In an image-based clinical trial, images are a subset of the overall data that is generated for a trial subject. Typically the site investigator orders images from another department that is dealing with images. This can be the radiology department of the same institution, a special imaging department (e.g., for neurology), or a remote imaging center. The subject is sent to the department or center and image data is generated with an imaging modality. 

The imaging data—we exclude film data for the purpose of clarity although we know that it is still being used—is electronically available and adheres in most cases to a global and well-recognized standard for medical images called DICOM. The imaging procedure is conducted by a specially trained technician. To ensure that the image data from different patients is comparable, the configuration parameters of the imaging modality (slice thickness, contrast, etc.) must be exactly re-used for all the subjects of a trial at the given site. Those parameters differ for different vendor models. 

In the typical hospital setting, most of the images are generated for clinical routine purposes. This means that an imaging procedure for a clinical trial is a rare event with a likelihood for deviations of the configuration parameters—thus impacting the comparability and quality of the image data. 

For each patient visit an image data set is created that contains between one image (mammography) and hundreds of single images (magnetic resonance tomography). The size of the image sets can vary between megabytes and gigabytes. After the image acquisition, the images are stored directly on the imaging modality or in a picture archiving and communication system (PACS). 

Disconnected from this process of image storage, a study nurse is periodically collecting image data for the subjects—maybe every week. This means the study nurse is searching for the patient's image data in the systems (imaging modality or PACS). This step is similar to the current EDC approach for nonimaging data, which is generated by the physician, stored in a patient record (electronic or paper), and later on extracted from the record and populated into case report forms by a study nurse. 

Back to the image data. The study nurse creates a copy, stores it on portable media, fills out a data transmittal form, and ships the portable media and the form to the image recipient—an imaging CRO or sponsor. A large number of image shipments that arrive daily, are opened, checked for completeness, assigned to the specific clinical trial, and further processed. An alternative for shipping the portable media is sending the data with electronic solutions. Both approaches deliver the data to the recipient. 

The recipient used to be exclusively the imaging CRO, which would process, analyze, and store the image data. The imaging CROs only passed on extracted information from the image data to the sponsor (e.g., tumor diameter, volumes) but not the image data. However, in recent years we have seen a growing request by sponsors to get the image data in addition to the extracted information. 

Due to the complex nature of medical images, ensuring image quality is a challenging task. The sponsor requires data of a predefined quality level in order to use it for validating the trial hypothesis. The quality level today leaves much room for improvement. One expert stated that between 15% and 25% of the incoming images in a Phase III trial cannot be used at all.

At the 2008 DIA Annual Meeting several presentations described strategies on "doing clinical trials right."

  Among the use of standards, economies of scale, and other topics, the use of EDC to bridge the health care and pharmaceutical world was also discussed. For trial data that results from clinical findings of an investigator (nonlaboratory and nonimage data) this is still a future vision because of the missing scalable business and missing organizational and technological processes.

Comparing lab data and imaging data, there are two criteria that result in differences: 					

 Both are originally of electronic format and both adhere to a standard.

 Lab data in most cases is provided by central labs in a clinical trial and standardized references to varying acquisition methodologies exist to ensure comparability; image data for one trial that is generated across many sites and imaging modalities are made comparable by pretrial phantom measurements. However, more uncertainties in the image acquisition process exist that affect comparability.

In image-based trials, the quality of the image data from beginning to end is the critical factor that needs to be the center of attention. In the classical EDC approach, built-in edit checks at the point of data entry are the first steps to assuring data quality. Currently, there is no "image edit check" at the place of data entry in available technical tools. Why not? Images are the visualization of a biological response to a given signal. The response is complex and needs human expertise for assessment. The same holds true for the signal generation conditions. Current EDC vendors do not have this expertise. 

On the CRO side, there are special imaging CROs that provide such expertise. They are using their own IT infrastructure (e.g., Perceptive) or are partnering with IT vendors (e.g., PhaseForward and AG MedNet) to solve the EDC task. With the current focus on fast image transfer only and not on image edit checks, quality assurance becomes a separate process at the imaging CRO. As we learned from the traditional EDC story, this is not good enough (see Figure 1). 

