Patent Publication Number: US-2023144124-A1

Title: Base dose calculation

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to U.S. Provisional Application No. 63/263,909, filed on Nov. 11, 2021. The entirety of the aforementioned application is incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     Biological outcomes from a given dose of radiation vary based on specifics of particle type, three-dimensional dose distribution, dose rate, fractionation schedule, etc. Accordingly, physical dose alone does not provide an accurate method of comparing the expected biological effects between two or more courses of radiation therapy that vary by delivery modality, fractionation schedule, and particle energy and/or type. Examples of radiation treatment modalities for which direct physical dose comparison is inappropriate for biological effect considerations include conventional and hypofractionated external beam photon radiation therapy (EBRT), proton therapy, high dose rate (HDR) brachytherapy, and low dose rate (LDR) brachytherapy (BT). Further, simple summations of physical dose distributions from dissimilar treatment courses are not useful indicators of expected tumor control or normal tissue complication probabilities. 
     Biologically Effective Dose (BED) has been introduced to quantitatively model the biological effect of radiation therapy. The BED concept has evolved to account for several factors, including but not limited to dose rate and time between fractionated deliveries, to account for repair of sub-lethal damage and cellular repopulation during treatment. BED distributions from different forms of radiation therapy may be directly compared or summed together to appropriately evaluate an expected biological outcome from combined use of the radiation therapies. 
     BRIEF SUMMARY OF THE INVENTION 
     A simplified summary is provided herein to help enable a basic or general understanding of various aspects of exemplary, non-limiting embodiments that follow in the more detailed description and the accompanying drawings. This summary is not intended, however, as an extensive or exhaustive overview. Instead, the sole purpose of the summary is to present some concepts related to some exemplary non-limiting embodiments in a simplified form as a prelude to the more detailed description of the various embodiments that follow. 
     In various, non-limiting embodiments, a system and associated methods are provided for determining a base dose input to a treatment planning system (TPS). Prior therapy information related to prior radiation therapy performed on a patient is acquired. Further, plan therapy information related to an additional radiation therapy to be performed on the patient is obtained. A base dose is determined based on the prior therapy information and the future plan therapy information. The base dose is determined in accordance with a base dose relationship derived from a biological effective dose (BED) model associated with the plan therapy. The base dose is exported to a TPS for planning the future radiation therapy. 
     These and other embodiments are described in more detail below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
       Various non-limiting embodiments are further described with reference to the accompanying drawings in which: 
         FIG.  1    illustrates desired external beam radiation therapy dose per voxel for an exemplary therapy according to one or more aspects; 
         FIG.  2    illustrates a base dose optimization concept to achieve a desired dose distribution in accordance with various aspects; 
         FIG.  3    is a schematic block diagram of an exemplary, non-limiting embodiment for base dose determination according to one or more aspects; 
         FIG.  4    is a schematic block diagram of an exemplary, non-limiting embodiment for base dose determination according to one or more aspects; 
         FIG.  5    illustrates an exemplary, non-limiting embodiment of a method for determining a base dose according to various aspects; 
         FIG.  6    is a schematic block diagram of an exemplary system for determining a base dose in accordance with one or more aspects; 
         FIG.  7    is a schematic block diagram of an exemplary system for determining a base dose according to various aspects; 
         FIG.  8    is a schematic block diagram of an exemplary system for determining a base dose in accordance with one or more aspects; and 
         FIG.  9    is a schematic block diagram of an exemplary, non-limiting embodiment for a computing device associated with the system of  FIGS.  6 - 8   . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     As discussed in the background, biologically effective dose (BED), unlike physical dose, can be utilized to evaluate and/or compare biological outcomes from various radiation therapies, and/or the combined use of various radiation therapies. Conversion of physical dose to BED may be based on a linear-quadratic (LQ) model that describes the probability of cells surviving after receiving varying amounts of radiation dose from varying particle types delivered at varying dose rates and/or fractionation schedules. The LQ model results from fitting a surviving fraction of irradiated cells as a function of physical dose through a second-order polynomial with coefficients α and β. Specifically, the LQ model indicates the surviving fraction of cells (S) irradiated with a dose (D) over a time period (T) as generally indicated by Equation 1. 
         S=e   −E   Equation 1
 
     In Equation 1, E represents a biological effectiveness of radiation exposure to a population of cells that causes inactivation that accounts for cell population. E may be defined according to Equation 2. 
     
