Optimization of thermoradiotherapy treatment

Abstract

The set of combined treatment plans including radiotherapy and thermotherapy treatment can be optimized by optimizing both plans together in a co-optimization or by optimizing one of the plans and then considering the predicted effects of the first optimized plan for the other plan.

Description

TECHNICAL FIELD

[0001] The present invention relates to a multi-modal treatment plan combining radiation therapy and thermal therapy treatment. This treatment is commonly referred to as hyperthermia or thermo- enhanced radiotherapy. BACKGROUND

[0002] Radiotherapy or radiation therapy (RT) refers to the placement of the patient's body under radiation. Hyperthermia therapy (HT) refers to the treatment of malignant disease by heating the tissue. Various heating techniques can be used, including but not limited to liquid preparations, capacitive heating systems, exposure to electromagnetic radiation (radiofrequency, microwave or infrared), sonic waves (ultrasound). The use of HT also enhances the effect of RT and some systemic types of treatment - such as chemotherapy. It has been found that elevating the temperature of the tumor to approximately 39 to 45°C and combining it with RT and / or systemic therapy increases the tumor control probability (TCP) and almost does not increase the normal tissue complication probability (NTCP) compared to single modality treatment. This synergistic effect is due to various sensitizing effects of HT, which include inhibition of DNA repair mechanisms and reoxygenation, as well as direct killing of radioresistant hypoxic tumor cells by HT and triggering of various immune responses. The radiosensitizing effect can be mathematically quantified using known concepts, such as the equivalent radiation dose (EQD), which includes a temperature-dependent parameter for the radiation; biological models, which include TCP and NTCP that can depend on the temperature-dependent EQD; the thermal enhancement ratio (TER), which is defined as the ratio of the radiation dose required to produce a specific therapeutic effect without HT treatment to the radiation dose required to produce the same therapeutic effect with HT treatment.

[0003] Generally, HT treatment and RT treatment use different equipment, and therefore, for practical reasons, HT treatment and RT treatment are usually applied to the patient sequentially, with an interval of approximately 0 to 4 h between the treatments. The delivery of the different treatment modalities can also be simultaneous. RTHT treatment usually consists of approximately 5 to 35 RT fractions delivered daily, with 1 to 2 HT boosts per week, resulting in a total of approximately 1 to 10 HT fractions. Generally, the same RT treatment is delivered regardless of whether the HT fractions are delivered on the same day or not, but there are also strategies of adapting the RT plan on the days of HT delivery.

[0004] The optimization of this multi-modal treatment involves the optimization of at least one RT plan and at least one HT plan.

[0005] The overall goal of all such treatments is to optimize the therapeutic effect of the treatment on the patient. For multi-modal treatment, this involves exploiting the synergistic effect as much as possible. For RTHT treatment, this includes exploiting the effect of hot or cold temperature voxels being co-localized with the RT dose, respectively, to account for the temperature-dependent radiosensitization in each voxel.

[0006] Therefore, international patent application PCT / EP2016 / 074609 discloses a method of planning a thermally enhanced radiotherapy, wherein first a plan for a thermotherapy or a radiotherapy is made. After delivering this plan to the patient, the results are evaluated and taken into account when optimizing another plan. In other words, a thermotherapy treatment plan is optimized and delivered, and the results of the delivery are taken into account to optimize a radiotherapy treatment plan, and vice versa. SUMMARY

[0007] The present disclosure aims to provide an improved planning method for multimodal treatment involving radiotherapy and thermotherapy.

[0008] The present disclosure relates to a computer-based method for generating a set of treatment plans for thermoradiotherapy treatment of a treatment volume of a patient, the set of treatment plans comprising a radiotherapy treatment plan and a thermotherapy treatment plan, the method comprising the steps of:

[0009] a. obtaining an optimization problem comprising at least one objective function related to a multimodal treatment comprising thermotherapy and radiotherapy, based on a model that predicts the effect of a combination of heat and radiation dose,

[0010] b. generating at least one of the radiotherapy treatment plan and the thermotherapy treatment plan by optimizing an optimization function value evaluated on the predicted combined effect of the set of treatment plans.

