Chinese Medical Sciences Journal ›› 2021, Vol. 36 ›› Issue (3): 187-195.doi: 10.24920/003985

Special Issue: 人工智能与精准肿瘤学

• Review • Previous Articles     Next Articles

Radiomics in Antineoplastic Agents Development: Application and Challenge in Response Evaluation

Jiazheng Li, Lei Tang()   

  1. Department of Radiology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing 100142, China
  • Received:2021-08-19 Accepted:2021-09-14 Published:2021-09-30 Online:2021-09-23
  • Contact: Lei Tang
Traditional response evaluation criteria for anti-tumor treatment are difficult to meet the requirement for precise and individual assessment. This review introduces advances, obstacles, and future directions of radiomics in investigations of response evaluation on antineoplastic agents, aiming to provide new ideas for researchers. 

The recent spring up of the antineoplastic agents and the prolonged survival bring both challenge and chance to radiological practice. Radiological methods including CT, MRI and PET play an increasingly important role in evaluating the efficacy of these antineoplastic drugs. However, different antineoplastic agents potentially induce different radiological signs, making it a challenge for radiological response evaluation, which depends mainly on one-sided morphological response evaluation criteria in solid tumors (RECIST) in the status quo of clinical practice. This brings opportunities for the development of radiomics, which is promising to serve as a surrogate for response evaluations of anti-tumor treatments. In this article, we introduce the basic concepts of radiomics, review the state-of-art radiomics researches with highlights of radiomics application in predictions of molecular biomarkers, treatment response, and prognosis. We also provide in-depth analyses on major obstacles and future direction of this new technique in clinical investigations on new antineoplastic agents.

Key words: radiomics, deep learning, machine learning, antineoplastic agents, response evaluation

Funding: This work is supported by the Beijing Natural Science Foundation(Z180001);This work is supported by the Beijing Natural Science Foundation(Z200015);PKU-Baidu Fund(2020BD027);the National Natural Science Foundation of China(91959205)

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