Therapy support system and therapy support method

Abstract

To provide a therapy support system capable of easily determining a radiation therapy device appropriate for a patient.SOLUTION: An arithmetic processing device 101 predicts a therapeutic effect index for each therapeutic modality of a radiation therapy device 202 by using a region of interest and parameter information. The region of interest corresponds to a part of a patient related to irradiation of a radiation ray in a three-dimensional transparent image obtained by imaging the patient. The parameter information indicates a value of a parameter for predicting the therapeutic effect index for evaluating an effect of a radiation therapy, for each therapeutic modality of the radiation therapy device 202 that performs the radiation therapy.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a medical treatment support system and a medical treatment support method. [Background technology]

[0002] Particle beam therapy using proton beams or carbon beams has superior dose concentration to tumors compared to conventional X-ray therapy, because the depth of the energy peak, called the Bragg peak, can be adjusted to match the depth of the tumor. For this reason, it is expected to reduce exposure to normal tissue and OARs (Organ At Risk) and the occurrence of side effects.

[0003] However, particle beam therapy systems for performing particle beam therapy require large equipment such as accelerators and rotating gantries that require a large installation area, and are generally more expensive than X-ray therapy systems for performing X-ray therapy, so there are only a limited number of facilities that can possess particle beam therapy systems. For this reason, there is a limit to the number of patients who can receive particle beam therapy at one time.

[0004] Therefore, rather than administering particle beam therapy to all cancer patients, it is necessary to identify patients who can fully benefit from the benefits of particle beam therapy and preferentially guide them to particle beam therapy. For example, in cases with small tumor volumes, it is known that conventional X-ray therapy can sufficiently reduce exposure to OARs and provide treatment with the same level of effectiveness as particle beam therapy.

[0005] As a judgment index for determining whether a patient is suitable for particle beam therapy, the normal tissue complication probability (NTCP) of OAR has attracted attention (see Non-Patent Document 1). The NTCP of OAR is the probability that irreversible damage occurs in the OAR adjacent to the tumor due to irradiation with radiation, and is generally calculated by a mathematical model constructed based on past treatment data. As an input to the mathematical model, a three-dimensional dose distribution in the patient's body calculated by treatment planning software for drawing up a treatment plan for radiation therapy, etc., is used. According to a representative mathematical model, the NTCP is calculated from Equation 1.

number

number

number

[0006] Also, D' k is the converted total radiation dose in voxel k, and the radiation dose per exposure is d ref = 2 [Gy], the total irradiation dose D k It indicates the total irradiation dose required to obtain the same biological effect as irradiation with a total fraction number N. According to the linear-quadratic model (LQ model), which is a general evaluation model for evaluating the relationship between radiation dose and biological effect, the total irradiation dose D k And when the total number of divisions is N, the probability that a normal tissue cell in voxel k survives is λ k teeth,

number

number

number

[0007] NTCP calculated from the dose distribution of X-ray therapy is x , NTCP calculated from the dose distribution of particle beam therapy is NTCP p Then, the difference between them, NTCP difference (ΔNTCP = NTCP x -NTCP p For patients with a large NTCP difference, particle therapy is considered to be of great benefit, and preferential induction to particle therapy is considered. On the other hand, for patients with a small NTCP difference, X-ray therapy is considered to be equivalent to particle therapy, and induction to X-ray therapy is considered. [Prior art documents] [Non-patent literature]

[0008] [Non-Patent Document 1] Yoshimura T, Kinoshita R, Onodera S, Toramatsu C, Suzuki R, Ito YM, Takao S, Matsuura T, Matsuzaki Y, Umegaki K, Shirato H, Shimizu S. NTCP modeling analysis of acute hematologic toxicity in whole pelvic radiation therapy for gynecologic malignancies - A dosimetric comparison of IMRT and spot-scanning proton therapy (SSPT). Phys Med. 2016 Sep;32(9):1095-102. doi: 101016 / j.ejmp.2016.08.007. Epub 2016 Aug 25. PMID: 27567089. Summary of the Invention [Problem to be solved by the invention]

[0009] As mentioned above, the NTCP difference is useful as a judgment index for judging whether a patient is suitable for particle beam therapy. However, in order to calculate the NTCP difference, it is necessary to draw up treatment plans for both X-ray therapy and particle beam therapy, and obtain the three-dimensional dose distribution in the patient's body from these treatment plans by simulation, which requires a lot of time and effort.

