INTRA-OBSERVER REPRODUCIBILITY AND ACCURACY OF

1D, 2D, AND 3D LUNG TUMOUR MEASUREMENTS

6 WEEK PROJECT REPORT

Laura Close

Medical Biophysics 3970Z

The University of Western Ontario

April 1, 2011

1

ACKNOWLEDGEMENTS

Dr. Grace Parraga, for welcoming me into her lab group with the opportunity to perform

an educational six-week research project.

Mr. Amir Owrangi, for all of his help and guidance in terms of answering questions and

offering suggestions that best lead to a successful project.

Ms. Lauren Villemaire, for training me on the software and techniques required to

complete this project, with additional help and guidance throughout.

Mr. Andrew Wheatley, for organizing all requirements in setting me up at Robarts

Research Institute, as well as technical support.

2

ABSTRACT

Development and assessment of lung cancer treatments depend on several factors,

including tumour measurement techniques. With use of X-ray computed tomography images,

1D, 2D, and 3D lung tumour measurement methods are compared. Intra-observer

reproducibility of each method is assessed on two patient tumours at nine time points and three

phantom tumours at four slice thicknesses. Accuracy of each method is additionally assessed on

the phantom tumours. 3D measurements display high intra-observer reproducibility and the

highest potential for accurate ground truth measurement reproduction. This provides insight into

the appropriateness of introduction of 3D measurement methods into clinical settings.

INTRODUCTION

It is known that cancer has become the leading cause of death in Canada, and more

specifically, lung cancer is the leading cause of cancer-related death (1). With such a vast

number of people affected, the importance of research in this area is highly stressed. There are

countless variables contributing to the effectiveness of patient treatment that must be recognized.

Such variables include choice of treatment (e.g. chemotherapy, radiation therapy), the length of

time for which the treatment is administered, and how that treatment is monitored longitudinally.

Imaging can play a very important role both in initial tumour assessment and monitoring changes

over time. In order to best utilize cross-sectional tumour images in obtaining useful data,

appropriate tumour measurement techniques are essential. Measurements have great

implications, as the ability to successfully quantify tumour size can directly impact and improve

upon decisions involving treatment requirements and effectiveness. Although two-dimensional

and one-dimensional measurements have been implemented into clinical settings thus far, three-

dimensional measurements have the greatest potential in terms of accurately incorporating

tumour size properties from all planes. Despite the more apparent benefits of volume

measurements, there are other criteria that must be met in order to ensure three-dimensional

methods are reliable. The main objectives of this project are to:

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1) Determine the intra-observer reproducibility of one-dimensional, two-dimensional, and

three-dimensional measurement methods.

2) Determine the accuracy of one-dimensional, two-dimensional, and three-dimensional

measurements in relation to ground truth measurements.

3) Use these factors to gain insight into whether three-dimensional measurements are

appropriate for use in clinical settings.

THEORY

Methods of Tumour Measurements

Tumour measurement methods have undergone a number of revisions since initial

implementation of such techniques into clinical settings. The most fundamental changes relate to

the number of dimensions incorporated into the measurements. In 1979, the World Health

Organization introduced a 2D measurement defined as the longest diameter of the tumour

multiplied by the longest perpendicular bisector (2). However, there were apparent limitations

with this technique, including the lack of an established minimum lesion size deeming the

technique appropriate, as well as the possibility of longest diameters existing out-of-plane from

the cross-sections being observed (2). This challenged the reproducibility of such measurements

(2). In 2000, Response Evaluation Criteria in Solid Tumours came about, introducing 1D

tumour measurements (2). This overcame certain limitations present with the 2D measurement

technique, including the fact that a minimum lesion size was defined, and higher reproducibility

was exhibited. However, the longest diameter may still exist out-of-plane from the image slices

being observed, and general tumour sphericity is also assumed (2). In order to best overcome the

limitations of 1D and 2D measurements, 3D measurements are ultimately required. An example

of software that has been developed in order to obtain such measurements is 3D Quantify,

created at Robarts Research Institute in London, Ontario. Another software program called

ClearCanvas (ClearCanvas, Inc., Toronto, Canada) is an open-source picture archiving and

communication system (2). As opposed to software such as ClearCanvas, which is capable of

basic 1D and 2D measurements, 3D Quantify contains an algorithm enabling a volume to be

calculated from triangular three-dimensional mesh models of contoured images (2). Three-

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dimensional measurements account for irregular tumour shapes, and asymmetrical growth rates

or shrinkage rates due to treatment, demonstrating superiority in comparison to 1D and 2D

methods.

