Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
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DOI：
10.1093/annonc/mdz108
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发表时间：
2019-06-01
影响因子：
50.5
通讯作者：
Aerts, H. J. W. L.
中科院分区：
文献类型：
--
作者：
Trebeschi, S.;Drago, S. G.;Aerts, H. J. W. L.
简介 免疫疗法被认为是癌症治疗的重大突破之一。尽管取得了成功，但只有一小部分患者做出了反应，这促使人们寻求预测性生物标志物。我们假设人工智能 (AI) 算法可以自动量化与免疫治疗反应相关的放射学特征，因此可能作为免疫治疗反应的无创放射组学生物标志物。 患者和方法 在这项研究中，我们分析了 203 名接受抗 PD1 治疗的晚期黑色素瘤和非小细胞肺癌 (NSCLC) 患者的 1055 个原发性和转移性病灶。我们根据治疗前的对比增强 CT 成像数据对每个病变进行了基于人工智能的表征，以开发和验证能够区分免疫治疗有反应和无反应的非侵入性机器学习生物标志物。为了定义放射学生物标志物的生物学基础，我们在 262 名 NSCLC 患者的独立数据集中进行了基因集富集分析。结果生物标志物在 NSCLC 病变上达到了显着的效果（高达 0.83 AUC，P
Introduction Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.Patients and methods In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.Results The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P