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R. Mazurchuk, D. Glaves, D. Raghavan (1997)
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Gothenburg SE-413 45, Sweden. 2 Division of Medical Physics and Medical Engineering
J. Masciotti, F. Provenzano, J. Papa, A. Klose, J. Hur, X. Gu, D. Yamashiro, J. Kandel, A. Hielscher (2006)
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ParteckeIKaedingASendlerMAlbersNKuhnJ-PSpeerforckSRoeseSSeubertFDiedrichSKuehnSIn vivo imaging of pancreatic tumours and liver metastases using 7 Tesla MRI in a murine orthotopic pancreatic cancer model and a liver metastases modelBMC Cancer20111114010.1186/1471-2407-11-4021276229ParteckeIKaedingASendlerMAlbersNKuhnJ-PSpeerforckSRoeseSSeubertFDiedrichSKuehnSIn vivo imaging of pancreatic tumours and liver metastases using 7 Tesla MRI in a murine orthotopic pancreatic cancer model and a liver metastases modelBMC Cancer20111114010.1186/1471-2407-11-4021276229, ParteckeIKaedingASendlerMAlbersNKuhnJ-PSpeerforckSRoeseSSeubertFDiedrichSKuehnSIn vivo imaging of pancreatic tumours and liver metastases using 7 Tesla MRI in a murine orthotopic pancreatic cancer model and a liver metastases modelBMC Cancer20111114010.1186/1471-2407-11-4021276229
A. Schmitt, P. Bernhardt, O. Nilsson, H. Ahlman, L. Kölby, H. Maecke, Eva Forssell-Aronsson (2004)
Radiation therapy of small cell lung cancer with 177Lu-DOTA-Tyr3-octreotate in an animal model.Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 45 9
M. Tomayko, C. Reynolds (2004)
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Nitin Mukerji, Dorothy Wallace, Dipayan Mitra (2006)
Audit of the change in the on-call practices in neuroradiology and factors affecting itBMC Medical Imaging, 6
Background: Animal models are frequently used to assess new treatment methods in cancer research. MRI offers a non-invasive in vivo monitoring of tumour tissue and thus allows longitudinal measurements of treatment effects, without the need for large cohorts of animals. Tumour size is an important biomarker of the disease development, −2 −1 but to our knowledge, MRI based size measurements have not yet been verified for small tumours (10 –10 g). The aim of this study was to assess the accuracy of MRI based tumour size measurements of small tumours on mice. Methods: 2D and 3D T2-weighted RARE images of tumour bearing mice were acquired in vivo using a 7 T dedicated animal MR system. For the 3D images the acquired image resolution was varied. The images were exported to a PC workstation where the tumour mass was determined assuming a density of 1 g/cm , using an in-house developed tool for segmentation and delineation. The resulting data were compared to the weight of the resected tumours after sacrifice of the animal using regression analysis. Results: Strong correlations were demonstrated between MRI- and necropsy determined masses. In general, 3D acquisition was not a prerequisite for high accuracy. However, it was slightly more accurate than 2D when small (<0.2 g) tumours were assessed for inter- and intraobserver variation. In 3D images, the voxel sizes could be 3 3 3 3 increased from 160 μm to 240 μm without affecting the results significantly, thus reducing acquisition time substantially. Conclusions: 2D MRI was sufficient for accurate tumour size measurement, except for small tumours (<0.2 g) where 3D acquisition was necessary to reduce interobserver variation. Acquisition times between 15 and 50 minutes, depending on tumour size, were sufficient for accurate tumour volume measurement. Hence, it is possible to include further MR investigations of the tumour, such as tissue perfusion, diffusion or metabolic composition in the same MR session. Keywords: Animal model, Magnetic resonance, Volume determination, Cancer Background inexpensive, and do not require anaesthesia. Studies on The efficacy of new tumour treatment methods are usu- orthotopic and metastatic tumour models have increased ally tested in animal models prior to clinical trials [6-9]. Generally, such studies exclude external volume (e.g. [1-3]). The end-point studied is often the change in measurements and require non-invasive imaging techni- tumour size using mice with subcutaneous xenografts of ques, such as magnetic resonance