Feasibility study of beam orientation class-solutions for prostate IMRT

Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305-5847, USA.
Medical Physics (Impact Factor: 2.64). 11/2004; 31(10):2863-70. DOI: 10.1118/1.1797571
Source: PubMed


IMRT is being increasingly used for treatment of prostate cancer. In practice, however, the beam orientations used for the treatments are still selected empirically, without any guideline. The purpose of this work was to investigate interpatient variation of the optimal beam configuration and to facilitate intensity modulated radiation therapy (IMRT) prostate treatment planning by proposing a set of beam orientation class-solutions for a range of numbers of incident beams. We used fifteen prostate cases to generate the beam orientation class-solutions. For each patient and a given number of incident beams, a multiobjective optimization engine was employed to provide optimal beam directions. For the fifteen cases considered, the gantry angle of any of the optimized plans were all distributed within a certain range The angular distributions of the optimal beams were analyzed and the most selected directions are identified as optimal directions. The optimal directions for all patients are averaged to obtain the class-solution. The class-solution gantry angles for prostate IMRT were found to be: three beams (0 degrees, 120 degrees, 240 degrees), five beams (35 degrees, 110 degrees, 180 degrees, 250 degrees, 325 degrees), six beams (0 degrees, 60 degrees, 120 degrees, 180 degrees, 240 degrees, 300 degrees), seven beams (25 degrees, 75 degrees, 130 degrees, 180 degrees, 230 degrees, 285 degrees, 335 degrees), eight beams (20 degrees, 70 degrees, 110 degrees, 150 degrees, 200 degrees, 250 degrees, 290 degrees, 340 degrees), and nine beams (20 degrees, 60 degrees, 100 degrees, 140 degrees, 180 degrees, 220 degrees, 260 degrees, 300 degrees, 340 degrees). The level of validity of the class-solutions was tested using an additional clinical prostate case by comparing with the individually optimized beam configurations. The difference between the plans obtained with class-solutions and patient-specific optimizations was found to be clinically insignificant.

1 Follower
26 Reads
  • Source
    • "Engel and Tabbert (2005), Knowles and Corne (2001), Oldham et al. (1995), Thomas and Foster (1995), Zeinab et al. (2005) Beams configuration Determination of optimal number of beams, angle between the beams and their weights. Holder (2004), Li et al. (2005) Pugachev (2001), Pugachev and Xing (2002), Schreibmann and Xing (2004), Yang et al. (2006), Movement of organs Study of the movement of organs during a treatment and determination of the exact location of organs Bussels et al. (2003), McCarthy et al. (2007), Jaffray et al. (2001) Outline of the treatment volume Determination of the planning target volume, organs at risk and margin Stroom et al. (1999), Sttoom et al. (2002), Yamamoto (1999) Comparison of treatment methods Comparative study of different types of radiotherapy treatments. Knaup et al. (2002), Vaarkamp et al. (2002), Zabel et al. (2002) reasoning, deterministic search methods etc. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Radiotherapy planning is a complex problem which requires both expertise and experience of an oncologist. A case based reasoning (CBR) system is developed to generate dose plans for prostate cancer patients. The proposed approach captures the expertise and experience of oncologists in treating previous patients and recommends a dose in phase I and phase II of the treatment of a new patient considering also the success rate of the treatment. The proposed CBR system employs a modified Dempster–Shafer theory to fuse dose plans suggested by the most similar cases retrieved from the case base. In order to mimic the continuous learning characteristic of oncologists, the weights corresponding to each feature used in the retrieval process are updated automatically each time after generating a treatment plan for a new patient. The efficiency of the proposed methodology has been validated using real data sets collected from the Nottingham University Hospitals NHS, City Hospital Campus, UK. Experiments demonstrated that for most of the patients, the dose plan generated by our approach is coherent with the dose plan suggested by an experienced oncologist. This methodology can assist both new and experienced oncologists in the treatment planning.
    Full-text · Article · Sep 2011 · Expert Systems with Applications
  • Source
    • "However, we assume that the treatment planner knows how many beam angles will be used a priori. A number of researchers have reported different techniques for optimizing the beam orientations of an IMRT plan [7] [9] [15] [16] [21] [22] [24] [29] [30] [31] [32] [33] [34] [36] [37] [40] [41]. Some of the proposed objective functions for beam orientations are nonlinear and non-convex [4] [15] [41]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We present computational approaches for optimizing beam angles and fluence maps in Intensity Modulated Radiation Therapy (IMRT) planning. We assume that the number of angles to be used for the treatment is given by the treatment planner. A mixed integer programming (MIP) model and a linear programming (LP) model are used to find an optimal set of beam angles and their corresponding fluence maps. The MIP model is solved using the branch-and-bound method while the LP model is solved using the interior point method. In order to reduce the computational burden for solving the optimization models, we introduce iterative beam angle elimination algorithms in which an insignificant beam angle is eliminated in each iteration. Other techniques are also explored including feasible set reduction for LP and data reduction. Experiments are made to show the computational advantage of the iterative methods for optimizing angles using real patient cases.
    Full-text · Article · Jan 2008 · Operations Research-Spektrum
  • [Show abstract] [Hide abstract]
    ABSTRACT: The authors present a two-dimensional sliding discrete Fourier transform (DFT) in column direction (or row direction). Based on the formula, it is easy to obtain the DFT successively when the sampling signal is being input, so that high processing speed is reached
    No preview · Conference Paper · Jun 1992
Show more