N.MAFFEI, G.GUIDI, C.VECCHI, G.BALDAZZI Physics Department, University of Bologna, via Irnerio Bologna, Italy

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1 AN ARTIFICIAL NEURAL NETWORK TO PREDICT TIME OF REPLANNING FOR TOMOTHERAPY TREATMENTS N.MAFFEI, G.GUIDI, C.VECCHI, G.BALDAZZI Physics Department, University of Bologna, via Irnerio Bologna, Italy G. GUIDI, A.CIARMATORI, T. COSTI Medical Physics Department,University Hospital Policlinico, Modena Italy, via del Pozzo Modena, Italy Abstract The study has analyzed and validated methods for Adaptive Radiation Therapy (RT), whose goal is the optimization of the daily radiation treatment based on the anatomical variations and patient dosimetry. Using IGRT (Image-Guided Radiation Therapy) techniques, was conducted an analysis of 51 patients treated by Tomotherapy, subdivided by three pathology: Head and Neck (H & N), Prostate Adenocarcinoma (ADK) and Lung Stereotactic Body Radiation Therapy (SBRT). Neural networks, developed and implemented in this work, allow to identify, within the statistical sample, the cases that reveal criticality not in line with the average trend of the patients. Getting information about the complex anatomy affected by deviations from the initial constraints and setting them from the time point of view, it becomes possible to plan the clinical activity and make the methods of warping usable for correction of the daily treatment delivery. Among the various anatomical regions analyzed, the use of Adaptive RT techniques proved to be especially useful for parts subject to temporal variations in the course of the therapy. In particular, parotid glands, rectum, bladder, and lungs were sensitive organs for the study. The work lays the foundation for research studies regarding deformation of organ through biomechanical approach to validate a method intended, presumably in the near future, the real clinical practice. Keywords: Adaptive Radiation Therapy, Neural Network, IGRT, Tomotherapy, Biomechanics. 1

2 1. Introduction Volumetric-spatial changes of Organs At Risk (OAR) and tumor target, during Tomotherapy treatment, can alter the dose distribution with respect to the constraints outlined in the planning stages, causing possible recurrence of toxicity in short and long term. Mathematical methods for the evaluation of organ motion and registration algorithms make it possible to automatically generate hybrid Region Of Interest (ROI) on deformed Megavoltage CT (MVCT) images. To correct these anatomical and dosimetric differences compared to the original plan, it is necessary re-planning therapy. However, taking into account the time and cost for a daily re-planning, it is not, generally, sustainable for all patients in busy clinic. The neural networks presented in this work allow to identify cases that have developed critical volumetric and/or dosimetric variation, during the weeks of radiation treatment, making the Adaptive Radiation Therapy techniques truly applicable and sustainable. 2. Methods and materials 2.1 Clinical Data The work is part of a research project funded by the Ministry of Health and has been developed at the Medical Physics and Radiation Oncology Department of the University Hospital of Modena. The study focuses on the technique of Image Guided Radiation Therapy (IGRT) through analysis of 51 patients divided into three pathologies: H & N, Prostate ADK and Lung SBRT. Within a dedicated experimental software, a hybrid algorithm of deformation automatically generates a mesh grid on the deformed ROI on daily MVCT images, starting from the contours on the kvct image planning. The data are used to extract the volume and dose evolution during the course of therapy. 2.2 Neural Network Architecture To quantify the added value of Adaptive RT techniques, an algorithm has been developed in which the volume and dose differences are identified in comparison with the first day of treatment. The statistical divergence, between the classical treatment plan and its daily optimization, is investigated. We used and unsupervised procedure on the dataset. Into the research, a Cluster Analysis plays a discovery role of latent structures, in order to infer the most likely partition; then, Support Vector Machines (SVM) functions are used to identify its optimal separation hyper-plane. The algorithm was developed and implemented in MATLAB and it is structured in such a way to be able to identify three main phases (Figure 1). The parameters accounted for the analysis of each ROI are the volume (V) and the dose (D). In order to have all the information available, in addition to D average, were considered also the percentages of D relative to the total volume fractions: D 99, D 98, D 2, D 1. 2

3 Figure 1 Neural Network architecture flow chart Step 1: Clustering Weekly matrices of all patients analyzed are given in input and, by setting the ROI and parameters of the k-means algorithm (number of K clusters and distance metric), is obtained an output division of data into K groups, with information of the centroid coordinates, distances between each point from them and the Similarity Index. Step 2: SVM training It instructs the classifier to generate a curve of optimal separation hyper-plane D / V and generate acceptance ranges around the centroids of the clusters. The hyper-plane has been obtained by setting the kernel as a linear function, with a compromise between the complexity of the decision rules based on the learning and performance on generalized samples not yet analyzed. Phase 3: Testing New normalized patient data (in course of therapy) are initialized as input to the classifier. According to the arrangement of the latter in the plane, the algorithm assigns a class to new couples and quantifies the percentage changes in terms of D and V with respect to the patients behavior, emerged during the learning process. 3

