8 Consensus Recommendations for Acquisition of Dynamic Contrasted-Enhanced MRI Data in Oncology

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8 Consensus Recommendations for

Acquisition of Dynamic Contrasted-Enhanced MRI Data in Oncology

Jeffrey L . Evelhoch

J. L. Evelhoch, PhD

Director, Structural Imaging, World Wide Clinical Technol- ogy, Pfizer Global Research and Development, 2800 Plym- outh Road, Mail Stop: 50-M129, Ann Arbor, MI 48105, USA CONTENTS

8.1 Introduction 109

8.2 General Recommendations 109 8.3 Specific Recommendations 111 8.4 Discussion 111

8.5 Participants (Affiliation at Time of Workshop) of the Dynamic Contrast-Enhanced

Magnetic Resonance Imaging Workshop 112 8.6 Participants (Affiliation at Time of Workshop)

of the Future Technical Needs in

Contrast-Enhanced MRI of Cancer Workshop 112 References 113

8.1 Introduction

As demonstrated by many of the chapters in this book, dynamic contrast-enhanced magnetic reso- nance imaging (DCE-MRI), using clinically avail- able, diffusible, extracellular contrast agents [e.g., gadopentetate dimeglumine (Mitchell 1997)], has emerged over the past decade as a promising method for cancer diagnosis, staging, response assessment and evaluation of biological activity in early clinical trials of novel drugs targeting the tumor vascula- ture. Remarkably, these positive results have been obtained despite considerable variation in both the methods of data acquisition (e.g., pulse sequences, acquisition parameters, temporal resolution, spatial resolution, and coverage) and analysis (e.g., visual inspection, parametric analysis, pharmacokinetic, or physiologic modeling). This suggests there are sub- stantial physiologic / pharmacokinetic differences (e.g., between benign and malignant, or between non-responsive and responsive tumors) underlying these observations that are evident independent of the methods used for acquisition and analysis of the

DCE-MRI data. Clearly, there is a promising future for use of DCE-MRI as both a clinical research tool and in routine clinical practice. However, there are several issues that should be addressed to expedite the realization of this promise in both routine clini- cal practice and drug development.

In order to better define and address these issues, two consensus workshops were held by the Cancer Imaging Program of the United States National Cancer Institute on October 28 and 29, 1999 and November 13, 2000 to consider this problem (Evelhoch et al. 2000; see also http://www3.cancer.

gov/bip/DCEMRIrpt.htm). This chapter summa- rizes the consensus opinions of the attendees of those workshops

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(see Sects. 8.5 and 8.6) and pro- vides examples of recent research that may help to address some of these critical issues.

8.2 General Recommendations

A fundamental issue impeding realization of the potential of DCE-MRI in oncology is the need to com- pare and/or integrate results across multiple institu- tions. This is due to the variety of methods used for data acquisition and analysis that have resulted from the lack of an established consensus regarding how best to acquire and/or analyze DCE-MRI data. More- over, evaluation of the relative merits of the various data analysis methods is difficult, if not impossible, when disparate acquisition methods are used. Con- sequently, the clinical relevance of the information provided by different analysis approaches is difficult to assess, further hindering the establishment of a consensus on analysis methods.

1 These recommendations are an independent statement of the workshop participants and do not represent a policy statement of the United States National Institutes of Health or the United States Federal Government.

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Ultimately, for DCE-MRI to be useful for routine oncologic applications in a clinical setting, includ- ing clinical trials of new cancer therapies, it should be straightforward to implement and provide a 3D representation of relevant quantitative informa- tion, voxel-wise quantitative results, and a statisti- cal summary of quantitative results. Regardless of the method used for data analysis, some of the fac- tors that could impact the information derived from DCE-MRI data are intra- or inter-patient variation in either the initial T1 or the blood contrast agent con- centration as a function of time (i.e., ‘arterial input function’), tumor heterogeneity, patient motion during the study (Evelhoch 1999), and signal arti- facts, particularly in the baseline image (Dale et al.

2003). A recent paper from Rijpkema and co-work- ers (2001), which showed that use of rapidly and highly-enhancing pixels to define a ‘vascular’ input function in each subject substantially improves the test-retest reproducibility of DCE-MRI measure- ments, illustrates the importance of using an indi- vidual input function for analysis of DCE-MRI data (see also Chap. 6). Although the importance of each of these factors for clinical research tools and rou- tine clinical practice may differ depending on the specific application, it would be useful to establish baseline requirements for general research and/or clinical studies. This would facilitate both integra- tion of data from different institutions and compari- son of various approaches for analysis of the kinetic data.

