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Objective: The aim of this study was to investigate the feasibility of VMAT library-derived model transfer in the prediction of IMRT plans by dosimetry comparison among with three groups of IMRT plans: two groups of automatic IMRT plans generated by the knowledge-based the volumetric modulated arc therapy (VMAT) model and intensity-modulated radiation therapy (IMRT) model and one group of manual IMRT plans., Methods: 52 prostate cancer patients who had completed radiotherapy were selected and randomly divided into 2 groups with 40 and 12 separately. Then both VMAT and IMRT plans were manually designed for all patients. The total plans in the group with 40 cases as training datasets were added to the knowledge-based planning (KBP) models for learning and finally obtained VMAT and IMRT training models. Another 12 cases were selected as the validation group to be used to generated auto IMRT plans by KBP VMAT and IMRT models. At last, the radiotherapy plans from three groups were obtained: the automated IMRT plan (V-IMRT) predicted by the VMAT model, the automated IMRT plan (I-IMRT) predicted by the IMRT model and the manual IMRT plan (M-IMRT) designed before. The dosimetric parameters of planning target volume (PTV) and organ at risks (OARs) as well as the time parameters (monitor unit, MU) were statistically analyzed., Results: The dose limit of all plans in the training datasets met the clinical requirements. Compared with the training plans added to VMAT model, the dosimetry parameters have no statistical differences in PTV (P > 0.05); the dose of X% volume (Dx%) with D25% and D35% in rectal and the maximum dose (Dmax) in the right femoral head were lower (P = 0.04, P = 0.01, P = 0.00) while D50% in rectal was higher (< 0.05) in the IMRT model plans. In the 12 validation cases, both automated plans showed better dose distribution compared with the M-IMRT plan: the Dmax of PTV in the I-IMRT plans and the dose in volume of interesting (VOI) of bladder and bilateral femoral heads were lower with a statistically significant difference (P < 0.05). Compared with the I-IMRT plans, dosimetric parameters in PTV and VOI of all OARs had no statistically significant differences (P > 0.05), but the Dmax in left femoral heard and D15% in the right femoral head were lower and have significant differences (P < 0.05). Furthermore, the low-dose regions, which was defined as all volumes outside of the PTV (RV) with the statistical parameters of mean dose (Dmean), the volume of covering more than 5 Gy dose (V5Gy), and also the time parameter (MU) required to perform the plan were considered. The results showed that Dmean in V-IMRT was smaller than that in the I-IMRT plan (P = 0.02) and there was no significant difference in V5Gy and MU (P > 0.05)., Conclusion: Compared with the manual plan, the IMRT plans generated by the KBP models had a significant advantage in dose control of both OARs and PTV. Compared to the I-IMRT plans, the V-IMRT plans was not only without significant disadvantages, but it also achieved slightly better control of the low-dose region, which meet the clinical requirements and can used in the clinical treatment. This study demonstrates that it is feasible to transfer the KBP VMAT model in the prediction of IMRT plans., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objective . Intensity-modulated radiation therapy (IMRT) aims to distribute a prescribed dose of radiation to cancerous tumors while sparing the surrounding healthy tissue. A typical approach to IMRT planning uniformly divides and allocates the same dose prescription (DP) across several successive treatment sessions. A more flexible fractionation scheme would lend the capability to vary DPs and utilize updated CT scans and future predictions to adjust treatment delivery. Therefore, our objective is to develop optimization-based models and methodologies that take advantage of adapting treatment decisions across fractions by utilizing predictions of tumor evolution. Approach . We introduce a nonuniform generalization of the uniform allocation scheme that does not automatically assume equal DPs for all sessions. We develop new deterministic and stochastic multistage optimization-based models for such a generalization. Our models allow us to simultaneously identify optimal DPs and fluence maps for individual sessions. We conduct extensive numerical experiments to compare these models using multiple metrics and dose-volume histograms. Main results . Our numerical results in both deterministic and stochastic settings reveal the restrictive nature of the uniform allocation scheme. The results also demonstrate the value of nonuniform multistage models across multiple performance metrics. The improvements can be maintained even when restricting the underlying fractionation scheme to small degrees of nonuniformity. Significance . Our models and computational results support multistage stochastic programming (SP) methodology to derive ideal allocation schemes and fluence maps simultaneously. With technological and computational advancements, we expect the multistage SP methodologies to continue to serve as innovative optimization tools for radiation therapy planning applications., (© 2022 Institute of Physics and Engineering in Medicine.)

