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British Journal of Radiology (2004) 77, 405-413
© 2004 British Institute of Radiology
doi: 10.1259/bjr/85294528
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Full Paper

Quantification of tumour response to radiotherapy

Q Y Gong, MRad, MD, PhD 1,2, P R Eldridge, FRCS 3 A R Brodbelt, FRCS 3 M García-Fiñana, PhD 1,4,5 A Zaman, PhD 1 B Jones, MD, FRCR 6 and N Roberts, PhD 1

1 Magnetic Resonance and Image Analysis Research Centre (MARIARC), 2 Department of Medical Imaging, 3 Department of Neurosurgery, Walton Centre for Neurology and Neurosurgery, 4 Centre for Medical Statistics and Health Evaluation, University of Liverpool, Liverpool, UK, 5 Department of Mathematics, Statistics and Computation, University of Cantabria, Santander, Spain and 6 Department of Oncology and Radiotherapy, Hammersmith Hospital, London, UK


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Discussion and conclusions
 References
 
In 1979, the World Health Organization (WHO) established criteria based on tumour volume change for classifying response to therapy as (i) progressive disease (PD), (ii) partial recovery (PR), and (iii) no change (NC). Typically, the tumour volume is reported from diameter measurements, using the calliper method. Alternatively, the Cavalieri method provides unbiased volume estimates of any structure without assumptions about its shape. In this study, we applied the Cavalieri method in combination with point counting to investigate the changes in tumour volume in four patients with high grade glioma, using 3D MRI. In particular, the volume of tumour within the enhancement boundary, the enhancing abnormality (EA), was estimated from T1 weighted images, and the volume of the non-enhancing abnormality, (NEA) enhancing abnormality, was estimated from T2 relaxation time and magnetic transfer ratio tissue characterization maps. We compared changes in tumour volume estimated by the Cavalieri method with those obtained using the calliper method. Absolute tumour volume differed significantly between the two methods. Analysis of relative change in tumour volume, based on the WHO criteria, provided a different classification using the calliper and Cavalieri methods. The benefit of the Cavalieri method over the calliper method in the estimation of tumour volume is justified by the following factors. First, Cavalieri volume estimates are mathematically unbiased. Second, the Cavalieri method is highly efficient under an appropriate sampling density (i.e. EA volume estimates can be obtained with a coefficient of error no higher than 5% in 2–3 min). Third, the source of variation of the volume estimates due to disagreements between observers, and within observer, is much greater in the positioning of the calliper diameters than in the identification of the tumour boundaries when applying the Cavalieri method. Additionally, the error prediction formula, available to estimate the coefficient of error of Cavalieri volume estimates from the data, allows us to establish more precise classification criteria against which to identify potentially clinical significant changes in tumour volume.


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Discussion and conclusions
 References
 
High grade glioma represents the most malignant end of the spectrum of neuroglial tumours, accounting for more than 50% of all primary intracranial tumours in adults [1]. Despite current advances in multimodality therapy, i.e. surgery, radiotherapy and chemotherapy, outcome for patients with high grade glioma remains uniformly fatal with a median survival of less than 10 months, and few patients survive beyond 2 years [2]. MRI is established as the most useful imaging modality in the diagnosis of intracranial tumours due to its high soft tissue contrast and multiplanar capability. However, high grade glioma is an infiltrative disease [3–5], and difficulty in identifying tumour margins on standard MR images prevents effective treatment by surgery or radiation therapy. In this study we have applied image analysis and tissue characterization techniques in combination with MRI to monitor the response of cerebral glioma to radiotherapy in an exploratory study of four patients.

Tumour response is conventionally evaluated by measuring the change in the size of the tumour [6] and traditionally this has been via the use of callipers. The current definition of tumour response to treatment advised by the World Health Organization (WHO) [7] is based on the change in the product of two lengths, which correspond to the two perpendicular largest chords that are observed, respectively, on two orthogonal projections of the tumour structure, i.e. via conventional X-ray radiograph. However, with rapid development of imaging techniques such as CT and MRI, 3D information may now be readily obtained. The enhancing abnormality (EA), defined as the portion of contrast-enhanced tumour on post-contrast T1 weighted MR images, is considered a radiological gold standard for defining viable tumour mass. For tumours that are not spheroidal in shape, calliper assessment of tumour response may be unreliable [8], and there have been several reports of monitoring tumour volume changes with image analysis techniques [9–13], but the methods are labour-intensive, time-consuming, operator-dependant and not cost effective. There is therefore no widely accepted method available in clinical practice for assessing serial tumour volume response.

