- Email: [email protected]
214. Predictability models for waiting time for radiotherapy treatment
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Abstracts / Physica Medica 56 (2018) 133–278
Fig. 1 TPS and DIAPIX dose map distribution with gamma histogram of two different region of the detector for CK and EBM target and three in latero-lateral directions were necessary to cover the full EBM planning area. Bad pixels in both matrices were masked and they were assigned the average value of neighbour pixels and a global 3 3 pixel filter was applied. Dose distributions were registered with intensity-based algorithms and c-index was evaluated with an in-house developed MATLAB routine and focused on the best performing matrix. The passing rate ðc < 1Þ was evaluated for 3%/3 mm criteria. Results. CK passing rate over the whole dosimeter was 72% and for the EBM was 77%. These values increase to 84% and 85% for CK and EBM respectively just selecting the best performing matrix (Fig. 1). Moreover c analysis showed that pixels corresponding to the edge of the matrix have a different behaviour and excluding them the passing rate increases to 93% for CK and 95% for EBM. Conclusions. DIAPIX, a bi-dimensional pCVD diamond detector was tested with CK and EBM plans. Its intrinsic characteristics and performance make the DIAPIX pCVD diamond matrix a very innovative and promising technology to be used for pre-treatment verification of stereotactic plans. Future plans are focused on improving pixel contacts to get better device performance and uniformity.
Reference 1. Bartoli A, Cupparo I, Baldi A, Scaringella M, Pasquini A, Pallotta S, et al.. Dosimetric characterization of a 2D polycrystalline CVD diamond detector. JINST 2017;12:C03052. https://doi.org/10.1016/j.ejmp.2018.04.224
214. Predictability models for waiting time for radiotherapy treatment M. Rao a, S. Strolin b, S. Ungania b, V. Bruzzaniti b, G. Sanguineti c, L. Strigari b a ENEA, Development of Particle Accelerators and Medical Applications, Frascati, Italy b Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome, Italy c Radiotherapy Department, Regina Elena National Cancer Institute, Rome, Italy
Purpose. This work deal with the waiting time that patient that receive radiotherapy treatments pass in the queue [1], in order to organize treatment processes effectively and efficiently and to ensure both the patient health and to facilities work optimization [2,3]. Methods. Data belong to patients undergoing radiotherapy related to an observation period of 2 years were extrapolated from the record and verified system and classified into 8 macro categories. A simulation on the waiting time estimation was performed for all patients in the dataset. Our system considers: a priority assignment system coded by our institute staff; a characterization of machines and of their operation and maintenance; the historical data about patients and treatments; some different methods for calculating waiting time. Have been tested: a deterministic model, a Hidden Markov Model (HMM) to estimate the component not explained by the above parameters and a model based on a Support Vector Machine (SVM). Results. The first results demonstrate an average accuracy (forecast – real waiting time, measured on historical data) around 5–10 days.
Abstracts / Physica Medica 56 (2018) 133–278
Conclusions. The implications of waiting list for radiotherapy treatment include perceived lower cancer control rates and patient suffering. The model represents a tool for effectively managing the capacity in a radiotherapy department to optimize the waiting lists for treatment.
References 1. Fomundam, S., & Herrmann, J. (2007). A survey of Queuing Theory Applications in healthcare. ISR Technical Report 24. 2. Seshaiah C, Thiagaraj H. A queuing network congestion model in hospitals. Eur J Sci Res 2011;63:419–27. 3. Yasara O. Queuing Models and Capacity Planning. In: Yasara O, editor. Queuing Methods in Health care management. San Francisco: Jossey-Bass; 2009. p. 348–56.
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Results. For all the three cases investigated, the main difference regard dose to D2cc of the rectum. In particular, we observed a percentage difference between 0.6% and 0.2% for AW case, between 4.2% and 0.9% for UDV case and between 4.6% and 0.6% for HUB case. The variation of D100 for TV is about between 0.2% and 0.3% in AW, between 1.8% and 0.3% in UDV and between 1.4% and 0.2% in HUB. Conclusions. From AW, UDV and HUB cases we note it seems to be not necessary to use ACE algorithms in VUB. Using them we can say it is crucial to select the predefined material closer to the electron density of each structure. Furthermore, it is necessary to establish when, for a given structure, it is preferable to use the HU or mass density assignment. For example, an incorrect selection of material could result to percentage differences up to 15% in D2cc of the rectum. https://doi.org/10.1016/j.ejmp.2018.04.226
https://doi.org/10.1016/j.ejmp.2018.04.225
215. Comparison of dose calculation using TG43 dose formalism and Collapsed-Cone Algorithm in vaginal and uterine brachytherapy D. Becci a, A. Bruno a, M.G. Leo a, A. Terlizzi a, G. De Zisa a, D. Mola a, A. Crastolla b, A.R. Marsella b, G. Silvano b a b
ASL Taranto, Medical Physics Unit, Taranto, Italy ASL Taranto, Radiation Oncology Unit, Taranto, Italy
Purpose. The aim of this study is to investigate differences in dose calculation resulting from the use of TG-43 formalism and Collapsed Cone Algorithm in vaginal and uterine brachytherapy (VUB). Methods. In this study we present an experimental evaluation of Oncentra Brachytherapy Advanced Collapsed-Cone Engine (ACE) using three different applicators: vaginal, Fletcher CT/MR with and without the interstitial applicator. We applied ACE algorithm first considering all of these structures composed of water (AW). Then we choose for them an uniform density value (UDV) and finally an HU-based density assignment (HUB). However, in HUB, a value of uniform density has been set for bladder, CTV and brachytherapy applicator. Evaluation has been performed in terms of D0.1 cc, D1cc and D2cc for rectum, sigmoid and bladder and in terms of the dose received from the entire volume (D100) for target volume (TV), in five patients undergoing intracavitary brachytherapy for cervical and uterine cancer over the course of 5 HDR fractions.