imaging (MRI), com- human tumour tissue (e.g. [4]). Two or three perpen- puted tomography (CT), positron emission tomography dicular diameters of the tumour are measured and vol- or single photon emission computed tomography ume is calculated under assumptions of e.g. ellipsoidal [10, 11]. tumour shape (e.g. [5]). These methods are fast, MicroCT imaging allows accurate measurement of −2 very small tumour volumes (~10 g) in short acquisi- * Correspondence: mikael.montelius@radfys.gu.se tion time [12], but the radiation exposure might affect Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska tumour growth and thus confound the interpretation of Cancer Centre, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska the treatment results. MRI does not expose the animal University Hospital, Gothenburg SE-413 45, Sweden Full list of author information is available at the end of the article © 2012 Montelius et al. licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Montelius et al. BMC Medical Imaging 2012, 12:12 Page 2 of 7 http://www.biomedcentral.com/1471-2342/12/12 to ionizing radiation and gives no known side effects experiments were based on a respiration triggered and which opens the possibility to follow the same animal by fat suppressed T2-weighted rapid acquisition with relax- consecutive non-invasive measurements in longitudinal ation enhancement (RARE) sequence [22]. A 2D set of studies. In addition, MRI has intrinsic contrast abilities axial images was acquired (TR = 3438–4186 ms, which can be used to assess tumour tissue characteris- TE =30 ms, NSA=2–5, turbo factor Tf = 4, pixel size: eff tics, e.g. diffusion, perfusion and metabolic properties, ~150200 μm , slice thickness = 700 μm) prior to a 3D parameters reflecting cell proliferation, apoptosis, vascu- volume acquisition (TR = 3000 ms, TE =35–82 ms, eff larisation, etc. [11, 13-21]. NSA = 1–2, Tf = 4–14). In the 3D acquisition, the matrix 3 3 Multiparametric MR imaging requires optimised MR size was adjusted to achieve 160 μm voxels with min- methods for minimum time consumption. Small imal field-of-view (FOV) but full tumour coverage. The tumours require careful attention regarding imaging 3D sequence was repeated twice using voxel sizes of 3 3 3 3 parameters due to the partial volume effect (PVE). To 200 μm and 240 μm but with maintained FOV. The our knowledge, there is a lack of information on the ac- methods are henceforth designated 3D-160, 3D-200 and curacy and precision of MRI based size measurements 3D-240, respectively. Saturation slices were always used on small tumours. in the phase encoding direction. The aim of this study was 1) to develop and assess the During the MRI experiments anaesthesia was main- accuracy of an MRI based method for in vivo measure- tained using a mixture of air and isoflurane (1.5–2.0 %) ment of tumour size in a small animal tumour model, (Isoba vet., Schering-Plough Animal-health, Farum, and 2) to investigate the possibility to reduce acquisition Denmark). Body temperature was maintained with a heat- time by reducing image resolution, without affecting the ing pad on the animal bed and a pressure sensitive pad was accuracy. used for respiratory triggering. Methods Image processing and tumour mass estimation Tumour models and experimental setup The image series were processed on a PC workstation The study was performed on 17 tumour bearing female, using an in house developed volume estimation tool athymic BALB/c and SCID mice s.c. inoculated in the implemented in MATLAB (R2008b, The MathWorks, neck/back region with the human midgut carcinoid cell USA). Figure 1 shows the volume determination process. line, GOT1 (n = 15), or the medullary thyroid carcinoma, The average signal from a manually outlined region con- GOT2 (n = 2) under anaesthesia using tribromoethanol taining approximately equal amounts of tumour and (Avertin , Winthrop Laboratories, Surbiton, UK). Ani- background tissue was used for threshold segmentation mals had free access to water and standard diet. (Figure 1b). Successful segmentation was ensured by Within two days after MR imaging, animals were allowing threshold adjustment on an image by image sacrificed by surgical incision of the heart after i.p. injec- basis, and a