Variability of Three-Dimensional High-Frequency Ultrasound Measurements of Small Tumor Volumes Lauren A. Hastie, Kevin C. Graham, Alan C. Groom, Ian C. MacDonald, Ann F. Chambers, Aaron Fenster, and James C. Lacefield Robarts Research Institute, London Regional Cancer Centre, and University of Western Ontario London, Ontario, Canada
[email protected] Abstract—The intraobserver variability in volume measurements of small (less than 2 mm3) tumors using three-dimensional highfrequency ultrasound has been assessed in two murine liver metastasis models. The maximum coefficient of variation was 10.7% for five B16F1 murine melanoma liver metastases and 18.2% for seven HT-29 human colon carcinoma liver metastases. The intraobserver variability was small compared to the tumor growth measured at two-day intervals in the rapidly progressing B16F1 model. However, the maximum measurement variability was comparable to the four-day growth rate in the slowly progressing HT-29 model. Keywords-Biomedical ultrasound, three-dimensional imaging, image segmentation, intraobserver variability
I.
INTRODUCTION
As more mouse models of cancer and drug treatments are developed, better techniques are required to monitor and evaluate disease progression [1]. Three-dimensional imaging enables measurement of changes in tumor volume to track progression or regression of cancer in response to drug treatments. Previous studies using high-frequency ultrasound micro-imaging of murine cancer models have demonstrated the feasibility of longitudinal imaging to track tumor progression [2]. Development of this technique was initially limited by a lack of a fast method for generating 3-D images [2]. A new commercial high-frequency scanner with on board 3-D capabilities (Vevo 660, VisualSonics Inc., Toronto, ON, Canada) that became available in 2004 has addressed these limitations. Volume measurement variability is a critical performance characteristic for tumor progression studies, because the variability determines the minimum change in volume that can be confidently measured [3]. This study assessed the intraobserver variability in manually segmenting small (less than 2 mm3) liver metastasis in three-dimensional (3-D) highfrequency ultrasound images. In clinical ultrasound studies, operator variability in volume measurement has been shown to be highest for the smallest lesions [4]. Therefore, the volume measurement variability for small lesions can aid in planning appropriate imaging intervals for progression and treatment response studies in a particular model. L.A.H. is supported by the CIHR/UWO Strategic Training Initiative in Cancer Research and Technology Transfer. K.C.G. is supported by an NSERC Postgraduate Scholarship D. This research is funded by the Ontario Innovation Trust, Canada Foundation for Innovation, Ontario Research and Development Challenge Fund, and in-kind support from VisualSonics Inc.
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II.
METHODS
A. Image Acquisition Three-dimensional imaging was performed using the Vevo 660 ultrasound micro-imaging system, which is similar in design to the instrument described in [5]. A 40 MHz mechanically scanned transducer was used. The resolution of the single-element, 3-mm diameter transducer was 40×60×60 µm3 at the 6 mm focal depth. Images were acquired with the region of interest centered within the depth of focus. The transducer was translated in the elevation dimension and 3-D images of 8×8×6 mm3 volumes were reconstructed from parallel B-mode planes separated by 30 µm. Volumetric image reconstruction, using methods reviewed in [6], was performed in near real time as the B-mode images were acquired. Complete 3-D images were produced in approximately 20 s and could be manipulated and stored within the imaging software. B. Tumor Models All animal experiments were conducted according to protocols approved by the University of Western Ontario Council on Animal Care, Animal Use Subcommittee. Liver metastases were produced by injecting cancer cell lines into the mesenteric vein of female mice. Two cell lines were used to allow for comparison of fast- and slow-growing tumor models. The first model was produced by injecting 3×105 B16F1 murine melanoma cells [7] into the mesenteric vein of four C57bl/6 mice (Harlan, Indianapolis, Indiana). For the second model, metastases were formed by injecting 2×106 HT-29 human colon carcinoma cells into six beige-nude NIH III mice (Charles River, Wilmington, MA). For ultrasound examination, the mice were anesthetized with 1 mL ketamine, 0.25 mL xylazine and 2.5 mL of water given at 0.03 mL/10 g body weight and the abdomen depilated with commercial hair removal cream. The animals were restrained on a heated stage (THM-100, Indus Instruments, Houston, TX) during imaging and images were acquired in the sagittal orientation. The ribs restricted imaging to the caudal regions of the liver. The two most dorsal lobes of the liver could not be imaged from the ventral side of the mouse due to limited penetration of the 40 MHz ultrasound pulses. The animals were imaged every two to four days once tumors developed. Images used to assess the measurement
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variability were of tumors that developed within 12 to 20 days post injection for the B16F1 model and within 33 to 41 days for the HT-29 model.
