Automatic Definition of Planning Target Volume in Computer-Assisted Radiotherapy

Transcription

1 Automatic Definition of Planning Target Volume in Computer-Assisted Radiotherapy Angelo Zizzari Department of Cybernetics, School of Systems Engineering The University of Reading, Whiteknights, PO Box 255 Reading, RG6 6AY, England Abstract This paper describes a supporting tool for medical doctors, providing the automatic definition of radiation volumes (planning target volume, PTV) for conformal radiotherapy computed on detected tumor areas (gross tumor volume, GTV) within radiographic images. First, the recognized brain tumor is geometrically reconstructed and visualized in three-dimensional space. Then, the respective growth dynamics are described by a classical diffusion-reaction equation. An artificial neural network has been used in order to predict the radiation volume according to the detected tumor areas and to the expected growth dynamics. The proposed method extends a former polinomial approach, using some B-splines whose coefficients are defined by an artificial neural network. Keywords: Medical Imaging, Artificial Neural Networks, B-Splines, Computer-Assisted Radiotherapy. 1. Introduction Nowadays, Intensity Modulated Radiotherapy (IMRT) is very accurately applied, either in dose-rate calculations and in patient immobilization techniques [1, 2]. However, the target volume is still manually defined by a medical doctor, and it is subject to large variations from physician to physician and from institute to institute [3, 4]. Investigations have shown that definitions of the same target volume, performed by the same medical doctor at different times, differed significantly. These inaccuracies reduce the appeal of conformal radiotherapy, because they nullify its precision. Target volume is defined in different ways and figure 1 shows a schematic description in case of brain tumors. Based on the actual tumor volume (gross tumor volume, GTV), which is more or less successfully represented in the state of the art CT and MRI scanning techniques [5], a clinical target volume (CTV) is defined, which includes unvisible tumor spread, and a planning target volume (PTV), which includes additional physical parameters, such as motion and positioning inaccuracies. The planning target volume defines the volume to be radiated [6, 7] which, as already mentioned, is subject to large variations. Actually, the automatic generation of such a PTV is considered as an extremely difficult objective. As a first step in this direction, a relatively simple GTV and PTV situation has been chosen in research. It has been considered the glioblastoma multiforme (figure 2), which is a specific type of well-visible brain tumors. Using a standard visible GTV, which is marked in CT and MRI slices by a high contrast zone, the target volume PTV, restricted to few anatomical boundary conditions, is commonly defined as a safety area of approximately 2 cm. The proposal of using an artificial neural network for automatic definition of PTV in radiotherapy has been introduced for the first time in Kaspari et al. [8]. The typical features of artificial neural networks (learning from experience and generalizing from examples) have been considered as the most appropriate for this kind of applications. The preliminary results, described in [8], are just the first step in the direction of automatic definition of PTV. In fact, in the words of the authors : Anatomical constraints for planning target volumes need to be considered in future research work. The tumor can be automatically localized and described by image processing and image enhancement methods at a later stage. Actually, the mentioned problem of tumor localization and geometrical reconstruction has been investigated and solved in previous research work [9, 10]. Furthermore, the second problem of PTV prediction, with concern to the anatomical constraints of the patient,