[0010] In addition, there are various irradiation methods for X-ray therapy, such as 3D-CRT (Three Dimensional Conformal Radiotherapy), IMRT (Intensity Modulated Radiotherapy), and VMAT (Volummetric Modulated Arc Therapy), and each irradiation method has a different treatment modality (type of radiation therapy device). For this reason, the difference in NTCP for each treatment modality can be used as a judgment index for determining the treatment modality suitable for a patient, similar to the judgment index for determining whether a patient is suitable for particle beam therapy. However, since it is necessary to obtain the three-dimensional dose distribution in the patient's body for each treatment modality, additional time and effort are required.

[0011] An object of the present disclosure is to provide a treatment support system and a treatment support method that make it possible to easily determine an appropriate radiation therapy device for a patient. [Means for solving the problem]

[0012] A treatment support system according to one aspect of the present disclosure is a treatment support system that predicts the effects of radiation therapy on a patient, and includes a calculation processing device that predicts a treatment effect index for each type of radiation therapy device using a region of interest in a 3D fluoroscopic image of the patient that corresponds to a part of the patient that is related to radiation irradiation, and parameter information indicating parameter values ​​for predicting a treatment effect index evaluating the effect of the radiation therapy for each type of radiation therapy device that performs the radiation therapy. Effect of the Invention

[0013] According to the present disclosure, it becomes possible to easily determine an appropriate radiation therapy device for a patient. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram illustrating a treatment selection support system according to an embodiment of the present disclosure. [Diagram 2] 11 is a flowchart for explaining an example of a treatment selection process by a treatment selection support system according to an embodiment of the present disclosure. [Diagram 3] FIG. 13 is a diagram showing an example of a GUI used by an operator in the treatment selection process. [Figure 4] FIG. 13 is a diagram showing another example of a GUI used by an operator in the treatment selection process. [Diagram 5] FIG. 13 is a diagram showing another example of a GUI used by an operator in the treatment selection process. [Figure 6] FIG. 11 is a diagram for explaining an example of an algorithm of a DVH prediction model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0016] Fig. 1 is a diagram showing a treatment selection support system according to an embodiment of the present disclosure. The treatment selection support system 100 shown in Fig. 1 is a treatment support system configured with a device capable of various information processing, for example, an information processing device such as a computer device. The treatment selection support system 100 is placed in a treatment facility such as a hospital where radiation therapy is performed. In the example of Fig. 1, facilities A to C are shown as treatment facilities, and the treatment selection support system 100 of facility A is shown, but facilities B and C also have systems equivalent to the treatment selection support system 100 installed.

[0017] The treatment selection support system 100 includes a calculation processing device 101, an input device 102, a display device 103, a memory 104, a database 105, and a communication device 106. The calculation processing device 101 is connected to the input device 102, the display device 103, the memory 104, the database 105, and the communication device 106. The method of connecting these is not particularly limited, and may be, for example, a connection method via a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0018] The arithmetic processing device 101 is, for example, a processor such as a central processing unit (CPU), a graphic processing unit (GPU), or a field-programmable gate array (FPGA), and constitutes a control unit that controls the entire treatment selection support system 100.

[0019] The input device 102 is a device that receives various information from an operator who operates the treatment selection support system 100, and is, for example, a mouse and a keyboard. The operator is, for example, a doctor or a medical physicist. The display device 103 is a device that displays various information such as the calculation results calculated by the arithmetic processing device 101, and is, for example, a display.