[0011] Thus, according to the present invention, the synergistic effect of RTHT treatment can be enhanced, since these treatments are optimized and planned together. The present invention is able to determine a more correct co-localization of temperature distribution, radiation dose distribution and optionally additional systemic therapy effect for the patient, so that the synergistic effect of the treatment can be used more effectively. In a preferred embodiment, the radiotherapy treatment plan is an external beam radiotherapy treatment plan.

[0012] By taking into account the temperature sensitivity of the radiation dose, the dose in a voxel can be increased or decreased, which can be related to the problem of alleviating the co-localization of hot or cold temperature voxels with the corresponding RT dose. In some cases, even such problems can be exploited. For example, if the temperature in a target point is increased, the radiation dose to the target point can be decreased while maintaining a predetermined TCP.

[0013] In OARs, a combination of high temperature and high radiation dose should be avoided, as this combination can lead to increased NTCP. This can be achieved by reducing the temperature and / or radiation dose in the corresponding voxels in a sequential optimization strategy in which the amount of the second modality is adjusted in a second optimization considering the combined predicted effects or in a joint optimization strategy in which both modalities are optimized simultaneously. In target volumes, it can be desirable to enhance the effect by a combination of high temperature and high radiation dose. From this, it can be possible to co-locate high temperature and high radiation dose to the same voxels within the target volume of a multi-modality treatment to enhance TCP using both sequential and joint optimization strategies.

[0014] Before performing step a), the method can comprise a step of defining a set of treatment plans comprising a thermal treatment plan and a radiation treatment plan and at least one other type of plan, such as a surgery or a systemic treatment type comprising chemotherapy, immunotherapy and hormone therapy. Alternatively, which plans are optimized is predetermined.

[0015] For example, the set of treatment plans can comprise at least a first radiation treatment plan and a second radiation treatment plan, wherein the first radiation treatment plan is optimized for delivery on the same day as at least one thermal treatment plan and the second radiation treatment plan is optimized for delivery on a day in which another thermal treatment plan is delivered or no thermal treatment plan is delivered.

[0016] In addition to the RT plan and the HT plan, the set of treatment plans comprises at least one additional therapy plan related to a systemic treatment such as chemotherapy and at least one thermal treatment plan and at least one radiation treatment plan.

[0017] The joint optimization of HT and RT is advantageous as these types of treatments are known to have a strong direct influence on each other and a strong synergistic effect. The set of treatment plans can also comprise plans for one or more additional treatment types. These additional treatment types mainly include systemic therapies such as chemotherapy, hormone therapy and immunotherapy, but can also include surgery.

[0018] Using the embodiment with one RT plan and one HT plan as an example, three general embodiments are designed: generating both the RT plan and the HT plan simultaneously; generating the RT plan and optimizing the HT plan based on the predicted outcome of the RT plan; and optimizing the RT plan based on the predicted outcome of the HT plan. In the first case, the method includes a step of generating at least one of a radiotherapy plan and a hyperthermia treatment plan, which includes a co-optimization of the hyperthermia plan and the radiotherapy plan, the optimization problem including information about the predicted effect of a combination of temperature and dose for each voxel. In the second case, a pre-existing radiotherapy plan has been obtained, and the step of generating at least one of a radiotherapy plan and a hyperthermia treatment plan includes optimizing the hyperthermia plan taking into account the predicted effect of the at least one pre-existing radiotherapy plan, wherein the optimization problem includes the predicted effect of a radiation dose for each voxel. In the third case, a pre-existing hyperthermia plan has been obtained prior to the step of obtaining the optimization problem, and the step of generating at least one of a radiotherapy plan and a hyperthermia treatment plan includes optimizing the radiotherapy plan taking into account the predicted effect of the at least one pre-existing hyperthermia plan, wherein the optimization problem includes temperature dependence information for a biological parameter for each voxel.

