A system and method for generation and use of radiation outcome prediction (response & side effects) score for patients undergoing radiotherapy for various medical conditions wherein the score is a personalized score, provided by analyzing multiple parameters 5 including the tumor specific, patient specific, gene specific and treatment planning specific parameter(s), during and post therapy.

An example method of treating a subject having a tumor is described herein. The method can include determining a radiosensitivity index of the tumor, deriving a subject-specific variable based on the radiosensitivity index, and obtaining a genomic adjusted radiation dose effect value for the tumor. The radiosensitivity index can be assigned from expression levels of signature genes of a cell of the tumor. Additionally, the genomic adjusted radiation dose effect value can be predictive of tumor recurrence in the subject after treatment. The method can also include determining a radiation dose based on the subject-specific variable and the genomic adjusted radiation dose effect value.

Disclosed herein are methods for personalized treatment of individual patient tumors. In one embodiment, a method of calculating a personalized radiation therapy dosage for a subject comprises determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the gene expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a genomic adjusted radiation dose (GARD) value based on RSI, radiation dose and fractionation schedule of the subject, and calculating a personalized radiation dosage (RxRSI) for the subject based on a pre-determined GARD value.

In radiation treatment planning, a plurality of optimization loops are performed. In each optimization loop computes a dose distribution (60) in a patient represented by a planning image (42) with regions of interest (ROIs) defined in the planning image. Weights (64) for objective functions (50) are determined from objective function value (OFV) goals (52) for the objective functions. An optimized dose distribution is produced by adjusting the plan parameters to optimize the computed dose distribution respective to composite objective function (62). At least one optimization loop may include updating (70) at least one OFV goal to be used in at least the next performed optimization loop. At least one optimization loop may include updating an objective function quantifying compliance with a target dose for a target ROI based on a comparison of a metric of coverage of the target ROI and a desired coverage of the target ROI.

A method for delivering radiation treatment may include defining a preliminary trajectory including a plurality of control points. Each control point may be associated with position parameters of a gantry and a couch. The method may also include generating a treatment plan based on the preliminary trajectory by optimizing an intensity and position parameters of a collimator and MLC leaves for each control point. The method may also include decomposing the treatment plan into a delivery trajectory including the plurality of control points. Each of the plurality of control points may be further associated with the optimized intensity, the optimized position parameters of the collimator and the MLC leaves, an output rate, and a motion parameter of each of the gantry, the couch, the collimator, and the MLC leaves. The method may further include instructing a radiation delivery device to deliver the treatment plan according to the delivery trajectory.

A method for delivering radiation treatment may include defining a preliminary trajectory including a plurality of control points. Each control point may be associated with position parameters of a gantry and a couch. The method may also include generating a treatment plan based on the preliminary trajectory by optimizing an intensity and position parameters of a collimator and MLC leaves for each control point. The method may also include decomposing the treatment plan into a delivery trajectory including the plurality of control points. Each of the plurality of control points may be further associated with the optimized intensity, the optimized position parameters of the collimator and the MLC leaves, an output rate, and a motion parameter of each of the gantry, the couch, the collimator, and the MLC leaves. The method may further include instructing a radiation delivery device to deliver the treatment plan according to the delivery trajectory.

Disclosed herein are methods for personalized treatment of individual patient tumors. A computer software configured to integrate with a radiation therapy treatment planning system is presented. The computer software is configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

An ion-based radiotherapy plan for passive delivery of one or more beams (7) uses an optimization problem set up to allow variation in settings of the range modulating device, and/or settings of the aperture element during the delivery of the first beam, so that said plan will include modulation of the fluence of the beam during the delivery of the beam. The optimization problem is set up to allow variation of the settings of an aperture element (11), a range modulating device (9) during delivery of each beam, so that said plan will include modulation in depth of the beam during the delivery of the beam.

A method for generating a radiation treatment plan is provided. The method may include determining a set of one or more optimization goals for radiation delivery by a therapeutic radiation delivery apparatus. The method may also include determining a plan for radiation delivery from a radiation source of the therapeutic radiation delivery apparatus. The radiation source may be capable of continuously rotating around a subject. The plan may include a plurality of radiation segments. Each radiation segment may be characterized by at least one parameter selected from a start angle, a stop angle, a two-dimensional segment shape, or a segment MU value such that the plurality of radiation segments satisfy the set of one or more optimization goals by superimposing at least two radiation segments from at least two different rotations into a target volume of the subject.

A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. A quantitative time-dependent model-based charged particle pencil beam scanning optimization is then implemented for FLASH therapy.