[0020] The memory 104 and the database 105 are composed of the same or different recording media, and record a program (computer program) that defines the operation of the arithmetic processing device 101, and various information used and generated by the arithmetic processing device 101. The recording media are, for example, a magnetic storage medium such as a hard disk drive (HDD), a semiconductor storage medium such as a random access memory (RAM), a read only memory (ROM) and a solid state drive (SSD), and a combination of an optical disk and an optical disk drive such as a digital versatile disk (DVD). When the treatment selection support system 100 starts to operate (for example, when the power is turned on), the arithmetic processing device 101 reads a program from the recording medium and executes the read program to perform various processes.

[0021] The communication device 106 is a communication interface that is communicatively connected to an external device. In the example of Fig. 5, the communication device 106 is connected to an information system 201 and a radiotherapy device 202 installed in its own facility (facility A) as external devices, and an external database 203 installed outside the facility.

[0022] The information system 201 is a computer device that performs various information processing related to radiation therapy and stores various information used and generated in the information processing. For example, the information processing of the information system 201 performs planning processing to plan a treatment plan for radiation therapy, and stores a treatment plan that is a processing result of the planning processing, and a treatment planning CT (Computed Tomography) image that is a treatment planning image for planning the treatment plan.

[0023] The radiotherapy device 202 is a device that actually performs radiotherapy on a patient. There are a plurality of types of radiotherapy device 202, that is, treatment modalities, and in FIG. 1, two radiotherapy devices 202A and 202B with different treatment modalities are shown as the radiotherapy device 202. Examples of the treatment modalities include a proton beam therapy device, a carbon ion therapy device, a 3D-CRT device, an IMRT device, and a VMAT device. Note that radiotherapy devices 202 with different treatment modalities may be arranged in each treatment facility.

[0024] The external database 203 is a storage device that stores parameter information indicating the values ​​of parameters for predicting the value of a therapeutic effect index that evaluates the effect of radiation therapy on a patient. The external database 203 is configured, for example, from a recording medium similar to the memory 104 and the database 105. In this embodiment, the therapeutic effect indexes are DVH (Dose-Volume Histogram) and NTCP. The values ​​of the parameters for predicting the value of the therapeutic effect index differ for each treatment modality, and the parameter information indicates the parameter values ​​for each treatment modality.

[0025] FIG. 2 is a flowchart for explaining an example of treatment selection processing by the treatment selection support system 100. FIGS. 3 to 5 are diagrams showing an example of a GUI (Graphical User Interface) 300 used by an operator in the treatment selection processing. The GUI 300 of this embodiment has three tabs (a planning image prediction tab 301, a ROI (Region Of Interest) extraction tab 306, and a DVH prediction tab 307), and FIG. 3 shows the GUI 300 in a state where the planning image prediction tab 301 is selected, FIG. 4 shows the GUI 300 in a state where the ROI extraction tab 306 is selected, and FIG. 5 shows the GUI 300 in a state where the DVH prediction tab 307 is selected. The GUI 300 is displayed on the display device 103.

[0026] In the treatment selection process, first, the arithmetic processing device 101 acquires a diagnostic CT image as a diagnostic image for diagnosing a patient's disease (step S1). For example, the operator uses the input device 102 to select a planning image prediction tab 301 of the GUI 300 shown in Fig. 3, and further presses the file input button 302 to select a diagnostic CT image to be used in the treatment selection process. The arithmetic processing device 101 acquires the selected diagnostic CT image.

[0027] The arithmetic processing device 101 records the acquired diagnostic CT image in the memory 104 or the database 105 (step S2).

[0028] 3, the arithmetic processing device 101 creates a planning predicted CT image, which is a planning predicted image obtained by predicting a treatment planning CT image for creating a treatment plan for the patient, based on the diagnostic CT image recorded in the memory 104 or the database 105. The arithmetic processing device 101 records the planning predicted CT image in the memory 104 or the database 105 (step S3).

[0029] The planning predicted CT image is created using a planning image prediction model realized by an application program recorded in the memory 104 or the database 105. The planning image prediction model is a model that predicts a treatment planning CT image from a diagnostic CT image and outputs it as a planning predicted CT image. The planning image prediction model is constructed by machine learning such as deep learning, for example.