polygon function could be used to manually tion of sodium pentobarbital (Apoteket Farmaci, Stock- exclude regions where the segmentation had failed holm, Sweden). Gauge block measurements of the (Figure 1b, c). With the tumour border properly defined, length, l, width, w and height, h of the tumours were regions of low intra-tumour signal intensity, erroneously performed. Assuming ellipsoidal tumours, the volume assigned to the background compartment, were con- V=(π/6)lwh, was determined and converted to mass, verted using a growing seed algorithm, and the resulting m , assuming a tumour density of 1.0 g/cm .The image was used for volume calculation (Figure 1b, d). GB tumour was then surgically removed and weighed for The number of tumour voxels, multiplied by the voxel reference tumour mass, m , determination. All proce- volume, was converted to mass assuming a tumour dures were approved by the local Animal ethics commit- density of 1.0 g/cm . The estimated masses from the 2D tee in Gothenburg. and 3D images, denoted m and m respectively, were 2D 3D compared to m using regression analysis. MR imaging The operator determining the masses from the MRI was performed using a 7 T MR system (Bruker repeated scans with increasing voxel sizes (m , m 3D-160 3D- BioSpin MRI GmbH, Ettlingen, Germany; software: , and m ) was blinded to images with higher 200 3D-240 ParaVision 5.0) equipped with water cooled gradient resolution when such existed. coils (maximum gradient strength 400 mT/m). A 72 mm volume coil was used for transmission and an actively Intra- and interobserver variability decoupled 4 channel array rat brain coil was used for Repeated (n = 10) segmentation and mass calculation was signal receiving (RAPID Biomedical GmbH, Rimpar, performed to investigate the intraobserver variability. Germany). Proper animal positioning was assured using Five image series including a small tumour (m = 0.01, a fast gradient-echo localizer scan. All other imaging 2D and 3D-160), a medium sized tumour (m = 0.10 g, T Montelius et al. BMC Medical Imaging 2012, 12:12 Page 3 of 7 http://www.biomedcentral.com/1471-2342/12/12 Figure 1 a) 3D-160 MR image of a subcutaneous GOT1 tumour positioned in the neck of the mouse. The tumour is visible as the hyper-intense region central to the image, marked by the 1 cm vertical bar. b) The global segmentation threshold is applied and regions of failed segmentation appear outside the tumour. c) The manual delineation excludes the areas of failed segmentation, and the final result is shown in d) where white pixels represent the region that will be accounted for as tumour volume. 3D-160) and a large tumour (m = 0.87 g, 2D and 3D-160) Statistics were included in the assessment. The operator was Regression analyses were performed using the Microsoft blinded to the 3D images when evaluating the 2D images. Excel (2003) statistics toolkit. Pearson’s r squared correl- Learning effects were minimized by presenting the image ation coefficient was used. series in a random order and allowing at least several hours to pass between sessions. The coefficient of vari- Results ation (CV) was calculated for each image series. Determination of tumour mass Two additional observers repeated the process on the All tumours found at necropsy were well visualized in five image series to investigate interobserver variations. the MR images. The mass of the resected tumours, m , Their results were compared to the corresponding aver- was between 0.01 g and 2.28 g. Tumours with m <0.2 g age values obtained by observer 1 in the intraobserver are henceforth classified as small. assessment. A strong correlation was found between the tumour mass estimated from the 3D-160 MR images (m ) 3D-160 and m (Figure 2). The correlation was persistent when Estimation of influence of partial volume effect data only from the small tumours (m < 0.2 g) were A simulation was set up using MATLAB to quantify the included. maximum influence that PVE could have on volume estimations based on 3D MRI images of a spherically shaped tumour. Tumour diameter and voxel dimensions were used as input parameters. A 3D grid was used to simulate the image matrix, with grid elements acting as image voxels. A sphere, simulating the tumour, was superimposed