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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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Cancer Vaccines01:30

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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.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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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.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Updated: Jul 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Cancer immunotherapy efficacy and machine learning.

Yuting Fang1,2, Xiaozhong Chen1, Caineng Cao1

  • 1Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China.

Expert Review of Anticancer Therapy
|January 30, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) predicts cancer immunotherapy effectiveness, aiding clinical decisions. This approach analyzes radiomics, pathomics, and tumor microenvironment data for personalized treatments.

Keywords:
Immunotherapycancerdeep learningmachine learningomics

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Immunotherapy

Background:

  • Immunotherapy is a key cancer treatment, but patient response varies.
  • Predicting immunotherapy efficacy is crucial for clinical decision-making.

Purpose of the Study:

  • To review the application of machine learning (ML) in predicting cancer immunotherapy efficacy.
  • To highlight the potential of ML for personalized cancer treatment strategies.

Main Methods:

  • Literature search of PubMed and ClinicalTrials.gov (up to January 2023).
  • Analysis of ML applications in radiomics, pathomics, tumor microenvironment (TME), and immune-related gene analysis.
  • Inclusion of deep learning (DL) techniques.

Main Results:

  • ML is increasingly used in oncology research for predicting treatment outcomes.
  • ML models analyze diverse data types, including imaging and genomic information.
  • Studies show ML's potential to enhance individualized immunotherapy strategies.

Conclusions:

  • ML offers promising tools for predicting cancer immunotherapy response.
  • Further advancements in ML technology may lead to more efficient predictive methods.
  • ML integration can improve clinical decision-making for cancer patients undergoing immunotherapy.