[0030] In addition, since the treatment planning CT images are used for the simulation of the irradiation of the patient with radiation in the creation of the treatment plan, the patient is photographed in a different position from the diagnostic CT images, for example, with the patient's arms raised. In addition, in order to perform the simulation accurately, the treatment planning CT images need to capture the entire area from the body surface to the target. For this reason, the field of view (FOV) may be different between the treatment planning CT images and the diagnostic CT images. Furthermore, filters may be used in the diagnostic CT images to improve the visibility of the patient's internal structures. For this reason, the pixel values ​​of each voxel may be different between the treatment planning CT images and the diagnostic CT images. In addition, the resolution may differ between the diagnostic CT images and the treatment planning CT images due to the protocols of each medical institution, and the settings of the CT imaging device at the time of shooting (such as the tube settings of the X-ray source) may be different.

[0031] Next, the arithmetic processing device 101 displays the diagnostic CT image and the planning predicted CT image recorded in the memory 104 or the database 105 as a diagnostic CT image 304 and a predicted CT image 305 on the GUI 300 (step S4), as shown in Fig. 3. This allows the operator to check the diagnostic CT image and the planning predicted CT image.

[0032] Thereafter, when the operator selects the ROI extraction tab 306 of the GUI 300, the GUI 300 is displayed in the state shown in Fig. 4. Then, when the operator presses the extraction start button 401 of the GUI 300, the arithmetic processing device 101 extracts a predicted region of interest that predicts a region of interest corresponding to a part of the patient related to irradiation with radiation from the planning predicted CT image recorded in the memory 104 or the database 105, and records predicted region of interest information indicating the predicted region of interest in the memory 104 or the database 105 (step S5).

[0033] The region of interest includes a target region corresponding to a target, which is a site to be irradiated with radiation, and an exclusion region corresponding to an OAR, which is a site to avoid irradiation with radiation. For example, in radiotherapy for prostate cancer, a region corresponding to a tumor in the prostate corresponds to the target region, and a region corresponding to the rectum and bladder corresponds to the exclusion region. The region of interest is extracted using an automatic region of interest extraction model realized by an application program recorded in the memory 104 or the database 105. The automatic region of interest extraction model predicts a region of interest from a predicted CT image for planning, and outputs three-dimensional position information of the predicted region of interest, which is the predicted region of interest, as predicted region of interest information. The automatic region of interest extraction model is constructed, for example, by machine learning such as deep learning.

[0034] Next, the arithmetic processing device 101 displays the GUI 300 by superimposing the predicted region of interest indicated by the predicted region of interest information recorded in the memory 104 or the database 105 as a region of interest 402 on the planning predicted CT image 403 (step S6), as shown in FIG. 4. At this time, the arithmetic processing device 101 may further display detailed information 404 regarding the predicted region of interest, as shown in FIG. 4. The detailed information includes, for example, the type of the predicted region of interest (target region or excluded region) and the name of the part corresponding to the predicted region of interest. This allows the operator to confirm the predicted region of interest. The treatment selection support system 100 may be configured so that the operator can instruct the correction of the shape and position of the region of interest 402 using the input device 102. In this case, the arithmetic processing device 101 corrects the shape and position of the predicted region of interest according to the correction instruction.

[0035] Next, when the operator selects the DVH prediction tab 307 of the GUI, the GUI 300 shown in FIG. 5 is displayed. Then, when the operator presses the GUI 300 prediction button 501, the arithmetic processing device 101 calculates a predicted DVH by predicting the DVH as a three-dimensional dose distribution in the patient's body formed by radiation therapy for each treatment modality based on the predicted region of interest information recorded in the memory 104 or the database 105. Furthermore, the arithmetic processing device 101 calculates a predicted NTCP by predicting the NTCP from the predicted DVH for each treatment modality. Then, the arithmetic processing device 101 records predicted DVH information indicating the predicted DVH for each treatment modality and predicted NTCP information indicating the predicted NTCP for each treatment modality in the memory 104 or the database 105 (step S7). The DVH and NTCP are treatment effect indices, and the predicted DVH and predicted NTCP are prediction results of the treatment effect indices.

