Cancer immunotherapy efficacy and machine learning

ABSTRACT

Introduction

Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making.

Areas covered

Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023).

Expert opinion

An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.

KEYWORDS:

• Immunotherapy
• machine learning
• deep learning
• cancer
• omics

Article highlights

• The inherent complexity of cancer makes it extremely difficult to predict patient prognosis.

• Machine learning (ML) techniques combined with radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis have been demonstrated to predict the efficacy of cancer immunotherapy.

• Models or classifiers constructed using ML effectively predict the outcomes of patients receiving immunotherapy.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or material discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or mending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Authors’ contributions

Caineng Cao: Conceptualization, Data collection, Writing- Reviewing and Editing

Yuting Fang: Conceptualization, Data collection, Writing- Reviewing and Editing

Xiaozhong Chen: Conceptualization, Writing- Reviewing and Editing

Additional information

Funding

This paper was not funded.