Dynamic estimation of biological effects of non-photon radiation of variable composition.

Abstract

A method for dynamically estimating the biological effectiveness of variable combinations of non-photon radiation according to a relative biological effectiveness RBE model including at least one biological effectiveness multiplier δ(T,E) that depends on particle type T and / or particle energy E, wherein one or more non-photon radiation contributions D (i) (T,E), 1≦i≦N, where at least one of the contributions includes multiple particle types and / or multiple particle energies; storing a per-contribution dose-weighted average of the at least one biological effect multiplier for each of one or more contributions (Equation (1)), 1≦i≦N; and calculating non-negative coefficients k1, k2, ..., k applied to one or more contributions. N and in response to obtaining a combination assignment Π for ≧0, identifying a biological effect of the combination, the identification comprising calculating a combined dose-weighted average of the at least one biological effect multiplier (Equation (2)) based on the stored per-contribution dose-weighted averages. [Equation 1] TIFF2023530597000059.tif11170(1) [Equation 2] TIFF2023530597000060.tif9170(2)

Description

[Technical Field]

[0001] Technical Field The present disclosure relates to the field of radiation therapy, and more particularly to methods and devices for estimating the biological effects of non-photon radiation of variable composition according to a relative biological effectiveness (RBE) model. [Background technology]

[0002] background

[0002] Non-photon radiotherapy may utilize ionizing radiation, such as protons, helium ions, or carbon ions. According to common practice within the radiotherapy subfield, prescriptions, clinical objectives, and treatment planning protocols may include specifications regarding equivalent doses. The equivalent dose of non-photon radiation is biologically equivalent to a reference radiation, such as photon radiation, in which case the terms photon equivalent dose or photon dose equivalent may be used. The equivalent dose is calculated from the physical absorbed dose using the relative biological effectiveness (RBE) of the radiation used, which is defined as the ratio of doses required to produce the same level of biological effect. The term RBE factor is used to indicate the conversion factor between physical dose and equivalent dose: Equivalent dose = RBE coefficient x physical dose.

[0003] Alternatively, the RBE model may provide an equivalent dose, from which the RBE coefficient can be calculated as the ratio of equivalent dose to physical dose. The RBE model may provide the biological effect as a function of the physical dose, for example of non-photon radiation. Starting from the biological effect, the equivalent dose is found by a simple calculation involving the radiobiological parameters of the reference radiation. A commonly used measure of biological effect is the negative logarithm of cell survival S, and the relevant radiobiological parameter of the photon reference is often referred to in the literature as α X , β X Therefore, there is a one-to-one relationship between equivalent dose and biological effect for a given choice of reference radiation.

[0004]

[0004] RBE can be combined with specific radiobiological models derived from physical and physiological considerations, and possibly experimentally verified or refined. Depending on the underlying radiobiological model, the RBE coefficient can vary, for example, with respect to the magnitude of the physical dose or the radiobiological properties of the irradiated tissue. A physical dose-dependent RBE coefficient can establish a nonlinear relationship between the physical dose and the equivalent dose. For example, an RBE coefficient proportional to the (p-1)th power of the physical dose for some real number p>0 can cause the equivalent dose to depend on the pth power of the physical dose. In the case of proton therapy, a widely used RBE coefficient is the so-called 1.1 model, which states that the biological effectiveness of protons exceeds that of photons by 10%, regardless of the dose and other factors. The 1.1 model has been found to underestimate the dose at the distal end of the proton field. This and other effects can be addressed by more sophisticated RBE models, including those by authors Carabe, Chen & Ahmad, Kraemer & Scholz, McNamara, and Wedenberg.

