Treatment planning device, method, and program
The treatment planning device optimizes radiation therapy plans by using multiple computational models and parameter sets to achieve target bioefficacy values, addressing the variability in existing methods and ensuring consistent treatment outcomes.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- HITACHI LTD
- Filing Date
- 2023-02-28
- Publication Date
- 2026-07-17
AI Technical Summary
Healthcare professionals face difficulties in selecting a unique calculation model and parameter set for bioefficacy indicators in radiation therapy, leading to variations in treatment outcomes due to the use of multiple models and parameter sets.
A treatment planning device that utilizes multiple computational models and parameter sets to calculate bioefficacy indices, optimizing conditions to achieve predetermined target values, thereby creating robust treatment plans.
Enables the creation of treatment plans that are not dependent on a single calculation model and parameter set, ensuring consistency and effectiveness in radiation therapy outcomes.
Smart Images
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Abstract
Description
[Technical Field]
[0001] This disclosure relates to a technique for creating treatment plans for radiation therapy. [Background technology]
[0002] In radiation therapy, bioefficacy indicators are used to predict the treatment outcome. Tumor control probability (TCP) and normal tissue complication probability (NTCP) are frequently used as bioefficacy indicators.
[0003] Patent Document 1 discloses a treatment planning method that calculates TCP and NTCP based on a calculation model using a prescription (total dose, number of fractions, etc.) as input, and optimizes the prescription so that TCP and NTCP approach target values. Here, a prescription is considered better if TCP is closer to 1 and NTCP is closer to zero.
[0004] Non-patent document 1 proposes several computational models for calculating TCP. Here, we will explain the Poisson statistical model as an example. Generally, it is known that radiation-induced cell death follows Poisson statistics. If n is the number of cancer cells contained in the tumor (target) before the start of treatment, and λ is the probability of cancer cell survival associated with radiation therapy, then the probability P (χ=0) that makes the number of cancer cells χ in the target zero after the end of treatment, i.e., TCP, is given by equation (1).
[0005]
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[0006] In this case, the survival probability λ of tumor cells associated with radiation therapy is expressed by a linear-quadratic curve model (LQ model) as shown in equation (2).
[0007]
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[0008] Here, D is the total radiation dose to the target, and N is the number of fractionated radiation irradiations. α and β are parameters indicating the radiosensitivity of cancer cells. α and β can be determined by in vitro radiation irradiation experiments or the like. These α and β are not uniquely determined, and even for similar cases, different values may be adopted depending on the literature and facilities.
[0009] Also, in calculation models different from the above-mentioned Poisson statistical model (for example, equivalent uniform dose model, etc.), other parameters may be used instead of these α and β. Other parameters can be determined, for example, by fitting the calculation model to a graph with the vertical axis being the past treatment results (for example, no progression is 1 and progression is 0) and the horizontal axis being the equivalent uniform dose (Equivalent uniform dose, EUD) of the target.
Prior Art Documents
Patent Documents
[0010]
Patent Document 1
[0012] As mentioned above, multiple calculation models and parameter sets can be used for the same case. Naturally, the values of bioefficacy indicators such as TCP or NTCP calculated by different calculation models and parameter sets will differ. Therefore, healthcare professionals are required to consider and select which calculation model and parameter set to use for each individual patient when formulating a treatment plan based on bioefficacy indicators. Furthermore, healthcare professionals may have difficulty deciding on a unique calculation model and parameter set. However, even in such cases, it is difficult for healthcare professionals to find a compromise between multiple calculation models and parameter sets.
[0013] One of the purposes included in this disclosure is to provide a technology that enables the creation of treatment plans based on multiple computational models. [Means for solving the problem]
[0014] A treatment planning device according to one aspect of the present disclosure is a treatment planning device for creating a radiotherapy treatment plan, comprising a processing device and a memory. The memory stores a plurality of calculation models, and the processing device uses at least two of the plurality of calculation models to calculate calculated values of a bioefficacy index representing the effect of radiotherapy on radiotherapy conditions, and searches for conditions such that the at least two calculated values approach a predetermined target value. [Effects of the Invention]
[0015] According to one aspect included in this disclosure, it becomes possible to create treatment plans based on multiple computational models. [Brief explanation of the drawing]
[0016] [Figure 1] This is a schematic diagram showing an example configuration of the treatment planning device according to this embodiment. [Figure 2] This is a flowchart illustrating an example of the treatment planning process performed by a treatment planning system. [Figure 3] This figure shows an example of a GUI for inputting target and exclusion areas. [Figure 4] This figure shows an example of a GUI for inputting the target tumor control probability, calculation model, model weights, and parameter set to be used for the target region. [Figure 5] This is an example of a GUI for selecting a computational model. [Figure 6] This is an example of a GUI for selecting a parameter set. [Modes for carrying out the invention]
[0017] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.