[0036] The calculation of the predicted DVH is performed using a DVH prediction model realized by an application program recorded in the memory 104 or the database 105. The DVH prediction model is prepared for each treatment modality, and predicts the DVH for the corresponding treatment modality from the predicted region of interest information and outputs it as the predicted DVH. The DVH prediction model will be described in more detail later (see FIG. 6).

[0037] Next, the arithmetic processing device 101 displays the predicted DVH and predicted NTCP for each treatment modality recorded in the memory 104 or the database 105 as treatment effect information 510 on the GUI 300 (step S8), as shown in Fig. 5. This allows the operator to check the predicted DVH and predicted NTCP, which are treatment effect indexes of radiation therapy, for each treatment modality. The predicted DVH is calculated for each region of interest, and the operator can switch the displayed predicted DVH using the tag 502. In addition, the predicted NTCP is calculated for each type and grade of side effects of radiation therapy, and the operator can switch the displayed predicted NTCP using the tag 503.

[0038] Next, the operator uses the input device 102 to select a reference modality, which is a reference treatment modality, from the NTCP list 507, which is a list of predicted NTCPs for each treatment modality displayed on the GUI 300 (step S9). The reference modality can be selected arbitrarily by the operator, and for example, the treatment modality of the radiotherapy device 202 installed in the operator's own facility is selected. In the example shown in Fig. 5, "X-ray therapy system A", which is a 3D-CRT device, is selected as the reference modality.

[0039] Next, the arithmetic processing device 101 acquires reference modality information indicating the reference modality selected by the operator, and records the reference modality information in the memory 104 or the database 105. Furthermore, the arithmetic processing device 101 calculates an NTCP difference, which is the difference between the predicted NTCP of the comparison target modality and the predicted NTCP of the reference treatment modality, for each comparison target modality that is a treatment modality other than the reference modality. Then, the arithmetic processing device 101 records NTCP difference information indicating the NTCP difference for each treatment modality in the memory 104 or the database 105 (step S10).

[0040] Then, the arithmetic processing device 101 displays the NTCP difference information recorded in the memory 104 or the database 105 as the NTCP difference 508 on the display device 103 (step S11), and ends the process. This allows the operator to confirm the NTCP difference between the radiotherapy device 202 of the own facility set as the reference modality and other treatment modalities. In addition, in step S11, the arithmetic processing device 101 may suggest to the operator that the comparison target modality whose NTCP difference exceeds a predetermined reference value be adopted by giving an icon 509. In this case, it is possible to more easily confirm a treatment modality that is expected to have a higher therapeutic effect than the own facility. In addition, the operator may return to the process of step S9 and change the reference modality.

[0041] Fig. 6 is a diagram for explaining an example of an algorithm of a DVH prediction model for calculating a predicted DVH in the process of step S7 in Fig. 2. Note that the method of implementing the DVH prediction model is not limited to the following algorithm, and may be machine learning such as deep learning. The arithmetic processing device 101 performs the following processes according to the algorithm of the DVH prediction model.

[0042] The arithmetic processing device 101 first calculates an enlarged target area 602 by enlarging the target area 601. Specifically, the arithmetic processing device 101 first calculates the enlarged target area 602 by enlarging the target area 601 as shown in FIG. 6(a). i (0) Set the point P i (0) It is desirable to arrange the pixels evenly (at equal intervals). Also, i is an integer between 1 and N, where N is the number of pixels P i (0) The total number of points P may be determined in advance or may be determined according to the size of the target region 601. i (0) A normal vector r to the surface of the region of interest, passing from the inside to the outside of the region of interest at i (0) Furthermore, the arithmetic processing device 101 calculates each point P i (0) Let r be the normal vector passing through that point. i (0) Point P moved by i (1) (=P i (0) +r i (0) ) is calculated as the enlarged target area 602.

