Recognition of this opportunity was the impetus for the first CRPF lung cancer imaging response monitoring workshop, held April 15-16, 2004, in Washington, DC. This initial forum also brought together academic researchers, federal scientists, lung cancer advocates, and representatives of a number of pharmaceutical and diagnostic imaging companies to consider the potential of high-resolution spiral CT imaging tools for the assessment of therapeutic response in lung cancer clinical trials.
The remarkable improvement in spiral CT capabilities are in part related to technical refinements of the CT instrument with image acquisition, but the real driver has been the improved microprocessor capability allowing acquisition of profoundly larger amounts of imaging information. Considerable tension surrounds the rapid evolution of spiral CT imaging capabilities, as the resolution of CT scanners has doubled every 2 years for the past decade and this rate of improvement appears to be sustainable for the foreseeable future. Although the progressively greater data acquisition with higher-resolution CT studies represents a challenge for radiologists, this data load provides a richer information source from which image-processing can characterize the precise nature of the intrathoracic structures. After appropriate validation of this capability, clinical researchers would have an objective quantitative tool with which to establish the response of cancers to the administration of therapeutic drugs, potentially allowing a more efficient process of drug development.
Challenges and Prospects
At the first workshop, participants discussed the development of image-processing tools and computer-assisted diagnosis of lung cancer to catalyze progress in lung cancer research. To explore the challenge, representatives of the FDA reviewed the regulatory implications of using changes in tumor volume as a metric for clinical drug response. An NCI investigator discussed the current standard for phase II drug evaluation (as outlined in RECIST), involving serial comparisons of a single measurement of the diameter of a tumor in one dimension. The benefits and limitations of this standard were discussed, as were the steps required to validate a new standard for imaging-based evaluation of drug response.
A small number of diagnostic imaging companies dominate the development of helical CT imaging devices and computer-assisted diagnostic tools. If these powerful new capabilities are developed such that imaging studies can be compared across different commercial platforms, it would greatly facilitate the delivery of health care. It could also represent a major reduction in costs for governments, insurance providers, and the pharmaceutical industry. The prospects were discussed for enabling cross-platform comparisons of image changes from serial scan of the same individual across time points using different manufacturer's equipment. The aging of the baby boomers will be associated with an increased number of lung cancers, which comes at a time of unprecedented health-care costs. Developing tools that can accelerate the identification and validation of more successful drugs for lung cancer treatment would be a strategic breakthrough.
Since the focus of the 2004 workshop was to explore the prospects for developing effective CAD tools, there was considerable discussion about the best way to validate CAD tools. CT software development is typically commercialized under the premarket notification (510[k]) process of the FDA.[16,17] The 510(k)-type regulatory approval is the most accessible level of CAD approval and allows a vendor to sell a software-based measurement tool.
For software tools to be used as a basis for clinical management requires a more rigorous level of FDA review called a premarket approval application (PMAA). A major factor in this application process is the need to convincingly prove the robustness of a device for a particular application. A very large image database is required to validate convincing software performance given the biologic range of variation that would be reasonably expected in the relevant clinical setting. In addition, the images for the database have to be acquired on the same types of device and sensitivity settings that are anticipated for the eventual clinical application. With the rapid technologic evolution of CT scanners, developing and sustaining such an expensive validation resource is thought to be beyond the resources of a single institution. Consequently, developing this resource emerges as an important area for shared development.
Consensus and Concerns
A point of consensus from workshop participants was the strategic value in developing a large image/clinical outcomes database. This was agreed to be an essential resource to facilitate the development and validation of CAD and related image-evaluation tools for drug responses with high-resolution CT scanning. A working coalition of clinical and imaging scientists emerged, committed to developing image analysis for drug response evaluation for lung cancer clinical trials. This group defined a series of concrete steps to advance the field. These steps included the definition of techniques for scanner calibration to ensure the acquisition of quality images as well as the potential design of phantoms to enable cross-platform imaging comparisons.
Representatives from the NCI reported on their ongoing collection of high-resolution images to catalyze software development for the early detection of lung cancer. In the course of this work, particular attention has been focused on the optimal development of an imaging database. A particular concern relates to the establishment of "ground truth" in regard to whether a CT image file represents a "false-positive" condition or a confirmed cancer case.
Based on feedback from the initial workshop regarding strong potential for real progress, the consensus was that an ongoing workshop forum was needed for academic and federal scientists as well as medical imaging and pharmaceutical industry representatives to continue developing their future vision of image-processing technology and computer-assisted diagnosis. It was agreed to meet again to discuss these issues as they relate to lung cancer treatment and prevention applications, to facilitate improvements in lung cancer outcomes. The purpose of the second workshop is summarized in Table 1. A major impetus in sustaining this forum was that lung cancer was a particularly strategic focus for research since the disease is so lethal. Furthermore, many of the challenges in validating lung cancer CAD tools can be shared with other high-resolution imaging tools.
The second workshop was convened on April 21-22, 2005, in Annapolis, Md, with the intention of discussing interval progress. From the comments of the pharmaceutical representatives, it was clear that lung cancer was growing as a high-priority area of drug development with many potential drug targets being evaluated in clinical trials. It was also evident from the results of success with targeted therapies in particular lung cancer settings[4,18] that many disease subtypes and distinct patient populations could be of interest.
The rapid improvements with CT imaging appeared likely to continue, necessitating the continuous collection of relevant image data to sustain the development of evolving CT platforms. That is, higher-resolution imaging will continue to find earlier phases of lung cancer that have never been seen before. It is therefore critical to continuously collect appropriate forms of ground truth from large image databases to ensure rigorous CAD validation.
The concern of investigators at the workshop was whether the number of cases in the image/outcome database will be sufficient not only to capture the variance in patient conditions and lung cancer presentation, but also to reflect the expected variability in scanner acquisition parameters. The consensus was that the precision of the volumetric tool will be directly related to the amount of high-quality data available, the capability of the acquisition system used, and the sophistication of the algorithms employed. From the discussion, the ideal target size of the database ranged from 500 to over 1,000 cases of serial images.