a)
c)
C. Image Analysis Liver tumors were manually segmented for volume estimation. The borders of each tumor were outlined within the reconstructed volumetric data, as described in [4], in parallel B-mode planes separated by 50 µm. Volumes were calculated by summing the outlined areas and multiplying by the inter-slice distance.
b)
d)
The images used for this study were selected from a larger set based on the definition of small tumors (less than 2 mm3) after an initial segmentation. The images used included multiple tumors within the same mouse and images of the same tumor at different time points. Five B16F1 tumors and seven HT-29 tumors were analyzed to determine the intraobserver variability of volume measurement. Segmentation was repeated five times over several weeks by a non-blinded operator under controlled conditions, including the same monitor and lighting. Means and standard deviations in the repeated volume measurements were computed for each tumor. The intraobserver variability of the tumor measurements was assessed using the standard deviations and coefficients of variation (COV) of the volume measurements. The COV is equal to the standard deviation divided by the mean. III.
RESULTS
Small liver metastases were hypoechoic in both models. The B16F1 tumors were approximately spherical, but the HT-29 tumors developed in a more complex, irregular geometry (Fig. 1). TABLE I.
HT-29
Tumor #1 #2 #3 #4
Table I shows that the range of standard deviations is smaller for the B16F1 tumors (0.02 to 0.10 mm3) than the HT-29 tumors (0.005 to 0.32 mm3). The mean COV for the HT-29 tumors was larger than for the B16F1 model, however, the difference was not significant (p=0.0621) based on a t-test at a 0.05 significance level. The standard deviation is weakly correlated with mean volume (Fig. 2). The correlation coefficient was 0.87 for the B16F1 tumors and 0.72 for the HT-29 tumors. However, neither result was considered significant when testing a null hypothesis of zero correlation at a 0.05 significance level (p=0.055 and p=0.066 respectively). There is no significant correlation between the COV and the mean volumes for either of the tumor lines (Fig. 3). The correlation coefficient is -0.40 (p=0.50) for the B16F1 tumors and -0.33 (p=0.48) for the HT-29 tumors. IV.
TUMOR MEAN VOLUME, STANDARD DEVIATION AND COEFFICIENT OF VARIATION
Cell Line B16F1
Figure 1. a) 2-D view of B16F1 #2 liver metastasis; b) 3-D view of tumor in panel a, showing spherical shape; c) 2-D view of HT-29 #2 liver metastasis; d) 3-D view of tumor in panel c, showing irregular shape. Scale bar is 1 mm.
Mean (mm3) 0.45 0.50 0.16 1.0
Std. Dev. (mm3) 0.029 0.026 0.018 0.038
COV 0.066 0.052 0.11 0.038
#5
1.3
0.104
0.079
µ±σ #1 #2 #3 #4 #5 #6 #7
0.69 ± 0.46 1.1 1.8 1.6 0.027 0.76 0.73 1.7
0.043 ± 0.045 0.05 0.32 0.26 0.0049 0.11 0.10 0.08
0.068 ± 0.026 0.044 0.17 0.16 0.18 0.14 0.14 0.048
µ±σ
1.1 ± 0.67
0.13 ± 0.11
0.13 ± 0.057
µ is the mean and σ is the standard deviation of results for each cell line. COV denotes coefficient of variation. Values are reported to two significant figures
DISCUSSION
It is necessary to be able to distinguish changes in tumor volume in order to assess whether a tumor is progressing, stable, or regressing in response to treatment. These two cell lines produce different intraobserver variability and growth rates, which demonstrates that both the measurement variability and growth rate must be known for a specific model to determine the smallest measurable volume change and an appropriate time interval for longitudinal examinations. Typical growth curves of these two tumors are shown in Fig. 4. Exponential curves fit the data for both tumors (r2 > 0.99). The time constant for the B16F1 growth curve was 1.6 days and the HT-29 growth curve time constant was 6.4 days. The intraobserver variability of the B16F1 tumors is small compared to the growth seen over a two day period. A maximum variability of 10.7% was seen in these tumors, which often double in volume in two days. The HT-29 tumors progress slowly relative to B16F1 tumors. Volume increases as low as 23% over four days were observed with the HT-29 model which was not significantly different from the maximum coefficient of variation, 18.2%. There are several differences between the two cell lines that could lead to differences in volume measurement variability.
