marți, 29 martie 2022

Applying artificial intelligence for cancer immunotherapy

 




Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs.

Although immunotherapy is a great breakthrough in the field of cancer treatment, the judgment of whether a particular patient can respond to the therapy is occasionally confusing. However, the appearance of AI increases the chance of successful cancer immunotherapy through forecasting the therapeutic effect based on the establishment of immunotherapy predictive scores, including immunoscore and immunophenoscore. These two scoring systems were developed to predict the response to immune checkpoint blockade (ICB) therapy. Meanwhile, some limitations, such as unknown predictive power of individual biomarkers, difficulty of integrating diverse biomarkers into one system and lack of ICB response prediction models that can integrate different biomarkers, are the main barriers that warrant further study. A previous study showed that the integration of an AI-based diagnostic algorithm with physicians’ interpretations can be positively related to improving diagnostic accuracy for indiscernible cancer subtypes. AI technology obtains approximately 91.66% accuracy when recognizing major histocompatibility complex patterns associated with immunotherapy response. More importantly, AI can be applied to standardize assessments across institutions instead of depending on the interpretation of clinicians that occasionally is inherently subjective. Therefore, the application of AI in cancer immunotherapy may lead to positive outcomes in patients.

To date, most notable is the successful application of AI in immunotherapy in cancer research. Machine Learning can match the pace with modern medicine regarding generated data and the detection of phenotypic varieties that sneak through human screening. The range of machine screening can also be adjusted to detect only interested phenotype changes or to screen for broader phenotypes. Currently, AI-based methods have shown good results in the prediction of MHC-II epitopes on the strength of amino acid sequences and the development of vaccines targeting MHC-II immunopeptidome , which demonstrate the increasingly extensive application of AI in immunotherapy.

 



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