Given this and considering the standardized environment in the imaging world described earlier, we can do better and implement processes that fit into the end user's workflow (technician, study nurse). We can provide an integrated solution and not just a copy of the old EDC approach. 

In order to do so, we need to cover all process steps. This starts with the requirements in the trial protocol and ends with the image data being provided to the recipient, and includes: 					

Site preparation according to protocol requirements (ideal: pushing image acquisition protocol to the modality to guarantee comparable data)

Site qualification (same protocol for all trial subjects in a clinical routine environment)

Image acquisition at the modality using the correct acquisition protocol

Automatic transfer of the de-identified image data to a centralized server

Automatic quality control based on the DICOM header data

Automatic transfer to the recipient (imaging CRO, pharmaceutical company).

In the case of image data, we find ideal prerequisites like the electronic data standardization. This could pave the way for faster adoption of an integrated approach. But there is still work needed to extend the health care–oriented standards so that they fit clinical trial and research needs. In addition, it requires a new competency that relates to defining, ensuring, and managing image quality across all process steps. Years from now we might have functions such as trial-specific imaging protocol upload to a modality, integrated basic quality checks at the scanner during the scan while the patient is still available, and direct image transfer from the scanner to an imaging CRO or sponsor for processing. All of this should be integrated in the modality software, but this can only be achieved if imaging vendors are added to the existing group of stakeholders in clinical trials. 

To get there we can start with solutions that provide the imaging sites with additional software to ensure direct data transfer of de-identified data from all existing modalities. Automatic image header data checks could give immediate feedback to the sites in case something went wrong. Those data can be transferred within several minutes after the imaging procedure to the imaging CROs and trial sponsors. Such a system enables direct engagement of the image recipients for further quality control—which cannot be done automatically currently—and image analysis. This is one step toward image-based adaptive trials. Such a system is in its piloting phase in one trial of a major pharmaceutical company, starting with a limited number of sites and growing into a network of qualified sites open to all imaging sites globally. 

The current approach to discussing standardizing the interfaces of such solutions by the pharmaceutical industry is very helpful in getting the basic requirements. However, the standardization should focus on data structures—the backend standardization—and leave the appearance to the different vendors of such solutions.

  Data structure standards would also form the basis for image quality assurance steps, the image edit checks. Getting the imaging sites and the imaging industry more involved will provide for the first time an integrated direct data capture solution that might become a blue print for the overall data capture process of the future. 

,* PhD, is director of imaging for pharma, email: 

 is project manager at Siemens Healthcare.References 

1. Confidential information from a major pharmaceutical company. 

2. S. Collins, EDC: Faster Horse or Model T? Presented at the DIA 44th Annual Meeting, Boston, 2008. 

3. Zahlmann et al., "EHR–EDC Integration in Reality: Evaluating the Munich Pilot," Applied Clinical Trials, In Press. 

4. "Slow Alignment of EDC, CRO Firms," ClinPage May 2008, http://

www.clinpage.com/article/slow_alignment_of_edc_cro_firms/C23

Articles

Feature Article

It doesn't have to be, but they do require an integrated approach that stresses quality assurance.

Image-Based Trials: Just Another Stressful EDC Story?

Much has been discussed and written about the potential value of metrics and how sponsors of clinical trials can use them to monitor the performance of organizations that they contract with to implement their development trials. 

One such vehicle is the Metrics Champion Consortium (MCC). The mission of the MCC is to develop, through a collaborative process, performance metrics within the biotech and pharma industry with the intent to jointly encourage performance improvement, effectiveness, efficiency, and appropriate levels of controls for both sponsors and service providers including imaging core labs. 

Initiatives like these should promote greater transparency between imaging labs and sponsors on what are believed to be important metrics. While most believe this is a step in the right direction, significant incremental benefits can be achieved for sponsors and imaging labs that focus on the operational improvements underlying the metrics—improvements that can accelerate the development process, allowing companies to ultimately bring new therapies to market quicker and at lower cost. 

