Original contributionPromise and pitfalls of quantitative imaging in oncology clinical trials
Introduction
The conduct of oncology clinical trials in the 21st century must address the paradigm-shifting recognition that effective cancer therapies will require individualized molecularly targeted drugs, either singly or in combination. Attempts to evaluate an array of molecularly targeted drugs with novel effects on virtually every aspect of tumor physiology have highlighted the limitations of response criteria based on anatomic imaging. Trials involving molecularly targeted drugs provide opportunities to develop equally novel functional imaging criteria for several important aspects of clinical design. These include noninvasive imaging approaches to pretreatment tumor phenotyping; early, often quantitative, imaging criteria for treatment response; pharmacodynamic measurements of drug delivery and target pathway modulation; and differentiation of tumor treatment response from other physiologic effects of the experimental therapy.
The optimal application of quantitative imaging (QI) to oncology clinical trials requires systemic evaluation of matching of modalities to the most appropriate physiologic parameters, the ability to standardize modalities and algorithms across different platforms and among multiple institutions, awareness of incremental cost of the imaging techniques and estimates of potential cost savings by enrichment of responders. The members of the Quantitative Imaging Network (QIN) contend that the investment of clinical trial funding resources by both industry and granting agencies into the innovation and development of validated QI tools will be necessary for the development of molecularly targeted therapeutics that are therapeutically effective and delivered at minimal cost. This article provides an overview of applications of QI to phase 0 through phase 3 oncology trials. We describe the most common uses of QI for several specific tumor types and identify challenges to standardization posed by the characteristics of these tumors. In the concluding section, we discuss potential constraints on QI, including complexity of trial design and conduct, impact on subject recruitment, incremental costs and institutional barriers. Strategies for overcoming these constraints are presented.
Section snippets
Clinical trial design and QI
In phase 1 clinical trials of molecularly targeted agents, maximal tolerated dose may not be relevant, and biomarkers for identifying the minimal dose required for biologically active drug delivery at the target site are needed. QI may be used to demonstrate actual drug delivery to tumor or physiologic changes associated with target modulation. Phase 0 “proof of mechanism” trials to demonstrate activity of novel compounds may use QI endpoints to measure target modulation or more downstream
PET
PET imaging produces a quantitative, three-dimensional (3D) image of the distribution of PET radioisotopes in the body. The PET radiotracer chosen is designed to reflect relevant biochemical processes in the body. A PET camera detects 511-keV gamma photons emitted by positron annihilation events due to positron emission by radioisotopes (e.g., F-18), which are linked to a molecule of interest (e.g., 2-deoxy-2-[18F]fluoro-d-glucose or FDG), which traces the early aspects of glucose metabolism in
Gliomas
Imaging has always played a critical role in assessing glioma response to treatment [17]. Repeated tumor biopsies are challenging in brain tumor patients, and imaging has the advantage of noninvasively capturing the heterogeneity of gliomas that may be missed through sampling error in only partially resectable gliomas. Unfortunately, though, as opposed to tissue-based makers such as MGMT methylation or IDH1 mutation, no validated imaging biomarkers have been discovered, and even seemingly
Lessons learned and future directions
While QI has promise for contributions to the conduct of clinical trials and oncology practice, QIN investigators have encountered several challenges when integrating QI into clinical trials. This section discusses and describes these challenges and the strategies developed to overcome them.
Uncertainty about measurement characteristics of QI biomarkers is a barrier to the planning of large-scale multicenter trials involving QI. Randomized phase 3 clinical trials may fail if they are
Acknowledgments
This work is supported by the National Institutes of Health, Quantitative Imaging Network (U01-CA148131). The authors are grateful to Robert Nordstrom, Larry Clarke, Hannah Linden, David Mankoff, Mark Muzi, Savannah Partridge, Mia Levy and Daniel Rubin for helpful discussions and to Alicia DePastino for administrative support.
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