Applying artificial intelligence for cancer immunotherapy.
10.1016/j.apsb.2021.02.007
- Author:
Zhijie XU
1
;
Xiang WANG
2
;
Shuangshuang ZENG
2
;
Xinxin REN
3
;
Yuanliang YAN
2
;
Zhicheng GONG
2
Author Information
1. Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China.
2. Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China.
3. Center for Molecular Medicine, Xiangya Hospital, Key Laboratory of Molecular Radiation Oncology of Hunan Province, Central South University, Changsha 410008, China.
- Publication Type:Review
- Keywords:
AI, artificial intelligence;
Artificial intelligence;
CT, computed tomography;
CTLA-4, cytotoxic T lymphocyte-associated antigen 4;
Cancer immunotherapy;
DL, deep learning;
Diagnostics;
ICB, immune checkpoint blockade;
MHC-I, major histocompatibility complex class I;
ML, machine learning;
MMR, mismatch repair;
MRI, magnetic resonance imaging;
Machine learning;
PD-1, programmed cell death protein 1;
PD-L1, PD-1 ligand1;
TNBC, triple-negative breast cancer;
US, ultrasonography;
irAEs, immune-related adverse events
- From:
Acta Pharmaceutica Sinica B
2021;11(11):3393-3405
- CountryChina
- Language:English
-
Abstract:
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. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.
