Gillies Robert J, Beyer Thomas
Department of Radiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
Department of Cancer Imaging, H Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
Cancer Res. 2016 Nov 1;76(21):6163-6166. doi: 10.1158/0008-5472.CAN-16-2121. Epub 2016 Oct 11.
Over the past decades, imaging in oncology has been undergoing a "quiet" revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ Important advances along this path have been the increasing power of MRI, which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, PET imaging allows quantitative assessment of the spatial localization of positron-emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules. Recent years have witnessed another technological advance, wherein these two powerful modalities have been physically merged into combined PET/MRI systems, appropriate for both preclinical or clinical imaging. As with all new enabling technologies driven by engineering physics, the full extent of potential applications is rarely known at the outset. In the work of Schmitz and colleagues, the authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both preclinical and clinical data. With combined PET and MRI and sophisticated machine-learning tools, they have been able identify multiple coexisting regions ("habitats") within living tumors and, in some cases, have been able to assign these habitats to known histologies. This work addresses an issue of fundamental importance to both cancer biology and cancer care. As with most new paradigm-shifting applications, it is not the last word on the subject and introduces a number of new avenues of investigation to pursue. Cancer Res; 76(21); 6163-6. ©2016 AACR.
在过去几十年里,肿瘤学成像领域正在经历一场“悄然”的变革,即将图像视为数据而非图片。这场变革是由技术进步引发的,这些技术进步使人们能够获取不仅反映解剖结构,还能反映组织原位代谢和生理状态的图像。在这条道路上取得的重要进展包括MRI功能的不断增强,它可用于测量细胞密度、组织结构、灌注和代谢在空间上的依赖性差异。与此同时,PET成像能够对正电子发射化合物的空间定位进行定量评估,并且可用于测量代谢和大分子表达的可成像示踪剂数量也在不断增加。近年来见证了另一项技术进步,即这两种强大的成像方式在物理上被整合到适用于临床前或临床成像的PET/MRI联合系统中。与所有由工程物理学驱动的新型使能技术一样,潜在应用的全部范围在一开始很少为人所知。在施密茨及其同事的研究中,作者结合了多参数MRI和PET成像,利用临床前和临床数据来解决乳腺癌瘤内异质性这一重要问题。通过PET和MRI联合以及复杂的机器学习工具,他们能够识别活体肿瘤内多个共存区域(“栖息地”),在某些情况下,还能够将这些栖息地与已知的组织学类型相对应。这项工作解决了一个对癌症生物学和癌症治疗都至关重要的问题。与大多数具有范式转变意义的新应用一样,它并非该主题的定论,而是引入了一些新的研究途径以供探索。《癌症研究》;76(21);6163 - 6。©2016美国癌症研究协会。