[0005]

[0005] The treatment plan Π is made up of coefficients k1, k2, ..., k N Two or more base plans P1, P2, ..., P are formed using NThe base plan may be a linear combination, particularly a convex combination, of the coefficients. The base plan may have been obtained by multicriteria optimization (MCO), thus corresponding to different weightings of different objective components that the user can explore to find a suitable tradeoff between competing goals, where each goal may represent a particular desirability of treatment, such as high tumor lethality or low exposure of organs at risk. Based on this, the user may proceed iteratively by first assigning a set of coefficient values, evaluating the generated linear combination (the "navigation plan"), assigning an improved set of coefficient values, evaluating the new navigation plan, and so on, until a satisfactory treatment plan is obtained. This type of treatment planning procedure can be likened to a feedback loop, where the coefficient values ​​are the input, the properties being evaluated are the output, and the treatment planner's heuristics and experience form the control law. It is generally desirable for each iteration to be computationally efficient so that the treatment planner does not stop trying to refine and improve too early. In particular, human treatment planners may be sensitive to the duration of the update interval.

[0006]

[0006] Because many biological effects of radiation are nonlinear in nature, as explained above, upscaling or downscaling a mixed radiation field can also consume substantial processing resources. The search for better or best scaling factors should not be prematurely terminated as a result of laborious iterations.

[0007]

[0007] Chinese Patent Application Publication No. 106902478A discloses a method for assessing the biological effect of systematic radiation therapy, in which the microscopic effect (double-strand breaks) is superimposed to estimate the total cell damage Δ i is obtained, which is applied as an initial condition to a system of ordinary differential equations (ODEs). The ODE system reflects the two-lesion kinematic (TLK) radiobiological model, and its solution corresponds to the biological effect predicted by this model. When the energy spectrum D(E) of the physical dose changes, the method of CN106902478A calculates the total cell damage Δ i It is necessary to perform a new calculation from this point onwards.

[0008] Currently, there is a need for a more computationally efficient method for evaluating the biological effects of combinations of mixed non-photon radiation fields, with the option for rapid re-evaluation if the combination changes as a result of navigation. This need is equally valid for the problem of scaling mixed non-photon radiation fields. Summary of the Invention [Problem to be solved by the invention]

[0009] overview

[0009] One object of the present invention is to propose an improved method and device for dynamically estimating the biological effect of variable combinations of non-photon radiation according to the RBE model. A particular object is to estimate the macroscopic biological effect of variable combinations of non-photon radiation. Another object is to propose an improved method and device for dynamically estimating how the biological effect changes during beam mixing. These and other objects are addressed by the present invention as defined by the independent claims. [Means for solving the problem]

[0010] In a first aspect, there is provided a method for dynamically estimating the biological effect of a variable combination of non-photon radiation according to an RBE model including at least one biological effect multiplier δ(T,E) that depends on particle type T and / or particle energy E. According to the method, one or more non-photon radiation contributions D (i) (T, E), 1≦i≦N, are obtained. In at least one voxel or volume, at least one of these contributions includes multiple particle types T and / or multiple particle energies E. Particle types may be characterized by particle mass or charge. Even in treatment plans that prescribe irradiation with a single particle type and a single energy layer, multiple particle types and / or particle energies may arise as a result of fragmentation, energy loss in tissue, etc. Even when a combination includes a single contribution, scaling issues are not trivial due to the presence of multiple particle types and / or particle energies, as will be described in more detail below.

[0011]

[0011] Once one contribution (N=1) or multiple contributions (N≧2) are obtained, a per-contribution dose-weighted average of the at least one biological effect multiplier for each of the one or multiple contributions is calculated.

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[0012] The next step of the method is carried out responsively. More precisely, when the combination assignment Π is obtained, the biological effect of the combination is identified. In order to combine or interpolate the contributions, the assignment is made by applying non-negative coefficients k1, k2, ..., k to one or more of the contributions. N It is understood that the biological effect of the combination is expressed in terms of a dose-weighted average of the stored contributions.

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[0013] The method may output the biological effect as a value for a location x, which may be a point in space or a volume in space. Alternatively, multiple biological effect values ​​at different locations may be output, forming a list, table, or function of spatial coordinates.

[0014]

[0014] This aspect of the invention is such that when a combination assignment is obtained, the biological effect is calculated based on the stored per-contribution dose-weighted average.

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[0015]

[0015] In general, all terms used in the claims should be interpreted according to their ordinary meaning in the art, unless expressly stated otherwise in this specification. All references to "a / an / an element, apparatus, component, means, step, etc." should be openly interpreted as referring to at least one instance of that element, apparatus, component, means, step, etc., unless expressly stated otherwise. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless expressly stated.

