716 May-June 2010 radiographics.rsna.org

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1 Note: This copy is for your personal non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, contact us at May-June 2010 radiographics.rsna.org Invited Commentary From: Alina Tudorica, PhD,* Charles R. Thomas, Jr, MD,* Wei Huang, PhD Department of Radiation Medicine, Oregon Health & Science University Knight Cancer Institute,* and Advanced Imaging Research Center, Oregon Health & Science University Portland, Oregon With its excellent soft-tissue contrast, anatomic MR imaging has often been used to evaluate changes in tumor size and morphologic features (eg, with the Response Evaluation Criteria in Solid Tumors system) in the assessment of cancer response to therapy. However, numerous imaging studies of gross tumor response to therapy have shown that during treatment, changes in tumor volume or morphologic features usually manifest later than do changes in underlying tumor functions, such as cell structure, vasculature, and metabolism. Recent technologic advances have made it possible to routinely use functional imaging modalities in the clinical setting for the purposes of (a) early prediction of response or nonresponse to cancer therapy, and (b) accurate assessment of residual cancer for preoperative staging following the completion of therapy. In their article, Barbaro et al (1) use individual cases to demonstrate in great detail the advantages and pitfalls of anatomic (mainly T2-weighted) MR imaging, diffusion-weighted MR imaging, and FDG PET in the restaging of locally advanced rectal cancer following CRT. The latter two modalities are functional imaging modalities. Like other investigators, Barbaro et al (1) conclude that purely anatomic MR imaging is insufficient for accurate and reliable assessment of therapeutic response of rectal cancer to CRT (and thus, for appropriate surgical planning), and that the use of functional imaging techniques that can noninvasively help monitor treatment results is highly desirable. FDG PET can be used to investigate treatment-induced metabolic changes and is useful for differentiating responding from nonresponding patients with rectal cancer during and after CRT. Therefore, PET is potentially complementary to anatomic MR imaging and diffusion-weighted MR imaging in this setting. However, as Barbaro et al (1) point out, the clinical utility of FDG PET is reduced by the need to limit repeated radiation doses, as well as by high cost and low spatial resolution. The ADC as measured with diffusion-weighted MR imaging provides a quantifiable assessment of the random motion of water molecules in the tissue microstructure. ADC has been shown to correlate inversely with cell density. In recent years, ADC has become a promising surrogate biomarker of tumor response to therapy. Viable tumor cells restrict the mobility of water, whereas necrotic tumor cells allow increased diffusion of water molecules as a result of treatment-induced tumor regression such as necrosis, decreased cellularity, and compromised cell membrane integrity. In a number of clinical and preclinical studies (2 9), increases in tumor ADC values were noted early during the course of therapy, allowing early prediction of treatment response. As Barbaro et al (1) point out, the advantages of diffusion-weighted MR imaging in the assessment of treatment response are that it is completely noninvasive, does not require exposure to ionizing radiation or injection of contrast agent, and requires only a few additional minutes of scanning time. Thus, diffusion-weighted MR imaging can easily be incorporated into a conventional rectal MR imaging protocol. In the rectal diffusion-weighted MR imaging studies performed by Barbaro et al (1), the authors correctly emphasized the importance of using high b values (eg, 1000 sec/mm 2 ) to eliminate possible microvascular contamination of the computed ADC values. Diffusion-weighted MR images were acquired with a section orientation, section thickness (4 mm), intersection gap (0.5 mm), and FOV (40 cm) that matched those with which the axial T2-weighted images were acquired. However, the interpretations of tumor size and morphologic features were all based on the oblique high-resolution T2-weighted images. This may have caused difficulties in spatially matching the tumor on the oblique anatomic im-