Imaging Modality

The details of the imaging process performed impact lung tumour images, and thus, the

measurements performed on these images. X-ray computed tomography is of principle use in

tumour measurements (3). It is able to adequately display the contrasted lung tumour boundary

in relation to background lung tissues (2), and advantageously compares to alternate imaging

modalities. For instance, X-ray radiograph displays flat plane limitations and merging of lung

structures from above or under the region of interest (2). X-ray CT addresses such interference

with slice selection. Additionally, a volume of information is available with the addition of a z-

plane, allowing for 3D representations (2). With availability of thinner slice thicknesses relating

to finer resolution in the z-plane, data within the overall volume of information can be rearranged

in order to generate constructed images in several plane orientations. X-ray CT remains to be the

favourable approach in comparison to other advanced imaging techniques as well, such as

magnetic resonance imaging, which demonstrates disadvantages including limited spatial

resolution and cardiac/respiratory motion, hindering detection of smaller lesions (2). Increasing

availability of X-ray CT scanners, in addition to improvements in resolution, have correlated

with increased detection of smaller lesions (4), which exemplifies one of the many important

implications of this imaging modality.

Lung Tumour Properties

Utilizing the best measurement and imaging techniques are important in terms of

grasping all characteristics of the tumours. Lung tumours, in particular, rarely possess a compact

shape, but are often spiculated with spikes or points on the surface (3). Additionally, tumours

may form against the chest wall or other structures, and differing numbers of blood vessels may

surround or grow within them (3). Over a course of treatment, tumour position may be altered

relative to surrounding blood vessels, and the tumour’s ability to attenuate x-rays may change

(3). Phantom tumours, which are artificially-constructed tumours available for imaging in

artificial lungs, are important in mimicking possible patient tumour geometries for research

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purposes. In particular, phantom tumours can be useful in applications in which known tumour

dimensions are needed, such as assessing accuracy of software from which tumour

measurements are calculated.

Reproducibility and Accuracy Analysis

With the introduction of new tumour measurement methods, two important statistical

criteria that must be satisfied include reproducibility and accuracy of the measurements.

Reliability, or reproducibility, refers to how reproducible a measurement is when it is randomly

repeated under the same conditions, often in order to assess performance of human observers (5).

The best method for determining reproducibility is by use of the intra-class correlation

coefficient (ICC) (5). Two types of ICC values exist. If systematic variability due to raters is

considered relevant, this rater variability contributes to the ICC calculation, producing an

absolute agreement ICC value (ICC(A)) (6). If rater variability is considered irrelevant, a

consistency ICC value (ICC(C)) is produced (6). Measurement accuracy can be assessed

through comparison of measured values with a set of ground truth measurements. Presence or

absence of statistical differences in data sets can be detected through two-sample, two-tailed t-

tests, with equal/unequal variances defined by preceding f-tests. ICC values ≥ 0.9 are

recommended for clinical measurements (7), and t-test p-values < 0.05 demonstrate evidence to

reject the notion that the means of the two data sets are equal, with a 5% chance of mistaken

rejection.

APPROACH/METHODS

Conditions

X-ray computed tomography images of lung tumours are acquired. Images at nine time

points of a patient undergoing a course of treatment over roughly two years are obtained, in

which two tumours of differing sizes exist in each image. Additionally, images of three irregular

phantom tumours of differing shapes and sizes at four slice thicknesses are acquired. 1D, 2D,

and 3D measurements of each individual tumour under each condition are performed in two

software programs. 1D and 2D measurements are made in ClearCanvas, while 3D

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measurements are obtained using 3D Quantify. A total of five rounds of measurements are made

for each image, and in order to ensure non-bias, all images are provided completely randomized.

Furthermore, known ground truth measurements of phantom tumours remain secretive at the

time that the measurements are performed, while the patient tumours, due to their nature, have no

such attainable ground truth measurements. One round of measurements per day is not

exceeded. Consistent conditions are also ensured in that images are displayed using the same

display monitor, in a room with consistent low-lighting.

1D Measurements

In order to perform 1D measurements, cross-sectional image slices are manually scrolled

through until the slice with the best potential for containing the longest diameter is visually

depicted. A line measuring this longest diameter is then drawn in ClearCanvas, as displayed in

Figure 1, and a length in centimetres is acquired on the pre-calibrated images.

(a) (b) (c) (d) (e)

Figure 1. 1-Dimensional Measurements. RECIST measurement examples for (a) large patient tumour at time

point 1, (b) small patient tumour at time point 1, (c) large phantom tumour at 0.5mm slice thickness, (d) medium

phantom tumour at 0.5mm slice thickness, and (e) small phantom tumour at 0.5mm slice thickness are displayed.

2D Measurements

2D measurements are also performed in ClearCanvas, in which the initial step in

acquiring the longest diameter measurement is completed in a similar fashion to obtaining the

RECIST measurement. Additionally, a ninety degree angle is measured and the longest

perpendicular diameter is visually depicted and measured, as displayed in Figure 2. The product

of the two diameters results in the WHO measurement.

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(a) (b) (c) (d) (e)

Figure 2. 2-Dimensional Measurements. WHO measurement examples for (a) large patient tumour at time point

1, (b) small patient tumour at time point 1, (c) large phantom tumour at 0.5mm slice thickness, (d) medium phantom

tumour at 0.5mm slice thickness, and (e) small phantom tumour at 0.5mm slice thickness are displayed.