[0018] Figure 1 is a schematic diagram showing an example of the configuration of the treatment planning device 100 according to this embodiment. The treatment planning device 100 is composed of an information processing device capable of various information processing, such as a computer device.
[0019] In the example shown in Figure 1, the treatment planning device 100 includes a calculation processing unit 101, an input device 102, a display device 103, a memory 104, a database 105, and a communication device 106. The calculation processing unit 101 is connected to the input device 102, the display device 103, the memory 104, the database 105, and the communication device 106. This connection method is not particularly limited and may be a connection method via a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.
[0020] The arithmetic processing unit 101 is a processor such as a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), and an FPGA (Field-Programmable Gate Array), and constitutes a control unit that controls the entire treatment planning device 100.
[0021] The input device 102 is a device that receives various information from the operator operating the treatment planning device 100, and is, for example, a mouse and keyboard. The display device 103 is a device that displays various information such as the treatment plan, and is, for example, a display.
[0022] The memory 104 and database 105 are composed of the same or different recording media and record programs (computer programs) that define the operation of the arithmetic processing unit 101, and various information used and generated by the arithmetic processing unit 101 (for example, calculation models described later). The recording media include, for example, magnetic storage media such as HDD (Hard Disk Drive), semiconductor storage media such as RAM (Random Access Memory), ROM (Read Only Memory), and SSD (Solid State Drive), and optical discs such as DVD (Digital Versatile Disk) and optical disc drives. When the treatment planning device 100 starts operating (for example, when the power is turned on), the arithmetic processing unit 101 reads a program from the recording media, executes the read program, and controls the entire treatment planning device 100 by performing various processes related to the treatment plan.
[0023] The communication device 106 is a communication interface that enables communication with an external device. In the example shown in Figure 1, the communication device 106 is connected to the radiotherapy device 200.
[0024] This embodiment describes an example in which the present invention is applied to a treatment planning device compatible with spot scanning particle beam therapy. However, the present invention can be similarly applied to X-ray therapy or particle beam therapy other than spot scanning, and equivalent effects can be obtained. In the spot scanning method, points (spots) are arranged three-dimensionally inside and around the target area to be irradiated with radiation, particularly tumors, within the patient's body, and a narrow beam is irradiated to each spot. A predetermined dose to be irradiated is set for each spot, and when the predetermined dose is irradiated to a certain spot, the beam is deflected to the next spot and the beam is irradiated sequentially. By irradiating all spots with the predetermined dose, a desired irradiation dose distribution is formed in the target area.
[0025] Examples of treatments other than spot scanning include treatment planning systems that support intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) using X-rays, and the present invention is applicable to these treatments as well. In X-ray therapy systems that perform intensity modulation, such as IMRT and VMAT, X-rays are irradiated onto the target from multiple directions. The dose distribution formed within the target by X-rays irradiated from each direction is non-uniform, but by superimposing the contributions from all directions, a uniform dose distribution that matches the three-dimensional shape of the target is applied to the patient's body. At this time, the fluence distribution of the X-rays to be irradiated from each direction can be obtained by solving an inverse problem using the treatment planning system. In addition, a multi-leaf collimator may be installed between the X-ray source and the isocenter in order to realize an arbitrary fluence distribution of X-rays. The present invention can obtain the same effects as in this embodiment even with treatment planning systems that support such radiation therapy systems.
[0026] Figure 2 is a flowchart illustrating an example of the treatment planning process performed by the treatment planning device 100. Note that, in the following, particle beams may sometimes be referred to as radiation.
[0027] In the treatment planning process, the processing unit 101 first receives the target area to be irradiated with radiation and the exclusion area to be avoided (step S1). For example, the processing unit 101 displays each slice image of the patient's CT image on the display device 103 and receives the target area and exclusion area for each slice image from the operator via the input device 102. The target area is, for example, an area such as a tumor. The exclusion area is, for example, an area of vital organs (Organ at Risk: OAR). Hereinafter, the exclusion area, which is a risk area, may be referred to as OAR. Also, both the target area and OAR may be referred to as the area of interest.