       
         
           
             
               
                 
                   E 
                   = 
                   
                     
                       α 
                       ⁢ 
                       D 
                     
                     + 
                     
                       β 
                       ⁢ 
                       G 
                       ⁢ 
                       
                         D 
                         2 
                       
                     
                     - 
                     
                       
                         
                           ln 
                           ⁡ 
                           ( 
                           2 
                           ) 
                         
                         
                           T 
                           P 
                         
                       
                       ⁢ 
                       
                         ( 
                         
                           T 
                           - 
                           
                             T 
                             k 
                           
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   2 
                 
               
             
           
         
       
     
     In Equation 2, α and β are coefficients that account for tissue radiosensitivity, G accounts for dose rate and repair of sublethal damage (e.g. a dose protraction factor), T P  is a tumor doubling time, and T K  represents a kick-off period after the onset of radiation therapy prior to initiation of cell repopulation. The α and β values are tissue-specific and express the radiosensitivity (e.g. sensitivity to fractionation). In exemplary applications, the LQ model provides an accurate description of fractionation effects at doses between 1 and 10 Gy per fraction. 
     BED may be related to Equation 1 and Equation 2, for example, according to Equation 3. 
     
       
         
           
             
               
                 
                   BED 
                   = 
                   
                     
                       E 
                       α 
                     
                     = 
                     
                       D 
                       + 
                       
                         
                           G 
                           ⁢ 
                           
                             D 
                             2 
                           
                         
                         
                           α 
                           β 
                         
                       
                       - 
                       
                         
                           
                             ln 
                             ⁡ 
                             ( 
                             2 
                             ) 
                           
                           
                             α 
                             ⁢ 
                             
                               T 
                               P 
                             
                           
                         
                         ⁢ 
                         
                           ( 
                           
                             T 
                             - 
                             
                               T 
                               K 
                             
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   3 
                 
               
             
           
         
       
     
     Bed for Fractionated External Beam Radiation Therapy 
     A total dose (D EBRT ) from external beam radiation therapy (EBRT) may be delivered in a prescribed number of fractions (n EBRT ) of equal dose with a sublethal damage repair factor represented as G=1/n EBRT . For EBRT, the effect of tumor cell repopulation may be ignored since a tumor “kick-off” period, T K , is greater than a total treatment time, T. Substitution of these values into Equation 3 results in the widely used BED model for EBRT: 
     
       
         
           
             
               
                 
                   
                     BED 
                     EBRT 
                   
                   = 
                   
                     
                       D 
                       EBRT 
                     
                     ( 
                     
                       1 
                       + 
                       
                         
                           
                             D 
                             EBRT 
                           
                           / 
                           
                             n 
                             EBRT 
                           
                         
                         
                           α 
                           / 
                           β 
                         
                       
                     
                     ) 
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   4 
                 
               
             
           
         
       
     
     Derivation of Method for Bed Informed Ebrt Planning Using Base Plan 
     This method assumes both an a priori known desired reference BED distribution (BED ref ) and the previously delivered BED distribution(s) (BED prior ). BED ref  values can be calculated by converting accepted physical dose constraints (such as tumor prescription values and organ at risk tolerances) corresponding to a specified fractionation scheme using the appropriate BED model. For radiation target volumes (e.g. tumor), the BED ref  distribution represents the desired minimum summation of the BED prior  and BED EBRT  at each point. For organs at risk (OAR), the BED ref  represents a maximum BED volumetric statistic of the desired of the BED prior  and BED EBRT . 
     Assuming the prior physical dose distribution is known and a model exists to convert physical dose from the prior therapy modality to BED, it is mathematically straightforward to calculate the BED prior  for each voxel. The remaining BED required to be delivered from future EBRT (BED EBRT ) is simply the difference between the BED ref  and the BED prior : 
       BED EBRT =BED ref −BED prior   Equation 5
 