[0016] In a second aspect, the present invention provides a treatment planning system for implementing the above method. In particular, the treatment planning system is adapted to calculate one or more non-photon radiation contributions D (i) (T,E), 1≦i≦N and non-negative coefficients k1, k2, ..., k NThe treatment planning system may include an interface configured to receive a dynamic allocation Π of combinations for ≧0. The treatment planning system may include a per-contribution dose-weighted average of the at least one biological effect multiplier.

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[0017]

[0017] The present invention provides a computer program having instructions for causing a computer, or in particular said treatment planning system, to carry out the above method. The computer program can be stored on or distributed on a data carrier. As used herein, a "data carrier" can be a temporary data carrier, such as a modulated electromagnetic wave or light wave, or a non-transitory data carrier. Non-transitory data carriers include volatile and non-volatile memories, such as permanent and non-permanent storage of magnetic, optical or solid-state type. While still within the scope of "data carrier", such memories can be fixedly mounted or portable.

[0018] BRIEF DESCRIPTION OF THE DRAWINGS Aspects and embodiments will now be described, by way of example, with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0019] [Figure 1] 10 illustrates schematically how the dynamic calculation of biological effect - ln Sπ is organized according to an embodiment of the present invention. [Figure 2] A detail of FIG. [Figure 3] 1 is a flowchart of a method according to one embodiment. [Figure 4] FIG. 1 is a block diagram of a treatment planning system according to one embodiment. [Figure 5]1 illustrates a radiation delivery system for implementing a treatment plan. DETAILED DESCRIPTION OF THE INVENTION

[0020] Detailed Description Aspects of the present disclosure will now be described in more detail with reference to the accompanying drawings, in which specific embodiments of the present invention are shown. However, the present invention may be embodied in many different forms, and the embodiments should not be construed as limiting, but rather are provided as examples so that this disclosure will be thorough and complete, and will fully convey the scope of all aspects of the present invention to those skilled in the art.

[0021] 3 is a flowchart of a method 300 for dynamically estimating the biological effect of a treatment plan that specifies non-photon irradiation of a patient, the treatment plan being a combination of non-photon radiation contributions. The non-photon radiation can be, for example, irradiation with ions or protons.

[0022]

[0021] A treatment plan may be performed by a radiation delivery system 500. As shown in Figure 5, such a system may include a gantry 510 having a radiation source and a couch 520 on which the patient is stationary and immobilized during treatment. Relative rotational and / or translational movement between the gantry 510 and the couch 520 is possible. In particular, the gantry 510 may be rotatable about one or two axes, and the couch 520 may be rotatable about a vertical axis and translatable in at least one dimension. This allows for many irradiation angles and irradiation positions (or incident directions), as may be described by a corresponding number of spots. The treatment plan may specify fluence and / or particle energy values ​​for all or some of the available spots.

[0023] The biological effectiveness should be calculated according to a relative biological effectiveness (RBE) model, which includes at least one biological effectiveness multiplier δ(T,E) that depends on the particle type T and / or particle energy E. The biological effectiveness multiplier may further be associated with a characteristic power value p>0. For particle type T and particle energy E, the biological effectiveness multiplier is δ(T,E)D(T,E) p corresponds to a contribution to the biological effect - ln S, which is equal to

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[0024]

[0023] The RBE model may be expressed as an RBE coefficient, which is a linear combination of one or more biological effect multipliers. Within the scope of the present invention, the RBE model may be: Local Effect Models (LEM) (e.g., an early version described in Scholz et al., “Computation of cell survival in heavy ion beams for therapy. The model and its approximation”, Radiat. Environ. Biophys. (1997), vol. 36, pp. 59-66 [doi:10.1007 / s004110050055]), - Microdosimetric-kinetic model (MKM) (see, for example, Hawkins, "A microdosimetric-kinetic model for the effect of non-Poisson distribution of lethal lesions on the variation of RBE with LET", Radiat. Res. (2003), vol. 160, pp. 61-69 [doi:10.1667 / RR3010]) It could be.

