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.

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.

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.

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.

The present invention relates to the technical field of cloud computing and relates to a standardized cloud radiotherapy planning method, a storage medium, and a system. The method comprises the following steps: (1) patient data is uploaded to a master cloud server (S210), where the patient data comprises a patient image and medical order data; (2) a target area is delineated on the basis of the patient image (S220); (3) the master cloud server assigns a computation task to a controlled computer, the controlled computer uses a standard radiotherapy equipment mode to compute a radiotherapy plan for a patient, thus generating a standard radiotherapy plan (S230); and, (4) a specific radiotherapy plan matching specific radiotherapy equipment is generated on the basis of the standard radiotherapy plan. The method provided in the present solution prevents a delay in treatment time for the patient and idling of treatment resources when a certain type of radiotherapy equipment in a hospital is malfunctioning while other radiotherapy equipment is idling, also balances differences in levels of treatment provided by physicians from different hospital campuses or regions, and also reduces work load for oncologists and medical physicist.

Example methods and systems for generating dose estimation models for radiotherapy treatment planning are provided. One example method may comprise obtaining model configuration data that specifies multiple anatomical structures based on which dose estimation is performed by a dose estimation model. The method may also comprise obtaining training data that includes a first treatment plan associated with a first past patient and multiple second treatment plans associated with respective second past patients. The method may further comprise: in response to determination that automatic segmentation is required for the first treatment plan, performing automatic segmentation on image data associated with the first past patient to generate an improved first treatment plan, and generating the dose estimation model based on the improved first treatment plan and the multiple second treatment plans.

Methods and apparatus are provided for planning and delivering radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. In some embodiments the radiation source is moved continuously along the trajectory while in some embodiments the radiation source is moved intermittently. Some embodiments involve the optimization of the radiation delivery plan to meet various optimization goals while meeting a number of constraints. For each of a number of control points along a trajectory, a radiation delivery plan may comprise: a set of motion axes parameters, a set of beam shape parameters and a beam intensity.

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.

Methods and apparatus are provided for planning and delivering radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. In some embodiments the radiation source is moved continuously along the trajectory while in some embodiments the radiation source is moved intermittently. Some embodiments involve the optimization of the radiation delivery plan to meet various optimization goals while meeting a number of constraints. For each of a number of control points along a trajectory, a radiation delivery plan may comprise: a set of motion axes parameters, a set of beam shape parameters and a beam intensity.

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.