Optimal properties for DCE-MRI methods include the following. The entire tumor should be imaged (3D measurements are preferred because single slice measurements may be prone to sampling error) with the best possible spatial and temporal resolution (trade-offs among coverage, spatial and temporal resolution depend on the specific applica- tion), while specification of the field of view (FOV) should be flexible. The results should be indepen- dent of system (e.g., site, manufacturer and field strength) and contrast dose, free from spurious cor- relations, motion artifacts, and errors due to tissue properties, include an estimate of error, and highly reproducible (although accurate assessment of the underlying pathophysiology should not be sacri- ficed for reproducibility).

Requirements for temporal resolution may vary over the course of a contrast-enhanced examination, with resolution on the order of seconds targeted for the initial contrast enhancement phase and becoming progressively longer to tens of seconds during later phases, which may occur minutes later. Thus, ‘adap-

tive imaging’ methods that allow dynamic trade-off between temporal and spatial resolution throughout the time course are preferable (see schematic repre- sentation of this concept in Fig. 8.1). Krishnan and Chenevert (2004) recently published an intuitive formalism applicable to DCE-MRI for a set of tar- geted/anticipated dynamic events as well as spatial features that should facilitate consideration of the trade-off between spatial and temporal resolution.

Many meritorious technical directions are possible for attempting to get high 3D spatial and good tem- poral resolution. Examples of methods allowing arbitrary selection from among several combina- tions of temporal/spatial resolutions during post- processing were recently published by Song et al.

(2001) and d’Arcy et al. (2002). However, they have yet to be used widely for DCE-MRI studies.

In order to have adequate patient power (see Chap. 15) clinical studies using a specific DCE-MRI technique will likely require the collaboration of oncologic investigators from multiple institutions.

Recruitment of such oncologic collaborators may likely involve sites that focus primarily on clini- cal MRI rather than those with extensive technical research capability. Furthermore, not all collabo- rators will necessarily be located near each other.

Most commercial MRI manufacturers provide to their academic collaborators some level of capabil- ity to develop acquisition techniques of their own.

In general, this requires a technical knowledge of the MRI machine at the academic site which is much deeper than that required for routine clinical opera- tion. Consequently, translation of new methods developed at one institution to other institutions for clinical use requires personnel dedicated to sup-

Fig.8.1. Recommended ‘adaptive’ imaging approach

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porting implementation at sites with considerably less technical knowledge of the MRI machine. In the short term, given the difficulty in translating new methods to multiple sites, it may be best to specify minimum requirements, rather than more sophisti- cated sequences, to facilitate cooperation from more clinically oriented sites.

8.3 Specific Recommendations

쎲 When feasible, entire tumor volume should be

included in ﬁ eld of view.

쎲 Prior to injection, T1 should be measured using the

same resolution and ﬁ eld of view used for acqui- sition of dynamic data, if possible (see Chap. 5).

This should be facilitated by the use of methods such as Look-Locker (Karlsson and Nordell 2000) and DESPOT1 (Deoni et al. 2003).

쎲 A power injector should be used for contrast agent

injection to minimize variation and a saline ﬂ ush should always be used.

쎲 The input function (preferably arterial) should be

measured with no in-ﬂ ow effect.

쎲 In general, a minimum of 5- to 30-s temporal reso-

lution (or the fastest sampling possible consistent with spatial resolution requirements) should be used for the ﬁ rst 90–150 s after bolus injection.

It should be noted that poorer temporal resolu- tion would not support the use of sophisticated data analysis methods (Henderson et al. 2000;

Jackson et al. 2002), but may be unavoidable if high spatial resolution is critical. Higher spatial resolution images could be acquired out to 10 min with 1- to 4-min temporal resolution. In some cases, sufﬁ cient spatial resolution may be achieved with higher temporal resolution and there is no need to change spatial resolution.

8.4 Discussion

The rationale behind the recommended strategy is to record potentially rapid signal changes as they occur (albeit at reduced spatial resolution), then transition to high spatial resolution imaging as the intensity changes become less abrupt. Use of the recom- mended ‘adaptive imaging’ approach would facili- tate both comparisons among different groups and

evaluation of the merits of the various approaches available for data analysis. The Workshop Attendees recognize that all interested investigators may not be able to implement the recommended ‘adaptive imaging’ approach immediately. This is primarily for two reasons: (1) ‘adaptive imaging’ and input function sampling requires very flexible control of clinical MR scanners to permit dynamic switch- ing between high spatial and temporal resolution;

and (2) methods optimizing the number of pixels imaged per unit time may not be available on all systems. Increased cooperation among investiga- tors, manufacturers and the relevant government agencies should facilitate more widespread use of the recommendations.