2 Tumor Brain Figure 1: Schematic view of target volumes for radiation therapy of brain tumors. Figure 2: Glioblastoma multiforme visualized in a radiographic image of the brain. has been here studied and a solution has been proposed. The integration of the tumor growth dynamics [11] has been useful in order to define more effective and automatic radiation plans. 2 System Architecture and Developed Method In this study, a method has been developed in order to improve the performance of the actual radiotherapy planning systems. The architecture of a computer system, implementing the same method, is illustrated in figure 3. The main modules of this computer system are : the module for tumor detection the module for tumor modeling the module for target volume prediction The module for tumor detection in radiographic images [9] introduces a semi-automatic tool supporting the object detection and extraction tasks, with a consequent advantage respect to the level of precision and the required time for the same procedure. The results have been further improved proposing some optimal feature functions on co-occurrence matrix [10]. A specific type of neural network, a Self Organizing Map (SOM) has been used in order to represent and generalize the information associated to the tumor structure and recognize it within the digital radiographic images. The module for tumor modeling introduces a useful tool predicting the tumor growth according to the anatomical conformation of the patient and to the related underlying biological processes [11]. A mathematical and geometrical model, including partial differential equations and tensor product splines, permits a very syntetic and efficient description of the tumor growth. The module for target volume prediction introduces another tool supporting the decisions of medical doctors in the definition of the volume of irradiation for conformal radiotherapy. Some tensor product splines whose coefficients are defined by an artificial neural network lead to the definition of standardized and accurate target volumes. For each single module, a set of images is used for training and tuning, and another set for performance evaluation of the computer system. 2.1 Artificial Neural Networks for Prediction on Time Series Data The prediction of future events from time series data is commonly done using various forms of statistical models [12]. Typical neural network models are closely related to statistical models and estimate Bayesian a posteriori probabilities when given an appropriately formulated problem [13]. For time series prediction, neural networks typically take a delay embedding of previous inputs which is mapped into a prediction. However, high noise, high non-stationarity time series prediction is fundamentally difficult for these models: 1. The problem of learning from examples is fundamentally ill-posed, i.e. there are infinitely many models which fit the training data well, but few of these generalize well. 2. The high noise and small datasets make the models prone to overfitting. Random correlations between the inputs and outputs can present great difficulty. 2.2 The Integrated Model for PTV Prediction In the above preliminary information, the tumor growth dynamics have been considered as a deterministic process, described by a diffusion-reaction equation, according to the initial distribution of tumor cells and to the underlying biological processes of proliferation and diffusion. Furthermore, the employment of an artificial neural network for time series prediction, in case of noisy and non-stationary data, has been introduced and discussed.

3 WORKING Standard Images Tumor Detection Tumor Modeling Target Volume Prediction Test Images Manual Labelling Self Organizing Map Mathematical and Geometrical Model TRAINING Artificial Neural Network Figure 3 : General Architecture of the proposed Radiation Therapy Planning System. In our application, there are two different aspects which are considered: the tumor growth dynamic, which is a partially unknown biological process conducting the patient to a sure death in a short time ; the tumor control action, which is a rational process, delivered by radiotherapy, defined on the basis of human decisions and experience. The basic idea is to introduce, in the actual radiotherapy planning systems, some information regarding the natural process of tumor growth and the rational process of tumor control action, in order to define a more effective and improved treatment against cancer. The tumor growth can be geometrically described as a sequence of surfaces in the three-dimensional space. Here following, our proposed system for automatic PTV prediction is presented and discussed. There are four possible alternative solutions: I. using only the diffusion-reaction equation it is possible to define the future expected tumor growth. If t det is the time at which the tumor has been detected in radiographic images, the equation defines the expected tumor volume at the future time t det +k. This information leads to an estimation of unvisible tumor extensions, and thus to a possible definition of the clinical target volume, CTV. The PTV can be defined just including an additional safety margin ; II. using only the artificial neural network it is possible to define the future expected tumor growth. A set of training samples concerning sequences of tumor surfaces in historical data permit to adapt the internal weights of the neural network in such a way that is possible the defininition of the future expected tumor volume. In a similar fashion, that leads to an estimation of the clinical target volume, CTV, and then, to the planning target volume, PTV ; III. using only the artificial neural network to define the PTV on the basis of the observed GTV (method used by Kaspari et al. [8]). This approach considers the tumor as a static object. The internal weights of the neural network are adapted on the basis of historical data, concerning manual definition of PTV by medical doctors with reference to respective observed GTV. This is a possibility to generalize the expertise of the medical doctors and, at the same time, to provide a specific tool for standardization of PTV definition ; IV. using a combination of diffusion-reaction equation (to define the future expected tumor growth) and artificial neural network (to define the PTV on the same expected tumor growth). That leads to a more realistic integrated model which describes the tumor according to the underlying deterministic biological processes. That makes it possible an estimation of unvisible tumor extensions and thus the definition of the CTV. The consequent PTV prediction is realized training an artificial neural network in order to generalize the expertise of medical doctors.