     However, EBRT TPS platforms optimize treatment plans using physical dose, not BED. So, it is necessary to convert the BED EBRT  to physical dose (D EBRT ). This is done by solving Equation 4 for the EBRT dose (D EBRT ) in terms of BED EBRT , the number of fractions (n EBRT ), and the alpha/beta ratio (α/β). This results in a quadratic equation, where the positive solution is used: 
     
       
         
           
             
               
                 
                   
                     D 
                     EBRT 
                   
                   = 
                   
                     
                       
                         
                           n 
                           EBRT 
                         
                         ( 
                         
                           α 
                           / 
                           β 
                         
                         ) 
                       
                       2 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           - 
                           1 
                         
                         + 
                         
                           
                             1 
                             + 
                             
                               
                                 4 
                                 ⁢ 
                                 
                                   BED 
                                   EBRT 
                                 
                               
                               
                                 
                                   n 
                                   EBRT 
                                 
                                 ( 
                                 
                                   α 
                                   / 
                                   β 
                                 
                                 ) 
                               
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   6 
                 
               
             
           
         
       
     
     Substitution for BED EBRT  in terms of BED ref  and BED prior  (Equation 5), gives: 
     
       
         
           
             
               
                 
                   
                     D 
                     EBRT 
                   
                   = 
                   
                     
                       
                         
                           n 
                           EBRT 
                         
                         ( 
                         
                           α 
                           / 
                           β 
                         
                         ) 
                       
                       2 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           - 
                           1 
                         
                         + 
                         
                           
                             1 
                             + 
                             
                               
                                 4 
                                 ⁢ 
                                 
                                   ( 
                                   
                                     
                                       BED 
                                       ref 
                                     
                                     - 
                                     
                                       BED 
                                       prior 
                                     
                                   
                                   ) 
                                 
                               
                               
                                 
                                   n 
                                   EBRT 
                                 
                                 ( 
                                 
                                   α 
                                   / 
                                   β 
                                 
                                 ) 
                               
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   7 
                 
               
             
           
         
       
     
     Performing this calculation on a voxel-by-voxel basis results in a distribution representing the total physical dose from an EBRT plan, consisting of n EBRT  fractions, that needs to still needs to be delivered to voxels in the target region and the maximum dose that should be delivered to voxels in organs at risk regions. A schematic representation of this process is shown in  FIG.  1   .  FIG.  1    illustrates the voxel-by-voxel values of D EBRT  calculated using Equation 7 for voxels in a target structure. Each dark green box represents the remaining required physical dose for each voxel required from EBRT to result in the desired composite BED. 
     An additional logistical issue remains to obtain the EBRT dose distribution outlined above. Commercial TPS inverse optimization tools do not allow users to specify the desired physical dose for each voxel, as illustrated in  FIG.  1   , but instead require the user to specify dose optimization goals that apply to all voxels within a volume of interest (i.e., maximum dose, minimum dose, uniform dose, etc.). To address this issue, a method is proposed that involves calculating a base dose (D base ) for each voxel. D base  is equal to the difference between the physical dose corresponding to the BED ref  calculated for the number of fractions for the future EBRT treatment (D ref ) and the D EBRT . 
         D   base   =D   ref   −D   EBRT   Equation 8
 
     D ref  is a structure-specific value for each target or OAR volume and is defined as the total physical dose that, when delivered in n EBRT  fractions, results in the BED ref . For target structures this represents the desired minimum physical dose value with a BED corresponding to those of the reference prescription tumor dose and for OARs this is the maximum physical dose value with a BED corresponding to that of commonly used maximum dose constraints from conventional RT. An expression for D ref  is derived similarly to Equation 6, except the BED term is given by BED ref : 
     
       
         
           
             
               
                 
                   
                     D 
                     ref 
                   
                   = 
                   
                     
                       