As mentioned previously, approaches providing dynamic trade-offs between high spatial resolu- tion adequate to fully characterize the tumor mor- phology and high temporal resolution adequate to characterize contrast pharmacokinetics have already been introduced. However, numerous other approaches, including those routinely applied to contrast-enhanced magnetic resonance angiog- raphy (CE-MRA; Mazaheri et al. 2002) may be valuable if adapted to DCE-MRI. In addition, new ways for providing improved spatial resolution per unit time could be applied to DCE-MRI [e.g. mul- tiple coil techniques such as sensitivity encoding, SENSE (Tsao et al. 2003), and simultaneous acqui- sition of spatial harmonics, SMASH (Sodickson and McKenzie 2001), and effective utilization of high capability gradient technology]. In any case, it is critical to verify that the fidelity of the quan- titative contrast agent concentration is maintained throughout the observation period.

The use of acquisition protocols meeting at least the minimum requirements recommended herein should facilitate the establishment of a database that could be used to evaluate data analysis approaches.

In this light, it is important to note that such pro- tocols would support the recommendations made by the Pharmacodynamic/Pharmacokinetic Tech- nologies Advisory Committee (PTAC) of the Cancer Research United Kingdom for use of DCE-MRI in clinical trials of drugs targeting the tumor vascu- lature (Leach et al. 2003). The Cancer Research UK PTAC recommended Ktrans (Tofts et al. 1999) and/or Initial Area Under the (concentration-time) Curve (IAUC) (Mattiello and Evelhoch 1991) should be the primary endpoint for clinical trials of anti-angiogenic or anti-vascular drugs. Both Ktrans and IAUC require calculation of instantaneous tumor contrast agent concentration, based on the

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change in relaxivity due to contrast uptake. Since that calculation requires a measurement of tumor T1 immediately prior to contrast uptake and the arte- rial input function is required for Ktrans and can be used as a reference for IAUC (Evelhoch 1999;

Redman et al. 2003), the recommended acquisition protocols would allow determination of the recom- mended primary endpoints.

8.5 Participants (Affiliation at Time of Workshop) of the Dynamic

Contrast-Enhanced Magnetic Resonance Imaging Workshop

Truman Brown (Fox Chase Cancer Center, Philadelphia, PA)

Thomas Chenevert (University of Michigan, Ann Arbor, MI)

Laurence Clarke (National Cancer Institute, Bethesda, MD)

Bruce Daniel (Stanford University, Palo Alto, CA) Hadassa Degani (Weizmann Institute, Rehovot,

Israel)

Jeffrey Evelhoch – Chair (Wayne State University, Detroit, MI)

Nola Hylton (University of California, San Francisco, CA)

Michael Knopp (German Cancer Research Center, Heidelberg, Germany)

Jason Koutcher (Memorial Sloan Kettering, New York, NY)

Ting-Yim Lee (University of Western Ontario, London, Ontario, Canada)

Nina Mayr (University of Iowa, Iowa City, IA) Daniel Sullivan (National Cancer Institute,

Bethesda, MD)

June Taylor (St. Jude Children’s Research Hospital, Memphis, TN)

Paul Tofts (University College London, London, UK)

Robert Weisskoff (EPIX Medical, Cambridge, MA) 8.6

Participants (Affiliation at Time of Workshop) of the Future Technical Needs in Contrast-Enhanced MRI of Cancer Workshop

Houston Baker (National Cancer Institute, Bethesda, MD)

Thomas Chenevert (University of Michigan, Ann Arbor, MI)

Laurence Clarke (National Cancer Institute, Bethesda, MD)

W. Thomas Dixon (General Electric Medical Systems, Schenectady, NY)

William Edelstein (General Electric Medical Systems, Schenectady, NY)

Jeffrey Evelhoch – Co-Chair (Wayne State University, Detroit, MI)

Robert Herfkens (Stanford University, Palo Alto, CA)

Alan Jackson (University of Manchester, Manchester, UK)

Andrea Kassner (Philips Medical Systems, Hammersmith, London, UK)

Larry Kasuboski (Marconi Medical Systems, Cleveland, OH)

Leon Kaufman (Toshiba America Medical Systems, Tustin, CA)

Elaine Keeler (Marconi Medical Systems, Cleveland, OH)

Michael Knopp (Ohio State University, Columbus, OH)

Charles Mistretta (University of Wisconsin, Madison, WI)

James Pipe (Barrow Neurological Institute, Phoenix, AZ)

Stephen Riederer – Co-Chair (Mayo Clinic, Rochester, MN)

Mitchell Schnall (University of Pennsylvania, Philadelphia, PA)

Edward Staab (National Cancer Institute, Bethesda, MD)

David Thomasson (Siemens Medical Solutions, Malvern, PA)

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Clinical Applications

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