3D Measurements

3D volume measurements are obtained using 3D Quantify. Once a cross-sectional image

slice best depicting the tumour is selected, a user-defined rotational axis is drawn across the

tumour. Next, the boundary of the tumour is traced using a sensor pad and pen. The tumour

image is then rotated eighteen degrees about the previously defined rotational axis, and tracing is

repeated. A total of ten tracings are completed with eighteen degrees of rotation between each

displayed cross-section, as illustrated in Figure 3. A resulting set of contours and corresponding

3D triangular mesh are created, and a volumetric measurement and model are displayed, as

shown in Figure 4.

Figure 3. Generation of a Mesh Model. An example of the

generation of a 3D mesh model for the medium phantom at 0.5mm

slice thickness is displayed. Ten required tumour tracings are shown,

beginning at the top left image, with eighteen degrees of rotation

between each tracing. The final model is indicated in the red circle.

8

(a) (b) (c) (d) (e)

Figure 4. 3-Dimensional Measurements. Volumetric measurement examples for (a) large patient tumour at time

point 1, (b) small patient tumour at time point 1, (c) large phantom tumour at 0.5mm slice thickness, (d) medium

phantom tumour at 0.5mm slice thickness, and (e) small phantom tumour at 0.5mm slice thickness are displayed.

RESULTS

Measured Tumour Size under Varying Conditions

Results obtained are based on a total of 450 measurements consisting of five rounds of

measurements of each tumour (patient/phantom) in each condition (time point/slice thickness).

Figures 5-7 act as visual representations of change in large and small patient tumour size over the

course of treatment lasting almost two years, as seen using RESIST, WHO, and volume

measurements, respectively. Each data point represents an average of five rounds of

measurements, with each error bar representing the corresponding standard deviation. General

decreasing tumour size trends are visually apparent with each measurement technique, with

greatest fluctuations existing in the case of 3D measurements.

(a) (b)

Figure 5. RECIST Measurements over Elapsed Treatment Time. Relationships between mean measured

patient tumour size and time in years using 1-dimensional measurements for (a) large and (b) small tumours are

displayed.

0

1

2

3

4

5

6

0.00 0.50 1.00 1.50 2.00

Mea

n R

EC

IST

Dia

met

er (

cm)

Time (years)

0

0.5

1

1.5

2

2.5

3

0.00 0.50 1.00 1.50 2.00

Mea

n R

EC

IST

Dia

met

er (

cm)

Time (years)

9

(a) (b)

Figure 6. WHO Measurements over Elapsed Treatment Time. Relationships between mean measured patient

tumour size and time in years using 2-dimensional measurements for (a) large and (b) small tumours are displayed.

(a) (b)

Figure 7. Volume Measurements over Elapsed Treatment Time. Relationships between mean measured patient

tumour size and time in years using 3-dimensional measurements for (a) large and (b) small tumours are displayed.

Visually descriptive plots are shown for phantom tumours in Figures 8-9, incorporating

large, medium, and small tumour measurements at four slice thicknesses for 1D, 2D, and 3D

measurement techniques. Again, data points represent averages of five measurement rounds

with corresponding standard deviations represented as error bars. Figure 8 displays RECIST and

WHO measurements, in which no obvious trends can be visually observed in relation to

0

2

4

6

8

10

12

14

16

18

20

0.00 0.50 1.00 1.50 2.00

Mea

n W

HO

(cm

^2

)

Time (years)

0

1

2

3

4

5

6

0.00 0.50 1.00 1.50 2.00

Mea

n W

HO

(cm

^2

)

Time (years)

0

20

40

60

80

100

120

140

160

0.00 0.50 1.00 1.50 2.00

Mea

n V

olu

me

(cm

^3

)

Time (years)

0

5

10

15

20

25

30

0.00 0.50 1.00 1.50 2.00

Mea

n V

olu

me

(cm

^3

)

Time (years)

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increasing slice thickness. Figure 9 displays volume measurements in which positive linear

trends are displayed for each phantom tumour with corresponding coefficients of determination.

In this case, increasing slice thickness correlates with increases in acquired measurements.

(a) (b)

Figure 8. RECIST and WHO Measurements over Increases in Slice Thickness. Relationship between mean

measured phantom tumour size and slice thickness for large, medium, and small phantom sizes using (a) 1-

dimensional measurements and (b) 2-dimensional measurements.

Figure 9. Volume Measurements over Increases in Slice Thickness. Relationship

between mean measured phantom tumour size and slice thickness for large, medium, and

small phantom sizes using 3-dimensional measurements is displayed.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 2 4 6

Mea

n R

EC

IST

Dia

met

er (

cm)

Slice Thickness (mm)

0

2

4

6

8

10

12

14

0 2 4 6

Mea

n A

rea (

cm^

2)

Slice Thickness (mm)

Phantom 1

Phantom 2

Phantom 3

R² = 0.9939

R² = 1

R² = 0.69550

5

10

15

20

25

0 1 2 3 4 5 6

Mea

n V

olu

me

(cm

^3

)

Slice Thickness (mm)

Phantom 1

Phantom 2

Phantom 3

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Intra-Observer Reproducibility

Statistics relating to intra-observer reproducibility are displayed in Tables 1-2, in which

absolute and consistency intra-class correlation coefficients are shown. In Table 1, ICC values

are displayed for the pair of patient tumours at each time point and each measurement method.