[0025]

[0024] The LEM by Kramer and Scholz (Kramer et al., "Rapid calculation of biological effects in ion radiotherapy", Phys. Med. Biol. (2006), vol. 51, pp. 1959-1970 [doi:10.1088 / 0031-9155 / 51 / 8 / 001]) calculates the biological effect D(T,E) of a dose as follows:

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[0026]

[0025] The biological effect of MKM can be expressed as follows:

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[0027] Furthermore, the RBE coefficient is - the Carabe model (see, e.g., Carabe-Fernandez et al., "The incorporation of the concept of minimum RBE (RBEmin) into the linear-quadratic model and the potential for improved radiobiological analysis of high-LET treatments", Int. J. Radiat. Biol. (2007), vol. 83, pp. 27-39 [doi:10.1080 / 09553000601087176]), - Chen & Ahmad model (see, for example, Chen et al., “Empirical model estimation of relative biological effectiveness for proton beam therapy”, Radiat. Prot. Dosim. (2012), vol. 149, pp. 116-123 [doi:10.1093 / rpd / ncr218]), - McNamara model (see, for example, McNamara et al., "A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data", Phys. Med. Biol. (2015), vol. 60, pp. 8399-8416 [doi:10.1088 / 0031-9155 / 60 / 21 / 8399]), - Wedenberg model (see, for example, Wedenberg et al., "A model for the relative biological effectiveness of protons: The tissue-specific parameter α / β of photons is a predictor for the sensitivity to LET changes", Acta Oncologica (2013), vol. 52, pp. 580-588 [doi:10.3109 / 0284186X.2012.705892]) The RBE models may follow one or more phenomenology-based parameterizations of linear energy transfer (LET) models, etc. In this disclosure, the named RBE models include not only the cited disclosures by the named authors, but also further developments by the same or other authors and quantitative and qualitative variations of the disclosed models.

[0028] A further option is to use external software that inputs a dose for a specified particle type T and particle energy E and outputs a biological effect multiplier, RBE coefficient, equivalent dose, or biological effect value. The software can be provided as source code executed by method 300. Alternatively, repeated calls to a local software library are made during execution of method 300. Yet alternatively, calls to a web application programming interface (API) are made, especially if low latency can be guaranteed. The software is external in the sense that it is opaque to the treatment planner, i.e., it returns an output (biological effect) for every allowable input (physical dose), but the treatment planner does not need to be aware of the RBE model being implemented or other considerations underlying the software.

[0029] The method 300 may be implemented in a treatment planning system 400 of the type shown in Figure 4. The treatment planning system may include an interface 410, a memory 420, and a processing circuit 430. The interface 410 may be configured to calculate one or more non-photon radiation contributions D via a text or graphical user interface, a script, or by data transfer. (i) (T,E), 1≦i≦N and non-negative coefficients k1, k2, ..., k N 0, and possibly further data. The interface 410 may connect the treatment planning system 400 to a data network (not shown), thereby enabling communication with users, clinicians, researchers, treatment planning personnel, radiation delivery systems, etc. The memory 420 may be configured to store a computer program 421 having instructions that cause the treatment planning system 400 to perform the method 300. The processing circuitry 430 may execute the instructions of the computer program 421 to, in particular, perform the steps described in the following paragraphs.

[0030] In a first step 310 of the method 300, one or more non-photon radiation contributions D (i)(T,E), 1≦i≦N, are obtained. The i-th contribution is the dose D at location x for the pair of particle type T and particle energy E. (i) The dose may be expressed as a list, table, or matrix providing the values ​​of (T, E). The location x may refer to a point, voxel, or other region, and is implied by notation herein. The dose representation may be discrete or continuous with respect to particle energy E. Each contribution may correspond to a beam or spot delivered in radiation therapy. Alternatively, each contribution may correspond to a preliminary treatment plan, such as a master plan or a Pareto-optimal plan. From a particular treatment plan, it may not be clear how much physical dose will be absorbed in a particular volume of the patient when the treatment plan is executed. If the treatment plan is expressed in terms of, for example, machine-level instructions rather than in terms of physical dose, relatively complex calculations may be required to identify or estimate the physical dose.