Stereological methods, such as the Cavalieri method, may be used to estimate, under a proper sampling design, geometric parameters of any structure. For example, the Cavalieri estimator is applied in combination with point counting to obtain an unbiased volume estimation of a given structure by estimating the area of interest on systematic and parallel sections through the structure. The high precision of these methods to estimate volume in combination with MRI, has been demonstrated for a variety of biological structures [14–16].

MR tissue characterization techniques, i.e., T2 and magnetization transfer (MT), may also be used to investigate tumour response to therapy. Rutter et al [17] found that tumour T2 values correlated with grade of astrocytoma. More detailed assessment is possible by using pixel by pixel analysis of T2 relaxation time [18] and this approach has been used to quantify lesion load in multiple sclerosis [19]. MT is a method of producing image contrast based on exchange of magnetization between free water and bound water after selectively saturating the magnetization of the latter with an off-resonance pulse [20–22]. Quantification of the magnitude of the effect by calculating the magnetization transfer ratio (MTR) has been reported to produce a valuable measure of the size of the macromolecular component of tissue in studies of malignant tumours in the liver, neck, and brain [20, 21]. In the present study, we used T2 and MTR mapping to assess non-enhancing abnormality (NEA). NEA refers to abnormalities observed on tissue characterization maps surrounding regions of tumour enhancement. We define T2-NEA and MTR-NEA as representing NEA on T2 and MTR maps, respectively. NEA may include oedema, the unenhanced portion of tumour, tumour cell infiltration and haemorrhage. Usually, oedema forms the major part of NEA.

Our objectives were to estimate the volume of EA on contrast enhanced 3D T1-weighted images; and NEA by using T2 relaxation time and MTR pixel by pixel maps with a threshold established from studies of age and sex matched healthy volunteers to define anomalous tissue. Both the Cavalieri and the calliper volume estimators were employed. We investigated the changes in volume of the above structures (i.e. EA, T2-NEA, MTR-NEA) occurring as a result of radiotherapy. Changes were classified, using the WHO criteria, as partial recovery (PR), no change (NC) or progressive disease (PD).


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Discussion and conclusions
 References
 
Subjects
Approval for this study was obtained from the local Ethics Committee. Four patients aged between 45 years and 63 years (mean=54.5 years) were prospectively recruited. Each patient gave fully informed written consent of willingness to participate. All four patients had histopathologically confirmed high grade glioma and an initial Karnofsky score [23] of 70 or above (Table 1Go). Four age and sex matched controls were also studied. All the controls had no history of neurological disorder.


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  		Table 1. Summary of the clinical information for the four cases in the study

 
MR examination
MR images were acquired on a 1.5 T SIGNA whole body imaging system (GE Medical Systems, Milwaukee, USA) using a standard head coil. The protocol included: 1) T1 weighted 3D fast spoiled gradient recalled acquisition in the steady state (FSPGR) sequence (repetition time (TR)/echo time (TE)=2.3/5.2 ms, slice thickness/gap=5/0 mm); 2) T2 weighted fast spin-echo (FSE) sequence (TR/TE=4000/25, 50, 75, 100 ms; slice thickness/gap=5/2.5 mm); 3) proton-density weighted FSE sequence with and without saturation pulse (TR/TE=2000/24 ms, offset frequency 600 Hz; slice thickness/gap=5/2.5 mm); 4) intravenous administration of Gd-DTPA (Magnevist®, Schering AG, Berlin, Germany) with a dose of 0.2 mmol kg–1 body weight; 5) T1 weighted 3D inversion recovery prepared sequence (3D IR-prep SPGR, TR/TE=450/3.5 ms, flip angle=30°, slice thickness/gap=1.6/0 mm). Only parts 1 to 3 were applied in controls.