[0043] Then, every time the calculation processing device 101 calculates the enlarged target area 602 according to the following formula (2), the calculation processing device 101 performs the above process again with the enlarged target area as the target area, and calculates each point P i (j) Let r be the normal vector passing through that point. i (j) Point P moved by i(j+1) (=P i (0) +r i (0) ), where j is the number of times the target region 601 has been generated. i (j) The absolute value of each point P i (j) For example, it is about 2mm to 10mm. Also, the normal vector r i (j) The absolute value of may vary depending on the protocol of each facility, etc. The maximum value of the number of times of expansion j is, for example, determined in advance.

[0044] Next, as shown in FIG. 6B, the arithmetic processing device 101 calculates an OV (Overlap Volume) area where the enlargement target area 602 and the OAR 603 overlap for each enlargement number j. Furthermore, the arithmetic processing device 101 calculates a differential OV area OV, which is a difference between the OV area in the enlargement target area 602 for the jth time and the OV area in the enlargement target area 602 for the j-1th time. j 604 Calculate the difference OV area OV j 604 and its volume dV j and are recorded in the memory 104 or the database 105. In addition, when j=0, the difference OV region ΔOV j 604 is the region where the target region 601 and the OAR 603 overlap.

[0045] Next, the arithmetic processing device 101 calculates the differential OV region ΔOV recorded in the memory 104 or the database 105. j Volume dV per 604 j Using this, the predicted DVHV(x) is calculated according to the following equation 6.

number

number

[0046] Also, D j is the differential OV domain ΔOV j The average dose given to 604, σ j is the differential OV domain ΔOV j 604. These parameters are parameters for predicting the therapeutic effect index, and their values ​​differ for each therapeutic modality and can be calculated from the treatment plan of the treatment performed in the past. As described above, the parameter information indicating the parameter values ​​for each therapeutic modality is stored in, for example, the external database 203. The parameter information may include parameter values ​​corresponding to radiation therapy devices installed in different facilities.

[0047] As shown in FIG. 5, when the operator presses the modality selection button 504 in step S7, a modality selection window 505 is displayed. When the operator uses the modality selection window 505 to select a treatment modality for checking the predicted result of the treatment effect index and presses the close button 506, the arithmetic processing device 101 accesses the external database 203 via the communication device 106. Then, the arithmetic processing device 101 acquires the parameter value corresponding to the treatment modality selected in the modality selection window 505 from the parameter information stored in the external database 203, and uses it for calculating the predicted DVH. Also, as shown in FIG. 5, even if the treatment system is the same, if the facilities are different, it can be registered as a different type of treatment modality. Also, similar treatment modalities such as treatment modalities with the same irradiation method may be registered as one treatment modality.

[0048] In addition, as shown in Equation 6 and Equation 7, in this embodiment, the differential OV region ΔOV j The histogram of the dose applied to 604 is modeled using a Gaussian distribution, although the distribution used for modeling can be changed as appropriate based on actual data from previous runs, such as a Cauchy distribution or a Landau distribution.

[0049] In the above-described embodiment, DVH and NTCP are calculated as the therapeutic effect index, but the therapeutic effect index is not limited to this example, and may be, for example, only DVH and NTCP, or may be another index such as TCP (Tumor Control Probability), or may be a combination of them. In calculating a therapeutic effect index such as TCP, in addition to a predicted CT image for planning, patient information on the patient may be required. In this case, the arithmetic processing device 101 further uses the patient information to calculate the therapeutic effect index. The patient information is stored in, for example, the information system 201, and is acquired by the arithmetic processing device 101 as necessary. In addition, the patient information includes, for example, age, sex, lifestyle history such as smoking and drinking, test results of various tests (blood tests, biopsies, genetic tests, etc.), and the presence or absence of concomitant use of medication such as anticancer drugs and immune checkpoint inhibitors.

[0050] In the present embodiment, the arithmetic processing device 101 generates a planning prediction CT image from a diagnostic CT image and predicts a treatment effect index based on the planning prediction CT image, but the image for predicting a treatment effect index is not limited to the planning prediction CT image. As an image for predicting a treatment effect index, for example, other three-dimensional fluoroscopic images such as a treatment planning CT image actually used to create a treatment plan for a patient can be used. When a region of interest has already been set for this treatment planning CT image, the arithmetic processing device 101 can omit the process of predicting the region of interest and use the already set region of interest for predicting a treatment effect index.