Metrics in and of themselves don't enable operational efficiencies. What can are continuous improvement initiatives applied to the underlying processes being measured. The mere existence of metrics doesn't necessarily provide any insight whatsoever as to the quality or even the existence of credible continuous improvement programs. 

Unfortunately this focus on continuous improvement has not begun to happen in a meaningful way. The focus of this article is to highlight the impact effective continuous improvement programs can have on both the organizations that implement them and their clients, who in this case are the drug and device companies that sponsor clinical trials. 

As sponsors look for new ways to accelerate their development programs, they are looking for imaging core labs that can do the following: 					

Commit to and deliver upon aggressive timelines and milestones. Committing can be the easy part. According to many anecdotal stories, delivering upon these commitments can prove challenging.

Provide more rapid and complete feedback of results to the study teams, allowing them to react much sooner.

Reduce cost where possible without sacrificing quality or adherence to timelines.

There are multiple ways an imaging core lab can react to these ever increasing demands. The method most frequently chosen is for the service provider to develop concurrent internal processes that essentially create two classes of customers within the organization. One class that represents the most demanding projects (i.e., those requiring tighter time lines and oversight by many in the organization), and a second class of customers or projects that are worked on when there is no priority work currently requiring attention. 

This methodology is sometimes referred to as the "squeaky wheel gets the grease strategy." This methodology can work as long as the quantity of special projects is kept to a minimum. It begins to fall apart as the number of priority projects grows to a point where it is hard to distinguish high priority from normal priority. Also, it is important to note that I have yet to meet a customer or study team that does not believe their project is high priority. 

The alternative to this multiple priority operational philosophy is to design the organization to deliver a level of performance that routinely meets or exceeds the most demanding customer requirements on a routine basis, not just for those high-priority projects or customers but for every project and customer. This is an objective that has obvious benefits from a timeline perspective, but I would also add that it is one that can yield significant benefits in terms of efficiency and cost. 

So, the far more interesting story is not one purely focused on metrics but one that highlights what is really happening within the organization putting forth the metrics to yield ongoing improvements that can make a real difference for sponsors and imaging core labs. 

At VirtualScopics we were faced with this exact scenario approximately 18 months ago. At that time the company was viewed as a very scientifically oriented provider of advanced imaging services for new and emerging biomarkers. We were experiencing significant growth in the number and size of projects that we were being awarded and had to make some very important decisions about how we were going to architect our operational infrastructure. 

Most importantly, would we adopt the more conventional philosophy that was described earlier with defined priorities or make the much more difficult decision to design our operations to meet what we believed would be a paradigm breaking level of performance—essentially wiping the slate clean, so to speak, and develop processes that minimize waste, time, and cost while yielding the turnaround times required by the most demanding clients across all projects. 

While we weren't sure at the time how we were going to achieve this, we knew the latter approach was the absolute right thing for the organization to focus upon and that a significant upfront investment would be necessary. 

To facilitate what would become a dramatic transformation of our operations organization we knew that change management would be critically important. To that end we adopted Lean Six Sigma principles as the change management methodology that would be used by the entire organization. This methodology, made famous by GE and Toyota, focused the organization on a relentless effort to reduce waste in all forms, with time being one of the key forms of waste. 

As a first step, all employees were given foundation training—especially the management team, as we needed to walk the talk and show very strong understanding of Lean Six Sigma principles and provide the strong leadership required to implement desired changes. We also brought in a number of black belts (i.e., experts in the implementation of these principles). 

We then chose some critical first projects to focus on to gain experience and get some early wins. One such project reduced the set up time of new projects by 50%. Subsequent efforts have reduced this by an additional 50%. In one of our first Kaizens, which is a Japanese term for take apart and put back together, we reduced the analysis time for certain oncology data by over 400%. 

These initial successes led us to create a very aggressive vision for the organization. A vision that stated, "We were to become an organization that processed imaging data we received today by the end of the day tomorrow." Not just high-priority data but all data. 

Although this may not seem to be an aggressive vision on the surface, it is routine practice for imaging core labs to queue up images for each trial for what could be a number of months before radiologists are contracted to evaluate those images. This batch and process work flow was and still is the more standard approach to processing imaging data by core labs. 