All MR images were acquired axially with a field of view (FOV) of 20 cm. ANALYZETM image analysis software (Biomedical Imaging Resource, Mayo Foundation, Rochester, Minnesota, USA) was used to process the images. T2 maps were computed from four echoes of multislice 2D FSE images using a log linear least squares fitting algorithm that assumes a mono-exponential decay. MTR maps were computed from multislice 2D FSE images as described by Dousset et al [24]. Examples of T2 and MTR maps, and pre-contrast and post contrast 3D T1 weighted images for Patient 1 are presented in Figure 1Go.



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  		Figure 1. Sample images for case 1. (a) T2 map; (b) magnetization transfer ratio (MTR) map; (c) pre-contrast 3D T1 weighted image; (d) post-contrast 3D T1 weighted image. T2 map (e) and MTR map (f) are thresholded so that the total abnormality is seen within the red shading. Note that stereological test systems for point counting are overlain on the enhancing abnormality (d), T2 total enhancing abnormality (e) and MTR total abnormality (f) with a uniform random position to obtain unbiased volume estimation. Each red cross (+) signifies one test point. Details of the calculation of tumour volume and precision are given in the text.

 
Thresholding T2 and MTR maps
White matter on T2 and MTR maps obtained for the four control subjects was separated from grey matter and cerebrospinal fluid (CSF) by boundary tracing using the region of interest (ROI) method in ANALYZETM. T2 and MTR histograms of these regions were normalized and combined. Mean T2 of normal white matter was 79 ms, with upper 95th percentile of 97 ms. Mean MTR of normal white matter was 0.46, with lower 5th percentile of 0.42. White matter in patients was considered abnormal for pixels with values higher than the 95% confidence limit on T2 maps or lower than the 5% confidence limit on MTR maps.

Volumetric analysis
Cavalieri volume estimator
According to the Cavalieri method, an unbiased estimate of the volume of any structure may be obtained by sectioning it, physically or with a non-invasive technique, with a series of systematic and parallel planes a constant distance T apart. To guarantee that the estimator is unbiased, the position of the first section has to be uniform random within an interval of length T [25, 26]. Volume is obtained by multiplying T by the sum of the section areas of the ROI. Planimetry is the most widely applied method of measuring section area on digital images but the technique of point-counting is generally more efficient without substantial loss of precision. In the point counting technique, a test system, with a square grid of side u, is overlain with uniform random position and new isotropic orientation on consecutive sections [26]. Each of the section areas is estimated by multiplying the area associated with each test point (u2) by the number of points that fall within the ROI on each section ({Pi; i=1,2,...,n}, where n is the total number of sections). Therefore, the Cavalieri volume estimator is To predict the precision of the estimator is a complex problem since the data are dependent. An error prediction formula has been developed based on approximations of the variance expression of the volume estimator and it shows a strong connection with the geometrical properties of the structure [25, 28]. In the present study, the Cavalieri volume estimate was achieved with a purpose built interface within ANALYZETM image software. The number of points falling within the ROI is manually recorded by the operator through clicking a computer mouse. Procedures have been established for determining the optimal number of sections to be analysed, and the optimal number of points to be counted per section, per volume estimate, as well as the method of predicting the coefficient of error (CE) of the volume estimates [25, 27, 28].

Calliper volume estimator
Calliper volume measurements were made by using the calliper device in ANALYZETM software. The calliper volume estimator is defined as , where a is the longest diameter that the structure exhibits in the axial plane, and b, c are the longest diameters perpendicular to a in the axial plane and in the direction perpendicular to it, respectively. Note that this volume estimator is biased for non-spherical structures. We also applied the volume estimator: , valid for structures with an ellipsoidal shape.

Judgement of significance of therapeutic response
WHO criteria for assessing tumour therapeutic response, i.e. NC, PR and PD, are arbitrarily based on percentage change in the product of two orthogonal calliper measurements. PR is defined as a 50% decrease in this parameter and PD as a 25% increase [7]. Assuming that tumours grow or shrink equally in all three dimensions, the WHO criteria can be translated to a decrease in tumour volume >65% for PR, and an increase >40% for PD. We compared the Cavalieri and calliper methods for assessing the significance of the tumour volume changes for our study group.