[0019] In addition to one or more objective functions, the optimization problem can include one or more constraints that define parameters that must be maintained during optimization. The optimization problem preferably includes a biological or physical objective. The optimization problem can also include at least one objective function for machine parameter optimization of one or both of the heat delivery system and the radiation delivery system. The optimization problem can also include at least one constraint related to machine limitations of at least one delivery machine to be used to deliver the hyperthermia treatment and / or the radiation treatment, as a constraint or as an objective. In some embodiments, the optimization problem includes a simplified machine model of the heating system or the RT system for which to optimize parameters, such as fluence optimization and power optimization for the heating system.

[0020] The model used as the basis for the optimization problem can be a biological model. The optimization problem advantageously includes a biological or physical objective. The model and objective can be related to factors such as: radiation dose; temperature; equivalent radiation dose (EQD); equivalent uniform distribution (EUD); biological effective dose (BED); and thermal enhancement ratio (TER) limits for target points and organs at risk (OARs) in treatment volume, dose volume histogram or temperature volume histogram (DVH and TVH) limits; probability limits for tumor control probability (TCP) and normal tissue complication probability (NTCP); complication-free cure probability, secondary cancer, and overall survival; linear energy transfer (LET) limits related to radiation; location and / or uniformity and consistency index of particle stops.

[0021] Aspects of the invention also relate to a computer program product comprising computer readable code which, when implemented in a processor, will cause the processor to carry out the method according to the embodiments discussed above. The computer program product can be stored on a non-transitory storage unit. Aspects of the invention also relate to a computer comprising a processor and a program memory, wherein the program memory holds such a computer program product for execution in the processor.

[0022] List of abbreviations

[0023] The following abbreviations are used herein:

[0024] BED - biological effective dose

[0025] DVH - dose-volume histogram

[0026] EQD - equivalent radiation dose

[0027] EQD2 - equivalent radiation dose in 2-Gy fractions

[0028] EUD - equivalent uniform dose

[0029] HT - hyperthermia therapy

[0030] LET - linear energy transfer

[0031] LQ - linear quadratic

[0032] NTCP - normal tissue complication probability

[0033] OAR - organ at risk

[0034] RT - radiotherapy

[0035] RTHT - radio-hyperthermia therapy

[0036] TCP - tumor control probability

[0037] TER - thermal enhancement ratio. Ratio of the radiation dose required without HT to the radiation dose required with HT for the same situation.

[0038] TVH - temperature-volume histogram. DETAILED DESCRIPTION

[0039] The method according to an embodiment of the application concerns the optimization of a treatment plan. As known in the art, this optimization is performed by applying an optimization problem designed for the current situation based on clinical objectives and one or more biological models. The optimization problem is defined by an objective function, which defines the objective that the optimization should strive to achieve, for example minimizing the dose to the tissue surrounding the target, and constraints, which set absolute requirements, for example a minimum dose to the target. The constraints can also reflect machine limitations of at least one delivery machine to be used for delivering the thermotherapy treatment and / or the radiotherapy. Such constraints are well known to the skilled person and include maximum speed of mechanical parts, maximum amount of radiation delivered per time unit, and other limitations.

[0040] The method according to a first embodiment of the present disclosure is described in detail below, wherein an RT plan is optimized taking into account the predicted effect of a pre-obtained HT plan. In a first step S11, a set of treatment plans corresponding to the treatment modalities involved is defined. In the present embodiment, the set of plans comprises a thermotherapy plan and a radiotherapy plan, and can optionally comprise plans of other modalities, such as surgery, immunotherapy, hormone therapy or chemotherapy.

[0041] At this stage, the following information can be required:

[0042] • the temperature distribution of the plans with respect to the outcome from HT,

[0043] • the time interval between the delivery of the two treatment modalities, if included in the model,

[0044] • the number of HT fractions and the delivery schedule, if not included in the optimization.

