Lung cancer is the leading cause of cancer death throughout the world. Despite massive public health efforts, global tobacco consumptionthe driver of the lung cancer mortalitycontinues to grow. In the United States, tobacco-control efforts have had significant success, resulting in a large cohort of close to 50 million former smokers. Unlike the observed decline in cardiovascular disease risk after smoking cessation, former smokers accrue a persistent lifelong elevated risk of lung cancer. This trend may account for the recent emergence of lung cancer as the leading cause of tobacco-related death. The "War on Cancer" is being reinvigorated with the goal of significantly improving outcomes by 2015. Given the dominant impact of lung cancer mortality, it is timely to consider opportunities for strategic breakthroughs in improving outcomes in lung cancer.
Progress has been modest in improving outcomes for patients with this disease. At the time of initial diagnosis, lung cancer is typically already established in regional or distant metastatic sites, meaning chemotherapy is the mainstay of treatment. Chemotherapy is rarely curative in this setting, but recent randomized reports suggest that chemotherapy added to surgical management of early-stage lung cancer is associated with a significant improvement in 5-year survival.[3-5] This finding suggests that as with breast cancer, chemotherapy in treating early lung cancer-potentially with new targeted therapiesmay have greater impact than in treating late disease. Chemotherapy has been recently shown to convincingly reduce the rate of recurrence after surgical management in several major studies, including one of a well tolerated oral drug from Japan.
The US Food and Drug Administration (FDA) has signaled that it would favor the evaluation of adjuvant therapy with tyrosine kinase inhibitors in conjunction with a predictive test to predict responsiveness. This opportunity is already the subject of clinical trials. The purpose of this research is to find effective therapy that is also safer and potentially less toxic to improve outcomes in patients with early lung cancer, so there are promising developments in the area of lung cancer therapeutics.
There has been considerable interest in improving the effectiveness of drug therapy for lung cancer, but this process has been slowed by the enormous cost and long duration of drug development. One approach to this problem is the use of tumor imaging as a surrogate marker of disease response, which allows for more rapid validating clinical trials than waiting for standard clinical endpoints. The precedent for this approach would be the use of cholesterol levels for evaluating lipid-lowering drugs rather than evaluating the frequency of heart attack or cardiovascular death. Over the past 3 decades, the transition from surgically based management of advanced cardiovascular disease to medical management of early cardiovascular disease has been associated with a 4-year increase in average life expectancy for all Americans. This achievement provides a model of success that is attractive to pursue in the setting of lung cancer, where success in treating advanced disease has been elusive.
Recent promising reports have suggested that spiral CT may be a more effective tool for early detection of lung cancer than chest x-ray.[9,10] The enhanced sensitivity of this rapidly improving tool is presenting a challenge for conventional radiologic interpretation in the detection of early lung cancers. In response to that challenge, the National Cancer Institute (NCI) and others (the Lung Image Database Consortium, or LIDC) have begun an innovative collaboration to collect a database of spiral CT images generated to find early lung cancer. This resource can be used as a validation matrix to assist in the development of computer-assisted detection (CAD) software. The database would allow software algorithm development and validation.
Spiral CT imaging is particularly suited for such a database resource since a standardized CT image file format (DICOM) already exists. This means that transporting and storing CT images for pooled analysis can be done readily.
The need for these tools is already pressing, as the current generation of CT scanners are capable of considerably higher resolution than is used in standard clinical practice. A growing problem with this enhanced sensitivity is the inability of the radiologist to manage and extract all useful clinical information from a high-resolution CT study. To address this problem, major CT vendors and others have been working to develop software tools to enhance work flow with the large CT image files. However, there remains a challenge in understanding the clinical significance of some of the newly identifiable features evident on high-resolution CT images. The difference between the potential information on a CT image and the ability of a reader to appreciate that information has been termed a "software gap."
Important questions concern whether CAD approaches have relevance to the assessment of drug response and whether they could improve on current approaches as codified in the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. RECIST is the only validated tool for lung cancer drug evaluation in a regulatory setting. This metric involves the measurement of tumor size on CT scan in one dimension. Its utility was established by comparing unidimensional measurement to the bidimensional World Health Organization (WHO) criteria in a study of over 4,000 patients from 14 different clinical trials. While RECIST and WHO criteria generally result in comparable assessments of response status (partial or complete) and disease status (stable or progressive), the RECIST determination involves subjectivity. Therefore, RECIST is frequently associated with response classification errors.
Since the time that RECIST was validated, there have been explosive improvements in the speed and resolution of spiral CT. Current 64-detector CT systems are capable of acquiring high-resolution imaging of the thoracic cavity (0.6-mm slice thickness) in a matter of seconds. While it remains to be determined how to best use this enhanced-resolution imaging capability in a clinical trial setting, recent FDA trends suggest a growing appreciation of the utility of imaging as a practical metric for drug approval. In light of CAD developments in the early detection of breast cancer and lung cancer, it may be that computer-assisted image-processing approaches represent an opportunity to improve decision-making in a variety of clinical settings.
An important question is whether CAD approaches also have relevance to the assessment of drug response and could improve on the current approach to cancer image evaluation with RECIST. Like the LIDC initiative, it would be useful to have a database of characterized lung cancer CT images from individuals undergoing chemotherapy to allow the development and validation of CAD capabilities for drug response assessment in this setting.
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