on the grid with coinciding origins. Grid elements located inside and outside the sphere were defined as tumour and background elements, respect- ively. Elements intersected by the rim of the sphere were defined as PVE elements. Each one-element thick sec- tion of the grid (representing the image slices) was con- sidered for two extreme cases of segmentation, where PVE elements were classified either as tumour elements, or as background elements, thus overestimating or underestimating the true tumour volume, respectively. Thetotal influenceofPVE on thetumour volumeesti- V V mation was defined as 100 %, where V represents the total volume of tumour elements in the simulation and V is 3 3 3 thetruetumour volume. Voxelsizes of 100 –250 μm and Figure 2 Tumour mass calculated from 3D-160 MR images, m , vs. tumour mass measured after resection, m , n = 17. 3–9 mm (diameter) tumours were simulated, and the 3D-160 T There is a strong correlation between the parameters. The inserted resulting data were presented as relative volume errors figure shows the same data when only small tumours (<0.2 g) are for different number of voxels-per-tumour-diameter included (m = 0.90m + 0.00, R = 0.98, n = 10). 3D-160 T ratios. Montelius et al. BMC Medical Imaging 2012, 12:12 Page 4 of 7 http://www.biomedcentral.com/1471-2342/12/12 In twelve mice, tumours were measured with gauge Intra- and interobserver variability blocks in addition to MRI and digital balance. The cor- The intraobserver variation was in general low for each relation between m and m was strong when data method and tumour size (CV of 2–3 %), but an indica- GB T from all tumours were included (Figure 3a). When ana- tion of increased variability (CV of 7 %) was observed in lysing small tumours only, the correlation was markedly the 2D image set of the smallest tumour (Table 1). reduced (R = 0.65). The same subset of tumours ana- The interobserver results indicated a difference be- lysed with the 3D-160 method yielded R = 0.97. tween the 2D and 3D-160 methods (Table 2). The rela- Comparing m to m (n = 15, one image series was tive deviation from the average mass was higher when 2D T lost, and one did not cover the entire tumour) a strong determined from the 2D images compared to the 3D-160 correlation was obtained (R = 0.99) (Figure 3b). Also for images. The effect was more pronounced for the smallest small tumours a strong correlation was found (R =0.96), tumour size. i.e. the 2D method was comparable to the 3D-160 method regarding accuracy. Computer simulation To exclude the inherent uncertainty of the digital Table 3 shows data from the simulations of the max- balance, m was compared directly to m . The lin- imum possible influence that PVE could have on volume 2D 3D-160 ear relation in the regression and the correlation estimation. The largest relative volume error was ap- approached unity m = 1.01m –0.00, R = 1.00, proximately 40 % for a voxels-per-tumour-diameter ratio 2D 3D-160 n = 15. For small tumours the corresponding relation of 12, corresponding to, e.g., a tumour of 3 mm diameter 2 3 3 was m =1.04 m + 0.00, R = 0.99, n = 9. (~0.01 g) imaged with the voxel size set to 250 μm , i.e. 2D 3D-160 The analysis of m , m ,and m vs. m the smallest tumour included in this study, in combin- 3D-160 3D-200 3D-240 T did not reveal any obvious voxel size dependence in the ation with the largest voxel size used in the imaging range of tumour- and voxel sizes investigated. The ac- experiments. quisition time required for the smallest tumour studied (m = 0.01 g) was clearly reduced with larger voxels: 55, Discussion 3 3 3 3 34 and 22 minutes for the 160 , 200 and 240 μm The present study evaluated the possibilities and require- images, respectively. ments for accurate measurement of small tumour sizes Figure 3 Tumour mass calculated from a) gauge block measurements, m , and b) 2D images, m , vs. tumour mass measured after GB 2D resection, m .a) m vs. m (n = 12). The correlation was strong when all tumour sizes were included (R = 1.0), but was lower in the assessment T GB T of small tumours only (inserted figure; m <0.2 g, n = 9, m = 0.78m + 0.00, R = 0.65). The corresponding correlation for m vs. m for the T GB T 3D-160 T 2 2 same set of tumours was m = 0.93m + 0.00, R = 1.00 (m <0.2 g: m = 0.90m + 0.00, R = 0.97). b) m vs. m (n = 15). 