[0051] Furthermore, the treatment selection support system 100 may evaluate the appropriateness of the treatment modality actually selected by the operator.

[0052] When the operator selects a treatment modality, a CT image for treatment planning is actually acquired by a CT device for treatment planning (not shown). The information system 201 sets a region of interest in the CT image for treatment planning according to an instruction from the operator. The information system 201 also creates a treatment plan in which the dose distribution in the patient is optimized using treatment planning software corresponding to the selected treatment modality so that a high dose is applied to the target region as uniformly as possible and exposure to the OAR is avoided as much as possible. The information system 201 calculates the DVH based on the dose distribution and the region of interest according to the treatment plan. The information system 201 stores the CT image for treatment planning, region of interest information indicating the region of interest, and the DVH. If a treatment modality that does not exist in the own facility is selected, the patient is introduced to another facility equipped with the treatment modality, and the above-mentioned series of procedures are performed at the other facility.

[0053] The arithmetic processing device 101 of the treatment selection support system 100 acquires the treatment planning CT image, the region of interest information, and the DVH from the information system 201 and records them in the memory 104 or the database 105. The arithmetic processing device 101 calculates the degree of agreement between the treatment planning CT image, the region of interest information, and the DVH, and the planning predicted CT image, the predicted region of interest information, and the predicted DVH calculated in the treatment selection process described with reference to FIG. 2. The arithmetic processing device 101 judges whether or not the degree of agreement is equal to or greater than a predetermined reference value. If the degree of agreement is less than the reference value, it is considered that the accuracy of the prediction of the treatment planning CT image, the predicted region of interest, and the predicted DVH by the treatment selection support system 100 is not sufficient. In this case, the arithmetic processing device 101 outputs an alert by displaying a message on the GUI 300 indicating that there is a doubt about the validity of the selected treatment modality, and prompts the operator to reconsider the treatment modality. The arithmetic processing device 101 may output an alert when at least one of these degrees of match is below a reference value, or may output an alert when all of these degrees of match are below a reference value.

[0054] As described above, according to this embodiment, the arithmetic processing device 101 predicts a therapeutic effect index for each therapeutic modality of the radiotherapy device 202 by using a region of interest corresponding to a part of the patient related to radiation irradiation in a 3D fluoroscopic image of the patient and parameter information indicating a parameter value for predicting a therapeutic effect index for evaluating the effect of radiation therapy for each therapeutic modality of the radiotherapy device 202 that performs radiation therapy. Therefore, since it is possible to predict a therapeutic effect index for each therapeutic modality, it is not necessary to obtain a 3D dose distribution in the patient's body for each therapeutic modality by simulation. Therefore, it is possible to easily determine a radiotherapy device appropriate for the patient.

[0055] In this embodiment, the arithmetic processing device 101 generates a planning prediction CT image, which is a prediction of a treatment planning CT image for planning a treatment plan for radiation therapy based on a diagnostic CT image for diagnosing a patient's disease, as a 3D fluoroscopic image for predicting a treatment effect index. This makes it possible to predict a treatment effect index for each treatment modality before capturing a treatment planning CT image, that is, before creating a treatment plan. Generally, a treatment modality is already determined at the time of capturing a treatment planning CT image, so predicting a treatment effect index for each treatment modality before capturing a treatment planning CT image as in this embodiment is very useful for easily determining a radiation therapy device appropriate for a patient.

[0056] In addition, in this embodiment, the calculation processing device 101 outputs an alert if the degree of agreement between the treatment planning CT image, region of interest information, and DVH used when actually creating the treatment plan and the planning predicted CT image, predicted region of interest information, and predicted DVH predicted in the treatment selection process is less than a reference value.

[0057] In this embodiment, the arithmetic processing device 101 extracts a region of interest from a planning prediction CT image. Therefore, the operator does not need to set a region of interest to predict a treatment effect index, and it is possible to reduce the effort required for determining an appropriate radiotherapy device 202 for a patient.

