Reardon David A, Ballman Karla V, Buckner Jan C, Chang Susan M, Ellingson Benjamin M
Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts (D.A.R.); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (K.V.B.); Department of Oncology, Mayo Clinic, Rochester, Minnesota (J.C.B.); Department of Neurological Surgery, University of California - San Francisco, San Francisco, California (S.M.C.); Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California (B.M.E.); Department of Biomedical Physics, David Geffen School of Medicine at UCLA, Los Angeles, CA (B.M.E.); Department of Bioengineering, Henry Samueli School of Engineering and Applied Science at UCLA, Los Angeles, California (B.M.E.); Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California (B.M.E.); UCLA Neuro-Oncology Program, David Geffen School of Medicine at UCLA, Los Angeles, California (B.M.E.).
Neuro Oncol. 2014 Oct;16 Suppl 7(Suppl 7):vii24-35. doi: 10.1093/neuonc/nou286.
We provide historical and scientific guidance on imaging response assessment for incorporation into clinical trials to stimulate effective and expedited drug development for recurrent glioblastoma by addressing 3 fundamental questions: (i) What is the current validation status of imaging response assessment, and when are we confident assessing response using today's technology? (ii) What imaging technology and/or response assessment paradigms can be validated and implemented soon, and how will these technologies provide benefit? (iii) Which imaging technologies need extensive testing, and how can they be prospectively validated? Assessment of T1 +/- contrast, T2/FLAIR, diffusion, and perfusion-imaging sequences are routine and provide important insight into underlying tumor activity. Nonetheless, utility of these data within and across patients, as well as across institutions, are limited by challenges in quantifying measurements accurately and lack of consistent and standardized image acquisition parameters. Currently, there exists a critical need to generate guidelines optimizing and standardizing MRI sequences for neuro-oncology patients. Additionally, more accurate differentiation of confounding factors (pseudoprogression or pseudoresponse) may be valuable. Although promising, diffusion MRI, perfusion MRI, MR spectroscopy, and amino acid PET require extensive standardization and validation. Finally, additional techniques to enhance response assessment, such as digital T1 subtraction maps, warrant further investigation.
我们提供关于影像反应评估的历史和科学指导，以便纳入临床试验，通过解决三个基本问题来促进复发性胶质母细胞瘤的有效和快速药物开发：（i）影像反应评估的当前验证状态如何，以及我们何时有信心使用当今技术评估反应？（ii）哪些影像技术和/或反应评估范式可以很快得到验证和实施，以及这些技术将如何带来益处？（iii）哪些影像技术需要广泛测试，以及如何对它们进行前瞻性验证？T1 +/- 对比、T2/FLAIR、扩散和灌注成像序列的评估是常规操作，能为潜在肿瘤活动提供重要见解。尽管如此，这些数据在患者内部和之间以及跨机构的效用受到准确量化测量的挑战以及缺乏一致和标准化图像采集参数的限制。目前，迫切需要制定针对神经肿瘤患者优化和标准化MRI序列的指南。此外，更准确地区分混杂因素（假进展或假反应）可能很有价值。尽管扩散MRI、灌注MRI、磁共振波谱和氨基酸PET很有前景，但它们需要广泛的标准化和验证。最后，增强反应评估的其他技术，如数字T1减法图，值得进一步研究。