[0045] In a second step S12, an optimization problem is obtained for optimizing the set of plans together. Typically, to achieve this, the optimization problem must include at least one objective function related to a combined treatment comprising thermotherapy and radiotherapy, and optionally one or more plans included in the set. In the present embodiment, the optimization problem comprises an optimization function, wherein for each voxel of interest the temperature dependence of the biological parameter is included.

[0046] For example, the linear-quad (LQ) model of cell survival can be used to optimize the equivalent radiation dose (EQD) in a 2-Gy fraction (EQD2) of a multi-modal treatment. The LQ parameters a and b are known to vary with temperature, with increasing temperature increasing radiosensitivity. By incorporating the temperature dependence of the LQ parameters into the model, the combination of radiation dose and temperature can be directly optimized on EQD2 to predict the efficacy of the treatment. For example, goals can be set for maximum / minimum EQD2, DVH constraints, and uniform EQD2. As a further development, the EQD2 can be incorporated into one or more suitable optimization models. Such models include the equivalent uniform dose (EUD), tumor control probability (TCP), and normal tissue complication probability (NTCP) models. This can include the use of an objective function defined based on EQD2 that includes temperature dependence. It can also include the use of an objective function defined as a model based on EQD2 that includes temperature dependence. In this way, the temperature-dependent EQD2, EUD, TCP, and / or NTCP will be optimized to achieve the stated clinical goals.

[0047] The pre-existing temperature distribution of the hyperthermia plan is used as an input for the optimization of the radiotherapy plan, and in step S14 the RT plan is optimized while taking into account the radiosensitization caused by the temperature distribution resulting from the pre-existing HT plan S13. The output S15 of step S14 is a set of plans, which includes the pre-existing HT plan and the RT plan optimized using the optimization problem obtained in step S13. The delivery order of the HT plan or the number of fractions can be modified to enhance the combined effect of RT and HT.

[0048] A second embodiment of the method according to the application is described below, in which the HT plan is optimized while taking into account the combined effect of the radiation dose distribution resulting from the pre-existing RT plan. In a first step S21, a hyper-radiotherapy treatment is defined as a set of treatment plans corresponding to the treatment modalities involved. As in the first embodiment of the method according to the application, this set of plans includes a hyperthermia plan and a radiotherapy plan, and can also optionally include other types of plans, such as surgery, immunotherapy, hormone therapy or chemotherapy, or a second radiotherapy plan related to a different type of radiotherapy.

[0049] In a second step S22, an optimization problem is obtained for optimizing the set of plans together. To achieve this, the optimization problem must include at least one objective function related to the combined treatment including hyperthermia and radiotherapy, and optionally one or more other plans in the set. In this embodiment, the objective function includes an optimization function in which the effect of the radiation dose resulting from the previously obtained radiotherapy plan is included for each voxel of interest. As in the first embodiment of the method according to the application, optimization strategies using for example EQD2, TCP and NTCP models can be used for the optimization of the hyperthermia plan.

[0050] The pre-existing dose distribution of the radiotherapy treatment plan is used as input for the optimization of the hyperthermia treatment plan, and in step S24 the HT plan is optimized taking into account the dose distribution resulting from the pre-existing RT plan. The output S25 of step S24 is a set of treatment plans comprising the previously obtained RT plan and the HT plan optimized using the optimization problem obtained in step S22.

[0051] A third embodiment of the method of the application is described in the following. In a first step S31 a hyper-radiation therapy treatment is defined as a set of treatment plans corresponding to the type of treatment involved. As in the previous embodiments, the set of plans comprises one or more hyperthermia plans and one or more radiotherapy plans, and can optionally comprise other types of plans, such as surgery, immunotherapy, hormone therapy or chemotherapy.