2 RG Volume 30 Number 3 Barbaro et al 717 ages and the diffusion-weighted images. It is not intuitive how the authors managed to draw tumor ROIs on the ADC maps by using information from the anatomic images. In addition, it is not clear how the tumor ADC value was calculated as the median value of four to six measurements (1). If the tumor ADC values listed in the article represent average ADC values for the whole tumor, these values should be calculated by averaging the tumor ROI ADC from each of the sections that together cover the entire tumor, weighted by the number of pixels in each ROI. Furthermore, regional tumor necrosis as a result of treatment may cause a significant increase in the ADC value of the whole tumor, leading to overestimation of therapeutic effects. Thus, to eliminate bias, care should be taken to exclude areas of necrosis when estimating whole-tumor ADC. Clearly, pixel-by-pixel ADC mapping should be performed to remove partial volume averaging effect from the ROI analysis and depict the heterogeneous tumor response to CRT. The usefulness of this approach has been well documented by the authors (1). Perhaps histogram analysis of the pixel ADC values should be performed to provide better quantitative functional assessment of rectal cancer response to CRT. There are several drawbacks to the use of diffusion-weighted MR imaging, as currently implemented in clinical settings, for monitoring therapeutic response. Diffusion-weighted MR images are usually characterized by low spatial resolution and the presence of artifacts owing to the frequent use of single-shot echoplanar pulse sequences. Furthermore, diffusion-weighted MR imaging is usually not very helpful in characterizing nonmass tumors. The diffusion-weighted MR images acquired by Barbaro et al (1) have a relatively large nominal voxel size of mm 3, and thus, as the authors acknowledged, can help detect small clusters of residual tumor cells only with difficulty. Other challenges in the use of diffusion-weighted MR imaging for the assessment of rectal cancer response to therapy include differentiation of therapy-induced fibrosis from residual cancer, discrimination between residual neoplastic tissue and scarring, and standardization of data acquisition and analysis. Although it was not used by the authors, dynamic contrast-enhanced MR imaging is another promising functional imaging method for the assessment of rectal cancer response to therapy. Dynamic contrast-enhanced MR imaging primarily measures the contrast agent related leakiness (or permeability) of the tumor blood vessels. The usefulness of dynamic contrast-enhanced MR imaging in characterizing rectal tumors (10,11) and their response to treatment (12 17) has been demonstrated in several clinical and preclinical studies. With use of pharmacokinetic models to fit the signal intensity time-course data and extract tissue parameters, quantitative dynamic contrastenhanced MR imaging is often the more desirable approach for the assessment of tumor response to therapy. These biomarkers are physiologic quantities that, in principle, are independent of the data acquisition details. The parameters are usually variants of (a) K trans, a rate constant for passive contrast agent plasma-interstitium transfer; (b) v e, the interstitial space volume fraction (the putative contrast agent distribution volume); and (c) v p, the plasma volume fraction. The K trans value is directly related to tumor vessel wall permeability and blood flow, whereas v e is a complementary measure of tumor cellularity. So far, the findings regarding the ability of dynamic contrastenhanced MR imaging to help assess tumor therapeutic response have been contradictory. The semiquantitative approaches used for signal intensity time-course analysis in some studies may be partly responsible for this inconsistency. The computed semiquantitative parameter values, such as enhancement ratio and area under the curve, are dependent on data acquisition details as well as contrast agent dose and injection rate, which vary from one institution to another. However, for quantitative pharmacokinetic modeling of the dynamic contrast-enhanced MR imaging time-course data, the commonly used standard model, or Tofts model (18), incorrectly assumes that the exchange kinetics of water molecules between the intra- and extracellular compartments in the interstitium is infinitely fast. In several recent studies, it has been found that this assumption causes underestimation of the pharmacokinetic parameters (19 22). The recently developed shutter speed model for dynamic contrastenhanced MR imaging data analysis (19,20) takes into account the effects of finite equilibrium transcytolemmal water exchange kinetics. The K trans parameter derived with the shutter speed model and the difference between K trans values obtained with this model and those obtained with the standard model were found to be extremely