In Table 2, ICC values are displayed for the set of phantom tumours at each slice thickness and

each measurement method. In both tables, the values that are ≥ 0.9, as indicated by the lightest

background colour, demonstrate recommended reliability for clinical measurements (7). For

patient tumours in Table 1, all values are clinically acceptable except for those corresponding to

select time points in the case of the 3D measurements. For phantom tumours in Table 2, all

values displayed are clinically acceptable.

1D 2D 3D

ICC(A) ICC (C) ICC(A) ICC (C) ICC(A) ICC (C)

1 0.983 0.983 0.982 0.977 0.98 0.974

2 0.966 0.96 0.999 0.999 0.839 0.888

3 0.971 0.966 0.977 0.996 0.993 0.991

4 0.995 0.996 0.986 0.989 0.992 0.993

5 0.975 0.979 0.991 0.993 0.847 0.849

6 0.975 0.972 0.948 0.958 0.891 0.897

7 0.982 0.982 0.973 0.973 0.718 0.712

8 0.982 0.99 0.972 0.978 0.923 0.925

9 0.987 0.99 0.957 0.957 0.858 0.843

Table 1. Intra-Observer Reproducibility Statistics for Patient Tumours. Absolute and consistent ICC values

for the set of two patient tumours at each time point are displayed.

1D 2D 3D

ICC(A) ICC (C) ICC(A) ICC (C) ICC(A) ICC (C)

0.5mm 0.996 0.997 0.995 0.993 0.997 0.997

1.0mm 0.997 0.998 0.995 0.998 0.997 0.997

2.0mm 1 1 0.991 0.995 0.996 0.997

5.0mm 0.995 0.995 0.993 0.995 0.984 0.997

Table 2 . Intra-Observer Reproducibility Statistics for Phantom Tumours. Absolute and consistent ICC values

for the set of three phantom tumours at each slice thickness are displayed.

Accuracy

Measurement accuracy is depicted in Table 3, in which performed measurements are

compared to the known sets of ground truth measurements for phantom tumours. P-values from

two-tailed t-tests, assuming unequal variance (as all F-test p-values are less than 0.05) are shown

for each phantom tumour at each slice thickness and measurement technique. Using an alpha

ICC Values

≥0.9

≥0.8

≥0.7

ICC Values

≥0.9

≥0.8

≥0.7

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value of 0.05, values with the lightest background colour demonstrate situations for which the

performed measurements are not statistically different from the ground truth measurements.

Such values are only apparent in the case of the 3D measurements.

Slice Thickness Tumour 1D 2D 3D

0.5mm

Large 0.000 0.000 0.008

Medium 0.000 0.000 0.038

Small 0.019 0.000 0.540

1.0mm

Large 0.000 0.000 0.291

Medium 0.000 0.000 0.002

Small 0.001 0.001 0.001

2.0mm

Large 0.000 0.000 0.957

Medium 0.000 0.000 0.008

Small 0.000 0.001 0.010

5.0mm

Large 0.000 0.000 0.020

Medium 0.000 0.000 0.001

Small 0.003 0.000 0.001

Table 3. Accuracy Statistics for Phantom Tumours. P-values obtained by 2-sample, 2-tailed

t-tests assuming unequal variance, comparing each phantom tumour at each slice thickness

against corresponding ground truth measurements are displayed.

DISCUSSION

The results that have been obtained incorporate any error or variation due to every step in

the process of obtaining final measurements including the imaging process, software capabilities,

measurement technique, and performance of the individual. The choice between 1D, 2D, and 3D

measurements methods, however, is shown to be a large factor in influencing tumour size

estimates. In the case of measured tumour size at various time points over a course of treatment,

all methods display general decreasing tumour size trends over time. It is noted, however, that

the volume measurements display the largest fluctuations, or the least consistent decreasing

trends, in comparison to the 1D and 2D measurements. Taking ten dimensions into account, as

opposed to only one, appears to provide a higher likelihood of such fluctuations. Furthermore,

data points displayed with large error bars (standard deviations), likely correspond to less clearly

defined tumour images due to factors such as poor contrast in relation to background structures.

For poorer images, ten boundary estimations likely provide even more room for error in

comparison to straight-forward linear measurements. Though relatively large error bars are

present with the first, and especially the second, time points in the 3D measurements (Figure 7),

p<0.001

0.001<p<0.01

0.01<p<0.05

p>0.05

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this pattern is not consistent, as relatively negligible error bars exist for time points in which

more properly defined images are likely present.