[0052] In a second step S32 an optimization problem is obtained for optimizing the set of plans together. To achieve this, the optimization problem must include at least one objective function related to the combined treatment comprising hyperthermia and radiation, and optionally one or more other plans in the set. In this embodiment, the objective function comprises an optimization function in which for each voxel of interest a predicted combined effect of temperature and dose is included. As in the embodiments discussed above, optimization strategies can be used, for example using EQD2, EUD, TCP and NTCP models; however, in this embodiment, both the temperature and dose distribution are co-optimized in the process of finding the optimal treatment according to the combined predicted treatment effect.

[0053] In step S33 the HT plan and the RT plan are optimized in one optimization operation. The output S34 of step S33 is a set of treatment plans comprising the HT plan and the RT plan optimized together using the optimization problem obtained in step S32.

[0054] The first step Sll, S21, S31 of each of the methods described above can not be needed in many cases. For example, it can be predetermined which treatment plans are included or which possible treatment types are limited by the available equipment. The co-optimization of HT and RT is advantageous because these types of treatment are known to have a strong direct influence on each other and a strong synergistic effect.

[0055] In each of the methods discussed in relation to the first, second and third embodiments of the method according to the application described above, the step of obtaining an optimization problem can comprise retrieving an existing optimization problem or defining a new optimization problem, or adapting a predetermined optimization problem to the specific situation by including or modifying objective functions and / or constraints.

[0056] Also, in each of the methods discussed above, in addition to the thermal therapy plan, two radiotherapy plans can be included in the treatment plan set. Including two separate radiotherapy plans can be useful in cases where a combination of two different types of radiation therapy should be administered. It is also possible to include a first radiotherapy plan comprising fractions delivered in combination with thermal therapy treatment and a second radiotherapy plan comprising fractions delivered without the effect of thermal therapy. The combination within this scenario refers to being very close in time such that the effects of radiation and thermal therapy overlap and reinforce each other.

[0057] A computer that can perform the method according to the present application is described below. As is common in the art, the computer comprises a processor, a program memory and a data memory. As will be appreciated, the program memory and the data memory can be implemented in any suitable way, in the form of one or more memories, inside or outside the computer. The computer comprises or is connected to one or more user input / output devices, such as a screen, a keyboard, a mouse, audio input / output devices and any other suitable devices. The program memory holds a computer program that is arranged to run in the processor to perform the method according to the present application, e.g. according to one of the embodiments discussed above. The one or more data memories hold input data used in the method, such as patient data, information about the plan set, and in the case of the first and second embodiments of the method according to the present application, information of previously obtained plans used as input to the optimization. The data memories can also hold the resulting plan set forming the output from the method.

[0058] The treatment plans can be designed to be delivered by any suitable type of delivery device. As mentioned above, it is common to use different devices for delivering HT and RT, respectively. The HT delivery device can use any suitable technique, including but not limited to liquid formulation, capacitive heating systems, exposure to electromagnetic radiation, sound waves (ultrasound) and magnetic HT, where nanoparticles are injected into the tumor and subsequently heated by a varying magnetic field over the tumor area. Exposure to electromagnetic radiation is often used, and can include electromagnetic radiation of any suitable wavelength, including radio frequency, microwave or infrared.

[0059] The RT delivery device is preferably an external beam radiotherapy apparatus for delivering any type of radiotherapy to the patient, including photon, electron or ion radiotherapy. Delivery devices for both RT and HT are known in the art and are not discussed in further detail here. Also, the skilled person is familiar with apparatuses and devices for other types of treatment, such as surgery and systemic treatment, and their effects. Systemic treatment includes chemotherapy, hormone therapy and immunotherapy, all of which are commonly used in the treatment of cancer patients.

Claims

1. A computer-based method for generating a set of treatment plans for applying hyperthermia to a patient's treatment volume, the set of treatment plans including a radiotherapy plan and a hyperthermia plan, the method comprising the following steps: a. Based on a model that predicts the combined effects of heat and radiation dose, an optimization problem (S12; S22; S32) is obtained, which includes at least one objective function related to multimodal treatment involving both heat therapy and radiation. b. By optimizing the objective function value for evaluating the predicted combination effect on the set of treatment plans, generate (S14; S24; S33) at least one of the radiotherapy plan and the hyperthermia treatment plan.