3D-160 T T 3D-160 T 2D T The correlation was strong when all tumour sizes were included (R = 0.99), and persisted in the assessment of small tumours only (inserted figure, m <0.2 g, n = 9, m = 0.94m + 0.00, R = 0.96). The corresponding correlation for m vs. m for the same set of tumours T 2D T 3D-160 T 2 2 was m = 0.93m + 0.01, R = 1.0 (<0.2 g: m = 0.91m + 0.00, R = 0.98). 3D-160 T 3D-160 T Montelius et al. BMC Medical Imaging 2012, 12:12 Page 5 of 7 http://www.biomedcentral.com/1471-2342/12/12 Table 1 The coefficient of variation (CV) calculated for Table 3 Influence of partial volume effect on volume the five intraobserver variability assessments. The CV is determination. The maximum possible influence of the based on 10 volume calculations performed on each of partial volume effect (PVE) on tumour volume the five image series estimations, assuming isotropic voxels and a spherical tumour, determined by simulations m (g) Method CV (%) Voxels/diameter Maximum relative volume difference (%) 0.01 3D-160 3.1 −1 (mm ) overestimated underestimated 2D 6.9 70 6 −6 0.10 3D-160 3.1 45 10 −10 0.87 3D-160 2.1 28 16 −14 2D 1.9 12 39 −31 The voxels-per-tumour-diameter ratio is listed with the corresponding results in mice using MRI. A linear, highly correlated relation- from the simulation. The relative difference between over- and underestimated tumour volumes and the analytically calculated volume are given as a percentage ship between weighed and MRI measured tumour of true tumour volume. −2 masses down to 10 g was found. 3D acquisition should −1 be considered when tumour masses of 10 gram or less In the 3D method, the turbo factor (Tf) was adjusted to are expected, due to the relative increase of PVE. In very −2 reduce acquisition time. An increase in Tf results in an small tumours (10 g) image acquisition at high reso- 3 3 increased point spread function (PSF). Computer simula- lution (in our setup 160 μm voxels) should also be con- tions assuming similar acquisition parameters as those sidered. The increased acquisition time using high used, and T2 values common at 7 T [23] showed that the resolution is compensated for by the smaller FOV needed PSF was broadened only by a factor of 1.6 compared to the to cover the tumour. Short acquisition times allow either value for Tf = 1 (data not shown). The minimum TE is additional MR investigations on the same animal, such as eff also affected by the Tf, i.e. the image contrast will vary determining tumour diffusion, perfusion or metabolic slightly with Tf. However, tumours were always easily visua- parameters within one MR session, or a higher animal lized and, altogether, the range of Tfs used in the study throughput. mightonlyaffect theresults to aminor extent. Initially, the 2D method was included only for anatom- The most time consuming process in the volume de- ical reference since gradient performance limited the termination was probably when adjusting the threshold minimum slice thickness to 700 μm, resulting in sub- value in images where the global segmentation had stantial PVE. However, the accuracy of volume estima- failed. To reduce the time of analysis one could e.g. cal- tions based on the 2D images was similar to that based culate an average volume based on two extreme segmen- on the 3D images (Figures 2 and 3b). This might be due tations; one including most of the border, and one to the possibility to study adjacent image slices, which excluding it. Such a procedure would, however, overesti- probably improves the view of the tumour shape and de- mate the volume, especially for small voxel-to-diameter lineation of the tumour border. However, the 2D method ratios, where asymmetry between over- and underestimated has a higher interobserver variability (Table 2), which volume errors is more pronounced (Table 3). In situations supports the use of a 3D method for very small tumours, when small tumours require polygon delineation of the in order to limit the subjectivity in the evaluation. tumour border, the decision to assign voxels intersected by the polygon line to the tumour or the background compart- ment, will require asymmetry consideration since it might Table 2 Interobserver