Department of Radiation Oncology, University Hospital Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer Ufer 1-3, 68167, Mannheim, Germany.
Department of Radiation Oncology, Medical Faculty and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
Strahlenther Onkol. 2020 Oct;196(10):888-899. doi: 10.1007/s00066-020-01615-x. Epub 2020 Apr 15.
Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.
目前的研究，尤其是肿瘤学领域的研究，越来越关注定量、多参数和功能成像数据的整合。在这个快速发展的研究领域，放射组学可以实现对成像数据的更精细分析，远远超出对可见组织变化的定性评估。通过使用定量成像数据，未来可以为肿瘤患者提供更具针对性和肿瘤特异性的诊断方法和个体化治疗方案。这在影像学和放射肿瘤学等横断面学科中尤为重要，这些学科在日常临床实践中已经大量使用，而且使用量还在不断增加。肝脏靶区通常采用立体定向体部放射治疗（SBRT）进行治疗，在保护周围正常组织的同时，可以提高局部剂量。随着带有植入标记的在线靶区监测技术的引入，3D 超声技术在常规直线加速器上的应用以及混合磁共振成像（MRI）-直线加速器的应用，个体化自适应放疗正在成为现实。放射组学等大数据的使用以及人工智能技术的整合，有可能进一步改善基于图像的治疗计划和结构化随访，实现对疗效/毒性的预测以及（寡）进展的即时检测。目前在这一创新领域的研究范围是确定和批判性地讨论放射组学的可能应用形式，因此，这篇综述试图总结放射组学在肿瘤患者中的跨学科整合的最新知识，重点探讨将多参数、定量数据纳入肝癌或寡转移患者的（放）肿瘤学工作流程的研究，包括疾病诊断、治疗计划、治疗实施和患者随访。