It is relatively easy to see how this approach to batch and process images is quite contrary to providing real time feedback to sponsors on the results of their trials. Not only is this process somewhat resistant to change, but it can be quite inefficient and tolerant of substandard quality as well. 

Today at VirtualScopics, approximately 80% of all oncology data we receive is processed within 24 hours. We expect that during 2009 we will approach 100% of all data to be processed in one day. Not just for those projects that require it but for all projects. To enable performance like this we had to modify or change almost every process in the organization. Most notably how we manage our work on a daily basis. 

Most organizations develop a monthly schedule of activities they need to accomplish during a particular month. During that month it is not uncommon for the last week of the month to be extremely busy, as some work planned for early in the month misses deadlines and moves to later in the month, only to have more work than can reasonably be accommodated stack up in the last week. This creates a challenge for the entire organization to work long hours and use brute force to get accomplished what you can by midnight on the last day of the month. Monthly or quarterly on time delivery metrics don't begin to characterize this phenomenon. 

At VirtualScopics we were no different at that time. Our implementation of Lean led us to translate our monthly schedule to a series of daily schedules. We determined that the only way to meet the month's needs consistently was to meet each day. Today we manage this through a morning market process where requirements in each area are defined at the end of each day for the next day. These requirements are determined by the key stakeholders in each area, from project managers to department personnel. 

Each morning there is a short 15-minute meeting where the previous day's schedule attainment is reviewed and the current day's requirements are discussed and committed to on the spot. This process of managing on a daily basis has not only made us a much more predictable organization in terms of setting a schedule and meeting it, it has also afforded us the added benefit of being extremely responsive to the needs of our clients. We have had more than one client ask how we were able to turn around their new requests or changes in timelines so quickly. The answer is that our standard process of daily scheduling allows for this. 

While still very early in our continuous improvement journey, we have been able to achieve what we believe are very extraordinary results. These results have not only had a significant impact on our company but the services we provide to our customers. Our focus on continuous improvement and the needs of our customers have positioned us well to address the significant growth we have experienced in our business. 

During 2008 we experienced a greater than 80% increase in our project backlog when compared to the prior year and a 2500% increase in the quantity of incoming data for analysis, all while not significantly increasing our cost structure. We have also identified many more opportunities we believe can yield even greater impact that we have targeted for 2009. 

We believe that continued investment in operational improvements can provide successful, efficient, and effective collaborative efforts between imaging core labs and sponsors. By rapidly providing information that provides sponsors the knowledge they need to make better and faster decisions about compounds in their pipeline, both patients and sponsors should benefit, as new therapies reach the market quicker and in a more cost effective manner. 

is president and chief executive officer of VirtualScopics, Inc., 500 Linden Oaks, Rochester, NY 14625, email: 

How one lab is using continuous process improvement to decrease timelines and redefine itself.

The Changing Core Laboratory

Medical treatment of human disease is in a period of tremendous change. In the past, small molecule drugs comprised the overwhelming majority of treatment choices for physicians. The continued emergence of biopharmaceuticals is providing new hope to millions of patients around the world. There are approximately 250 biologic therapy products currently in the market, with hundreds more in development pipelines. 

The growing role of biopharmaceuticals presents new challenges for the pharmaceutical and biotech companies guiding their development, as well as for the central laboratories performing the subject testing during a clinical trial. Many biopharmaceuticals are antibody-based compounds, which often elicit a response from the immune system. Thus, rigorous and objective quantification of immune cells and assessment of immune function is required to ensure optimal subject safety. 

In addition, since much of the biology underlying the new classes of therapeutics remains unknown, clever and innovative experimental designs are necessary in clinical trials to enhance our knowledge regarding these treatment options. 

One of the most robust methods available for immune system monitoring is flow cytometry. Flow cytometry utilizes lasers and sensitive optical detectors to measure properties of cells at rates upwards of 10,000 cells per second. The design of flow cytometers is such that measurements are recorded on individual cells as they pass the laser beam. Thus, although a large number of heterogeneous cells may be present in the sample the cytometer can identify subpopulations, and by appropriate gating strategies, measurements can be confined to particular cell subsets. 

