The grid-dose-spreading algorithm for dose distribution calculation in heavy charged particle radiotherapy

A new variant of the pencil-beam (PB) algorithm for dose distribution calculation for radiotherapy with protons and heavier ions, the grid-dose spreading (GDS) algorithm, is proposed. The GDS algorithm is intrinsically faster than conventional PB algorithms due to approximations in convolution integral, where physical calculations are decoupled from simple grid-to-grid energy transfer. It was effortlessly implemented to a carbon-ion radiotherapy treatment planning system to enable realistic beam blurring in the field, which was absent with the broad-beam (BB) algorithm. For a typical prostate treatment, the slowing factor of the GDS algorithm relative to the BB algorithm was 1.4, which is a great improvement over the conventional PB algorithms with a typical slowing factor of several tens. The GDS algorithm is mathematically equivalent to the PB algorithm for horizontal and vertical coplanar beams commonly used in carbon-ion radiotherapy while dose deformation within the size of the pristine spread occurs for angled beams, which was within 3 mm for a single proton pencil beam of $30^\circ$ incidence, and needs to be assessed against the clinical requirements and tolerances in practical situations.Comment: 7 pages, 3 figure

A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling

Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the "node size" parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin

Dosimetric verification of the anisotropic analytical algorithm for radiotherapy treatment planning

BACKGROUND AND PURPOSE: To investigate the accuracy of photon dose calculations performed by the Anisotropic Analytical Algorithm, in homogeneous and inhomogeneous media and in simulated treatment plans. MATERIALS AND METHODS: Predicted dose distributions were compared with ionisation chamber and film measurements for a series of increasingly complex situations. Initially, simple and complex fields in a homogeneous medium were studied. The effect of inhomogeneities was investigated using a range of phantoms constructed of water, bone and lung substitute materials. Simulated treatment plans were then produced using a semi-anthropomorphic phantom and the delivered doses compared to the doses predicted by the Anisotropic Analytical Algorithm. RESULTS: In a homogeneous medium, agreement was found to be within 2% dose or 2mm dta in most instances. In the presence of heterogeneities, agreement was generally to within 2.5%. The simulated treatment plan measurements agreed to within 2.5% or 2mm. Conclusions: The accuracy of the algorithm was found to be satisfactory at 6MV and 10MV both in homogeneous and inhomogeneous situations and in the simulated treatment plans. The algorithm was more accurate than the Pencil Beam Convolution model, particularly in the presence of low density heterogeneities