                         
                           n 
                           EBRT 
                         
                         ( 
                         
                           α 
                           / 
                           β 
                         
                         ) 
                       
                       2 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           - 
                           1 
                         
                         + 
                         
                           
                             1 
                             + 
                             
                               
                                 4 
                                 ⁢ 
                                 
                                   BED 
                                   
                                     r 
                                     ⁢ 
                                     e 
                                     ⁢ 
                                     f 
                                   
                                 
                               
                               
                                 
                                   n 
                                   EBRT 
                                 
                                 ( 
                                 
                                   α 
                                   / 
                                   β 
                                 
                                 ) 
                               
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   9 
                 
               
             
           
         
       
     
     Substitution of Equations 6 and 9 into Equation 8 and simplifying terms results in an expression for D base : 
     
       
         
           
             
               
                 
                   
                     D 
                     
                       b 
                       ⁢ 
                       a 
                       ⁢ 
                       s 
                       ⁢ 
                       e 
                     
                   
                   = 
                   
                     
                       
                         
                           n 
                           EBRT 
                         
                         ( 
                         
                           α 
                           / 
                           β 
                         
                         ) 
                       
                       2 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           
                             1 
                             + 
                             
                               
                                 4 
                                 ⁢ 
                                 
                                   BED 
                                   ref 
                                 
                               
                               
                                 
                                   n 
                                   EBRT 
                                 
                                 ( 
                                 
                                   α 
                                   / 
                                   β 
                                 
                                 ) 
                               
                             
                           
                         
                         - 
                         
                           
                             1 
                             + 
                             
                               
                                 4 
                                 ⁢ 
                                 
                                   ( 
                                   
                                     
                                       BED 
                                       ref 
                                     
                                     - 
                                     
                                       BED 
                                       prior 
                                     
                                   
                                   ) 
                                 
                               
                               
                                 
                                   n 
                                   EBRT 
                                 
                                 ( 
                                 
                                   α 
                                   / 
                                   β 
                                 
                                 ) 
                               
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   Equation 
                   ⁢ 
                       
                   10 
                 
               
             
           
         
       
     
