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Radiotherapy Physics
Published in Debbie Peet, Emma Chung, Practical Medical Physics, 2021
Andrea Wynn-Jones, Caroline Reddy, John Gittins, Philip Baker, Anna Mason, Greg Jolliffe
Treatment planning is often performed by a Dosimetrist or Clinical Scientist. Resulting plans will specify the positions of the sealed source and the period of time that the source dwells there, known as the dwell time as described by Haie-Meder et al. (2005) and Poetter et al. (2006). Plans usually start from templates known to provide uniform dose distributions over the intended treatment area based on the geometry of applicators and or needles selected. Optimisation is then achieved by adjusting the source positions and dwell times so that the dose distribution is adapted to the individual anatomy of the specific patient. In common with standard external beam techniques, independent checks are performed on all plans before they are used to treat patients and this task is usually performed by a Clinical Scientist.
Linac-Based SRS/SBRT Dosimetry
Published in Arash Darafsheh, Radiation Therapy Dosimetry: A Practical Handbook, 2021
Karen Chin Snyder, Ning Wen, Manju Liu
MLCs are characterized dosimetrically in the treatment planning system with several measured parameters. The parameters of the MLC that are input in the treatment planning system to be modeled may include inter- and intra-leaf leakage, transmission, as well as leaf tip transmission and penumbra. Ion chamber and films are often used to characterize the MLC [56]. Due to the small size of MLCs used for radiosurgery, films are useful in accurately characterizing the edge and penumbra regions of the MLC. However, small field detectors as well as multi-detector arrays are often used to verify, validate, and modify different MLC modeling parameters to better match measurements. Different treatment planning systems require different measurements. Depending on the treatment planning system different factors can be tweaked.
From model-driven to knowledge- and data-based treatment planning
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Morteza Mardani, Yong Yang, Yinyi Ye, Stephen Boyd, Lei Xing
It is useful to reiterate that treatment planning based on prior knowledge, or historical patient plans, has been discussed previously.20,27–30 These approaches are generally classified into three categories, namely (1) those that predict the weighting factors for different structures using machine learning algorithms, (2) those that generate initial solutions for the “warm startup” of the subsequent optimization, and (3) those that estimate the desired DVH curves to be achieved by the subsequent optimization. It is worth noting that estimating the permissible range of the final plan is only part of the inverse planning problem. Ultimately, the beam parameters that produce the best possible dose distribution are needed for patient treatment. The subsequent search process after knowledge-based plan prediction would still demand manual trial and error.
The risk of radiation-induced neurocognitive impairment and the impact of sparing the hippocampus during pediatric proton cranial irradiation
Published in Acta Oncologica, 2023
Daniel Gram, N. Patrik Brodin, Thomas Björk-Eriksson, Karsten Nysom, Per Munck af Rosenschöld
This study shows that it is possible to reduce the dose to the hippocampus considerably with minimal impact on whole-brain target coverage with IMPT, in particular when inspecting dose-volume histograms. Even with acceptable target coverage, there might, however, be hot- and cold-spots throughout that would affect clinical acceptability, which is why this was explicitly evaluated. The high HI can be explained by the fact that the hippocampus only constitutes roughly 1% of the total irradiated volume. Gondi et al. [27] found that the HS volume with added planning-risk expansion accounted for about 2.1% of the whole-brain in adults. The lowest HS dose constraints tested in this study (5 GyRBE) might be difficult to achieve for some patients, especially depending on tumor location and GTV size. This is in agreement with results from a previous study [6] where plans were not based on a clinical protocol for treatment planning as well as on robust plan optimization. For the 9 GyRBE HS constraint, all plans were deemed clinically acceptable, demonstrating the possibility to lower the mean dose to the hippocampus by 20 GyRBE and still achieve acceptable plans.
Clinicopathological characteristics and risk factors in elderly patients with biopsy-proven IgA nephropathy
Published in Renal Failure, 2022
Jiaxing Tan, Xinyao Luo, Jiaqing Yang, Nuozhou Liu, Zheng Jiang, Yi Tang, Wei Qin
Identifying the risk factors for the disease has an important guiding role in treatment planning. To date, there are some well-recognized risk factors associated with the progression of poor renal outcomes in patients with IgAN, including proteinuria of more than 1 g/24h, sustained hypertension and kidney insufficiency at onset [5–7]. However, the impact of hematuria, aging and sex on the progression of IgAN remains controversial [8–11]. Meanwhile, most of these relevant studies were specifically designed for the evaluation of younger IgAN patients under the age of 50 years old, but no consensus was reached in older patients [9]. Considering the physiological age-related decline in kidney function and other chronic diseases in elderly individuals, it is possible that IgAN patients diagnosed at an advanced age may have other predictive factors than their younger counterparts.
Patient-derived breast model repository, a tool for hyperthermia treatment planning and applicator design
Published in International Journal of Hyperthermia, 2022
Ioannis Androulakis, Kemal Sumser, Melanie N. D. Machielse, Linetta Koppert, Agnes Jager, Remi Nout, Martine Franckena, Gerard C. van Rhoon, Sergio Curto
Figure 5 summarizes treatment planning results for all patients in the repository. The THQ varied between 0.50 and 1.20, with a median of 0.79 (Figure 5(a)). TC25 varied between 96% and 100%, with a median of 100%. TC50 varied between 4% and 100%, with a median of 65%. TC75 varied between 0% and 100%, with a median of 6% (Figure 5(b)). In terms of temperature distribution, the maximum temperature in healthy tissue reached the maximum allowed temperature of 44 °C in 11 cases. The median maximum healthy tissue temperature was 44.0 °C with a range of 42.9–44.0 °C. Water bolus temperature was set to 40 °C in two patients (12 and 22) where the water bolus temperature was affecting the intratumoral temperature, i.e., because the tumor was very close to the skin. In the other 19 patients, the water bolus temperature was kept at 30 °C. The median T10, T50, and T90 values (Figure 5(c)) were 43.5 °C, 42.6 °C, and 41.3 °C, respectively. In one patient with a very superficial tumor, the temperature increase in the tumor was limited by the maximum water bolus temperature, leading to a T90 value barely just below 40 °C (39.8 °C). In general, an adequate temperature (T90 > 40 °C) can be reached in all but one of the patients in the repository.