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

所属专题: 人工智能与精准肿瘤学

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  1. 北京大学肿瘤医院医学影像科,北京市肿瘤防治研究所,恶性肿瘤发病机制及转化研究教育部重点实验室,北京 100142,中国
  • 收稿日期:2021-08-19 接受日期:2021-09-14 出版日期:2021-09-30 发布日期:2021-09-23
  • 通讯作者: 唐磊

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



关键词: 影像组学, 深度学习, 机器学习, 抗肿瘤药物, 疗效评价


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

基金资助: 北京自然科学基金(Z180001);北京自然科学基金(Z200015);北大百度基金资助项目(2020BD027);国家自然科学基金重大研究计划(91959205)

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