Several academic and commercial groups outlined the advances being made in measuring size and change of subcentimeter lung lesions, with the potential use of these methods to assess therapy. They pointed out, however, that significant algorithmic challenges remain in measuring change of the larger lesions present in late-stage lung cancer. It was evident from the algorithm-development community that in addition to advancing volumetric segmentation algorithms, new approaches to measuring change need to be investigated. These approaches include measuring change using image deformation and subtraction (which has been investigated in early Alzheimer's disease detection in magnetic resonance imaging exams) and approaches that factor in additional image metrics, such as lesion texture and vascular properties.[19,20]
In addition, it was recognized that significant research is needed to explore, identify, and validate the method(s) that best assess the outcome of therapy. Likewise, it will be important to establish which algorithmic techniques are resilient to variation in scanner capability, as is commonly found when recruiting sites for large clinical trials. Scanner variability exists not only from one patient to the next but also occurs within a patient's longitudinal study, as imaging may be performed at different locations with different scanners, potentially changing throughout the study. The current rapid evolution of scanner capability ensures that variation in acquisition will continue to be a challenge to algorithms for the foreseeable future.
Reducing Technical Variability
During the discussions, a number of additional technical issues emerged. For example, although the newer scanners, including those with 16 or more detectors, are capable of acquiring very-thin-slice information (1.25 mm or less), in clinical practice most radiologists are still using the much thicker 2.5- to 5-mm slice thickness. The concern expressed by the image vendor community was that the thicker images would contain less precise information for optimal CAD-based volume determination. This would be particularly important in "segmentation," which is the process of defining the borders of adjacent structures. Considerable discussion focused on the relative merits of acquiring thicker-slice images that reflect current clinical radiology practice vs images with slice thickness less than 1.25 mm, where the prospect for successful volume analysis may be more favorable.
While some investigators contended that CAD algorithms should be robust enough to handle even suboptimal clinical images, others argued that the time to impose quality standards for imaging to minimize measurement variability through time is now, ie, early in the development of the field. Since it is not clear how these tools will evolve, it was felt that a broad range of images (acquired under variable degrees of resolution) should be obtained, to address all possible scenarios.
Compounding this problem is the fact that standardized scan acquisition parameters have never been established to ensure that scans obtained for volume analysis are generated in a way that reduces technical variability. Unresolved issues include standardization of radiation dose, instrument calibration approaches, or acquisition parameters for the scanner itself to normalize for patient size and geometry.
Other Technical Issues
Further discussion focused on whether volumetric measures of tumors implied merely defining new standards as to how much of a change constitutes a meaningful clinical response. Alternatively, does the enhanced resolution provide more information that links more reliably to ultimate clinical outcome? These issues led to a technical discussion on how to define imaging endpoints and acquisition parameters to allow for robust measurements. Ultimately, it was agreed that the accuracy of the new tools may vary depending on the feature (subject to location, geometry, density, etc) that was being evaluated, and so performance standards need to be established. Such qualification will require the type of well-defined database that was proposed in the first workshop.
Other critical imaging issues for using these tools in clinical management relate to cross-platform compatibility among different manufacturers. Inevitably, with patients being evaluated for drug response, a different vendor's instrument will be used for the baseline and final imaging studies. In this regard, the performance of volume change or a related analysis would be subject to the conditions required for the drug evaluation, which must be defined in a regulatory setting. This situation again underscores the need for a large image database to allow the responsible development of this class of drug response evaluation tools.
At the most recent workshop, held on April 19-20, 2006, in Bethesda, Md, participants explored the implications of the fast pace of improved performance of CT scanners. It was agreed that current scanners already achieve high enough resolution when looking for large changes in lung cancer volumes. If it becomes necessary to look more quickly for very small changes in tumor characteristics, then new calibration methods will be necessary. This capability would be particularly important in looking at serial imaging over short intervals to determine drug responsiveness.
This use of imaging as a biomarker of drug response is consistent with FDA efforts to responsibly accelerate drug approval under their new Critical Path Initiative (www.fda.gov/oc/initiative/criticalpath/). Other metrics of responsiveness such as tumor nodule density may also be of value. Such new metrics require standardization and calibration of the image-acquisition protocol (Table 2). Such provisions could simplify image data analysis. Participants suggested that some level of measuring compatibility was emerging, and there are currently some standards for calibration.
Role of Industry and FDA
Further dialogue, especially among the imaging manufacturers, is needed to resolve many of these issues, but the clear message from the pharmaceutical industry was a desire to have access to such powerful imaging tools that could evaluate response across vendor platform types. We urgently need to establish proper methodologies in this regard, to test algorithm performance in drug assessment.
A consensus emerged as to the FDA being a critical participant in this process, including divisions of the FDA responsible for clinical trial drug evaluation (Center for Drug Evaluation and Research) and device evaluation (Center for Devices and Radiological Health). A representative from the FDA outlined the agency's extensive interest in this promising yet challenging area. An important issue from a regulatory perspective is establishing the robustness of imaging information.
and Data Sharing
During the workshop, the suggestion was made several times for the field to consider the use of a phantom to assist in validating the reproducibility of a spiral CT scanner's sensitivity settings. Phantom studies would assist in making imaging comparisons across vendor platforms, which may be of value when comparing serial images acquired over time. Development of a standard phantom model would also have utility in CAD algorithm development and validation.
The issue of sharing data from drug research trials was discussed. While some representatives of the pharmaceutical industry expressed sensitivity regarding this issue, most companies were quite comfortable with sharing anonymized data from experimental drug trials. It was noted that the development of image databases prompts Health Insurance Portability and Accountability Act (HIPAA) and institutional review board (IRB) issues that must be addressed. Further, representatives of pharmaceutical companies expressed a willingness to insist on standardization of imaging measures with new clinical trial protocols.
A summary of the conclusions and recommendations from the workshop appears in Table 3. A strong motivation for this forum has been the shared recognition that no single institution can bear the recurring costs of keeping a large database current. Yet the pace of progress in the field was restricted by the lack of such an imaging database. The absence of such a resource would constitute a structural bottleneck to realizing the full benefit of the rapidly improving resolution of spiral CT in managing the lethal consequences of lung cancer.
The collaboration embodied in this process involves a public-private partnership to validate imaging tools for the application of CAD in lung cancer management. It was understood by all parties that this effort is a model for developing other emerging high-resolution imaging tools for disease management so that the shared goal of improving patient outcomes can be realized much more quickly. Future plans for this effort include ongoing discussions about the refinement of strategies for optimal database design.
Additionally, it is critical that information about the existence of the database continues to be disseminated. Finally, and most critically, more contributors must be identified to provide high-quality, serial images with clinical follow-up information as available, to rapidly populate the database. These measures will greatly accelerate the maturation of this promising field.