Worked example
An example of tumour volume estimation is illustrated for Patient 1 at two time points corresponding to 21/2; months (time point 3) and 31/2; months (time point 4) after treatment with radiotherapy (Figure 2Go). Stereological volume estimates of EA were obtained from a systematic series of axial MR images separated by 0.39 cm, i.e. on every fifth image from the reformatted datasets. A test system for point counting, available in ANALYZETM, was overlain with random position on each section with a grid of side equal to 8 and 9 pixels for time point 3 and 4, respectively (which is equivalent to a unit side of 0.625 cm and 0.703 cm, respectively). Figure 2aGo (left and middle columns) illustrates the point counting process for both time points. The number of points (red crosses) at time point 3, recorded as overlying the tumour on consecutive sections, were 3, 10, 21, 33, 38, 38, 26, and 8, which gives a total of 177 counted points. Therefore, the tumour volume estimate for Patient 1, at time point 3, was . The corresponding volume estimate of EA tumour at time point 4 was =41.7 cm3 (Figure 2bGo). The stereological error associated with each of these two volume estimates was predicted as indicated in [27 Section 3.2]. We obtained CE()=3% and 2%, respectively (where CE() denotes the coefficient of error estimate of ). For the calliper method (Figure 2aGo, right columns), the measurements of EA were made using the calliper device in ANALYZETM. The longest diameters that the tumour exhibits on the axial plane (red and blue lines), and on the direction perpendicular to it (green line on a coronal image) were {6.7 cm, 3.9 cm, 3.0 cm} and {7.1 cm, 4.7 cm, 3.4 cm} for time points 3 and 4, respectively. Then, based on the assumption that the tumour has spherical shape, the volume estimate was and =68.1 cm3, for time points 3 and 4, respectively. If we assume that the tumour has ellipsoidal shape, then, and =59.4 cm3, for point times 3 and 4, respectively (Figure 2bGo).



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  		Figure 2. (a) 3D T1 weighted images of patient 1 showing examples of the volume measurement corresponding to (i) the Cavalieri method (left and middle columns) and (ii) the calliper method (right column), where the first and second rows correspond to the third time point, and the third and fourth rows to the fourth time point. (i) A stereological test system for point counting, available in ANALYZETM software, is overlain in four Cavalieri sections with uniform random position to obtain the enhancing abnormality (EA) volume estimation. (ii) Calliper measurements were made using the calliper device in ANALYZETM software. The measurement of the EA included the greatest diameter found on the axial images (red line), the diameter perpendicular to it on the same image (blue line), and the greatest diameter on a coronal image (green line). Details of the calculation of tumour volume and the precision of the Cavalieri method are given in the text. (b) Graph illustrating the mathematical analysis of tumour volume change between two time points according to the Cavalieri and calliper methods (spherical and ellipsoidal models), see worked example. Error bars indicate an approximate confidence interval for the true volume obtained as the Cavalieri volume estimate±twice the predicted standard error (we have assumed that the Cavalieri estimator is normally distributed, however its distribution is still an open problem).

 
Based on WHO criteria, volume change of EA observed between time points 3 and 4 can be classified as PD for the calliper method (ellipsoidal model) and also for the Cavalieri method, since the relative change in volume was higher than 40% (Figure 2bGo). The increase in volume using the spherical model was 39.5%, and therefore, although the volume change is substantial, this case was classified, under the calliper method (spherical model), as NC.

Statistical analysis
An analysis of variance (with S-plus software; StatSci, Washington, USA) was performed to quantify some of the sources of variability affecting the volume estimates. We assessed the error due to the discrepancies between observers (interobserver error), and the error inherent to each observer (intraobserver error), in the identification of tumour boundaries for the Cavalieri estimates, and positioning of the longest chords for the calliper measurements. To perform this analysis, measurements of tumour volume were repeated for two of the patients by two different observers (reproducibility) on two occasions (repeatability), using Cavalieri and calliper methods.

Results
Tumour volume was estimated for all patients by applying the calliper method, under the assumption that the tumour exhibits a spherical and ellipsoidal shape, and also by using the Cavalieri method in conjunction with point counting (Figure 3Go). The volume of EA was obtained by using 3D T1 weighted images, while the volume of EA+NEA required T2 and MTR-maps. The NEA volumes (i.e. T2-NEA and MTR-NEA) were extracted from the total volumes of abnormal tissue on T2 and MTR maps by subtracting the volume of EA (Figure 4Go).



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  		Figure 3. Volume estimates of the enhancing abnormality obtained by the Cavalieri and calliper methods (spherical and ellipsoidal models).