3 718 May-June 2010 radiographics.rsna.org accurate diagnostic markers for breast cancer detection (21,22). By correcting the underestimation of the pharmacokinetic parameters associated with use of the standard model, the shutter speed model derived parameters have greater dynamic ranges, and thus may be more sensitive to therapy-induced microvascular changes than are the standard model derived parameters. Dynamic contrast-enhanced MR imaging requires the intravenous injection of contrast agent during the acquisition of a series of T1-weighted images. It is routinely included in clinical MR imaging protocols, such as diagnostic breast MR imaging protocols. Dynamic contrast-enhanced MR imaging usually has robust signal-to-noise and contrast-to-noise ratios and is often performed with spatial resolution comparable to that of anatomic MR imaging, so that it can help detect small areas of residual cancer. The combined use of anatomic, diffusion-weighted, and dynamic contrast-enhanced MR imaging should provide more comprehensive and accurate assessment of rectal cancer response to therapy (23). References 1. Barbaro B, Vitale R, Leccisotti L, et al. Restaging locally advanced rectal cancer after chemoradiation therapy with MR imaging. RadioGraphics 2010;30 (3): ; discussion Galons JP, Altbach MI, Paine-Murrieta GD, Taylor CW, Gillies RJ. Early increases in breast tumor xenograft water mobility in response to paclitaxel therapy detected by non-invasive diffusion magnetic resonance imaging. Neoplasia 1999;1(2): Theilmann RJ, Borders R, Trouard TP, et al. Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. Neoplasia 2004;6(6): Morse DL, Galons JP, Payne CM, et al. MRImeasured water mobility increases in response to chemotherapy via multiple cell-death mechanisms. NMR Biomed 2007;20(6): Chenevert TL, Meyer CR, Moffat BA, et al. Diffusion MRI: a new strategy for assessment of cancer therapeutic efficacy. Mol Imaging 2002;1(4): Lee KC, Moffat BA, Schott AF, et al. Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res 2007;13(2 pt 1): Kim H, Morgan DE, Zeng H, et al. Breast tumor xenografts: diffusion-weighted MR imaging to assess early therapy with novel apoptosis-inducing anti-dr5 antibody. Radiology 2008;248(3): Aliu SO, Wilmes LJ, Moasser MM, et al. MRI methods for evaluating the effects of tyrosine kinase inhibitor administration used to enhance chemotherapy efficiency in a breast tumor xenograft model. J Magn Reson Imaging 2009;29(5): Buijs M, Kamel IR, Vossen JA, Georgiades CS, Hong K, Geschwind JF. Assessment of metastatic breast cancer response to chemoembolization with contrast agent enhanced and diffusion-weighted MR imaging. J Vasc Interv Radiol 2007;18(8): Zhang XM, Yu D, Zhang HL, et al. 3D dynamic contrast-enhanced MRI of rectal carcinoma at 3T: correlation with microvascular density and vascular endothelial growth factor markers of tumor angiogenesis. J Magn Reson Imaging 2008;27(6): Tuncbilek N, Karakas HM, Altaner S. Dynamic MRI in indirect estimation of microvessel density, histologic grade, and prognosis in colorectal adenocarcinomas. Abdom Imaging 2004;29(2): George ML, Dzik-Jurasz AS, Padhani AR, et al. Non-invasive methods of assessing angiogenesis and their value in predicting response to treatment in colorectal cancer. Br J Surg 2001;88(12): Morgan B, Thomas AL, Drevs J, et al. Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK , an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: results from two phase I studies. J Clin Oncol 2003;21(21): Mross K, Fasol U, Frost A, et al. DCE-MRI assessment of the effect of vandetanib on tumor vasculature in patients with advanced colorectal cancer and liver metastases: a randomized phase I study. J Angiogenes Res 2009;1: de Lussanet QG, Backes WH, Griffioen AW, et al. Dynamic contrast-enhanced magnetic resonance imaging of radiation therapy-induced microcirculation changes in rectal cancer. Int J Radiat Oncol Biol Phys 2005;63(5): Ceelen W, Smeets P, Backes W, et al. Noninvasive monitoring of radiotherapy-induced microvascular changes using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in a colorectal tumor model. Int J Radiat Oncol Biol Phys 2006;64(4): Bradley DP, Tessier JJ, Lacey T, et al. Examining the acute effects of cediranib (RECENTIN, AZD2171) treatment in tumor models: a dynamic contrast-enhanced MRI study using gadopentate. Magn Reson Imaging 2009;27(3): Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999;10(3): Yankeelov TE, Rooney WD, Li X, Springer CS Jr. Variation of the relaxographic shutter-speed for transcytolemmal water exchange affects the CR bolus-tracking curve shape. Magn Reson Med 2003;50(6):