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Standard Deviation versus Volume 0.35
0.25 0.20
0.15
30 25 20
15
0.10
10
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5 0 0
0
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1.6
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Coefficient of Variation versus Volume
Coefficient of Variation
4
8
6
10
12
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Figure 4. B16F1 tumors are fast growing and HT-29 tumors are slow growing. Tumors were first imaged on day 0. Each volume is measured a single time as variability data has only been assessed for tumors less than 2 mm3.
identify opportunities for improving the performance of ultrasound micro-imaging for tumor volume measurements by separating the lesion and observer-dependent components [4].
0.20 B16F1 tumors HT-29 tumors
0.18
2
Day
2.0
Figure 2. Standard deviation shows no significant trend with mean volume.
0.16
The intraobserver variability was assessed for only one observer. The observer for this study was also the operator who acquired the images originally, so the measured variability may be optimistic as the observer was very familiar with the appearance and shape of these tumors prior to segmentation. In clinical practice the operator who acquires the image is rarely the observer who assesses the image. To obtain a more general assessment of variability for measuring these tumors, an observer not familiar with these particular images and tumors could be used. In addition, a multi-observer study could be performed to obtain a more accurate estimate of intra- and interobserver variability [3,4].
0.14 0.12 0.10 0.08 0.06 0.04 0.02 0
B16F1 tumors HT-29 tumors
35 Volume (mm)3
Standard Deviation (mm)3
0.30
0
Tumor Growth Curve
40
B16F1 tumors HT-29 tumors
0.2 0.4
0.6
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Mean Volume (mm)3 Figure 3. COV shows no trend with mean volume
The HT-29 metastases form and grow in more complex geometries than the B16F1 cells. This increases the difficulty in segmenting the HT-29 images. If it is known a priori that the lesions are spherical the operator has to make fewer decisions regarding shape and boundary position [8]. As a result, higher overall variability is expected for HT-29 metastases compared to the B16F1 metastases. Within the set of HT-29 images, the sharpness of the margins also varies, which may contribute to the large range of standard deviations and COVs within the HT-29 tumors. Biological variability between tumors resulted in different contrast and shape between tumors of the same model. As a result it is difficult for the operator to consistently define the borders. The observer-dependent component of measurement variability can be decreased by improved image quality, improved segmentation algorithms and increased observer experience. Therefore, an analysis of variance study can
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An additional challenge with animal imaging is that the 3-D images have respiratory motion artifacts that make segmentation more difficult for human observers and automated algorithms. The breathing motion of the liver produces an offset between some B-mode planes. When the tumor moves relative to the 2-D field of view, the observer or algorithm is not able to use the segmentation result from the previous slice to make an initial estimate of the boundary location in neighboring slice. Prospective or retrospective respiratory gating would be desirable and would be expected to reduce volume measurement variability. During the mouse respiratory cycle there is a long period of minimal movement once expiration begins and before the next inhalation commences. This interval of minimal movement can occupy up to two-thirds of the respiratory period [9], so the volume acquisition time should increase by no more than 15 s, if the 3-D micro-imaging system used in this study was employed, with prospective respiratory gating.
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V.
CONCLUSIONS
Longitudinal 3-D ultrasound micro-imaging is a feasible method of tracking tumor progression in mouse liver metastasis models. The two models studied exhibited different tumor growth rates and different volume measurement variabilities, indicating that both parameters must be determined for each model to enable proper planning of progression and treatment response studies. Between the two models, the volume measurement variability was higher in the HT-29 tumors, which grow in irregular shapes and with inconsistently defined margins, than in the B16F1 tumors, which were approximately spherical with well defined margins. Further research is needed to determine if the observed variabilities are sufficiently small to support biological studies of treatment response in B16F1 and HT-29 tumors.
[3]
[4]
[5]
[6]
ACKNOWLEDGMENTS
[7]
The authors thank Lisa T. MacKenzie for assistance with mouse handling and Carl O. Postenka for histology work.
[8]
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