 


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  		Figure 4. Graphs showing serial changes in volume of the enhancing abnormality (EA), T2 non-enhanceing abnormality (NEA) and magnetization transfer ratio (MTR)-NEA for Patient 1 (top left), Patient 2 (top right), Patient 3 (bottom left) and Patient 4 (bottom right). The horizontally shaded bar on the x-axis represents the period of radiotherapy.

 
According to WHO criteria, when the volume was obtained by the calliper method, analysis of the relative change in volume between 15 pairs of consecutive time points indicated 13 NC and 2 PD for the spherical model and 14 NC and 1 PD for the ellipsoidal model (Table 2Go). When tumour volume was estimated by the Cavalieri method we obtained 13 NC and 2 PD. On the other hand, the predicted error of the volume estimates available for the Cavalieri method indicated 5 significant tumour increases and 10 significant tumour decreases.


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  		Table 2. Number of cases with relative change in tumour volume classified as NC, PD or PR, by using Cavalieri method and calliper methods (spherical and ellipsoidal models)

 
Changes in volume of NEA based on T2 maps, indicated 9 NC, and 5 PD (calliper spherical model), and 11 NC and 3 PD (calliper ellipsoidal model), whereas the Cavalieri method provided 7 NC, 4 PD and 3 PR. On the other hand, changes in volume of NEA based on MTR maps, indicated 13 NC and 2 PD (calliper spherical model), and 12 NC and 3 PD (calliper ellipsoidal model), whereas the Cavalieri method provided 12 NC and 3 PR (Table 2Go).

Change in EA volume showed no significant correlation with change in T2 or MTR-NEA volume. MTR-NEA volume is consistently larger than T2-NEA volume during and following radiotherapy (p<0.01), by an average of 11%, 9%, 13% and 18% for cases 1 to 4, respectively. A negative trend was observed between clinical performance status measured by Karnofsky score and NEA volume obtained from either T2 or MTR maps. However, these relationships did not reach statistical significance (p=0.12).

Volume estimates are affected by measurement errors produced by the quantitative method involved in the study. While the error coming from the Cavalieri and point counting methods can be assessed from the data (see worked example), it is difficult to quantify the error of the calliper method without having relevant information about the geometry of the tumour structure. Also, there is an additional error in the volume estimate produced by the observer in the recognition of the boundaries of the structure. Discrepancies between observers, and between measurements by the same observer, are mainly due to inadequate resolution in the MR image, i.e. partial volume effects and inhomogeneities in the image intensity. This limitation of the MRI technique produces a source of bias that is difficult to quantify. Table 3Go shows the coefficient of error of tumour volume estimates due to discrepancies between observers, and within observer. There is a significant disagreement in the identification of the position of the calliper diameters, especially when using T2 and MTR maps. On the other hand, the error produced by the observer in the volume estimates is significantly smaller when applying the point counting technique in the Cavalieri method, although this error should still be taken into consideration. It is reasonable to expect that discrepancies within observer with repeated measures are smaller than discrepancies between observers' measurements. This situation, however, does not hold for the EA volume estimation using the calliper method.


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  		Table 3. Coefficients of error of the tumour volume estimates (%) produced by discrepancies between observers and within observer (intraobserver and interobserver variability)

 

   Discussion and conclusions
Top
 Abstract
 Introduction
 Materials and methods
 Discussion and conclusions
References
 
WHO [7] proposed ad hoc, relatively conservative, criteria for identifying cases where tumour volume has increased or decreased. The Cavalieri method is proposed as an alternative to the conventional calliper method in the estimation of tumour volume for three main reasons. First, the Cavalieri volume estimator is unbiased (volume estimates are located around the true volume). Second, it is highly efficient (CE()<=3% in the worked example) with the additional advantage that the coefficient of error of the volume estimate can be predicted from the data set. And third, discrepancies between observers, and within observer, are much less in the Cavalieri than in the calliper method (Table 3Go). The application of the Cavalieri method for tumour volume estimation has been previously recommended by Bentzen [29]. We recommend additionally that the clinical utility of a mathematical classification of NC, PD and PR, based on the predicted precision of the Cavalieri volume estimates, should be investigated.

