Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.pre-prin
Purpose We describe a treatment plan optimization method for intensity-modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume, while limiting degradation of the best possible physical dose distribution. Methods To allow fast optimization based on dose and LET, a GPU-based Monte-Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LETxD). To first approximation, LETxD represents a measure of the additional biological dose that is caused by high LET. Results The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intra-cranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) where the target volume overlaps with the brainstem and optic structures. In all cases, high LETxD in critical structures could be avoided while minimally compromising physical dose planning objectives. Conclusion LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and RBE-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects due to elevated relative biological effectiveness (RBE) of proton beams near the end of range.
Motion and uncertainty in radiotherapy is traditionally handled via margins. The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose. However, the PTV concept has several limitations, especially in proton therapy. Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV. Initial work focused on random and systematic setup errors in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus. Over the past ten years, IMPT has emerged as a new application for robust planning methods. In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams -a problem that cannot generally be addressed by margins. Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use. This paper first summarizes the limitations of the PTV concept. Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.Abstract. Motion and uncertainty in radiotherapy is traditionally handled via 31 margins. The clinical target volume (CTV) is expanded to a larger planning target 32 volume (PTV), which is irradiated to the prescribed dose. However, the PTV 33 concept has several limitations, especially in proton therapy. Therefore, robust and 34 probabilistic optimization methods have been developed that directly incorporate 35 motion and uncertainty into treatment plan optimization for intensity modulated 36 radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the 37 explicit definition of a PTV becomes obsolete and treatment plan optimization is 38 directly based on the CTV. Initial work focused on random and systematic setup errors 39 in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became 40 a research focus. Over the past 10 years, IMPT has emerged as a new application for 41 robust planning methods. In proton therapy, range or setup errors may lead to dose 42 degradation and misalignment of dose contributions from different beams a problem 43 Robust radiotherapy planning 2 that cannot generally be addressed by margins. Therefore, IMPT has led to the first 44 implementations of robust planning methods in commercial planning systems, making 45 these methods available for clinical use. This paper first summarizes the limitations 46 of the PTV concept. Subsequently, robust optimization methods are introduced and 47 their applications in IMRT and IMPT planning are reviewed. 48 1. Introduction 49Radiotherapy aims at delivering curative doses of radiation ...
Recent success in identifying gene regulatory elements in the context of recombinant adeno-associated virus vectors have enabled cell type-restricted gene expression. However, within the cerebral cortex these tools are presently limited to broad classes of neurons. To overcome this limitation, we developed a strategy that led to the identification of multiple novel enhancers to target functionally distinct neuronal subtypes. By investigating the regulatory landscape of the disease gene Scn1a, we identified enhancers that target the breadth of its expression, including two that are selective for parvalbumin and vasoactive intestinal peptide cortical interneurons. Demonstrating the functional utility of these elements, we found that the PV-specific enhancer allowed for the selective targeting and manipulation of these neurons across species, from mice to humans. Finally, we demonstrate that our selection method is generalizable to other genes and characterize four additional PV-specific enhancers with exquisite specificity for distinct regions of the brain. Altogether, these viral tools can be used for cell-type specific circuit manipulation and hold considerable promise for use in therapeutic interventions.Large-scale transcriptomic studies are rapidly revealing where and when genes associated with neuropsychiatric disease are expressed within specific cell types (1-4). Approaches for understanding and treating these disorders will require methods for targeting and manipulating specific neuronal subtypes. Thus, gaining access to these populations in non-human primates and humans has become paramount. AAVs are the method of choice for gene delivery in the nervous system but have a limited genomic payload and are not intrinsically selective for particular neuronal populations (5). We and others have identified short regulatory elements capable of restricting viral expression to broad neuronal classes. In addition, systematic enhancer discovery has been accelerated by the recent development of technologies allowing for transcriptomic and epigenetic studies at single-cell resolution (6-12). Despite these advances, the search space for enhancer selection remains enormous and to date success has been limited. To focus our enhancer selection, we chose to specifically examine the regulatory landscape of Scn1a, a gene expressed in distinct neuronal populations and whose disruption is associated with severe epilepsy (13).Combining single-cell ATAC-seq data with sequence conservation across species, we nominated ten candidate regulatory sequences in the vicinity of this gene. By thoroughly investigating each of these elements for their ability to direct viral expression, we identified three enhancers that collectively target the breadth of neuronal populations expressing Scn1a. Among these, one particular short regulatory sequence was capable of restricting viral expression to parvalbumin-expressing cortical interneurons (PV cINs). To fully assess the utility of this element beyond reporter expression, we validated it in a v...
Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of the existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 s, which confirms earlier results. We propose an iterative procedure for QP that allows us to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iterative procedure is comparable in speed to the LP model and produces better plans than the non-iterative QP. We formulate a new dose-volume-based model that maximizes V(100%) while satisfying pre-set DVH criteria. This model optimizes both catheter positions and dwell times within a few minutes depending on prostate volume and number of catheters, optimizes dwell times within 35 s and gives better DVH statistics than dose-based models. The solutions suggest that the correlation between the objective value and the clinical plan quality is weak in the existing dose-based models.
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