216. Biological treatment planning with multiple ion beams O. Sokol a, E. Scifoni b, S. Hild b, M. Durante b, M. Krämer a a
GSI, Biophysics, Darmstadt, Germany Trento Institute for Fundamental Physics and Applications (TIFPA), Istituto Nazionale di Fisica Nucleare (INFN), Trento, Italy b
Purpose. To exploit the potential and different advantages of different ion beams when used at the same time, in a mixed irradiation, accounting for biological effects (relative biological effectiveness (RBE) and oxygen enhancement ratio (OER)) at an advanced level in inverse planning. Methods. Firstly, a biologically oriented treatment planning system for particles (TRiP98) was upgraded with the possibility to perform ‘‘kill painting” [1] in hypoxic targets, i.e. restoring a uniform cell killing in the all over the target, accounting for the differently oxygenated regions. The code is further enabled to perform simultaneous biological optimization of multiple ion species (MIBO), especially tuned for cases of hypoxic tumors. Calculation on idealized geometries as well as on selected patient cases have been considered. Results. A comparative assessment of treatment plans using different ion beams singly or in combinations has been performed. In particular, the use of oxygen beams appears to be more effective when used in combination with lower LET ions like He, rather than using only oxygen beams [2]; improvements up to a 12% relative effect
Abstracts / Physica Medica 56 (2018) 133–278
Fig. 1 TPS and DIAPIX dose map distribution with gamma histogram of two different region of the detector for CK and EBM target and three in latero-lateral directions were necessary to cover the full EBM planning area. Bad pixels in both matrices were masked and they were assigned the average value of neighbour pixels and a global 3 3 pixel filter was applied. Dose distributions were registered with intensity-based algorithms and c-index was evaluated with an in-house developed MATLAB routine and focused on the best performing matrix. The passing rate ðc < 1Þ was evaluated for 3%/3 mm criteria. Results. CK passing rate over the whole dosimeter was 72% and for the EBM was 77%. These values increase to 84% and 85% for CK and EBM respectively just selecting the best performing matrix (Fig. 1). Moreover c analysis showed that pixels corresponding to the edge of the matrix have a different behaviour and excluding them the passing rate increases to 93% for CK and 95% for EBM. Conclusions. DIAPIX, a bi-dimensional pCVD diamond detector was tested with CK and EBM plans. Its intrinsic characteristics and performance make the DIAPIX pCVD diamond matrix a very innovative and promising technology to be used for pre-treatment verification of stereotactic plans. Future plans are focused on improving pixel contacts to get better device performance and uniformity.
Reference 1. Bartoli A, Cupparo I, Baldi A, Scaringella M, Pasquini A, Pallotta S, et al.. Dosimetric characterization of a 2D polycrystalline CVD diamond detector. JINST 2017;12:C03052. https://doi.org/10.1016/j.ejmp.2018.04.224
214. Predictability models for waiting time for radiotherapy treatment M. Rao a, S. Strolin b, S. Ungania b, V. Bruzzaniti b, G. Sanguineti c, L. Strigari b a ENEA, Development of Particle Accelerators and Medical Applications, Frascati, Italy b Laboratory of Medical Physics and Expert Systems, Regina Elena National Cancer Institute, Rome, Italy c Radiotherapy Department, Regina Elena National Cancer Institute, Rome, Italy
Purpose. This work deal with the waiting time that patient that receive radiotherapy treatments pass in the queue [1], in order to organize treatment processes effectively and efficiently and to ensure both the patient health and to facilities work optimization [2,3]. Methods. Data belong to patients undergoing radiotherapy related to an observation period of 2 years were extrapolated from the record and verified system and classified into 8 macro categories. A simulation on the waiting time estimation was performed for all patients in the dataset. Our system considers: a priority assignment system coded by our institute staff; a characterization of machines and of their operation and maintenance; the historical data about patients and treatments; some different methods for calculating waiting time. Have been tested: a deterministic model, a Hidden Markov Model (HMM) to estimate the component not explained by the above parameters and a model based on a Support Vector Machine (SVM). Results. The first results demonstrate an average accuracy (forecast – real waiting time, measured on historical data) around 5–10 days.