4 RG Volume 30 Number 3 Barbaro et al Li X, Rooney WD, Springer CS. A unified magnetic resonance imaging pharmacokinetic theory: intravascular and extracellular contrast reagents. Magn Reson Med 2005;54(6): [Published correction appears in Magn Reson Med 2006;55(5):1217.] 21. Huang W, Li X, Morris EA, et al. The magnetic resonance shutter speed discriminates vascular properties of malignant and benign breast tumors in vivo. Proc Natl Acad Sci U S A 2008;105(46): Li X, Huang W, Morris EA, et al. Dynamic NMR effects in breast cancer dynamic-contrast-enhanced MRI. Proc Natl Acad Sci U S A 2008;105(46): Batchelor TT, Sorensen AG, di Tomaso E, et al. AZD2171, a pan-vegf receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 2007;11(1): Authors Response From: Brunella Barbaro, MD,* Renata Vitale, MD,* Lucia Leccisotti, MD,* Fabio M. Vecchio, MD, Luisa Santoro, MD, Vincenzo Valentini, MD,* Claudio Coco, MD, Fabio Pacelli, MD, Antonio Crucitti, MD, Roberto Persiani, MD, Lorenzo Bonomo, MD* Departments of Bioimaging and Radiological Sciences,* Pathology, and Surgery, Catholic University, School of Medicine Rome, Italy We are grateful for the opportunity to reply to the commentary by Tudorica et al of Oregon Health & Science University, in response to which we would like to provide additional information. The accuracy of high-resolution T2-weighted sequences for primary staging of the extent of rectal cancer has been validated in a number of studies and confirmed by the MERCURY Study Group (1), which demonstrated that MR imaging is feasible and its findings reproducible in a multicenter setting, and that it allows accurate preoperative prognostication. In a study conducted by our team in which high-resolution T2-weighted MR imaging was performed both before and after CRT, the relevant morphologic and volumetric changes had a high positive predictive value for the assessment of treatment response (2). A high-resolution T2-weighted sequence was performed with a small FOV and a section thickness of 3 mm, with the sections being placed perpendicular to the longitudinal axis of the tumor. With this sequence, it was possible to precisely evaluate the tumor and its relationship to the intestinal wall, the mesorectal fascia, the pelvic organs, and possibly the peritoneal folds. Moreover, mesorectal lymph nodes could be evaluated. Thus, we performed an oblique high-resolution T2-weighted sequence in a plane orthogonal to the tumor before and after CRT to facilitate interpretation of the relative size and morphologic features of the tumor and lymph nodes. Precise tumor and lymph node localization was then achieved with an axial T2-weighted sequence performed with a large FOV and a section thickness of 4 mm, with imaging encompassing the entire pelvis up to the aortic bifurcation. The same parameters were used for the diffusionweighted sequence to match up the tumor and lymph nodes. Spatial correlation was achieved by identifying anatomic and morphologic landmarks (lymph nodes, blood vessels, perirectal fascia, and bowel contour) that were visible on the oblique high-resolution and axial T2-weighted images. With regard to calculation of tumor ADC, there are as yet neither standardized techniques nor agreement regarding the best method to use. Recently, Sun et al (3) demonstrated that, in rectal carcinoma, an early increase in mean tumor ADC at the end of the first week of therapy and a low pretherapy mean ADC correlate with good response to CRT. These authors manually outlined the contour of the tumor as an ROI on each of the relevant imaging sections, avoiding regions of distortion and artifact. The number of imaging sections on which the tumor was visible ranged from three to 10. The mean ADC of the whole tumor was derived from the mean of all voxel ADCs within each ROI recorded for the entire lesion.