4 Figure 4 : Schematic view of the use of an artificial neural network for the prediction of control points associated to a tensor product spline. Each, of the abovesaid approaches, presents some advantages and disadvantages. The solutions I and II are more focused on tumor growth dynamics, instead the solutions III and IV consider very important and critical matter the contribution of medical doctors in PTV definition. The solution II presents all the problems and the difficulties described in the above Section 2.1. Instead, the solution IV introduces a significant knowledge in the system, concerning either the natural process of tumor growth and the decisions and experience of the experts. This approach leads to a more effective and appropriate PTV definition. 2.3 Implementation of the Integrated Model In our chosen approach, it has been used a combination of a diffusion-reaction model and a specific type of artificial neural network, a feed forward neural network. The diffusion-reaction equation, with respective initial conditions, describes the future expected tumor growth dynamics. The same equation, with additional constrains related to the anatomical shapes of the patient, is able to describe a more realistic tumor growth. This information leads to an estimation of unvisible tumor extensions, and then, to a definition of the clinical target volume, CTV. The artificial neural network has been used in order to define, on the same computed CTV, the predicted PTV. All the closed volumes considered in this approach, GTV, CTV and PTV, are supposed to have a regular shape, so that their respective surfaces could be effectively described by tensor product splines. The control points Qi,j(CTV) of the tensor product spline, approximating the surface of the computed CTV, are the input information of the neural network (figure 4). Some respective control points Qi,j(PTV), output information of the same neural network, are associated to another tensor product spline, which describes another surface, approximating the predicted PTV. The abovesaid tensor product splines are both defined on the same space of parameters. In order to obtain a significant association between input control points Qi,j (CTV) and output control points Qi,j (PTV), the artificial neural network has been trained using some appropriate training samples. These samples have been extracted from an archive of radiographic images concerning 75 respective patients affected by glioblastoma multiforme. For each patient, a set of digital images (CT-scan) has provided the visual information. Some additional information, the so-called structure, has provided the gross tumor volume (GTV) and the planning target volume (PTV) manually defined by a medical doctor. The association between visual information and GTV has been subject of our previous work [9, 10], in order to obtain a semi-automatic tumor detection in digital images. The association between GTV (automatically detected or manually defined by a medical doctor) and CTV is realized using the a diffusion-reaction equation, which provides an estimation of the future tumor growth dynamics [11]. The computed CTV, approximated by a tensor product spline, provides the control points Qi,j (CTV) of the training samples, input information of the neural network. The approximation of the planning target volume (PTV), manually defined by a medical doctor in the training samples, using again a tensor product spline, provides the control points Qi,j (PTV), output information for the trained neural network. A standard back-propagation algorithm has been used in order to train the artificial neural network. In order to evaluate the level of quality of the proposed method, a set of test samples, extracted from an archive of 30 other patients affected by the same tumor, the glioblastoma multiforme, has been submitted to a similar process.

5 Figure 5 : Experimental results on CT-scan images: lines with crosses are the respective GTV and PTV from a medical doctor, line without crosses is the predicted PTV from the automatic system. Then, the results on the automatic PTV prediction have been compared to independent definition by medical doctors using the same digital images. The experimental results have been quite satisfactory respect to the actual clinical practise. In the following section, more details are presented. 3 Experimental results Some radiographic images (CT-scan), available from real patients, have been considered for prediction of planning target volumes (PTV). The archive consisted of 75 clinical samples, used for training the neural network, and other 30 used for testing the performance. For each patient, a number between 25 and 50 of single CT-scan images has been available with reference to each longitudinal level. The predictions of the PTV were deemed to be acceptable by the team of medical doctors in charge. Table 1 illustrates the results for different numbers of hidden neurons and different sizes of training set. The reported numbers are mean values on the respective data set (training or test set). The mean prediction error of the target volume (represented by the radius R) was specified as a rough performance index respect to a physician s requirement. The mean value was generated across the entire surface (square error). In the right columns of table 1, the results on test data are given. A large training set (250 samples) and a number of 20 hidden neurons appear to be the best choice for accurate PTV predictions. In figure 5 the results are illustrated on CT-scan images for a given glioblastoma-multiforme case within small error margins. The yellow lines describe the respective GTV and PTV manually designed by a medical doctor, the white line is the predicted PTV from the automatic system. These preliminary results have been deemed satisfactory for the medical team working in the Radiotherapy Department of the University of Magdeburg. Some additional effort is necessary in case that the tumor is localized close to the border of the skull. In this situation, the predicted PTV should be refined in order to adapt to the anatomical conformation of the patient. Further research is now addressed in order to provide an automatic refinement according to the tumor location inside the brain of the patient. 4 Discussion The proposed method gives good results for analysed tumor data. It provides the automatic definition of radiation volumes (planning target volume, PTV) for conformal radiotherapy computed on detected tumor areas (gross tumor volume, GTV) within radiographic images. These results could be further improved introducing some different neural network architectures and respective training algorithms. Furthermore, the system for PTV prediction has been designed as part of a larger computer system, providing automatic tumor detection on radiographic images and geometrical reconstruction and modeling of tumor growth dynamics. Training set size # hidden units train test train test train test Table 1: Average Root-Mean-Square Deviation of Radius R [mm] on predicted PTV.