Abstracts / Physica Medica 56 (2018) 133–278
Conclusions. The implications of waiting list for radiotherapy treatment include perceived lower cancer control rates and patient suffering. The model represents a tool for effectively managing the capacity in a radiotherapy department to optimize the waiting lists for treatment.
References 1. Fomundam, S., & Herrmann, J. (2007). A survey of Queuing Theory Applications in healthcare. ISR Technical Report 24. 2. Seshaiah C, Thiagaraj H. A queuing network congestion model in hospitals. Eur J Sci Res 2011;63:419–27. 3. Yasara O. Queuing Models and Capacity Planning. In: Yasara O, editor. Queuing Methods in Health care management. San Francisco: Jossey-Bass; 2009. p. 348–56.
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Results. For all the three cases investigated, the main difference regard dose to D2cc of the rectum. In particular, we observed a percentage difference between 0.6% and 0.2% for AW case, between 4.2% and 0.9% for UDV case and between 4.6% and 0.6% for HUB case. The variation of D100 for TV is about between 0.2% and 0.3% in AW, between 1.8% and 0.3% in UDV and between 1.4% and 0.2% in HUB. Conclusions. From AW, UDV and HUB cases we note it seems to be not necessary to use ACE algorithms in VUB. Using them we can say it is crucial to select the predefined material closer to the electron density of each structure. Furthermore, it is necessary to establish when, for a given structure, it is preferable to use the HU or mass density assignment. For example, an incorrect selection of material could result to percentage differences up to 15% in D2cc of the rectum. https://doi.org/10.1016/j.ejmp.2018.04.226
https://doi.org/10.1016/j.ejmp.2018.04.225
215. Comparison of dose calculation using TG43 dose formalism and Collapsed-Cone Algorithm in vaginal and uterine brachytherapy D. Becci a, A. Bruno a, M.G. Leo a, A. Terlizzi a, G. De Zisa a, D. Mola a, A. Crastolla b, A.R. Marsella b, G. Silvano b a b
ASL Taranto, Medical Physics Unit, Taranto, Italy ASL Taranto, Radiation Oncology Unit, Taranto, Italy
Purpose. The aim of this study is to investigate differences in dose calculation resulting from the use of TG-43 formalism and Collapsed Cone Algorithm in vaginal and uterine brachytherapy (VUB). Methods. In this study we present an experimental evaluation of Oncentra Brachytherapy Advanced Collapsed-Cone Engine (ACE) using three different applicators: vaginal, Fletcher CT/MR with and without the interstitial applicator. We applied ACE algorithm first considering all of these structures composed of water (AW). Then we choose for them an uniform density value (UDV) and finally an HU-based density assignment (HUB). However, in HUB, a value of uniform density has been set for bladder, CTV and brachytherapy applicator. Evaluation has been performed in terms of D0.1 cc, D1cc and D2cc for rectum, sigmoid and bladder and in terms of the dose received from the entire volume (D100) for target volume (TV), in five patients undergoing intracavitary brachytherapy for cervical and uterine cancer over the course of 5 HDR fractions.
216. Biological treatment planning with multiple ion beams O. Sokol a, E. Scifoni b, S. Hild b, M. Durante b, M. Krämer a a
GSI, Biophysics, Darmstadt, Germany Trento Institute for Fundamental Physics and Applications (TIFPA), Istituto Nazionale di Fisica Nucleare (INFN), Trento, Italy b
Purpose. To exploit the potential and different advantages of different ion beams when used at the same time, in a mixed irradiation, accounting for biological effects (relative biological effectiveness (RBE) and oxygen enhancement ratio (OER)) at an advanced level in inverse planning. Methods. Firstly, a biologically oriented treatment planning system for particles (TRiP98) was upgraded with the possibility to perform ‘‘kill painting” [1] in hypoxic targets, i.e. restoring a uniform cell killing in the all over the target, accounting for the differently oxygenated regions. The code is further enabled to perform simultaneous biological optimization of multiple ion species (MIBO), especially tuned for cases of hypoxic tumors. Calculation on idealized geometries as well as on selected patient cases have been considered. Results. A comparative assessment of treatment plans using different ion beams singly or in combinations has been performed. In particular, the use of oxygen beams appears to be more effective when used in combination with lower LET ions like He, rather than using only oxygen beams [2]; improvements up to a 12% relative effect