[0028] FIG. 3 is a diagram showing an example of a GUI (Graphical User Interface) for an operator to input a target region and an exclusion region. When the operator inputs the target region 301 and the OAR 302 on a certain slice image using a mouse or a stylus pen, etc., the target region 301 and the OAR 302 are drawn on the GUI (Graphical User Interface) on the display device 103 as shown in FIG. 3. As shown in FIG. 3, in this embodiment, an example where there is one target region and one OAR is shown, but there may be two or more target regions and OARs respectively.
[0029] Return to the description of FIG. 2. The arithmetic processing unit 101 records the received target region and exclusion region in the memory 104 or the database 105 as three-dimensional position information (step S2).
[0030] Next, the arithmetic processing unit 101 receives the target tumor control probability TCP model which is the target value of the treatment effect for the target region 301 (step S3). Also, the arithmetic processing unit 101 receives the target normal tissue complication probability NTCP obj which is the target value of the treatment effect for the OAR 302. Further, the arithmetic processing unit 101 receives weights w TCP , w NTCP for the target region 301 and the OAR 302 respectively. The region of interest with a larger set weight is considered preferentially in the optimization of the prescription.
[0031] Return to the description of FIG. 2 again. The arithmetic processing unit 101 records the received target tumor control probability TCP obj , the target normal tissue complication probability NTCP obj , and the respective weights w TCP , w NTCP in the memory 104 or the database 105 (step S4)
[0032] Furthermore, the arithmetic processing unit 101 has a calculation model for calculating TCP or NTCP for the target region 301 and the OAR 302, and the weight w modelThe system accepts the parameter set of the calculation model (step S5). The arithmetic processing unit 101 accepts the accepted calculation model and the weights w model Then, the parameter set is recorded in memory 104 or database 105 (step S6). Weight w TCP , w NTCP Similarly, if you set up multiple computational models, the weight w model The larger the value set in the calculation model, the more it will be given priority in optimizing prescriptions.
[0033] Figure 4 shows the target tumor control probability TCP used by the operator for the target region 301. obj TCP computation model, model weights w model This figure shows an example of a GUI for inputting parameter sets. The operator can call up the GUI shown in Figure 4 for each region of interest by selecting the target region setting screen call button 303 shown in Figure 3 using the input device 102. As shown in Figure 4, multiple calculation models can be set for a single region of interest. In this embodiment, it is also possible to combine multiple different parameter sets for the same calculation model and register them as separate calculation models in the treatment planning device 100.
[0034] The operator can select a calculation model and parameter set from a list of those pre-stored in database 105. When the operator selects the model addition button 401 shown in Figure 4, the model selection screen (Figure 5), described later, is called up. Once a calculation model has been added, it can be deleted by selecting the model deletion button 402.
[0035] Figure 5 shows an example of a GUI for selecting a computation model. It displays a list of computation models that match the specified region of interest type (target, OAR) and are pre-stored in database 105. By selecting the model selection button 501, the user can select a new computation model to add. By selecting the model selection cancel button (back button) 502, the user can cancel the selection of a computation model and return to the settings screen shown in Figure 4. Once a computation model is selected in Figure 5, a screen for selecting the parameter set is called up.
[0036] Figure 6 shows an example of a GUI for selecting a parameter set. This GUI displays a list of parameter sets pre-stored in database 105 that correspond to the calculation model selected on the model selection screen shown in Figure 5. By selecting the parameter set selection button 601, the operator can select the parameter set to be used for the selected calculation model. By selecting the parameter set selection cancel button (back button) 602, the operator can cancel the parameter set selection and return to the model selection screen shown in Figure 5.
[0037] The above uses Figures 4 to 6 to illustrate the target tumor control probability TCP used by the operator for the target region 301. obj TCP computation model, model weights w model The procedure for inputting the parameter set and the target normal tissue injury probability NTCP for OAR302 has been explained, but obj NTCP computation model, weights of the computation model w model The procedure for inputting the parameter set for the calculation model is the same. By selecting the OAR settings screen call button 305 shown in Figure 3, the operator can select the target tumor control probability TCP to be used for the target region 301. obj TCP computation model, model weights w model A GUI is called for inputting the parameter set. This GUI is the same as the GUI in Figure 4, and the target normal tissue injury probability NTCP for OAR302 is calculated from the GUI similar to that in Figures 4 to 6. objNTCP computation model, weights of the computation model w model You can input the parameter set for the calculation model, as well as the parameters for the calculation model.