variation. The interobserver have a significant effect on the volume estimation (Table 3). variation of tumour mass measurements for three The tumour density assumption (1.0 g/cm )might be an observers given as relative deviation (in per cent) from underestimation that would account for the fact that the the average value of the mass (n = 10) obtained by observer 1 for three different tumours and two imaging relations between the determined masses (m ,m 3D-160 2D methods studied and m )and theweight(m ) were less than unity. An- GB T Mean m (g) Method Interobserver variation (%) other contributing factor could be the inherent uncertainty in the digital balance, since errors in the predictor used in Obs1 Obs2 Obs3 regression analysis are typically manifested as a decrease of 0.02 3D-160 − 0.7 −4.7 the slope coefficient towards zero [24]. These two consid- 0.02 2D − 23 11 erations are justified by the fact that the slope was 1.01 0.10 3D-160 − 1.2 −4.3 when m and m were compared directly to each 3D-160 2D 0.91 3D-160 −−1.4 0.1 other, thus excluding the density effect and predictor 0.99 2D − 2.2 −9.0 uncertainties. Montelius et al. BMC Medical Imaging 2012, 12:12 Page 6 of 7 http://www.biomedcentral.com/1471-2342/12/12 Few studies were found in the literature where the ac- Acknowledgements The authors thank Lilian Karlsson and Ann Wikström for excellent animal curacy of MRI based tumour size measurement was veri- handling. This study was supported by grants from the European fied by e.g. comparison with weight after resection. He Commission FP7 Collaborative Project TARCC HEALTH-F2-2007-201962, the et al. found a correlation of R = 0.96 (n = 7) when com- Swedish Research Council, the Swedish Cancer Society, BioCARE - a National Strategic Research Program at University of Gothenburg, the King Gustav V paring volumes of pancreatic tumours in mice from T2- Jubilee Clinic Cancer Research Foundation. The work was performed within weighted 2D MR images (similar to our 2D method), the EC COST Action BM0607. acquired at 4.7 T, for 0.2–2.0 g tumours [6]. We Author details obtained a similar correlation (R = 0.96, n = 9) but for Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska smaller tumours (0.01–0.2 g) since we used six times Cancer Centre, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska smaller voxel volume, i.e. the influence of PVE might be University Hospital, Gothenburg SE-413 45, Sweden. Division of Medical Physics and Medical Engineering, Sahlgrenska University Hospital, comparable. Other groups have reported MRI tumour Gothenburg SE-413 45, Sweden. Department of Radiology, University of size measurements in mice but without verification with Würzburg, Würzburg, Germany. tumour weight, e.g. [7-9]. One group reported MRI mea- Received: 8 June 2011 Accepted: 15 May 2012 surements of tumours in mouse pancreas down to Published: 30 May 2012 0.14 g at 7.0 T using sequence parameters similar to our 2D method (T2-weighted RARE sequence, 0.015 mm References voxels), but verified the volume determination by one 1. Schmitt A, Bernhardt P, Nilsson O, Ahlman H, Kolby L, Maecke HR, phantom measurement only [9]. Forssell-Aronsson E: Radiation Therapy of Small Cell Lung Cancer with Using high resolution microCT (7 min acquisition time, 177Lu-DOTA-Tyr3-Octreotate in an Animal Model. J Nucl Med 2004, 3 3 3 45(9):1542–1548. voxel size of 81 μm , voxel volume of 0.0005 mm )aclose 2. Masciotti J, Provenzano F, Papa J, Klose A, Hur J, Gu X, Yamashiro D, correlation was found for 0.02–0.25 g s.c. tumours in mice, Kandel J, Hielscher A: Monitoring Tumor Growth and Treatment in Small verified by weight after resection (R = 0.97, n = 20) [12]. 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Frost C, Thompson SG: Correcting for regression dilution bias: comparison of methods for a single predictor variable. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2000, 163(2):173–189. doi:10.1186/1471-2342-12-12 Cite this article as: Montelius et al.: Tumour size measurement in a mouse model using high resolution MRI. BMC Medical Imaging 2012 12:12. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit
BMC Medical Imaging – Springer Journals
Published: May 30, 2012
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