     Performing this calculation on a voxel-by-voxel basis results in a base dose distribution that is intended to be imported into an EBRT TPS. In some exemplary implementations herein, inverse optimization will be employed to generate EBRT plans. Modern TPS platforms offer the ability to specify a “base dose” or “bias dose” to allow an EBRT plan to be generated so that the sum of the base dose and new EBRT plan attempt to achieve the input optimization goals. This technique will be used to account for dose delivered by the prior treatment by optimizing an EBRT plan so that the sum of the dose to each voxel from the IMRT plan (D EBRT,actual ) and D base  is equal to D ref  for voxels in a target volume and less than D ref  for all non-target voxels. 
     In an embodiment where a TPS supports voxel-wise rather than contour-wise dose objectives, or currently if a contour is defined for every voxel, D EBRT  could be also imported directly into the TPS to define the voxel-wise desired dose component from the EBRT course or the optimizer could compute Equation 8 within its objective function to incorporate the already delivered D base  when optimizing D EBRT  to achieve D ref . 
     Exemplary Planning Procedure 
     An exemplary planning procedure based on the concepts above may commence with acquiring previous treatment planning CT and/or MRIs with corresponding target and organ at risk (OAR) structures and previously delivered dose distributions (D prior ) following base radiotherapy (RT) treatment and importing into a TPS program. 
     A secondary EBRT radiotherapy simulation CT is performed and imported into the TPS program. 
     Subsequently, tools provided in the TPS may be utilized to register the previous imaging and second radiotherapy simulation imaging. In cases where acceptable anatomic fusion is not possible due to tissue/cavity deformation, deformable image registration may be used. Dose from the previous treatment is mapped onto the secondary EBRT simulation CT using the image registration. If deformable registration tools are used, the D prior  distribution may be deformably mapped using the deformation registration information. Then, secondary RT planning contours are generated. Next, BED ref  and pertinent tissue-specific BED parameters (i.e., etc.) for each RT planning contour are specified. The tissue-specific BED parameters are used to convert the prior treatment physical dose mapped onto the EBRT simulation CT to BED prior  using the appropriate BED model. Finally, D base  is calculated using Equation 10 for each voxel on the EBRT simulation CT.  FIG.  3    illustrates the conversion process. 
     The secondary EBRT simulation CT, secondary RT planning contours, and D base  distribution are then imported into a secondary EBRT treatment planning system. For this step, the pertinent DICOM header information (e.g. patient demographic values) in the base TPS may be edited to ensure the information matches the data in the secondary TPS to avoid import incompatibility issues. An RT plan (D EBRT,actual ) is generated using inverse optimization tools with the base dose as a foundation to achieve the dose objectives. The composite dose distribution consisting of the sum of D base  and D EBRT,actual  is evaluated and re-optimized, if needed, until an acceptable secondary RT plan is generated. 
     In other embodiments, the conversion from base RT physical dose to D base  may be computed using a separate third dose conversion system. And in other embodiments the conversion from base RT physical dose to D base  may be computed using the secondary RT TPS. 
     Improved Determination of Base Dose 
     In the clinic, if a patient is getting external beam radiotherapy following any prior radiotherapy (e.g. radioembolization, molecular radiotherapy, brachytherapy, proton therapy, etc.) or other techniques that may affect the surviving fraction of cells, the expected biological implications must be accounted for in order to define a prescription dose which will optimize treatment outcomes. One conventional approach to this is either (I) non-voxelized estimation using models on summarized values from the dose distribution or (II) biological treatment planning plugins, which are expensive, not widely adopted, and do not conceive of building one treatment onto another where the treatments have different biologic dose effects. 
     Described herein is a general and improved technique for determining a base dose employed in treatment planning. Using this technique, prior therapy doses are considered on a voxelized basis such that subsequent treatments may be planned. The base dose output is operable with legacy treatment planning systems and is not computationally intensive. 
     As noted above, a biologically effective dose (BED) is a more useful quantity to express expected biological effects of radiation therapy or other therapies. Physical dose is not as useful an indicator of biological effects, particularly when considering expected tumor control or normal tissue complication probabilities. When considering situations with multiple treatment sessions and/or treatment utilizing multiple, different therapies, additivity becomes desirable to design a treatment plan that mitigates complications. 
     In general, a treatment plan may satisfy two conditions. A first condition is a biological condition and provides that a biologically effective dose (BED) of prior treatment(s) in addition to a biologically effective dose (BED) of a planned treatment should achieve a reference or threshold BED. A second condition is a physical condition and provides that a prior physical dose or base dose plus a planned physical dose for a subsequent treatment should achieve a reference dose (e.g. dose constraint). It is to be appreciated that these conditions may be evaluated on a point-by-point basis. In practical terms, since treatment planning is performed based on medical imaging, these conditions may be considered per voxel. 
