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Contains fulltext : 232797.pdf (Publisher’s version ) (Open Access) PURPOSE: Although various studies have reported that stereotactic body radiation therapy (SBRT) for liver metastases has high local control rates and relatively low toxicity, most series included a small number of patients. We aimed to validate these outcomes in a large multi-institution patient cohort treated in accordance with a common protocol. METHODS AND MATERIALS: A shared web-based registry of patients with liver metastases treated with SBRT was developed by 13 centers (12 in the Netherlands and 1 in Belgium). All the centers had previously agreed on the items to be collected, the fractionation schemes, and the organs-at-risk constraints to be applied. Follow-up was performed at the discretion of the centers. Patient, tumor, and treatment characteristics were entered in the registry. Only liver metastases treated individually as independent targets and with at least 1 radiologic follow-up examination were considered for local control analysis. Toxicity of grade 3 or greater was scored according to the Common Terminology Criteria of Adverse Events (v4.03). RESULTS: Between January 1, 2013, and July 31, 2019, a total of 515 patients were entered in the web-based registry. The median age was 71 years. In total, 668 liver metastases were registered, and 447 were included for local control analysis. The most common primary tumor origin was colorectal cancer (80.3%), followed by lung cancer (8.9%) and breast cancer (4%). The most-used fractionation scheme was 3x18-20 Gy (36.0%), followed by 8x7.5 Gy (31.8%), 5x11-12 Gy (25.5%), and 12x5 Gy (6.7%). The median follow-up time was 1.1 years for local control and 2.3 years for survival. Actuarial 1-year local control was 87%; 1-year overall survival was 84%. Toxicity of grade 3 or greater was found in 3.9% of the patients. CONCLUSIONS: This multi-institutional study confirms the high rates of local control and limited toxicity in a large patient cohort. Stereotactic body radiation therapy should be considered a valuable part of the multidisciplinary approach to treating liver metastases.

Simple Summary In treatment planning for intensity-modulated radiotherapy (IMRT), optimization objectives and beam angle settings are individualized for the anatomy of each patient. This is a complex interactive process that is usually performed by a treatment planner. In this study, a novel system for automated optimization of IMRT plans with integrated beam angle optimization (BAO) was developed, and used to systematically investigate the impact of selected beam angles on treatment plan quality for locally advanced non-small cell lung cancer (LA-NSCLC). Automatically generated plans were of a higher quality than the manually generated, clinically delivered plans, while dramatically reducing the planning workload. The study demonstrates the potential for automated planning with integrated BAO to enhance radiotherapy for LA-NSCLC patients. Abstract In this study, the novel iCE radiotherapy treatment planning system (TPS) for automated multi-criterial planning with integrated beam angle optimization (BAO) was developed, and applied to optimize organ at risk (OAR) sparing and systematically investigate the impact of beam angles on radiotherapy dose in locally advanced non-small cell lung cancer (LA-NSCLC). iCE consists of an in-house, sophisticated multi-criterial optimizer with integrated BAO, coupled to a broadly used commercial TPS. The in-house optimizer performs fluence map optimization to automatically generate an intensity-modulated radiotherapy (IMRT) plan with optimal beam angles for each patient. The obtained angles and dose-volume histograms are then used to automatically generate the final deliverable plan with the commercial TPS. For the majority of 26 LA-NSCLC patients, iCE achieved improved heart and esophagus sparing compared to the manually created clinical plans, with significant reductions in the median heart Dmean (8.1 vs. 9.0 Gy, p = 0.02) and esophagus Dmean (18.5 vs. 20.3 Gy, p = 0.02), and reductions of up to 6.7 Gy and 5.8 Gy for individual patients. iCE was superior to automated planning using manually selected beam angles. Differences in the OAR doses of iCE plans with 6 beams compared to 4 and 8 beams were statistically significant overall, but highly patient-specific. In conclusion, automated planning with integrated BAO can further enhance and individualize radiotherapy for LA-NSCLC.

Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries.