Flow cytometry is a powerful technology capable of quickly and accurately making single cell measure- ments of properties of cells that have been tagged with fluorescent compounds. Modern flow cytometers typically incorporate multiple lasers to confer the capability of simultaneously measuring many different fluorescent dyes, record digital data which facilitates later analysis, and possess multiple detectors so they have the ability to collect eight or more fluorescent channels depending on which tags have been attached to the cells. Thus, flow cytometers are powerful cellular analysis instruments capable of performing highly complex analyses for quantifying and monitoring multiple immune system parameters. 

Since a single cell suspension is the required sample type for analysis on the cytometer, blood is ideally suited for assays designed for monitoring subject immune systems during a clinical trial. 

Flow cytometers quantify fluorescence tags on cells that have been added prior to placement of the sample on the instrument. Depending on the biological question and the assay being applied to answer it, these fluorescent tags can take different forms. Most common is the use of fluorochrome-conjugated monoclonal antibodies for phenotyping assessment. These antibodies can be chosen so that they bind to plasma membrane proteins whose expression is restricted to specific cell populations. For example, green, orange, and red fluorescently conjugated monoclonal antibodies might be added to subject blood samples to enumerate the percentages of T, B, and NK lymphocyte subpopulations and identify any effect from therapy. The flow cytometer is ideally suited to perform such tasks, as its function is to measure particle-associated fluorescence. 

Flow cytometry technology can also be used to identify and quantitate intracellular proteins. Once again, a fluorochrome-conjugated monoclonal antibody will be incubated with the cell sample, in this case following a permeabilization step, which opens holes in the cell's membrane allowing the fluorescent tag to enter the cytoplasm of the cell. For example, the apoptotic regulatory protein bcl-2 can be quantified by labeling permeabilized cells using specific fluorochrome-conjugated monoclonal antibody. 

Alternatively, the fluorescent tag can be a dye that binds to a specific cellular component. The common dye propidium iodide is fluorescent (so it is applicable for use in flow cytometric assays) and specifically binds to nucleic acid. Propidium iodide is a red dye, which will bind to both DNA and RNA inside the cell—by additionally adding RNase enzyme to eliminate the RNA, measured fluorescence will represent DNA content of the cells. Since cells increase their DNA in preparation to divide, the rate of cell division can be determined for a particular population. Such measurements are important in cancer to determine how quickly the tumor is growing. 

In addition, flow cytometric assays are available that are capable of measuring particular processes within the cell. For example, many oncology therapies induce death of cancer cells by apoptosis, a process of self-directed cell destruction that typically occurs after initiation by an appropriate stimulus. As part of the cascade of events that occurs in the cell, DNA is cleaved into fragments. By permeabilizing cells and then incubating them with an enzyme, which incorporates fluorescently conjugated nucleic acids into sites of breakage in the DNA, cells that are undergoing apoptosis can be fluorescently tagged and identified by flow cytometry. 

Diligent subject monitoring is an essential component of clinical trials, and central laboratories are well positioned to perform testing on subject samples during the course of a trial. Since subjects are dosed and clinically assessed at multiple sites, samples from these visits can be sent to a central laboratory, where all testing can be performed under one roof. 

The central laboratory provides an environment characterized by strict quality control for all testing, technical staff who are trained and certified, and a highly evolved database for data transmission to the sponsor. In addition, central laboratories incorporate client service departments that assist with issues arising during the trial, and project management departments for monitoring sites and subject visits, while providing sites with kits stocked with all the necessary items for sample acquisition and shipment. In short, central labs have matured as highly efficient testing sites that can receive subject samples during a trial and provide high-quality data to the pharmaceutical or biotech company developing the therapy. 

Now let's consider for a moment the dilemma of a biotech or pharmaceutical company entering into the clinical trial phase with its new biopharmaceutical therapeutic compound. The needs of this company consist of routine safety testing and complex immune monitoring of the samples from the participants in the trial. The central laboratory with the expertise and experience to perform high-end flow cytometry, which also has in place the complete lab and database infrastructure to perform hematology and chemistry, is the solution. 