5 720 May-June 2010 radiographics.rsna.org Kim et al (4) published the value of adding post-crt diffusion-weighted MR imaging to conventional MR imaging for the evaluation of complete response to neoadjuvant CRT in patients with locally advanced rectal cancer. These authors performed qualitative analysis (residual tumor as hyperintense signal in the corresponding tumor) and quantitative analysis with diffusion-weighted imaging. For the latter, the post-crt ADC values were measured in the three ROIs, and these measurements were then averaged. A circular ROI of at least 4 mm 2 (minimum diameter = 2 mm) was placed within three different portions of the heterogeneous tumor to obtain average ADC values for the tumor. In their study, the mean ADC for the complete response group was significantly higher than that for the incomplete response group (4). To date, studies of diffusion-weighted imaging in rectal cancer have been small and have varied with respect to technical parameters and instrumentation; thus, the reliability of ADC measurement after neoadjuvant therapy must be further investigated to standardize data acquisition and analysis, as we indicated in our article. Because it includes the entire tumor, this analysis has become less observer dependent and is more easily translated to a clinical setting; however, tumor response to therapeutic intervention may be heterogeneous. Nevertheless, the use of imaging could assist in delineating therapy-induced spatial heterogeneity within a tumor by providing information regarding specific regions that are either resistant to or responsive to treatment. As shown in Figures 1 and 5 from our article, it is possible to recognize, in residual heterogeneous post-crt tumor, (a) lakes of post-crt mucin differentiation at morphologic T2-weighted imaging and on the color ADC map (Fig 1), or (b) a focal area with higher signal intensity than that of background tumor at diffusion-weighted MR imaging that manifests as a corresponding area of restricted diffusion on the ADC map (Fig 5). Thus, we suggest segmentation of the ROI according to the difference in signal intensity on diffusionweighted images and the ADC map, to avoid overestimation of the therapeutic effects. To investigate the relevance of ADC heterogeneities within a rectal tumor, DeVries et al (5) generated relative frequency histograms of the ADCs within the tumor. These data showed a significantly higher proportion of high ADCs in patients who did not respond to therapy compared with those who did respond, whereas mean ADC values did not differ. As Tudorica et al indicated, this suggests the potential use of histogram analysis for investigating the distribution of any single-voxel ADC value in the tumor region. Our choice of FOV was a compromise between voxel size, volume coverage, and signalto-noise ratio, with a maximum FOV of 40 cm. Smaller FOVs (34 40 cm) were also used, depending on patient body habitus. The current literature (6,7) suggests that the administration of intravenous contrast material does not improve the staging of rectal tumors with MR imaging; however, as Tudorica et al pointed out, dynamic contrast-enhanced MR imaging is another promising functional imaging method for the assessment of rectal cancer response to therapy, and thus should be incorporated into a conventional rectal MR imaging protocol. In recent years, there has been considerable effort devoted to the performance of diffusionweighted MR imaging, and the clinical utility of this modality in oncology practice is still under active investigation. The considerations put forth by Tudorica et al in their commentary could help in such endeavors. References 1. MERCURY Study Group. Extramural depth of tumor invasion at thin-section MR in patients with rectal cancer: results of the MERCURY study. Radiology 2007;243(1): Barbaro B, Fiorucci C, Tebala C, et al. Locally advanced rectal cancer: MR imaging in prediction of response after preoperative chemotherapy and radiation therapy. Radiology 2009;250(3): Sun YS, Zhang XP, Tang L, et al. Locally advanced rectal carcinoma treated with preoperative chemotherapy and radiation therapy: preliminary analysis of diffusion-weighted MR imaging for early detection of tumor histopathologic downstaging. Radiology 2010; 254(1): Kim SH, Lee JM, Hong SH, et al. Locally advanced rectal cancer: added value of diffusion-weighted MR imaging in the evaluation of tumor response to neoadjuvant chemo- and radiation therapy. Radiology 2009;253(1): DeVries AF, Kremser C, Hein PA, et al. Tumor microcirculation and diffusion predict therapy outcome for primary rectal carcinoma. Int J Radiat Oncol Biol Phys 2003;56(4): Vliegen RF, Beets GL, von Meyenfeldt MF, et al. Rectal cancer: MR imaging in local staging is gadolinium-based contrast material helpful? Radiology 2005;234(1): Klessen C, Rogalla P, Taupitz M. Local staging of rectal cancer: the current role of MRI. Eur Radiol 2007;17 (2):