Use of High-Resolution CT Imaging Data in Lung Cancer Drug Development: Measuring Progress

Use of High-Resolution CT Imaging Data in Lung Cancer Drug Development: Measuring Progress

Lung cancer is the leading cause of cancer death throughout the United States.[1] Despite massive efforts, tobacco consumption continues to grow, with a large and predictable impact on premature mortality across the globe.[2] There is an urgent need to improve outcomes for lung cancer patients, but the process of developing more effective drugs for lung cancer is hampered by the extraordinary cost of pharmaceutical development and a cumbersome development process. While many factors contribute to high development costs, one of the most significant is the cost of clinical validation trials. This cost is increased by the lack of precision inherent in using the manual measurement technique for drug response evaluation using the current standard approach, called the RECIST criteria (Response Evaluation Criteria in Solid Tumors).[3] RECIST is the validated tool used in most clinical trials.

The Lung Cancer Workshops have focused on developing image-processing tools to be used in evaluating drugs to manage small, early-stage cancers. The use of RECIST has not been validated for use with tumors under 1 cm in diameter. Since management of early cancer will be a progressively greater focus of drug development, defining and validating a new approach to drug response assessment is a critical challenge to progress in treating early lung cancer.

The purpose of the Fifth Prevent Cancer Foundation (PCF) Lung Cancer Workshop was to review progress in the management of drug therapy using image-processing approaches with spiral computed tomography (CT),[4] including consideration of drug response in chronic obstructive lung disease (COPD). This combined focus was based on the frequent comorbidity and clinical overlap between these two tobacco-related diseases, in part, related to shared molecular mechanisms of pathogenesis (Table 1).

The Workshop was held in Oak Brook, Ill, on April 26-27, 2008. It was cosponsored by the PCF and the Optical Society of America (OSA). A major interest of PCF is to encourage development of drug therapies for early lung cancer, and OSA is promoting the innovative use of image-processing tools in clinical management.

Early Lung Cancer Management
Dr. James Mulshine of Rush University Medical Center gave an overview of the goals of the Workshop and reviewed past progress and the current lung cancer research environment. Small-volume primary lung cancer is the area where image-processing techniques for quantitative evaluation may have their greatest impact.

Supporting this focus on early lung cancer management, a recent report from Taipei analyzing the results from six prospective CT screening studies using a three-state Markov model with a Bayesian approach suggested that this approach was promising.[5] Evaluating all available published lung cancer screening data that met their a priori criteria and assuming a 10-year time horizon of follow-up, they found that spiral CT had a median sensitivity of 97% and advanced the diagnosis of lung cancer to 1 year earlier than chest x-ray. The authors concluded that with annual CT screening there would be an estimated 23% mortality reduction with a relative risk of 0.77 (95% confidence interval = 0.43–0.98). From a comparative perspective, this preliminary result approached the mortality benefit seen with mature breast cancer screening trial results,[6] highlighting the significant potential in managing early lung cancer.

Slow Progress in Image Acquisition
Publications from past Workshops have included an article that reviewed progress up to the fifth meeting[4] and the first OSA monograph, which included in-depth reviews on aspects of quantitative imaging for drug evaluation from previous Workshop participants.[7] A key strategy outlined in the monograph is to make Digital Imaging and Communications in Medicine (DICOM) image files from research papers available for postpublication evaluation as a research resource, to address the challenge of accumulating sufficient numbers of useful images associated with clinical outcomes data.

This led to a dialogue with Dr. Anna Barker, Deputy Director of the National Cancer Institute, which resulted in the development of the Response Imaging Database for Evaluating Response.[8] However, in terms of accumulating images for drug-response assessment software, the acquisition of sufficient numbers of paired, characterized high-resolution, thoracic CT image files from individuals before and after receiving cancer drug therapy remains a challenge.

Defining the reasons for the slow progress in acquiring images has been a persistent concern of all the previous Workshops. Issues that have been identified include establishing “ownership” of the images, and Health Insurance Portability and Accountability Act (HIPAA) concerns, institutional review board (IRB) concerns, and other regulatory issues. One surprising aspect of this problem has been the amount of professional time required to acquire, de-identify, distribute, and then verify the image file-sharing. Much of this strategic problem relates to the heterogeneous way patient images are managed across inpatient and outpatient health-care settings. This makes it very difficult to link serially acquired images from an individual patient undergoing cancer therapy with the eventual clinical outcome of that patient.

A strategy was proposed for academic medical centers to more effectively organize their image storage and distribution infrastructure as an integrated enterprise resource. The goal would be to have the imaging data systematically stored in relationship to other clinical data provided by electronic medical record. If the clinical and imaging information systems are aligned, this may allow for rapid and efficient acquisition of imaging and related translational research data at incremental cost, so that validated clinical outcomes data will accurately define evolving “ground truth.”

This approach is general strategy for all types of imaging tools, including magnetic resonance imaging and positron-emission tomography. The gap in the availability of images linked to clinical follow-up is an important, underrecognized challenge for improving imaging research, especially in early disease management. Imaging research is generally perceived to be a radiological problem, but this strategic gap cannot be overcome unless radiologists work closely with clinicians to allow images to be routinely linked to clinical outcomes in large numbers of patients.

Imaging Issues in Lung Cancer
The specific application of imaging in lung cancer management is limited by significant unmet needs. Series of cases are required for imaging tool validation in each clinical management setting, and in these different settings, there are distinct challenges for image analysis.

Tumor-node-metastasis (TNM) staging has been the cornerstone of defining patient prognosis and also provides a useful framework for discussing the nature of the distinctive image-processing challenges across the spectrum of lung cancer. For clinical trials in stage IV lung cancer, imaging is typically concerned with characterizing the sites of metastatic disease (M+ disease). In this setting, since the disease can extend throughout the body, volumetric CT of the primary lung cancer is often not the pivotal basis for determining drug response. In this circumstance, disease progression is commonly at a known site of metastatic disease or with detection of a new site of metastatic involvement.

In the setting of stage II/III lung cancer, there is typically involvement of regional nodal sites with cancer (N+ disease). This is also a challenging setting since the involvement of mediastinal nodal structures can be complex and bulky. In addition, since the mediastinum is the confluence of so many vascular, lymphatic, and bony structures, reliably segregating the volume involved with nodal cancer can be quite challenging. For this reason, image-processing strategies involving only volumetrics may have limited utility.

In the setting of stage I lung cancer, the standard approach is to use surgical resection. Recently, a new trial structure has been employed to evaluate the effect of brief exposure of new drugs on stage I lung cancer patients, while they are awaiting surgery. This experimental trial structure is called a neoadjuvant window-of-opportunity trial (referring to the 2- to 3-week time interval during which a patient is awaiting thoracic surgery to remove his or her cancer). In this trial design, the primary endpoint is determining volume changes in the primary lung cancer. This measurement can be quite accurate, as the boundary of the tumor is surrounded by air-filled alveolar tissue allowing for an exquisite signal-to-noise ratio.


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