[0031] In an optional second step 312, the total dose of each contribution

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[0032] In a similar optional third step 314, a per-contribution dose-weighted average of at least one biological effect multiplier is calculated. The calculation is performed using the following formula:

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[0033] In the next step 316, a per-contribution dose-weighted average of at least one biological effect multiplier for each of the N contributions is calculated.

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[0034]

[0033] In a subsequent step 318 of the method 300, an allocation Π of the combination of the N contributions is obtained. The allocation Π is calculated by applying non-negative coefficients k1, k2, ..., k to the N contributions. N ≥ 0. The allocation Π is given by the user or another processor with coefficients k1, k2, ..., k N Alternatively, the allocation Π may be obtained by polling the memory space where the allocation Π is found. In FIG. 3, block 318 has one right exit indicating that no coefficients have been input, in which case execution of method 300 loops back. The bottom exit from block 318 indicating that the coefficients have been obtained is -ln S ΠThis is followed by a subsequent step 320 of identifying the biological effect of the combination according to this assignment Π, denoted as Π′. After completion of step 320, execution of method 300 may loop back to block 318 to receive a new assignment Π′.

[0035] The first set of coefficients k1, k2, ..., k N can sum to 1,

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[0036]

[0035] Step 320 is more precisely the stored contribution-weighted dose average.

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[0037] For RBE models with more than one biological effectiveness multiplier, the equivalent dose contributions are summed. In the particular case of the Kramer & Scholtz model mentioned above, such summation is

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[0038] As seen above, the combination is −ln S over all (T,E) pairs for which there is a non-zero dose for each assignment Π. Π The sum of the contributions to the radiation field provides a mixed radiation field with a total biological effect. The calculation is structured as presented above, with coefficients k1, k2, ..., k N This allows for a computationally efficient refresh when

[0039] The computational structure is visualized in FIG. 1, where the input quantities

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[0040] FIG. 2 shows the overall biological effect—ln S Π The internal operation of block 102 is shown to reveal that is calculated in two steps. First, the combined dose-weighted average of each biological effect multiplier is calculated.

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[0041] An advantage of the embodiments disclosed herein is that the new set of coefficients

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[0042]

[0041] The computational structure of Figure 1 may also provide advantages in the case of scaling of treatment plans. A scaled treatment plan can be considered as a basic plan P1 constituting a single contribution N=1, whose coefficient k1 is the scaling coefficient. The total dose is therefore

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[0043] Aspects of the present disclosure have been described above primarily with reference to a few embodiments. However, as will be readily apparent to those skilled in the art, embodiments other than those disclosed above are equally possible within the scope of the present invention as defined by the appended claims.

Claims

1. 1. A computer-based method (300) for dynamically estimating the biological effectiveness of variable combinations of non-photon radiation according to a relative biological effectiveness (RBE) model including at least one biological effectiveness multiplier δ(T,E), wherein the at least one biological effectiveness multiplier δ(T,E) is determined such that its contribution to the biological effectiveness is δ(T,E)D(T,E) p where p>0 is a characteristic power of the biological effect multiplier, and the method is one or more non-photon radiation contributions D (i) (T, E), 1≦i≦N (310), where at least one of the contributions includes multiple particle types and / or multiple particle energies; a per-contribution dose-weighted average of said at least one biological effect multiplier for each of said one or more contributions; [Equation 1] , 1≦i≦N is expressed by the following formula [Equation 2] Calculating (314) as [Equation 3] is the total dose of the i-th contribution; and storing (316) the per-contribution dose-weighted average; A non-negative coefficient k applied to said one or more contributions 1 , k 2 ,... ,k N In response to obtaining (318) the combination's assignment Π for ≧0, determining (320) a biological effect of the combination, the biological effect being a joint dose-weighted average of the at least one biological effect multiplier based on the stored per-contribution dose-weighted averages. [Equation 4] , the respective coefficients k i and the total dose of the contributions [Equation 5] Each stored contribution-dose weighted average is weighted by [Equation 6] , the power average with exponent 1 / p [Equation 7] and calculating the total dose of the combination Π as [Equation 8] to the pth power of the combined dose-weighted average of the at least one biological effect multiplier [Equation 9] calculating the biological effect by multiplying the biological effect by The method (300).

