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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影像组学在抗肿瘤药物临床试验疗效评估中的应用和挑战

李佳铮,唐磊()   

  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 E-mail:terrytang78@163.com

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 Online:2021-09-30 Published:2021-09-23
  • Contact: Lei Tang E-mail:terrytang78@163.com

摘要:

抗肿瘤药物的快速发展改善了恶性肿瘤患者预后。以CT、MRI和PET为代表的影像学手段作为重要的终点替代指标,在新药临床试验中发挥着越来越重要的作用。但因抗肿瘤药物作用靶点、应用线数等不同,治疗后影像学征象变化的个体差异较大,单用RECIST标准已无法满足个体化精准评效的需求。影像组学作为一种依托于新兴计算机技术的特征提取和模型构建手段,有望协助临床进行抗肿瘤药物治疗疗效的精准评估。本文介绍了影像组学的基本概念,回顾了影像组学在抗肿瘤药物疗效评价方面的最新进展,深入分析影像组学在预测肿瘤分子标志物、评价治疗疗效和预后预测方面的应用潜力,探讨了目前影像组学在抗肿瘤药物临床研究中存在的主要问题和未来发展方向。

关键词: 影像组学, 深度学习, 机器学习, 抗肿瘤药物, 疗效评价

Abstract:

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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