Background and purpose: To explore the use of texture analysis (TA) features of patients' 3D dose distributions to improve prediction modelling of treatment complication rates in prostate cancer radiotherapy. Material and methods: Late toxicity scores, dose distributions, and non-treatment related (NTR) predictors for late toxicity, such as age and baseline symptoms, of 351 patients of the hypofractionation arm of the HYPRO randomized trial were used in this study. Apart from DVH parameters, also TA features of rectum and bladder 3D dose distributions were used for predictive modelling of gastrointestinal (GI) and genitourinary (GU) toxicities. Logistic Normal Tissue Complication Probability (NTCP) models were derived, using only NTR parameters, NTR + DVH, NTR + TA, and NTR + DVH + TA. Results: For rectal bleeding, the area under the curve (AUC) for using only NTR parameters was 0.58, which increased to 0.68, and 0.73, when adding DVH or TA parameters respectively. For faecal incontinence, the AUC went up from 0.63 (NTR only), to 0.68 (+ DVH) and 0.73 (+ TA). For nocturia, adding TA features resulted in an AUC increase from 0.64 to 0.66, while no improvement was seen when including DVH parameters in the modelling. For urinary incontinence, the AUC improved from 0.68 to 0.71 (+ DVH) and 0.73 (+ TA). For GI, model improvements resulting from adding TA parameters to NTR instead of DVH were statistically significant (p

The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset.

While interior-point methods share the same fundamentals, the implementation determines the actual performance. In order to attain the highest efficiency, different applications may require differently tuned implementations. In this paper we describe an implementation specifically designed for optimisation in radiation therapy. These problems are large-scale nonlinear (and sometimes nonconvex) constrained optimisation problems, consisting of both sparse and dense data. Several application-specific properties are exploited to enhance efficiency. Permuting, tiling and mixed precision arithmetic allow the algorithm to optimally process the mixed dense and sparse data matrices (making this step 2.2 times faster, and overall runtime reduction of $$55\%$$ ) and scalability (16 threads resulted in a speed-up factor of 9.8 compared to singlethreaded performance, against a speed-up factor of 7.7 for the less optimised implementation). Predefined cost-functions are hard-coded and the computationally expensive second derivatives are written in canonical form, and combined if multiple cost-functions are defined for the same clinical structure. The derivatives are then computed using a scaled matrix–matrix product. A cheap initialisation strategy based on the background knowledge reduces the number of iterations by $$11\%$$ . We also propose a novel combined Mehrotra–Gondzio approach. The algorithm is extensively tested on a dataset consisting of 120 patients, distributed over 6 tumour sites/approaches. This test dataset is made publicly available.

For conventional radiotherapy treatment units, automated planning can significantly improve plan quality. For robotic radiosurgery, systems for automatic generation of clinically deliverable plans do not yet exist. For prostate stereotactic body radiation therapy (SBRT), few studies have systematically compared VMAT with robotic treatment.The multi-criteria autoplanning optimizer, developed at our institute, was coupled to the commercial treatment planning system of our robotic treatment unit, for fully automated generation of clinically deliverable plans (autoROBOT). The system was then validated by comparing autoROBOT plans with manually generated plans. Next, the autoROBOT system was used for systematic comparisons between autoROBOT plans and VMAT plans, that were also automatically generated (autoVMAT). CTV-PTV margins of 3 mm were used for autoROBOT (clinical routine) and autoVMAT plan generation. For autoVMAT, an extra plan was generated with 5 mm margin (often applied for VMAT). Plans were generated for a 4 × 9.5 Gy fractionation scheme.Compared to manual planning, autoROBOT improved rectum D[Formula: see text] (16%), V[Formula: see text] (75%) and D[Formula: see text] (41%), and bladder D[Formula: see text] (37%) (all p [Formula: see text] .002), with equal PTV coverage. In the autoROBOT and autoVMAT comparison, both with 3 mm margin, rectum doses were lower for autoROBOT by 5% for rectum D[Formula: see text] (p=.002), 33% for V[Formula: see text] (p=.001) and 4% for D[Formula: see text] (p=.05), with comparable PTV coverage and other OAR sparing. With 5 mm margin for VMAT, 18/20 plans had a PTV coverage lower than requested (95%) and all plans had higher rectum doses than autoROBOT (mean percentage differences of 13% for D[Formula: see text], 69% for V[Formula: see text] and 32% for D[Formula: see text] (all p.001)).The first system for fully automated generation of clinically deliverable robotic plans was built. Autoplanning did largely enhance robotic plan quality, compared to manual planning. Using autoplanning for both the robotic system and VMAT, superiority of non-coplanar robotic treatment compared to coplanar VMAT for prostate SBRT was demonstrated.