[0058] In this embodiment, the arithmetic processing device 101 acquires parameter information from an external database 203 that stores parameter information indicating parameter values ​​corresponding to multiple types of radiotherapy devices 202 installed in multiple facilities A to C, and uses the parameter information to predict a therapeutic effect index. This makes it possible to determine whether a radiotherapy device 202 of a therapeutic modality that does not exist in the facility is appropriate or not, making it possible to provide a more appropriate radiotherapy for the patient.

[0059] In this embodiment, the therapeutic effect index includes at least one of DVH, NTCP, and TCOP. The arithmetic processing device 101 may further use patient information on the patient to predict the therapeutic effect index. This makes it possible to predict an appropriate therapeutic effect index depending on the situation.

[0060] The above-described embodiments of the present disclosure are illustrative examples of the present disclosure, and are not intended to limit the scope of the present disclosure to only these embodiments. A person skilled in the art can implement the present disclosure in various other forms without departing from the scope of the present disclosure. [Explanation of symbols]

[0061] 100: Treatment selection support system 101: Processing device 102: Input device 103: Display device 104: Memory 105: Database 106: Communication device 201: Information system 202: Radiation therapy device 203: External database

Claims

1. A treatment support system for predicting an effect of radiation therapy on a patient, comprising: a calculation processing device that predicts a therapeutic effect index evaluating the effect of the radiation therapy for each type of radiation therapy device using a region of interest in a three-dimensional fluoroscopic image of the patient that corresponds to a part of the patient that is related to radiation irradiation, and parameter information indicating parameter values ​​for predicting a therapeutic effect index evaluating the effect of the radiation therapy for each type of radiation therapy device that performs the radiation therapy.

2. 2. The treatment support system according to claim 1, wherein the arithmetic processing device generates, as the three-dimensional fluoroscopic image, a predicted planning image that predicts a treatment planning image for formulating a treatment plan for the radiation therapy based on a diagnostic image for diagnosing a disease of the patient.

3. 3. The treatment support system according to claim 2, wherein the arithmetic processing device calculates a degree of agreement between a treatment planning image used when the treatment plan was actually created and the three-dimensional fluoroscopic image, and outputs an alert when the degree of agreement is less than a reference value.

4. The treatment support system according to claim 2 , wherein the arithmetic processing device extracts the region of interest from the three-dimensional fluoroscopic image.

5. 5. The treatment support system according to claim 4, wherein the arithmetic processing device calculates a degree of agreement between the region of interest set in a treatment planning image used when the treatment plan was actually created and the extracted region of interest, and outputs an alert when the degree of agreement is less than a reference value.

6. 3. The treatment support system according to claim 2, wherein the calculation processing device calculates a degree of agreement between a treatment effect index calculated from the region of interest and the parameters set in a treatment planning image used when actually formulating the treatment plan and the predicted treatment effect index, and outputs an alert when the degree of agreement is less than a reference value.

7. 2. The treatment support system according to claim 1, wherein the arithmetic processing device acquires the parameter information from a storage device that stores the parameter information indicating values ​​of the parameters corresponding to a plurality of types of the radiation therapy devices installed in a plurality of treatment facilities, and uses the parameter information to predict the treatment effect index.

8. The treatment support system according to claim 1 , wherein the calculation processing device predicts the treatment effect index by further using patient information relating to the patient.

9. The treatment support system according to claim 1 , wherein the treatment effect index includes at least one of a dose-volume histogram, a probability of normal tissue damage, and a probability of tumor control.

10. A treatment support method using a treatment support system for predicting an effect of radiation therapy on a patient, comprising: A treatment support method for predicting a treatment effect index for evaluating the effect of the radiation therapy for each type of radiation therapy device, using a region of interest in a three-dimensional fluoroscopic image of the patient that corresponds to a part of the patient that is related to radiation irradiation, and parameter information indicating parameter values ​​for predicting a treatment effect index for evaluating the effect of the radiation therapy for each type of radiation therapy device that performs the radiation therapy.

Images