Imaging for Response Assessment in Cancer Clinical Trials.
Imaging for Response Assessment in Cancer Clinical Trials.
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DOI：
10.1053/j.semnuclmed.2020.05.001
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发表时间：
2020-11
影响因子：
4.9
通讯作者：
Smith AD
中科院分区：
文献类型：
--
作者：
Sorace AG;Elkassem AA;Galgano SJ;Lapi SE;Larimer BM;Partridge SC;Quarles CC;Reeves K;Napier TS;Song PN;Yankeelov TE;Woodard S;Smith AD
生物标志物的使用是癌症患者日常管理中不可或缺的一部分，包括疾病的诊断、临床分期和对治疗干预的反应。在新药开发和癌症研究中，先进的CT、MRI和PET成像指标被用于评估反应，以预测反应指标。确定合适的成像生物标记物的关键组成部分和挑战是选择整体和综合生物标记物，选择适当的终点和方式，以及合作和多中心试验的成像生物标记物的标准化。成像生物标记物依赖于最初提出的来自成像的量化指标，如肿瘤大小或最长尺寸，临床试验中最常用的指标来自RECIST标准，然后分别用于免疫治疗反应和PET成像的改编版本，如iRECIST和PERCIST。在临床试验中，已有许多广泛应用的MRI生物标志物，包括DW-MRI(ADC)和DSC或DCE-MRI(KTrans，rCBV)分别描述细胞密度和血管密度的指标。此外，描述葡萄糖代谢、增殖和缺氧的分子标志物的FDG-、Flt-和FMISO-PET成像已经被应用于各种类型的癌症，以评估对各种靶向治疗和化疗的治疗反应。近年来，有许多正在开发的功能和分子新型成像生物标记物正在迅速集成到临床试验中(并有望应用于临床工作流程)，如人工智能和机器学习计算策略、抗体和多肽特异性分子成像、先进的扩散磁共振成像。这些包括PSMA-和曲妥珠单抗-PET，从增强CT提取的血管肿瘤负荷，扩散峰度成像，CD8或颗粒酶B PET成像。更令人兴奋的是治疗过程，例如68Ga/111In-和177Lu-DOTATE的组合，使用集成的生物标记物来指导护理和个性化治疗。然而，在实施仍然存在的成像生物标记物方面存在许多挑战，包括了解这些成像生物标记物的采集和分析的准确性、重复性和再现性。尽管与新的成像生物标记物的生物学和技术验证相关的挑战，但已经创建了一个独特的路线图，该路线图正被应用于许多临床试验，以推进开发和实施，以创建治疗反应的特定和敏感的新成像生物标记物，以继续改变医学肿瘤学。
The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with CT, MRI, and PET are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral versus integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the RECIST criteria, and then adapted versions such as iRECIST and PERCIST for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from DW-MRI (ADC) and DSC or DCE-MRI (Ktrans, rCBV), respectively. Furthermore, FDG-, FLT- and FMISO- PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemo-therapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipated of being implemented into clinical workflow), such as AI and machine learning computational strategies, antibody and peptide specific molecular imaging, advanced diffusion MRI. These include PSMA- and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.
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影响因子：
3.1
作者：
Bowen SR;van der Kogel AJ;Nordsmark M;Bentzen SM;Jeraj R
通讯作者：
Jeraj R
影响因子：
50.3
作者：
Batchelor, Tracy T.;Sorensen, A. Gregory;Jain, Rakesh K.
通讯作者：
Jain, Rakesh K.
影响因子：
4.8
作者：
通讯作者：
--
影响因子：
4.2
作者：
Allen, Brian C.;Florez, Edward;Smith, Andrew D.
通讯作者：
Smith, Andrew D.
DOI：
10.1007/s00259-018-4090-4
发表时间：
2018-12
影响因子：
9.1
作者：
Brahm CG;den Hollander MW;Enting RH;de Groot JC;Solouki AM;den Dunnen WFA;Heesters MAAM;Wagemakers M;Verheul HMW;de Vries EGE;Pruim J;Walenkamp AME
通讯作者：
Walenkamp AME