4 2.3 Predictive output cases The output of new patient may reveals 4 different placement cases (Table 1) of the parameters compared to 2 cluster: classical treatment plan and simulation of Adaptive RT, identified by the classifier based on the training. A. Predictive. The new patient will follow a weekly trend that is reasonably comparable with the average values of V and D obtained from gold patients post-processed. B. Adaptive. The warping methods highlighting differences in the morphodosimetric plan, provide added value not obtainable with the current gold standard. A physicist-physician meeting would be advisable to discuss a possible therapeutic intervention to reschedule re-planning. C. Error. Output unlikely, due to a bias of the software. D. Warning. The present patient should be monitored carefully. The latter may be characterized by an anatomical behavior or delivered dose out of the ordinary; otherwise, there may be a systematic error of patient positioning during treatment setup. The latter possibility can be identified with a multiple blinds on the behavior of other ROI of the same patient. Case Cluster A: not Deformed (Blue) Cluster B: Deformed (Red) Example images A Inside Inside B Inside Outside C Outside Inside D Outside Outside Table 1 Neural Network Output cases 4

5 3. Results Neural networks have given information related to the usefulness and applicability of the Adaptive RT techniques in the clinical setting. Given the learning phase, obtained by the morphological information of 1500 MVCT analyzed, the implemented algorithm was validated on test patients during treatment: 8 test cases of H&N region and prostate treatment were used. Regarding H&N patients, the Figure 2 shows that in the first 3 weeks of therapy we can be predictive. At least 75% of patients exhibits an output in line with the average behavior of the patients analyzed. From the third week onwards occurs a reversal of the trend. In the last two weeks of therapy, all test patients would need a re-plan intervention due to changes on the body and especially in the parotid glands. The parotid glands are the evidence of a volume reduction of 30%, with an increase in the mean dose of approximately 10%. Figure 1 Test Patients Frequency vs. Week (H&N) Figure 3 Test Patients Frequency vs. Week (ADK) In the case of Prostate ADK is not possible to identify a clear time dependence (Figure 3) due to the fact that this anatomical region is characterized by OAR, as the rectum and bladder, where changes are strictly dependent by the arbitrary degree of readiness with which the individual patient comes to the therapy session (Figures 4 and 5). The methods of warping assume even greater importance in this context since, in the absence of predictive techniques for monitoring, would not be possible to notice any changes if not after the successful delivery of the dose to the patient. Volumetric changes of over 30% and 40% respectively for the rectum and bladder were found for 25% of the patients already in the mid-course of treatment. Figure 4 3D rectum reconstruction with/without adequate clinical preparation Figure 5 3D bladder reconstruction with/without adequate clinical preparation 5

6 4. Conclusion The research work conducted provides a tool that discriminates individuals or treatments not in line with the average patient trend and allows predicting an event or anomaly. The added value inherent the techniques of Adaptive RT, becomes in this way useful in clinical practice, applying it not to the totality of patients undergoing radiation oncology treatments, but targeted to a subset of these. Adaptive RT techniques have proved most useful for organs subject to temporal variations in the course of the therapy. These morphological changes can be analyzed and simulated in order to test the biomechanical prediction of moving target and to validate the clinical data in real-time Radiation Therapy. The realization of anthropomorphic phantoms that simulate the processes of organ deformation and the creation of realistic models relating to the dynamic movement of organs such as the parotid glands, rectum, bladder, ribs and lungs are currently under develop in the areas of experimental research activities suited to a steady increase in accuracy and efficiency for clinical practice of radiotherapy. 5. Acknowledges The research is partially co-funded by the MoH (GR ) and Tecnologie Avanzate S.r.l.(Italy) 6. References [1] [Lee 2008] Lee C, Langeon K M, Lu W, Evaluation of geometric changes of parotid glands during head and neck cancer radiotherapy using daily MVCT and automatic deformable registration, Radiotherapy and Oncology 89, [2] [Gulliford 2004] Gulliford S L, Webb S, Rowbottom C G et al., Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate, Radiotherapy and Oncology 71, [3] [Murphy 2008] Murphy M J, Using Neural Networks to Predict Breathing Motion, 7 th International Conference on Machine Learning and Applications. [4] [Schwartz 2013] Schwartz D L, Garden A S, Shah S J et al., Adaptive Radiotherapy for Head and Neck Cancer: Dosimetric Results from a Prospective Clinical Trial, Radiotherapy and Oncology 106, [5] [Nishi 2013] Nishi T, Nishimura Y, Shibata T, Volume and dosimetric changes and initial clinical experience of a two-step adaptive intensity modulated radiation therapy (IMRT) scheme for head and neck cancer, Radiotherapy and Oncology 106, [6] [Barker 2004] Barker J L, Garden A S, Ang K K et al., Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head and neck cancer using an integrated CT/linear accelerator system, Int. J. Radiation Oncology Biol. Phys., Vol. 59, No. 4, [7] [Scalco 2013] Scalco E, Fiorino C, Cattaneo G M et al., Texture analysis for the assessment of structural changes in parotid glands induced by radiotherapy, Radiotherapy and Oncology 109,