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Related Concept Videos

Tumor Immunotherapy01:27

Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Cytotoxic T Cells-mediated Immune Response01:27

Cytotoxic T Cells-mediated Immune Response

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Cytotoxic T cells are a vital component of the immune system. They have the remarkable ability to identify and target antigens on infected or abnormal cells. These antigens often originate from intracellular pathogens such as viruses or abnormal proteins cancer cells produce.
Immunological surveillance is the ability of immune cells to monitor and eliminate infected cells with intracellular pathogens, neoplastically transformed cells, and cells with non-self antigens. Cytotoxic T cells and NK...
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Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
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Cancer treatment vaccines are a rapidly evolving field that offers a promising approach to immunotherapy. Unlike traditional vaccines that prevent diseases, cancer treatment vaccines are designed to treat existing cancers by stimulating the immune system to recognize and attack cancer cells.
Cancer vaccines come in two categories: preventive (prophylactic) and treatment (active). Preventive vaccines, such as the Human Papillomavirus (HPV) vaccine, protect against viruses that cause certain...
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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Just how transformative will AI/ML be for immuno-oncology?

Daniel Bottomly1, Shannon McWeeney2

  • 1Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, USA.

Journal for Immunotherapy of Cancer
|March 26, 2024
PubMed
Summary

Artificial intelligence and machine learning (AI/ML) offer powerful tools to advance immuno-oncology research and clinical applications. This review explores how AI/ML can address key challenges in data analysis, workflow efficiency, and cohort studies for better cancer immunotherapy outcomes.

Keywords:
BiostatisticsImmunotherapyReview

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Area of Science:

  • Oncology
  • Immunology
  • Biomedical Research
  • Clinical Operations
  • Artificial Intelligence
  • Machine Learning

Background:

  • Immuno-oncology harnesses the patient's immune system to combat cancer.
  • Technological advancements are generating vast amounts of data in biomedical research and clinical operations.
  • Artificial intelligence and machine learning (AI/ML) show significant potential for improving insights and outcomes in immuno-oncology.

Purpose of the Study:

  • To review critical considerations for evaluating the clinical impact of AI/ML in immuno-oncology.
  • To highlight key clinical and biomedical challenges within immuno-oncology.
  • To explore how AI/ML advancements can address these challenges.

Main Methods:

  • Review of current AI/ML applications and their relevance to immuno-oncology.
  • Identification and discussion of four specific challenges in immuno-oncology: clinical workflow efficiency, image data curation, text knowledge synthesis, and small cohort sizes.
  • Exploration of future AI/ML advancements, including reinforcement learning and federated learning, and ethical data practices.

Main Results:

  • AI/ML can potentially enhance efficiency in clinical workflows.
  • AI/ML can aid in the curation of high-quality image data for research.
  • AI/ML facilitates the extraction and synthesis of knowledge from text data.
  • AI/ML methods can help address challenges related to small cohort sizes in immunotherapeutic evaluation.

Conclusions:

  • AI/ML holds substantial promise for advancing immuno-oncology research and clinical practice.
  • Addressing challenges in data quality, workflow, and cohort analysis through AI/ML is crucial.
  • Future innovations in AI/ML, coupled with ethical data practices, will likely drive significant progress in immuno-oncology.