[0038] Once the model settings are complete for all areas of interest, the operator selects the optimization start button 304 shown in Figure 3. When the optimization start button 304 is selected, the search for the optimal prescription using the configured biological effect indicators, computational models, model weights, and parameter sets begins. Let's return to the explanation of Figure 2.
[0039] The arithmetic processing unit 101 sets an objective function for prescription search based on the information recorded in the memory 104 or the database 105 (step S7). The objective function is shown in equation (3).
[0040]
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[0041] Next, as an example of how the processing unit 101 calculates TCP, we will explain a Poisson statistical model as an example of a model. It is known that the probability of cancer cells receiving radiation follows Poisson statistics. Therefore, the probability P that the number of cancer cells in voxel k within the target region 301 becomes zero at the end of treatment is... k (χ=0) is expressed by equation (4).
[0042]
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[0043] At this time, the survival probability of voxel k cancer cells λ associated with radiation therapy k This can be expressed using the linear-quadratic model (LQ model) as shown in equation (5).
[0044]
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[0045] D k α is the total radiation dose to voxel k, and N is the number of fractions. i , β i is a parameter of model i that indicates the radiosensitivity of cancer cells.
[0046] TCP is the probability that the number of cancer cells in the target region 301 will be zero after the end of treatment. Therefore, it is calculated by multiplying the probabilities P(χ=0) for all voxels in the target region 301, as shown in equation (6).
[0047]
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[0048] In this embodiment, a method for calculating TCP using a Poisson statistical model was illustrated, but it is also possible to use other models, such as an equivalent uniform dose model, and the same effects as in this embodiment can be obtained in that case as well.
[0049] Next, an example of how the arithmetic processing unit 101 calculates NTCP will be explained. According to a typical model, the NTCP of OAR is calculated according to equation (7).
[0050]
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[0051]
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[0052] Furthermore, D max V np This is expressed by equation (9).
[0053]
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[0054] Here, V represents the total volume of the OAR, v represents the volume per voxel, and k is the voxel number contained within the OAR. p , m p and TD 50 These are parameters determined based on past clinical data, etc., and are pre-stored in database 105 by the operator.
[0055] D k ' is the converted total radiation dose in voxel k, and d is the radiation dose for a single exposure. ref If we set =2Gy, the total radiation dose is D k This shows the total radiation dose required to obtain a biological effect equivalent to irradiation with N fractionation. As mentioned above, the total radiation dose is D. k The probability λ of normal tissue surviving when irradiated with N fractionation rounds is given by the irradiation. k This can be expressed by equation (10).
[0056]
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[0057] Here, α j , β j This is a parameter of the computational model j that indicates the radiosensitivity of normal tissue cells. Therefore, the converted total radiation dose D k ' can be found using equations (11) and (12).
[0058]
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[0059] It should be noted that the NTCP calculation method shown in this embodiment is just one example, and it is also possible to calculate NTCP using a different model formula, in which case the same effect as in this embodiment can be obtained.
[0060] The dose per voxel required for TCP and NTCP calculations is D. kThis is calculated based on equation (13), which shows the relationship between a vector whose elements are the absorbed dose of each voxel included in the target region 301 and OAR302 (hereinafter also referred to as the absorbed dose vector) and a vector whose elements are the beam irradiation dose for each spot (hereinafter also referred to as the spot dose vector).
[0061]
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[0062] Matrix A represents the dose that radiation delivered to each spot delivers to each voxel k, and is calculated based on the irradiation direction set in advance by the operator and the internal body information obtained from CT images.
[0063] Returning to the explanation of Figure 2, once the objective function is generated in step S7, the processing unit 101 iteratively searches for the spot-by-spot irradiation dose and the number of divisions that minimize the objective function (step S8). The termination conditions are not particularly limited, but the total calculation time of the iterative calculations in the iterative search, the number of times the calculation is repeated in the iterative search, or the amount of change in the objective function per iterative calculation can be used as indicators, and conditions can be set according to the indicators.