2. The method according to claim 1, wherein, The radiotherapy plan is an external beam radiotherapy plan.

3. The method according to claim 1 or 2, wherein, The step of generating at least one of the radiotherapy plan and the hyperthermia plan includes joint optimization of the hyperthermia plan and the radiotherapy plan (S33), wherein the optimization problem includes information on the predicted combined effect of temperature and dose for each voxel.

4. The method according to claim 1 or 2, wherein, Prior to obtaining the optimization problem described in (S14), a pre-existing hyperthermia plan has been obtained in (S13), and the step of generating at least one of the radiotherapy plan and the hyperthermia plan includes optimizing the radiotherapy plan in (S14) by considering the predicted effect of at least one pre-existing hyperthermia plan, wherein the optimization problem includes temperature dependence information of biological parameters for each voxel.

5. The method according to claim 1 or 2, wherein, A pre-existing radiotherapy plan has been obtained (S23), and the step of generating at least one of the radiotherapy plan and the hyperthermia plan includes optimizing (S24) the hyperthermia plan by considering the predicted effect of at least one pre-existing radiotherapy plan, wherein the optimization problem includes the effect of the radiation dose for each voxel.

6. The method according to claim 1 or 2, wherein, The model includes one or more of the following: equivalent radiation dose (EQD), equivalent uniform distribution (EUD), bioeffective dose (BED), thermal enhancement ratio (TER), tumor control probability (TCP), normal tissue complication probability (NTCP), complication-free cure probability, secondary cancer and / or overall survival.

7. The method according to claim 1 or 2, wherein, The optimization problem includes constraints, which are defined as parameters maintained during optimization.

8. The method according to claim 1 or 2, wherein, The optimization problem includes biological or physical objectives.

9. The method according to claim 1 or 2, wherein, The optimization problem is defined as optimizing machine parameters of at least one of the heat delivery system and the radiation delivery system, which are variables.

10. The method according to claim 1 or 2, wherein, The optimization problem includes at least one constraint, as a constraint or objective, related to machine limitations of at least one delivery machine used for delivering hyperthermic and / or radiotherapy treatments.

11. The method according to claim 1 or 2, wherein, The optimization problem includes a simplified machine model of a heating system or RT system for optimizing its parameters.

12. The method according to claim 11, wherein, The optimization problems include radiation flux optimization and power optimization for the heating system.

13. The method according to claim 1 or 2, further comprising the step of defining (S11; S21; S31) the treatment plan set performed prior to steps a and b of claim 1, the treatment plan set including the hyperthermia treatment plan and the radiotherapy plan, and at least one other type of plan.

14. The method according to claim 13, wherein, The at least one other type of plan is a surgical or systemic treatment plan that includes chemotherapy, immunotherapy, and hormone therapy.

15. The method according to claim 1 or 2, wherein, The treatment plan set includes at least a first radiotherapy plan and a second radiotherapy plan, wherein the first radiotherapy plan is optimized for delivery on the same day as at least one hyperthermia plan, and the second radiotherapy plan is optimized for delivery on days when no hyperthermia plan is delivered.

16. The method according to claim 1 or 2, wherein, The treatment plan set includes at least one additional therapy plan, at least one thermotherapy plan, and at least one radiotherapy plan, wherein the at least one additional therapy plan is related to systemic treatment.

17. The method according to claim 16, wherein, The at least one additional treatment plan is related to chemotherapy.

18. A computer program product comprising a computer-readable code means, which, when executed in a processor, causes the processor to perform the method according to any one of claims 1 to 17.

19. A computer comprising a processor and a program memory, wherein the program memory stores a computer program product according to claim 18 for execution in the processor.