Artificial Intelligence in Predicting Efficacy and Toxicity of Immunotherapy: Applications, Challenges, and Future Directions.

Journal: Cancer letters
Published Date: Jun 16, 2025

Abstract

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, becoming a standard approach for various tumor types. Consequently, accurately predicting their efficacy has become crucial in clinical practice. Artificial intelligence (AI) has emerged as a powerful tool for extracting meaningful insights from complex clinical datasets, showing immense potential to transform medical decision-making. Therefore, the integration of AI techniques into immunotherapy facilitates the development of predictive models for immunotherapeutic efficacy based on radiological, genomic, and pathological data, ultimately refining the precision treatment of tumors. In this review, we systematically summarize the application of AI in predicting the efficacy of ICIs, and briefly address the challenges and future directions in this field.

Authors

  • Qiang Wen
    School of Artificial Intelligence and Big Data, Economic and Technological Development Zone, Hefei University, Hefei City, Anhui, China.
  • Liang Qiu
  • Chenhui Qiu
    Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, HangZhou, 310000, China.
  • Keying Che
    Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, 250021, China.
  • Renya Zeng
    Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, 250021, China.
  • Xi Wang
    School of Information, Central University of Finance and Economics, Beijing, China.
  • Pingdong Cao
    Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, China.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Zhe Yang
    Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Jinming Yu
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, 250021, China. Electronic address: sdyujinming@163.com.

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