[0064] The absorbed dose vector shown on the left side of equation (13) is used to determine the dose within the target region 301, even when the spot dose vector included on the right side of equation (13) is unknown. mean It can be estimated based on the assumption that it is uniformly distributed and decreases isotropically outside the target area according to the distance from the target region 301. Under this assumption, the dose D can be used instead of the spot dose vector. mean These can be used as search parameters. This reduces the number of search parameters, so faster convergence can be expected. However, the number of divisions N and the central dose D mean After the search is complete, it is necessary to determine the spot dose vector again based on the objective function F' shown in equation (14).
[0065]
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[0066] However, the relationship between the absorbed dose vector and the spot dose vector is as shown in equation (13).
[0067] Returning to Figure 2, the processing unit 101 calculates a three-dimensional dose distribution based on the determined beam irradiation dose for each spot, displays the calculation result on the display device 103, and records it in the memory 104 or database 105, thereby completing the treatment plan (step S9).
[0068] The embodiments described above are illustrative for explaining the present invention and are not intended to limit the scope of the present invention to those embodiments only. Those skilled in the art can implement the present invention in various other forms without departing from the scope of the present invention. Furthermore, the embodiments described above include the following matters. However, the matters included in these embodiments are not limited to those described below. (Item 1)
[0069] A treatment planning device for creating a radiotherapy treatment plan comprises a processing unit and a memory, wherein the memory stores a plurality of calculation models, and the processing unit uses at least two of the plurality of calculation models to calculate calculated values of a bioefficacy index representing the effect of radiotherapy on radiotherapy conditions, and searches for conditions such that at least two of the calculated values approach a predetermined target value. As a result, since conditions are searched to bring the calculated values of the bioefficacy index calculated using two or more calculation models closer to the target value, a robust treatment plan that does not depend on a single calculation model and parameter set can be created. (Item 2)
[0070] In the treatment planning device described in item 1, the processing device searches for the conditions using an objective function based on the difference between the calculated value and the target value. According to this, the conditions can be searched by optimizing the objective function. (Item 3)
[0071] In the treatment planning device described in item 2, the objective function is a function that weights and adds the difference between the calculated value and the target value according to the weights specified for the at least two calculation models. This allows for the optimization of prescriptions by assigning priority to each calculation model. (Item 4)
[0072] In the treatment planning device described in item 3, the processing device searches for the conditions to minimize the value of the objective function. (Item 5)
[0073] In the treatment planning device described in item 1, the processing device calculates the biological effect index for the target area to be irradiated with radiation and / or the risk area to which radiation should not be irradiated in radiotherapy. (Item 6)
[0074] In the treatment planning device described in item 5, the biological efficacy indicators are the probability of tumor control in the target region and the probability of normal tissue damage occurring in the risk region. This allows for the search of a formulation that optimizes the probability of tumor control and the probability of normal tissue damage occurring. (Item 7)
[0075] In the treatment planning device described in item 1, the conditions include the number of irradiation fractions and the irradiation dose per spot in spot scanning irradiation. This allows for the search for the optimal number of irradiation fractions and the irradiation dose per spot. (Item 8)
[0076] The treatment planning device described in item 1 further comprises a display device, the processing device displays on the display device a management screen showing information on the at least two calculation models used to calculate the biological efficacy index. This allows the treatment plan creator to create a treatment plan while confirming which calculation model to use. (Item 9)
[0077] In the treatment planning device described in item 8, the processing device displays a selection screen on the display device that allows the user to select from the plurality of calculation models stored in the memory to be used for calculating the biological efficacy index. This allows the treatment plan creator to select a calculation model on the selection screen and create a treatment plan. (Item 10)
[0078] The treatment planning device according to claim 5 further comprises a display device, wherein the processing device displays on the display device a management screen showing information of the at least two calculation models used to calculate the bioefficacy index, corresponding to the region of interest for which the bioefficacy index is to be calculated. This allows the treatment plan creator to create a treatment plan while confirming the region of interest and the calculation models on the screen. [Explanation of Symbols]
[0079] 100…Treatment planning device, 101…Calculation processing device, 102…Input device, 103…Display device, 104…Memory, 105…Database, 106…Communication device, 200…Radiation therapy device, 301…Target area, 302…OAR, 303, 305…Setting screen call button, 304…Optimization start button, 401…Add model button, 402…Delete model button, 501…Select model button, 502…Cancel model selection button, 601…Select parameter set button, 602…Cancel parameter set selection button
Claims
1. A treatment planning device for creating a treatment plan for radiotherapy, comprising a processing unit and a memory, The memory stores multiple computational models, The aforementioned processing apparatus is Using at least two of the aforementioned calculation models, a calculated value of a biological efficacy index representing the effect of radiotherapy for each radiotherapy condition is calculated. The conditions are searched so that at least two of the calculated values approach a predetermined target value. In terms of things, The processing device calculates the biological efficacy index for a target area to be irradiated with radiation and / or a risk area that should not be irradiated with radiation in radiotherapy, wherein the biological efficacy index is the probability of tumor control in the target area and the probability of normal tissue damage occurring in the risk area. Treatment planning device.