The measured phantom tumours at varying slice thicknesses also display interesting

results. There does not appear to be an obvious effect of slice thickness on 1D and 2D

measurements, while, based on the results, 3D measurements appear to increase as slice

thickness increases. Reasoning behind this perceived increase is somewhat visually apparent in

Figure 10. Increased slice thickness corresponds to blurrier, less defined boundaries. In

performing linear measurements, only a limited portion of the boundary is incorporated into

these measurements, and an increase in blurring does not seem to have a significant effect.

However, while performing 3D measurements, the entire tumour boundary comes into play ten

times, and a blurred boundary may cause a broader outline to be drawn, resulting in possible

overestimation of tumour volume. Other studies support the notion of increases in error with

increases in slice thickness, in which such error is found to be statistically significant (8).

(a) (b) (c)

Figure 10. Slice Thickness Effects on Tumour Appearance. Differences in

tumour appearance between 0.5mm slice thickness (top row) and 5.0mm slice

thickness (bottom row) are indicated for (a) large (b) medium and (c) small phantom

tumours.

High intra-observer reproducibility is an important requirement for any measurement

technique in order to demonstrate reliability required for practical use. Intra-class correlation

coefficients (Table 1) demonstrate that, in the case of the patient tumour measurements, all 1D

and 2D measurements, and four out of the nine 3D measurements display required

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reproducibility for clinical settings. As mentioned earlier, poorly contrasted tumour images can

correspond to increased variability, especially in the case of 3D measurements. Figure 11

demonstrates a time point at which the ICC values fall below the required magnitude.

Boundaries are not clearly defined in these images, as the larger tumour displays similar

attenuation to the surrounding structure, and the smaller tumour displays an unclear boundary

portion along the pleural surface.

(a) (b)

Figure 11. Images from which 3D Reproducibility is

Below Requirement. (a) Large and (b) small patient

tumours at time point 9 are displayed.

Alternatively, ICC values (Table 2) for 1D, 2D, and 3D measurements at all slice

thicknesses for the phantom tumours all demonstrate the required reproducibility for clinical

settings. The phantom tumours have more clearly defined boundaries and act as better

representations of true 3D measurement capabilities in terms of intra-observer reproducibility.

These results are based on less ambiguous measurements.

Accuracy of measurements, as displayed through the ability to recreate ground truth

measurements, is another important requirement that measurement methods in clinical settings

should satisfy. Using an alpha value of 0.05, the only cases in which performed measurements

are not statistically different from ground truth measurements occur under use of the 3D

measurement method (Table 3). If a lower alpha value is hypothetically allowable, an even

larger number of 3D measurements demonstrate accuracy, while the number of measurements

displaying 1D and 2D accuracy remain rather low. Based on these results, 3D measurements

display the greatest potential in terms of accurately defining true tumour size. It is quite likely

that 1D and 2D measurements display poor accuracy due to loss of the longest dimensions in

alternate planes from the cross-sections observed. Furthermore, it is interesting to note that

although only some measurements display accuracy, all phantom tumour measurements

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demonstrate high reproducibility. This indicates that measurements may have been consistent,

but consistently wrong. 1D and 2D measurements likely consist of repeated measurements of

only the longest dimensions that are actually displayed in the cross-sectional images, as opposed

to the true longest dimensions. All tumour measurements may have additionally been affected

by interference of other structures. For instance, airways or blood vessels of similar attenuation

may blend with the tumours and become incorporated into the measurements, causing

misjudgement of true boundaries. Perhaps a more highly trained individual, such as a

radiologist, would better be able to distinguish between such features, and potentially

demonstrate higher accuracy.

That fact that 3D measurements are shown to demonstrate adequate intra-observer

reproducibility and, additionally, display the greatest potential in terms of accurately recreating

ground truth measurements, leads to favouritism of this method. Aside from these factors, 3D

measurements logically have the potential to be favoured as only volume can incorporate all size

characteristics of a tumour, rendering it the truest measurement. The fact that the results

demonstrate accurate measurements of irregularly-shaped phantom tumours is promising, as

patient tumours tend to be of simpler geometries (2).

Tumour measurements have very important implications, as perceived changes in tumour

size can influence treatment plans and development of new therapies, thereby affecting lives.

With the apparent advantages of three-dimensional methods, the question may arise as to why

one-dimensional methods are in current use in clinical settings. One roadblock is overcome with

imaging advances relating to the third dimension. Through availability of thinner slice thickness

in X-ray CT, adequate spatial resolution for volume measurements can be displayed (3).

Another roadblock that is still present, however, is the issue of time-consumption (2). Manual

3D segmentations require much more time than simple linear measurements, which is an

obstacle that had been acknowledged for translation into clinical settings (2). If the advantages

of 3D measurements are stressed, perhaps increased time investment in measurements can be

deemed worthwhile. More realistically, however, mastered development of automated, yet

accurate techniques would be the best candidate for clinical use. This would additionally be

expected to further reduce both intra- and inter-observer variability (2). Regardless, three-

dimensional methods exhibit several advantages.