     As noted above, conventional treatment planning system (TPS) platforms typically optimize treatment plans based on physical dose. Optimizing based on physical dose (i.e. satisfying the second (physical) condition) may lead to violations of the first (biological) condition using conventional techniques. BED is a non-linear phenomenon. Consideration of physical dose alone does not account for all biological constraints. Similarly, BED is not a direct substitute for physical dose due to its non-linearity. 
     A technique for determining a base dose is described below. The base dose value determined can be input to conventional TPS platforms that optimize according to physical dose. The base dose is determined such that the TPS platforms, when optimizing for physical dose, consider the biological constraints defined using BED. The base dose does not have a direct physical meaning, but operates as a proxy to enable optimization by the TPS platform to meet a reference BED. The base dose is a value representing, corresponding to, and derived from a BED for prior therapy as opposed to an actual physical dose of the prior therapy. 
     According to an aspect, the base dose is determined based on a relationship generated between BED and a dose, such as a fractionated dose. According to an embodiment, Equation 4 above can be utilized to define this relationship. For example, the quadratic equation created by distributing, D, can be solved to generate the relationship between D and BED. Further to this embodiment, this value D, defined according to BED, can be used in the model described above. For instance, the expression for D may be utilized in place of the equivalent dose (EQD) and, subsequently, the base dose D base  may be determined. 
     In accordance with various aspects, a base dose determination tool is provided that receives, as input, a biological effective dose (BED) from any therapy and outputs a base dose that may be imported into a treatment planning system to achieve a plan satisfying BED constraints. In some examples, the tool utilizes relationships based on a Linear Quadratic (LQ) BED model as described above. 
     In an embodiment, generally shown in  FIG.  4   , the tool receives as inputs, for example, contours, user-defined parameters (e.g. radiobiological parameters, etc.), optimization parameters (e.g. dose constraints, and prior therapy BED map registered to a series to be used for treatment planning (e.g. a simulation image). The tool matches contours from the simulation image to ranked tissue-specific user-defined parameters and optimization parameters. The tool may create parameter maps and a reference dose from the ideal plan by substituting the user-defined parameters and optimization parameters into the contours according to rank. Then, the tool may apply the base dose relationship to the parameter maps, reference dose, and prior therapy BED map on a voxel-wise basis. As output, the tool provides a base dose. 
     In various examples, the contour matching defines how the parameter maps and reference dose are created. The parameter maps and reference dose are required arguments to the base dose relationship, which is utilized to determine the desired voxelized output using a lightweight method. 
     In an embodiment, the additional therapy may be for a cancer which has a suspected likelihood of recurrence which would subsequently require additional therapy in the future. In this embodiment, the “prior dose” may be a simulated dose anticipated in the future based on a statistical model of location of and time to expected recurrence and required future therapy dosimetry. In this embodiment, the result of the planned therapy optimization is a therapy plan with sufficiently dose-spared OARs to allow safe delivery of future therapy. 
       FIG.  5    illustrates a flow diagram of an exemplary, non-limiting method  100  for determining a base dose utilized for treatment planning of an additional therapy. In an aspect, method  100  is suitable while planning a radiation therapy that is subsequent to a prior therapy. The additional therapy may or may not be different from the prior therapy. For instance, as in the examples described above, the prior therapy may be LDR BT and the additional therapy may be EBRT. It is to be appreciated, however, that the technique described herein is not limited to these therapies and this technique may be employed for other combinations of therapies including one or more of BT, EBRT, radioembolization, molecular radiotherapy, proton therapy, etc. 
     Method  100  may begin at  102 , where a set of inputs are obtained. The set of inputs may include, for example, contours, radiobiological parameters for the additional therapy, and optimization parameters (e.g. dose constraints). The contours may indicate regions of interest such as, but not limited to, OARs and target volumes. The optimization parameters may be per contour and indicate respective reference doses for the corresponding regions. The radiobiological parameters may also be defined on a per contour basis and may be specific to the therapy. For instance, for EBRT, the biological modeling parameters may include n (e.g. number of fractions) and α/β (e.g. indicating tissue-specific radiosensitivities). 
     At  104 , a parameter map and a reference dose is generated based on the set of inputs. The parameter map and reference dose may be generated by matching the contours to the radiobiological parameters and optimization parameters. Once matched, the parameters and optimization parameters may be substituted into the contours to create the maps and reference dose. 
     At  106 , a biological effective dose (BED) map from prior therapy is obtained. As utilized herein, prior therapy may also be referred to as a base therapy. In an aspect, the prior BED map is registered to a simulation image (e.g. a series utilized for treatment planning). The prior BED map provides BED for regions of interest resulting from prior therapy. 