2. For use in the identification (320) of the biological effect of the combination Π, [Equation 10] The method of claim 1 , further comprising: storing (312)

3. formula: [0011] calculating (314) the per-contribution dose-weighted average of the at least one biological effect multiplier using p>0, where p>0 is a characteristic power of the biological effect multiplier; and [0012] The method of claim 1 or 2, wherein i is the total dose of the i-th contribution.

4. 4. The method of claim 1, wherein p=1 for at least one biological effect multiplier δ(T,E) of the RBE model.

5. The at least one biological effect multiplier is the α of the microdosimetry-kinetic model MKM. 0 Section or [0013] The method of claim 4 , including an α multiplier for a linear-quadratic model such as a term.

6. 6. The method of claim 1, wherein for at least one biological effect multiplier δ(T,E) of the RBE model, p=2.

7. 7. The method of claim 6, wherein the at least one biological effect multiplier comprises a beta multiplier of a linear-quadratic model.

8. The method of any one of claims 1 to 7, wherein each contribution represents a beam or spot delivered in a radiation treatment.

9. The method of any one of claims 1 to 7, wherein each contribution represents a radiation treatment plan, such as a base plan or a Pareto-optimal plan.

10. The method of claim 9 , wherein the coefficients represent a convex combination of the contributions.

11. The method of claim 10 , wherein the contributions represent a master plan obtained by a multi-criteria optimization (MCO), and the combination corresponds to a navigation plan.

12. The method of any one of claims 1 to 7, wherein the combination corresponds to a scaling of a radiation treatment plan.

13. The method of any one of claims 1 to 12, wherein the non-photon radiation comprises proton radiation, helium ions or carbon ions.

14. The method of any one of claims 1 to 13, further comprising using the identified biological effects of the combination to support radiation therapy planning.

15. 1. A treatment planning system (400) configured to dynamically estimate a biological effect of a variable combination of non-photon radiation according to a relative biological effectiveness (RBE) model including at least one biological effect multiplier δ(T,E), wherein the at least one biological effect multiplier δ(T,E) is determined such that its contribution to the biological effect is given by δ(T,E)D(T,E) p where p>0 is a characteristic power of the biological effect multiplier, depending on particle type T and / or particle energy E, such that p>0 is a characteristic power of the biological effect multiplier, and the system includes an interface (410), a memory (420), and a processing circuit (430); The interface (410) one or more non-photon radiation contributions D (i) (T,E), 1≦i≦N, where at least one of the contributions includes multiple particle types and / or multiple particle energies. (i) (T, E), 1≦i≦N, and a non-negative coefficient k applied to said contribution(s); 1 , k 2 ,... ,k N Dynamic allocation of the combinations for ≧0 configured to receive The processing circuitry (430) calculates a per-contribution dose-weighted average of the at least one biological effect multiplier for each of the one or more contributions. [0014] , 1≦i≦N, as expressed by the following formula: [Equation 15] where: [0016] is the total dose of the i-th contribution, the memory (420) is configured to store the per-contribution dose-weighted average; The processing circuitry (430) is configured to, for each received dynamic assignment Π of the combination, determine a biological effect of the combination, the combined dose-weighted average of the at least one biological effect multiplier based on the stored per-contribution dose-weighted average. [Equation 17] , the respective coefficients k i and the total dose of the contributions [Equation 18] Each stored contribution-dose weighted average is weighted by [Equation 19] , the power average with exponent 1 / p [Equation 20] and the total dose of the combination Π [0000] to the pth power of the combined dose-weighted average of the at least one biological effect multiplier [Equation 22] and calculating the biological effect by multiplying the biological effect by A treatment planning system (400).

16. A computer program (421) comprising instructions which, when executed by a computer, cause said computer to carry out the method according to any one of claims 1 to 14.

17. A data carrier carrying a computer program according to claim 16.

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