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Review
. 2023 Jan 4:13:1076883.
doi: 10.3389/fimmu.2022.1076883. eCollection 2022.

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Jindong Xie  1 , Xiyuan Luo  2 , Xinpei Deng  1 , Yuhui Tang  1 , Wenwen Tian  1 , Hui Cheng  2 , Junsheng Zhang  1 , Yutian Zou  1 , Zhixing Guo  1 , Xiaoming Xie  1
Affiliations
Review

Advances in artificial intelligence to predict cancer immunotherapy efficacy

Jindong Xie et al. Front Immunol. .

Abstract

Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.

Keywords: artificial intelligence; deep learning; genomics; immunotherapy; multi-omics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The workflow for using AI to predict immunotherapy efficacy. The First step is to collect multiscale medical data which include pathological tissue, CT/MR imaging-omics, genomics, proteomics, and more. The following steps are to gather, filter, segment, extract and select features. Then split these data into a training cohort and a validation cohort. Next, take the data from the training cohort, handing them over to the AI for learning and modeling. And then utilize the validation cohort to verify the learning results. Finally, a clinically applicable model will be developed.
Figure 2
Figure 2
AI-based evaluation of immunotherapy efficacy by histopathological features. This is an illustration of the use of AI to forecast the effectiveness of immunotherapy. With the help of AI, more specific information can be extracted from clinical pathological tissues, including components of the tumor microenvironment and small molecular components like receptors, ligands, cytokines, nucleic acids, etc. From these elements, AI gathers data related to immunotherapy to forecast the efficacy.
Figure 3
Figure 3
The dilemmas and solutions in predicting immunotherapy efficacy with AI. The left side outline the current difficulties AI encountered, and the right side proposes possible solutions. And only the excellent cooperation of artificial intelligence and human intelligence can achieve the best prediction effect.
See this image and copyright information in PMC

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