Abstract: Systems and methods for volumetric modulated arc therapy (VMAT) treatment planning include a processor determining, using a current solution of a non-convex VMAT optimization problem, a search region defining a corresponding spatial movement range for each leaf of a plurality of leaves of a MLC. The current solution can include first positions of the plurality of leaves of the MLC. The processor can merge, for each spatial movement range of a corresponding leaf, beamlets associated with the spatial movement range, and transform the nonconvex VMAT optimization problem into a convex VMAT optimization based on the merging of b camlets associated with each spatial movement range. The processor can solve the convex VMAT optimization problem to determine at least second positions of the plurality of leaves of the MLC.

Abstract: Disclosed are treatments for cancer that enhance a patient immune systems' ability to detect and attack cancer. The present disclosure provides methods for treating cancer in a subject in need thereof comprising administering to the subject an effective amount of a pregnancy specific glycoprotein (PSG) inhibitor.

Abstract: Systems and methods for multi-modal, multi-resolution deep learning neural networks for segmentation, outcomes prediction and longitudinal response monitoring to immunotherapy and radiotherapy are detailed herein. A structure-specific Generational Adversarial Network (SSGAN) is used to synthesize realistic and structure-preserving images not produced using state-of-the art GANs and simultaneously incorporate constraints to produce synthetic images. A deeply supervised, Multi-modality, Multi-Resolution Residual Networks (DeepMMRRN) for tumor and organs-at-risk (OAR) segmentation may be used for tumor and OAR segmentation. The DeepMMRRN may combine multiple modalities for tumor and OAR segmentation. Accurate segmentation is may be realized by maximizing network capacity by simultaneously using features at multiple scales and resolutions and feature selection through deep supervision. DeepMMRRN Radiomics may be used for predicting and longitudinal monitoring response to immunotherapy.

Abstract: Systems and methods for improved radiation treatment planning can employ hierarchical constrained optimization with relaxed constraints also referred to herein after as expedited hierarchical constrained optimization (ECHO). The systems and methods described herein employ a combination of constrained optimization and a correction loop involving unconstrained optimization to enhance the speed and efficiency of the radiation treatment planning process and improve the accuracy and quality of resulting radiation treatment plans. The use of a hierarchical constrained optimization approach leads to less complex and faster to solve optimization problems. The correction loop allows for compensating for optimization error associated with the sequence of constrained optimizations by incorporating such error in an unconstrained optimization. Hard and soft dose volume constraints can be efficiently incorporated.

Abstract: A computationally efficient and accurate procedure for calculating radiation dose distributions within a volume of interest in a patient incorporating variations in patient density and source particle energy, position, direction, and type. The procedure iteratively simulates energy depositions from source particles using the Monte Carlo radiation transport method and then applies methods for reducing noise inherent in Monte Carlo results to obtain an accurate and computationally efficient representation of an actual radiation dose distribution compared to that produced by Monte Carlo methods alone. The invention makes an analogy between real measured data and transformed Monte Carlo generated computer data and then applies data denoising techniques to reduce the noise in the Monte Carlo dose images. Conversely, the present invention can produce a dose distribution having a predetermined noise level in a reduced amount of computation time.

Abstract: A computationally efficient and accurate procedure for calculating radiation dose distributions within a volume of interest in a patient incorporating variations in patient density and source particle energy, position, direction, and type. The procedure iteratively simulates energy depositions from source particles using the Monte Carlo radiation transport method and then applies methods for reducing noise inherent in Monte Carlo results to obtain an accurate and computationally efficient representation of an actual radiation dose distribution compared to that produced by Monte Carlo methods alone. The invention makes an analogy between real measured data and transformed Monte Carlo generated computer data and then applies data denoising techniques to reduce the noise in the Monte Carlo dose images. Conversely, the present invention can produce a dose distribution having a predetermined noise level in a reduced amount of computation time.

Abstract: A method of proton tomotherapy provides uniform dose placement within a tumorous area on a slice by slice basis by locating a Bragg peak of the proton beam at the distal edge of the tumor and irradiating the tumor from a number of different angles without sweeping the range of the protons at the given angle. Uniform dose is provided by simultaneous intensity modulation of the beams. Treatment of the tumor in slices eliminates the need for a rotating gantry but allows the patient to be rotated instead. Accurate range placement is provided by a preliminary proton verification tomogram obtainable on the same equipment. A set of standard blocks permits this technique to be used with standard irradiation zones without additional range or intensity adjusting mechanism.

Abstract: A computationally efficient and accurate procedure for calculating electron beam dose within a volume of a patient incorporating knowledge about the variations in density of the patient as obtained from a CT machine or the like employs an iterative process of dividing the patient into a set of layers of elements where each layer is computed from the previous layer by algebraic approximations of the Fermi partial differential equation. Energy loss of the particles is accommodated through the addition of a simple functional relationship between depth and energy or by separately binning energy at each layer as determined by local energy loss.

Abstract: A radiation therapy apparatus, for a radiation therapy machine providing an irradiation of a tumor at 360.degree. about the tumor within a plane, determines a distribution of charges in a conductor that would produce a potential energy field matching the desired dose to the tumor in the plane. The fluence of any given ray through the tumor may be determined by summing the charges along the ray's path. The distribution may include areas of no irradiation which may require negative fluences. Physically realizable non-negative fluences are obtained by an iterative process of adjusting an input dose map in light of the actual dose produced by the calculated fluences.