2. A treatment planning device for creating a treatment plan for radiotherapy, comprising a processing device and a memory, The memory stores multiple computational models, The aforementioned processing apparatus is Using at least two of the aforementioned calculation models, a calculated value of a biological efficacy index representing the effect of radiotherapy for each radiotherapy condition is calculated. The conditions are searched so that at least two of the calculated values approach a predetermined target value. In terms of things, The above conditions include the number of irradiation fractions and the irradiation dose per spot in spot scanning irradiation. Treatment planning device.
3. The processing device searches for the conditions using an objective function based on the difference between the calculated value and the target value. A treatment planning device according to claim 1 or 2.
4. The objective function is a function that weights and adds the difference between the calculated value and the target value according to the weights specified for the at least two computational models. The treatment planning device according to claim 3.
5. The processing device searches for the conditions to minimize the value of the objective function. The treatment planning device according to claim 4.
6. It further has a display device, The processing apparatus displays a management screen on the display device showing information of the at least two calculation models used to calculate the biological effect index. A treatment planning device according to claim 1 or 2.
7. The processing device displays a selection screen on the display device that allows the user to select from the plurality of calculation models stored in the memory to be used for calculating the biological effect index. The treatment planning device according to claim 6.
8. It further has a display device, The processing apparatus displays on the display device a management screen showing information on the at least two calculation models used to calculate the biological effect index, corresponding to the region of interest for which the biological effect index is calculated. A treatment planning device according to claim 1 or 2.
9. A treatment planning method for creating a treatment plan for radiotherapy using a treatment planning device comprising a processing device and a memory, The aforementioned processing apparatus Multiple computational models are recorded in the aforementioned memory, Using at least two of the aforementioned calculation models, a calculated value of a biological efficacy index representing the effect of radiotherapy for each radiotherapy condition is calculated. The conditions are searched so that at least two of the calculated values approach a predetermined target value. In terms of things, The processing device calculates the biological efficacy index for a target area to be irradiated with radiation and / or a risk area that should not be irradiated with radiation in radiotherapy, wherein the biological efficacy index is the probability of tumor control in the target area and the probability of normal tissue damage occurring in the risk area. Treatment planning methods.
10. A treatment planning method for creating a treatment plan for radiotherapy using a treatment planning device comprising a processing device and a memory, The aforementioned processing apparatus Multiple computational models are recorded in the aforementioned memory, Using at least two of the aforementioned calculation models, a calculated value of a biological efficacy index representing the effect of radiotherapy for each radiotherapy condition is calculated. The conditions are searched so that at least two of the calculated values approach a predetermined target value. In terms of things, The above conditions include the number of irradiation fractions and the irradiation dose per spot in spot scanning irradiation. Treatment planning methods.
11. A treatment planning program for creating a treatment plan for radiotherapy using a treatment planning device comprising a processing unit and memory, Multiple computational models are recorded in the aforementioned memory, Using at least two of the aforementioned calculation models, a calculated value of a biological efficacy index representing the effect of radiotherapy for each radiotherapy condition is calculated. The conditions are searched so that at least two of the calculated values approach a predetermined target value. In a program for causing the processing device to perform the above, The processing device is made to calculate the biological efficacy index for the target area to be irradiated with radiation and / or the risk area to which radiation should not be irradiated, wherein the biological efficacy index is the probability of tumor control in the target area and the probability of normal tissue damage occurring in the risk area. Treatment plan program.
12. A treatment planning program for creating a treatment plan for radiotherapy using a treatment planning device comprising a processing device and a memory, Multiple computational models are recorded in the aforementioned memory, Using at least two of the aforementioned calculation models, a calculated value of a biological efficacy index representing the effect of radiotherapy for each radiotherapy condition is calculated. The conditions are searched so that at least two of the calculated values approach a predetermined target value. In a program for causing the processing device to perform the above, The above conditions include the number of irradiation fractions and the irradiation dose per spot in spot scanning irradiation. Treatment plan program.