Application of radiomics in lung immuno‐oncology

Q4 Medicine
Weisi Yan, C. Quan, Mourad Waleed, Jianda Yuan, Zheng Shi, Jun Yang, Qiuxia Lu, J Zhang
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引用次数: 1

Abstract

Radiomics is a rapidly evolving field of research that extracts and analyzes quantitative features within medical images. Those features are termed as radiomic features that can characterize a tumor in a comprehensive and quantitative manner with regard to its internal structure and heterogeneity. Radiomic features can be used, alone or in combination with demographic, histological, genomic, or proteomic data, for predicting prognosis or treatment response. Immunotherapy, or immune‐oncology, is the study of cancer treatment by taking advantage of the body's immune system to prevent, control, and eliminate cancer. In this review, we first provide a brief introduction to both radiomics and immune‐oncology in lung cancer. Then, we discuss the need for developing immune‐oncology biomarkers, and the advantages of radiomics in identifying biomarkers related to immunotherapy. We also discuss potential areas in and out of tumors, such as the intra‐tumoral hypoxic region and tumor microenvironment, where radiomic markers might be extracted, as well as a potential application of radiomic biomarkers in clinical lung cancer management. Finally, we present radiation and immune modulation in non‐small cell lung cancer, clinical trials and their design to incorporate radiomic biomarkers, and radiomics‐guided precision radiation therapy.
放射组学在肺部免疫肿瘤学中的应用
放射组学是一个快速发展的研究领域,提取和分析医学图像中的定量特征。这些特征被称为放射学特征,它可以对肿瘤的内部结构和异质性进行全面和定量的描述。放射组学特征可以单独使用,也可以与人口统计学、组织学、基因组学或蛋白质组学数据结合使用,用于预测预后或治疗反应。免疫疗法,或免疫肿瘤学,是一门利用人体免疫系统来预防、控制和消除癌症的癌症治疗研究。在这篇综述中,我们首先简要介绍了放射组学和免疫肿瘤学在肺癌中的应用。然后,我们讨论了开发免疫肿瘤学生物标志物的必要性,以及放射组学在识别与免疫治疗相关的生物标志物方面的优势。我们还讨论了肿瘤内外的潜在区域,如肿瘤内缺氧区和肿瘤微环境,放射组学标志物可能被提取,以及放射组学生物标志物在临床肺癌管理中的潜在应用。最后,我们介绍了非小细胞肺癌的放射和免疫调节,临床试验及其设计,以纳入放射组学生物标志物,以及放射组学指导的精确放射治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Precision Radiation Oncology
Precision Radiation Oncology Medicine-Oncology
CiteScore
1.20
自引率
0.00%
发文量
32
审稿时长
13 weeks
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