6 The resulting quality level in the entire chain is strongly dependent by the precision and accuracy in each single ring of the same chain. In fact, a small deviation in the detected tumor borderlines propagates itself in the chain as an error in tumor growth dynamics and thus a large deviation in the predicted planning target volume (PTV). The definition of target volumes in radiotherapy is receiving a lot of critical attention in the discussion on conformal radiotherapy. Very few attempts have been made up until now to automize the PTV definition process. The advantages of introducing such a supporting tool in the actual clinical practise would be very significant. It could be possible to increase the level of precision and standardization in the planning target definition and, at the same time, to decrease the amount of effort and time required by medical doctors. Further advantages would be introduced considering the same supporting tool as part of a larger integrated system for computer assisted-radiotherapy. However, the proposed system has been actually designed and realized only for the glioblastoma, a relatively simple target volume. The possibility of extending the field of application, including other organs and tumors has been further considered and it will be subject of future specific research. References [1] C.A. Perez et al., Design of a fully integrated threedimensional computed tomography simulator and preliminary clinical evaluation, Int. J. Radiat. Oncol. Biol. Phys. 30: , [2] W. Schlegel et al., Computer systems and mechanical tools for stereotactical guided conformation therapy with linear accelerators, Int. J. Radiat. Oncol. Biol. Phys. 24: , [3] C.H. Ketting et al., Consistency of three-dimensional planning target volumes across physicians and institutions, Int. J. Radiat. Oncol. Biol. Phys. 37: , [4] G. Leunens et al., Quality assessment of medical decisions making in radiation oncology: variability in target volume delineation for brain tumors. Radiother. Oncol. 29: , [5] R.K. Ten-Haken et al., A quantitative assessment of the addition of MRI to CT-bases, 3D treatment planning of brain tumors. Radiotherapy Oncol. 25: , [6] International Commission on Radiation Units, ICRU Report 50, Prescribing, Recording, and Reporting Photon Beam Therapy, Bethesda, USA, [7] International Commission on Radiation Units, ICRU Report 62, Prescribing, Recording, and Reporting Photon Beam Therapy (Supplement to ICRU Report 50), Bethesda, USA, [8] N. Kaspari, B. Michaelis and G. Gademann, Using an Artificial Neural Network to Define the Planning Target Volume in Radiotherapy, Journal of Medical Systems, Vol. 21, No. 6, [9] A. Zizzari, U. Seiffert, B. Michaelis et al., Detection of Tumor in Digital Images of the Brain. Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications SPPRA 2001, Rhodes, Greece, July 3-6, 2001, pp [10] A. Zizzari, B. Michaelis and G. Gademann, Optimal Feature Functions on Co-occurrence Matrix and Appli-cations in Tumor Recognition, Proceedings of the IASTED International Conference on Applied Simulation and Modelling (ASM 2002) - June 25-28, Crete, Greece. [11] A. Zizzari, B. Michaelis and G. Gademann, Simulation and Modeling of Brain Tumors in Computer-Assisted Radiotherapy, Proceedings of the IASTED International Conference on Applied Simulation and Modelling (ASM 2003), September 3-5, 2003, Marbella, Spain. [12] C.W.J. Granger and P. Newbold. Forecasting Economic Time Series. Academic Press, San Diego, second edition, [13] D.W. Ruck, S.K. Rogers, K. Kabrisky et al.. The multilayer perceptron as an approximation to an optimal Bayes estimator. IEEE Transactions on Neural Networks, 1(4): , 1990.