Since subject visits and sample acquisitions are taking place at multiple sites, a major issue to consider is the stability of samples during transport to the central lab. For most cell surface and intracellular proteins of interest, cells from whole blood samples can be successfully assayed within 72 hours of collection, following shipment at ambient temperature conditions. For unique or labile proteins, or for more complex methods such as functional assessment, time course studies are necessary to determine the window of time following sample acquisition during which the particular measurement can be made. 

New BCT phlebotomy tubes have lengthened blood sample stability times; and for particularly sensitive assays, PBMC can be isolated from the whole blood sample at the site, cryo-preserved, and shipped to the central laboratory. 

Another major issue for consideration is harmonization of subject sample testing during a clinical trial. Increasing utilization of global site selection has necessitated standardization between laboratories on different continents. 

Consider a small biotech company that has developed an experimental design for its new compound based on investigator expertise at four academic medical centers located in Washington, DC, Glasgow, Beijing, and Mumbai. Blood samples from participants in this trial need to undergo testing in hematology and chemistry as well as complex flow cytometric assays for receptor occupancy and apoptosis for the target cancer cell population. What is the solution to the challenge this company's team faces? ICON Central Laboratories [ICL] believes it has the solution in its harmonized testing facilities in New York, Dublin, Singapore, and Bangalore. 

These facilities perform hematology, chemistry, and flow cytometry, where samples from each of the four sites could be sent, respectively, at ambient temperature and processed for testing the following day. Both safety and flow cytometric assays would be performed at each of ICL's facilities, thus circumventing the concern of sample stability issues. 

In addition, reagent optimization and validation is performed at the New York ICL location—all reagents are purchased in New York and shipped to the other facilities. The template for the flow cytometer, which includes gating strategy and analysis for data acquisition, is created in New York, locked down, and provided to the other ICL locations. A standardized cell preparation protocol is written and shared among centers; cross-training takes place and sample concordance studies between sites are completed. All of these steps help to ensure that laboratory testing from subjects seen in Glasgow (performed in Dublin) is comparable to laboratory testing from subjects seen in Mumbai (performed in Bangalore). 

To further assist in standardization and quality control of flow cytometric data generated in the central laboratory environment, ICL has created a virtual flow cytometry network. 

In the earlier example, samples are shipped to, prepared, and analyzed on the flow cytometer at each of the four laboratory locations. Subsequently the data, via computer server, is transmitted to the central database at the New York lab, where the reviewer[s] can assess quality control, interpret the data, provide final approval for the sample, and, if necessary, reanalyze the data to ensure consistency for all the data in the trial. 

ICL uses the aftermarket flow cytometry software data analysis program FCS Express for this purpose. Flow cytometric listmode data from different laboratory sites around the world can be loaded into FCS Express and analysis regions can be reviewed, thus minimizing operator variation. 

As a further step, ICL has in place a system that allows availability of flow cytometric list mode data to clients for review by logging into a secure account from their own computer. All these steps can provide reassurance to the sponsor that laboratory testing results impacting the outcome of their clinical trial are being generated in a setting that utilizes innovative strategies and procedures, which in the final analysis will provide the best data quality possible for their hematology and blood chemistry results as well as for their flow cytometric assays for receptor occupancy and quantification of apoptotic cells. 

ICL combined the power of flow cytometric analysis with information technology capable of connecting the world. The result: a global solution that assured the integrity of subject samples, uniform test results, flow cytometric data directly correlated for individual subjects, consistent data review and final approval by a single team of flow cytometry experts, and access to the raw flow cytometric data so that the sponsor's scientists and those from ICL could work collaboratively to ensure final data quality. 

, PhD, is associate director, cellular immunology, research and development, 

* is director of research and development at ICON Central Laboratories, 123 Smith Street, Farmingdale, NY 11735, email: 

*To whom all correspondence should be addressed.

A look into how flow cytometrycan benefit global clinical trials and what an IT savvy central lab can contribute.

Supplements-01-02-2009

Image-Based Trials: Just Another Stressful EDC Story?

The Changing Core Laboratory

Discovering Flow Cytometry