The aim of this study was to describe a detailed instruction of intensity modulated radiotherapy (IMRT) planning simulation using BEAMnrc-DOSXYZnrc code system (EGSnrc package) and present a new graphical user interface based on MATLAB code (The MathWorks) to combine more than one. 3ddose file which were obtained from the IMRT plan. This study was performed in four phases: the commissioning of Varian Clinac iX6 MV, the simulation of IMRT planning in EGSnrc, the creation of in-house VDOSE GUI, and the analysis of the isodose contour and dose volume histogram (DVH) curve from several beam angles. The plan paramaters in sequence and control point files were extracted from the planning data in Tan Tock Seng Hospital Singapore (multileaf collimator (MLC) leaf positions - bank A and bank B, gantry angles, coordinate of isocenters, and MU indexes). VDOSE GUI which was created in this study can display the distribution dose curve in each slice and beam angle. Dose distributions from various MLC settings and beam angles yield different dose distributions even though they used the same number of simulated particles. This was due to the differences in the MLC leaf openings in every field. The value of the relative dose error between the two dose ditributions for "body" was 51.23 %. The Monte Carlo (MC) data was normalized with the maximum dose but the analytical anisotropic algorithm (AAA) data was normalized by the dose in the isocenter. In this study, we have presented a Monte Carlo simulation framework for IMRT dose calculation using DOSXYZnrc source 21. Further studies are needed in conducting IMRT simulations using EGSnrc to minimize the different dose error and dose volume histogram deviation., (© 2020 Greater Poland Cancer Centre. Published by Elsevier B.V. All rights reserved.)

Purpose: Intensity-Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge-intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge-based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge-based approaches in IMRT and recent clinical validation results., Methods: In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here., Results: The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose-volume points, voxel-level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation., Conclusions: The number of KBP-related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi-institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology., (© 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The purpose of this study is to develop and evaluate a statistics-based model to predict the achievability of the planning criteria for intensity-modulated radiation therapy planning. A statistics-based model was proposed to predict the achievability of the planning criteria based on the structure set. A retrospective study was performed to validate the proposed model. A total of 160 prostate cases and 134 nasopharynx (NP) cases were used as the training set to build the model while 200 cases for each treatment site were used to validate the proposed model. An overlapping ratio and the minimum distance between organ at risks (OARs) and planning target volumes were used to predict the achievability of the planning criteria for serial organs. Since both prostate and NP cases were treated with simultaneous boost using multiple targets with different prescription doses, the effect of each parameter on the OARs was studied by binary logistic regression. For parallel organs, average distance to dose level was introduced and it described the dose falloff gradient within each OAR. By studying the distribution of average distance to dose level in the training set for each OAR, the result was used to predict the dose volume and average dose criteria. The accuracy of the proposed model was evaluated by comparing the predicted proportion and the actual proportion of the validation group that can achieve the clinical planning criteria. For prostate cases, the differences between the actual and predicted proportions were small for all criteria of rectum and bladder. The maximum deviations were 7% for bladder, 9% for rectum, and 5% for femur. For NP cases, the difference ranged from 0% to 7% with the largest difference found at the criterion D50% < 30 Gy of the parotids. In conclusion, the proposed statistics based plan prediction model was presented and the results showed the model could achieve acceptable prediction accuracy. The proposed tools could be used to calculate the probability of the OARs to achieve the clinical goals. This statistics based model can be further developed to adapt into the planning objective estimation in the auto-planning process., (Copyright © 2018 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)