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CONCLUSION

The main objectives of this project relate to reproducibility and accuracy of lung tumour

measurements, and the implications of these findings. 1D, 2D, 3D measurements all display

high intra-observer reproducibility for use in clinical settings. 3D measurements, however,

display the greatest potential in accurate reproduction of ground truth measurements. These

findings, coupled with the fact that a measurement of volume best represents true tumour

geometry, prove to be very advantageous. Once the drawback of time-consumption is overcome

for reliable, accurate three-dimensional lung tumour measurements, it is hopeful that transition

into clinical settings can be achieved.

REFERENCES

1. The future of cancer control in Canada. (2011). Canadian Partnership Against Cancer.

2. Wilson, L.C.R. (2010). Development of multi-dimensional x-ray computed tomography

measurements of lung tumours (Master’s thesis).

3. Zhao, B., Schwartz, L.H., Moskowitz, C.S., Ginsberg, M.S., Rizvi, N.A., & Kris, M.G.

(2006). Lung cancer: computerized quantification of tumor response. Radiology, 241(3),

892-898.

4. Yankelevitz, D.F., Reeves, A.P., Kostis, W.J., Zhao, B., & Henschke, C.I. (2000). Small

pulmonary nodules: volumetrically determined growth rates based on CT evaluation.

Radiology, 217(1), 251-256.

5. Lu, L., & Shara, N. (2007). Reliability analysis: calculate and compare intra-class

correlation coefficients (ICC) in SAS. Statistics and Data Analysis, 1-4.

6. Nichols, D.P. (1998). Spss library: choosing an intraclass correlation coefficient. UCLA

Academic Technology Services, (67).

7. Portney LG, Watkins MP. Foundations of clinical research: applications to practice.

Norwalk, CT: Appleton & Lange 1993; 505-528.

8. Prionas, N.D., Ray, S., & Boone, J.M. (2010). Volume assessment accuracy in computed

tomography: a phantom study. Journal of Applied Clinical Medical Physics, 11(2), 168-

180.

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APPENDIX

Table A. Patient Tumour Data. Raw measurement data for large and small tumours are displayed with

corresponding means, standards deviations, and coefficients of variation. Patient Tumour (Large) Patient Tumour (Small)

Time

point Round

RECIS

T (cm)

Perpen

dicular

Axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

Time

point Round

RECIS

T (cm)

