinical results, facilitated by the expanding marketing of user-friendly computer interfaces for lab scientists, has hatched the newly defined society of immunoinformatics. Immunoinformatics flourished in the last decade by detangling tumor immunology, predicting cancer epitopes, and sequencing the adaptive immune receptor repertoires, as has been widely reviewed previously [1,2].…”

Section: Introductionmentioning

confidence: 99%

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

et al. 2021

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Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

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“…AI may be applied to cellular phenotype detection and classification to determine the presence of a particular disease or its outcome. Image‐based phenotype detection has been developed to overcome the limitations of the classical methods of microscopic visual inspection of tissues, which are time‐consuming, prone to subjectivity and unable to keep up with the data collected from high‐throughput studies 151 . These novel techniques can determine immune responses such as macrophage activation and lymphocyte infiltration, which has particular relevance in IO, where different lesions can have different TMEs resulting in heterogeneous response patterns.…”

Section: Novel Ici Approaches To Precision Oncologymentioning

confidence: 99%

Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

Watson

Doi

Hansen

et al. 2020

Brit J Clinical Pharma

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The development of immune checkpoint inhibitors (ICI) represents a major milestone in immune‐oncology. Over the years these agents have demonstrated efficacy in an increasing array of malignancies. Despite this success however, significant challenges remain. Novel approaches to both drug development and trial design are required to incorporate the unique pharmacokinetic and pharmacodynamic properties of ICIs. Further, it has also been established that the benefit of ICIs is limited to only a subset of patients. The molecular interactions between native immune cells and tumorigenesis and progression represent an active area of biomarker research, and elucidating the mechanisms of response and resistance is crucial to develop rational trial designs for the next wave of immune‐oncology (IO) clinical trials, particularly in patients with primary and/or acquired resistance. Efforts are now being made to integrate both biological and clinical information using novel multi‐omic approaches which are now being developed to further elucidate the molecular signatures associated with IO treatment response and resistance and enable rational drug development and trial design processes. As such, precision IO and the ability to deliver patient‐specific choices for ICI monotherapies or combination therapies has become an increasingly tangible goal. We herein describe the current landscape in ICI drug development and discuss the challenges and future directions in this exciting and evolving era in immune‐oncology.

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Artificial intelligence and immunotherapy

Cited by 30 publications

References 16 publications

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

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