The calliper method appeared to be subject to important sources of error. There is a systematic error (bias) due to departure of the real geometry of the tumour structure from the spherical or ellipsoidal models. Unfortunately, there is no error prediction formula available in the calliper method to measure such departure unless additional information on the tumour geometry is considered. A second, and important, source of variability is produced by the observer while positioning the longest chords. Poor reproducibility of the calliper measurement has already been reported by Fornage [8] and Clark et al [6], who demonstrated that variation in the placement of the cursors during the calliper measurement may lead to considerable errors in the resultant measurement, leading to inaccuracy in monitoring tumour response.

T2 and MTR tissue characterization maps may provide additional measures of tumour therapeutic response to that obtained using T1 weighted images with respect to revealing more significant lesion changes, i.e. PR and PD. T2 relaxation time is known to correlate with water content in biological systems while MT contrast is generated by exchange of magnetization between free water and bound water after selectively saturating the magnetization of bound water protons with an off resonance pulse [20–22]. An increase in free water in living tissue will lead to prolongation of T2 and reduction of MTR. Demyelination resulting from tumour infiltration or irradiation [24, 30] and tumour microscopic infiltration or subtle oedema will also cause increase in T2 or decrease in MTR. MTR-NEA volume tends to be larger than T2-NEA volume suggesting the possibility that MTR-NEA is more sensitive in detecting certain underlying pathologies, e.g. tumour microscopic extension, demyelination. Detailed histopathological investigation is required to answer the question of which is the principal factor (e.g. microscopic extension, demyelination, oedema) causing T2-NEA and MTR-NEA changes.

Previously published values of T2 and MTR for normal white matter in the literature are 74±5 ms [31] and 0.43±0.01 [32, 33]. However, both T2 and MTR values presented in this study should be treated as institutional units. It should be borne in mind that the absolute values of T2 and MTR can be variable due to numerous technical factors. MTR for cranial tissues, in particular, may vary with the use of different MT pulse parameters (i.e. pulse duration, pulse amplitude, and offset frequency) and imaging protocols [24, 34]. Recent studies have reported various MTR values for normal white matter of 0.41±0.01 [35], 0.44±0.01 [36] and 0.46±0.01 [37]. Therefore, it is recommended that the imaging protocol, pulse design and parameters are standardized to allow meaningful comparison of the data acquired from different institutions and also to share a range of data established for controls. An additional problem is that white matter in patients was considered abnormal when higher than the 95% confidence limit on T2 maps or lower than the 5% confidence limit on MTR maps, both defined for controls. This procedure creates an additional source of bias since pixels of healthy tissue have a probability greater than or equal to 5% of being classified as abnormal.

Cavalieri and calliper methods provide estimations of tumour volume at specific time points. However, the evolution-curve of tumour volume between these time points remains unknown. In fact, there are an infinitive number of possible trajectories joining two volume points. The clinical significance of the difference in volume and the effect of the treatment are dependent upon which of the unknown trajectories the tumour volume would have followed. Therefore, additional information on tumour evolution up to the time of treatment is desirable for a more reliable and precise classification of the response to treatment.

We have described approaches to assess therapeutic response in patients with high grade glioma. These methods are also applicable to other oncological diseases. Volume estimates of EA and NEA obtained using T2 and MTR mapping in combination with the Cavalieri method and point counting provide objective information for monitoring tumour progression, supplementary to conventional diagnostic imaging reports.


   Acknowledgments
 
We gratefully acknowledge support from the Cancer and Polio Research Fund and from the Endowment Trust Fund of the Walton Centre for Neurology and the Clatterbridge Centre for Oncology, Liverpool, UK. MGF also acknowledges the receipt of a grant from "Secretaría de Estado de Educación y Universidades de España" to visit the University of Liverpool. The authors wish to thank Sister Jane Chance and the radiographers of the Magnetic Resonance and Image Analyses Research Centre for their help in carrying out this study and Dr Mike Puddephat for assistance in computer programming.


   Footnotes
 
Address correspondence to Dr Q Y Gong, Department of Medical Imaging, Faculty of Medicine, University of Liverpool, Liverpool L69 3GB, UK. E-mail: qiyong.gong{at}liv.ac.uk Back

Received for publication January 22, 2003. Revision received September 9, 2003. Accepted for publication October 28, 2003.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Discussion and conclusions
 References
 

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