Radiomic biomarkers of tumor immune biology and immunotherapy response.
Radiomic biomarkers of tumor immune biology and immunotherapy response.
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DOI：
10.1016/j.ctro.2021.03.006
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发表时间：
2021-05
影响因子：
3.1
通讯作者：
Fuller CD
中科院分区：
文献类型：
--
作者：
Wang JH;Wahid KA;van Dijk LV;Farahani K;Thompson RF;Fuller CD
需要免疫疗法治疗反应的准确生物标志物。放射组学特征是肿瘤免疫生物学和免疫治疗反应的新兴生物标志物。Delta放射组学可能对预测免疫治疗反应至关重要。低维特征关联比纹理特征更鲁棒。复合模型的特征和其他变量显示出更大的预测能力。免疫疗法正在改善许多癌症的结果，包括那些具有毁灭性的癌症。随着免疫检查点抑制剂（ICI）等疗法成为治疗方案的主流，出现了许多并发的挑战-例如，从无应答者中划分临床应答者。由于缺乏一致和准确的生物标志物，肿瘤微环境（TME）的异质性以及对耐药机制的理解不足，因此预测反应已被证明是困难的。在大多数情况下，成像数据仍然是一个未开发的，但丰富的资源，以应对这些挑战。近年来，定量图像分析突出了医学成像在预测肿瘤表型、预后和治疗反应中的效用。这些研究受到图像特征（即放射组学）和人工智能的高通量挖掘资源爆炸的推动。在这篇综述中，我们重点介绍了放射组学的最新进展，以了解肿瘤免疫生物学和预测免疫治疗的临床反应。我们还讨论了这些研究的局限性和该领域的未来发展方向，特别是如果高维成像数据要在精准医学中发挥更大的作用。
Accurate biomarkers of immunotherapy treatment response are needed. Radiomic features are emerging biomarkers of tumor immune biology and immunotherapy response. Delta radiomics may be critical for predicting immunotherapy response. Lower-dimensional feature associations are more robust than for textural features. Composite models features and other variables show greater predictive power. Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen – for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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影响因子：
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作者：
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