Perpen

dicular

axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

1 1 5.3 3.4 18.0 99.6 1 1 2.4 2.0 4.8 27.2

2 5.0 3.2 16.0 120.5 2 2.4 2.1 5.0 20.3

3 4.5 3.3 14.9 116.7 3 2.4 2.2 5.3 26.0

4 4.6 3.1 14.3 136.9 4 2.4 2.2 5.3 21.9

5 4.8 3.4 16.3 120.6 5 2.4 2.1 5.0 22.7

Mean 4.8 3.3 15.9 118.9 Mean 2.4 2.1 5.1 23.6

SD 0.3 0.1 1.5 13.3 SD 0.0 0.1 0.2 2.9

CV 0.1 0.0 0.1 0.1 CV 0.0 0.0 0.0 0.1

2 1 5.5 3.4 18.7 125.7 2 1 2.4 2.1 5.0 25.0

2 4.7 4.0 18.8 48.3 2 2.5 2.0 5.0 3.5

3 4.5 4.2 18.9 127.0 3 2.5 2.0 5.0 22.8

4 4.5 4.0 18.0 163.8 4 2.5 2.1 5.3 21.8

5 4.6 4.0 18.4 132.7 5 2.5 2.1 5.3 20.3

Mean 4.8 3.9 18.6 119.5 Mean 2.5 2.1 5.1 18.7

SD 0.4 0.3 0.4 42.7 SD 0.0 0.1 0.1 8.7

CV 0.1 0.1 0.0 0.4 CV 0.0 0.0 0.0 0.5

3 1 3.8 3.4 12.9 26.6 3 1 2.1 1.7 3.6 6.9

2 4.1 3.1 12.7 26.0 2 2.1 1.8 3.8 6.4

3 3.8 3.2 12.2 29.4 3 2.0 1.7 3.4 6.1

4 4.5 3.0 13.5 29.9 4 2.0 1.7 3.4 5.7

5 3.7 3.4 12.6 27.1 5 2.1 1.8 3.8 5.8

Mean 4.0 3.2 12.8 27.8 Mean 2.1 1.7 3.6 6.2

SD 0.3 0.2 0.5 1.7 SD 0.1 0.1 0.2 0.5

CV 0.1 0.1 0.0 0.1 CV 0.0 0.0 0.1 0.1

4 1 4.3 3.0 12.9 65.8 4 1 1.9 1.6 3.0 14.6

2 4.0 2.6 10.4 70.7 2 1.9 1.4 2.7 12.9

3 4.0 2.7 10.8 77.7 3 1.8 1.6 2.9 15.2

4 3.9 2.8 10.9 68.2 4 1.8 1.6 2.9 14.7

5 4.0 2.8 11.2 75.9 5 1.9 1.6 3.0 14.1

Mean 4.0 2.8 11.2 71.6 Mean 1.9 1.6 2.9 14.3

SD 0.2 0.1 1.0 5.0 SD 0.1 0.1 0.2 0.9

CV 0.0 0.1 0.1 0.1 CV 0.0 0.1 0.1 0.1

5 1 3.7 2.1 7.8 11.4 5 1 1.7 1.4 2.4 3.3

2 3.1 2.3 7.1 29.6 2 1.6 1.3 2.1 3.3

3 3.1 2.3 7.1 20.9 3 1.6 1.4 2.2 3.0

4 3.5 2.2 7.7 17.1 4 1.6 1.4 2.2 3.0

5 3.6 2.3 8.3 17.0 5 1.6 1.4 2.2 2.9

Mean 3.4 2.2 7.6 19.2 Mean 1.6 1.4 2.2 3.1

SD 0.3 0.1 0.5 6.7 SD 0.0 0.0 0.1 0.2

CV 0.1 0.0 0.1 0.4 CV 0.0 0.0 0.0 0.1

18

Table A. Patient Tumour Data (Cont.)

Patient Tumour (Large) Patient Tumour (Small)

Time

point Round

RECIS

T (cm)

Perpen

dicular

Axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

Time

point Round

RECIS

T (cm)

Perpen

dicular

axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

6 1 3.3 2.2 7.3 8.8 6 1 1.6 1.1 1.8 2.5

2 3.0 2.2 6.6 16.7 2 1.6 1.3 2.1 2.7

3 2.7 1.7 4.6 12.4 3 1.6 1.0 1.6 2.2

4 3.2 1.9 6.1 10.7 4 1.5 1.3 2.0 2.1

5 3.0 2.1 6.3 9.3 5 1.5 1.3 2.0 2.3

Mean 3.0 2.0 6.2 11.6 Mean 1.6 1.2 1.9 2.4

SD 0.2 0.2 1.0 3.2 SD 0.1 0.1 0.2 0.3

CV 0.1 0.1 0.2 0.3 CV 0.0 0.1 0.1 0.1

7 1 3.5 2.0 7.0 11.6 7 1 1.4 1.2 1.7 2.3

2 3.2 1.8 5.8 5.6 2 1.4 1.2 1.7 2.0

3 2.9 1.8 5.2 3.4 3 1.4 1.2 1.7 2.1

4 3.0 1.9 5.7 12.3 4 1.4 1.2 1.7 1.6

5 3.3 2.0 6.6 12.6 5 1.4 1.2 1.7 2.0

Mean 3.2 1.9 6.1 9.1 Mean 1.4 1.2 1.7 2.0

SD 0.2 0.1 0.7 4.3 SD 0.0 0.0 0.0 0.3

CV 0.1 0.1 0.1 0.5 CV 0.0 0.0 0.0 0.1

8 1 2.8 2.0 5.6 28.4 8 1 1.4 1.1 1.5 7.2

2 2.5 2.3 5.8 55.7 2 1.4 1.1 1.5 7.4

3 2.4 1.8 4.3 41.8 3 1.3 1.1 1.4 7.1

4 2.5 1.9 4.8 45.8 4 1.3 1.1 1.4 5.4

5 2.6 2.1 5.5 37.5 5 1.4 1.2 1.7 5.4

Mean 2.6 2.0 5.2 41.8 Mean 1.4 1.1 1.5 6.5

SD 0.2 0.2 0.6 10.1 SD 0.1 0.0 0.1 1.0

CV 0.1 0.1 0.1 0.2 CV 0.0 0.0 0.1 0.2

9 1 2.8 2.2 6.2 3.8 9 1 1.4 1.1 1.5 2.2

2 2.6 1.8 4.7 8.8 2 1.5 1.1 1.7 2.0

3 2.8 2.3 6.4 6.4 3 1.4 1.1 1.5 2.1

4 2.5 1.9 4.8 8.4 4 1.3 1.1 1.4 1.7

5 2.7 2.3 6.2 7.4 5 1.4 1.1 1.5 1.9

Mean 2.7 2.1 5.6 7.0 Mean 1.4 1.1 1.5 2.0

SD 0.1 0.2 0.9 2.0 SD 0.1 0.0 0.1 0.2

CV 0.0 0.1 0.2 0.3 CV 0.1 0.0 0.1 0.1

19

Table B. Phantom Tumour Data. Raw measurement data for large, medium and small tumours are displayed

with corresponding means, standards deviations, and coefficients of variation.

