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作为临床试验终点的影像组学和体积测量——全面综述

Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.

作者信息

Funingana Ionut-Gabriel, Piyatissa Pubudu, Reinius Marika, McCague Cathal, Basu Bristi, Sala Evis

机构信息

Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK.

Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK.

出版信息

Cancers (Basel). 2022 Oct 17;14(20):5076. doi: 10.3390/cancers14205076.

DOI:10.3390/cancers14205076
PMID:36291865
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9599928/
Abstract

Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas-segmentation, validation and data sharing strategies-where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.

摘要

长期以来,肿瘤学药物研发的临床试验一直依赖替代结局生物标志物来评估肿瘤负荷的变化,以加速药物注册(即实体瘤疗效评价标准第1.1版(RECIST v1.1)标准)。药物诱导的肿瘤大小缩小是药物活性的一个不完美替代标志物,但放射学确定的客观缓解率是2期试验广泛使用的终点。随着针对免疫系统和DNA损伤修复途径等复杂生物系统的疗法的增加,纳入综合反应和结局生物标志物可能会增加更多预测价值。我们对四种代表性肿瘤类型(乳腺癌、直肠癌、肺癌和胶质母细胞瘤)的相关文献进行了综述,以评估体积测量和放射组学指标作为临床试验终点的准备情况。我们确定了三个关键领域——分割、验证和数据共享策略,需要共同努力,以使基于体积测量和放射组学的临床试验终点取得进展,以便更广泛地在临床中实施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c689/9599928/45382e0bc1d1/cancers-14-05076-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c689/9599928/9a28da4b2071/cancers-14-05076-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c689/9599928/45382e0bc1d1/cancers-14-05076-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c689/9599928/9a28da4b2071/cancers-14-05076-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c689/9599928/45382e0bc1d1/cancers-14-05076-g002.jpg

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2
Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis.免疫疗法治疗黑色素瘤患者的总生存早期结果:一种新型影像分析方法。
JAMA Oncol. 2022 Mar 1;8(3):385-392. doi: 10.1001/jamaoncol.2021.6818.
3
Texture analysis imaging "what a clinical radiologist needs to know".纹理分析成像:“临床放射科医生需要知道的”。
多参数单细胞蛋白质组学技术为卵巢肿瘤的生物学研究提供了新的见解。
Semin Immunopathol. 2023 Jan;45(1):43-59. doi: 10.1007/s00281-022-00979-9. Epub 2023 Jan 12.
Eur J Radiol. 2022 Jan;146:110055. doi: 10.1016/j.ejrad.2021.110055. Epub 2021 Nov 25.
4
Volumetric measurements of target lesions: does it improve inter-reader variability for oncological response assessment according to RECIST 1.1 guidelines compared to standard unidimensional measurements?靶病灶的体积测量:与标准的一维测量相比,根据RECIST 1.1指南进行肿瘤反应评估时,它是否能降低阅片者间的变异性?
Pol J Radiol. 2021 Oct 22;86:e594-e600. doi: 10.5114/pjr.2021.111048. eCollection 2021.
5
Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.深度学习在转移性结直肠癌治疗早期应答预测中的应用:基于系列医学影像学研究。
Nat Commun. 2021 Nov 17;12(1):6654. doi: 10.1038/s41467-021-26990-6.
6
Predicting cancer outcomes with radiomics and artificial intelligence in radiology.利用放射组学和人工智能技术预测癌症预后。
Nat Rev Clin Oncol. 2022 Feb;19(2):132-146. doi: 10.1038/s41571-021-00560-7. Epub 2021 Oct 18.
7
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8
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Sci Rep. 2021 May 11;11(1):9984. doi: 10.1038/s41598-021-88239-y.
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Cancer Manag Res. 2021 Apr 13;13:3235-3246. doi: 10.2147/CMAR.S295317. eCollection 2021.