Slice Thickness = 0.5 mm Slice Thickness = 1.0 mm

Phanto

m Round

RECIS

T (cm)

Perpen

dicular

axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

Phanto

m Round

RECIS

T (cm)

Perpen

dicular

axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

1 1 4.2 2.8 11.8 15.3 1 1 4.2 3.1 13.0 14.5

(large) 2 4.1 2.9 11.9 14.6 (large) 2 4.1 2.9 11.9 16.6

3 4.3 3.1 13.3 16.4 3 4.3 3.1 13.3 17.5

4 4.2 3.0 12.6 15.6 4 4.2 3.1 13.0 16.8

5 4.2 2.9 12.2 15.4 5 4.3 3.1 13.3 15.4

Mean 4.2 2.9 12.4 15.5 Mean 4.2 3.1 12.9 16.2

SD 0.1 0.1 0.6 0.6

SD 0.1 0.1 0.6 1.2

CV 0.0 0.0 0.1 0.0

CV 0.0 0.0 0.0 0.1

2 1 2.6 1.7 4.4 2.8 2 1 2.6 1.7 4.4 3.4

(mediu

m) 2 2.6 1.7 4.4 3.1 (mediu

m) 2 2.5 1.6 4.0 3.2

3 2.6 1.6 4.2 3.7 3 2.6 1.7 4.4 3.6

4 2.5 1.7 4.3 2.9 4 2.6 1.7 4.4 3.3

5 2.6 1.8 4.7 3.3 5 2.7 1.8 4.9 3.0

Mean 2.6 1.7 4.4 3.1 Mean 2.6 1.7 4.4 3.3

SD 0.0 0.1 0.2 0.4 SD 0.1 0.1 0.3 0.2

CV 0.0 0.0 0.0 0.1 CV 0.0 0.0 0.1 0.1

3 1 2.0 1.2 2.4 0.9 3 1 1.9 1.4 2.7 1.8

(small) 2 1.8 1.4 2.5 1.6 (small) 2 1.9 1.2 2.3 1.9

3 2.0 1.2 2.4 1.2 3 1.9 1.5 2.9 1.9

4 2.0 1.2 2.4 1.4 4 2.0 1.3 2.6 2.2

5 2.0 1.2 2.4 1.4 5 1.9 1.3 2.5 2.2

Mean 2.0 1.2 2.4 1.3 Mean 1.9 1.3 2.6 2.0

SD 0.1 0.1 0.1 0.3 SD 0.0 0.1 0.2 0.2

CV 0.0 0.1 0.0 0.2 CV 0.0 0.1 0.1 0.1

20

Table B. Phantom Tumour Data (Cont.)

Slice Thickness = 2.0 mm Slice Thickness = 5.0 mm

Phanto

m Round

RECIS

T (cm)

Perpen

dicular

axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

Phanto

m Round

RECIS

T (cm)

Perpen

dicular

axis

(cm)

WHO

(cm^2)

Volume

(cm^3)

1 1 4.2 3.1 13.0 16.5 1 1 4.2 3.1 13.0 20.5

(large) 2 4.2 2.8 11.8 17.6 (large) 2 4.2 3.0 12.6 21.5

3 4.2 3.0 12.6 17.6 3 4.0 2.9 11.6 23.1

4 4.2 3.1 13.0 16.0 4 4.1 3.0 12.3 17.8

5 4.2 2.7 11.3 16.6 5 4.2 3.2 13.4 18.9

Mean 4.2 2.9 12.3 16.8 Mean 4.1 3.0 12.6 20.4

SD 0.0 0.2 0.8 0.7 SD 0.1 0.1 0.7 2.1

CV 0.0 0.1 0.1 0.0 CV 0.0 0.0 0.1 0.1

2 1 2.6 1.9 4.9 3.3 2 1 2.7 1.8 4.9 5.0

(mediu

m) 2 2.5 1.7 4.3 3.3 (mediu

m) 2 2.5 1.7 4.3 4.3

3 2.6 1.8 4.7 4.3 3 2.6 1.7 4.4 4.9

4 2.6 2.0 5.2 3.8 4 2.7 1.7 4.6 4.8

5 2.6 1.8 4.7 3.4 5 2.7 1.8 4.9 4.0

Mean 2.6 1.8 4.8 3.6 Mean 2.6 1.7 4.6 4.6

SD 0.0 0.1 0.4 0.4 SD 0.1 0.1 0.3 0.4

CV 0.0 0.1 0.1 0.1 CV 0.0 0.0 0.1 0.1

3 1 1.9 1.5 2.9 2.2 3 1 2.0 1.2 2.4 2.0

(small) 2 1.9 1.2 2.3 1.6 (small) 2 1.9 1.2 2.3 3.0

3 1.9 1.4 2.7 2.4 3 2.0 1.2 2.4 2.4

4 1.9 1.3 2.5 1.7 4 1.9 1.2 2.3 2.4

5 1.9 1.3 2.5 2.6 5 2.0 1.2 2.4 2.6

Mean 1.9 1.3 2.5 2.1 Mean 2.0 1.2 2.4 2.5

SD 0.0 0.1 0.2 0.4 SD 0.1 0.0 0.1 0.4

CV 0.0 0.1 0.1 0.2 CV 0.0 0.0 0.0 0.1