     At  108 , a base dose is determined based on the parameter map, reference dose, and the BED map from prior therapy. For example, the parameter map and reference dose may provide a BED map for additional therapy. Using the relationships described above, the BED map from prior therapy and the parameter map/reference dose are input to the relationship, which produces a base dose suitable for treatment planning. At  110 , the base dose is exported to a treatment planning system. 
     Turning to  FIG.  6   , illustrated is a schematic block diagram of an exemplary, non-limiting embodiment for a base dose calculation system  200 . As shown, system  200  can include a computing device  210 , which implements a base dose calculation tool  300 . The computing device  210  can include a processor and various computer-readable storage media (e.g., volatile and non-volatile). The computer-readable storage media can store computer-executable instructions implementing at least a portion of functional modules comprising the base dose calculation tool  300 , described herein. When the computer-executable instructions are executed by the processor, the system  200  is thus configured to perform the operations described herein, such as those of method  100  described above. 
     Computing device  210  can further include various hardware devices (not shown) to implement portions of base dose calculation  300 . For instance, computing device  210  can include a graphics device having a graphics processing unit (GPU), dedicated memory, and/or hardware interfaces to couple the graphics device to a display. Moreover, computing device  210  can include physical hardware ports and/or wireless interfaces (e.g., Bluetooth, wireless USB, etc.) to couple computing device  210  to various devices of system  200 , such as, but not limited to a treatment planning system  400 . 
     Base dose calculation tool  300 , according to an aspect, determines a base dose that is exported to treatment planning system  400  to plan a radiation therapy. Base dose calculation tool  300  acquires feature information  212  indicating various features of interest. For instance, feature information  212  may include contours of regions of interest such as OARs and target volumes. Base dose calculation tool  300  also receives optimization information, such as therapy parameters or dose constraints,  214  that indicates dose constraints for each feature indicated by feature information  212 . The base dose calculation tool  300  also obtains additional therapy information  216  and prior therapy information  218 . In an aspect, additional therapy information  216  may include radiobiological parameters for a particular additional therapy. The parameters may be provided for each feature indicated by the feature information  212 . The prior therapy information  218 , in some examples, may be a BED map from prior therapy that is registered to a simulation image. The simulation image may be a DICOM series utilized by treatment planning system  400  during treatment planning. The prior therapy information  218  may also be referred to herein as base therapy information. In addition to information related to a prior or base therapy for a patient, the base therapy information or prior therapy information  218  may include information related to laboratory and/or patient notes. This information may have biological information relevant to biological planning even if no base therapy were performed. 
     Using these inputs, the base dose calculation tool  300  generates a base dose, which is exported to treatment planning system  400 . The base dose may be generated using a base dose relationship derived from the BED model described above. 
     Turning to  FIG.  7   , illustrated is a schematic block diagram of an exemplary, non-limiting embodiment for a base dose calculation system  500 . As shown in  FIG.  7   , system  500  includes the computing device  210 , base dose calculation tool  300 , and treatment planning system  400 . Additionally, in the embodiment of  FIG.  7   , a registration engine  510  is provided. According to an aspect, the registration engine  510  receives registration information  512  and registers corresponding voxels of various images together. The registration information  512  may, for example, include spatiotemporal registration information that accurately links points in space/time on the various images together. The registration engine  510 , in an aspect, provides a consistent basis for the base dose calculation tool  300  to determine and manipulate BED quantities when determining the base dose value. 
     Turning to  FIG.  8   , illustrated is a schematic block diagram of another exemplary, non-limiting embodiment for a base dose calculation system  600 . As shown in  FIG.  7   , system  600  includes the computing device  210 , base dose calculation tool  300 , and treatment planning system  400 . Additionally, in the embodiment of  FIG.  8   , a recurrent therapy engine  610  is provided. As mentioned above, the additional therapy may be for a cancer, which has a suspected likelihood of recurrence. Recurrence may subsequently require additional therapy in the future. In this embodiment, base therapy information  218  may include information related to location of and time to expected recurrence, which is received by a modeling engine  614  to generate a predictive model or time and location of recurrence. The simulation engine  612  may use the predictive model to generate a simulated therapy to be delivered in the future, and based on the simulated therapy, a predicted or future therapy dosimetry. This prediction may be utilized by the base dose calculation tool  300  to determine base dose value exported to the treatment planning system  400  for future planning of this recurrent therapy. 
       FIG.  9    illustrates a schematic block diagram of an exemplary, non-limiting embodiment for a computing device  210  associated with system  200  of  FIG.  7   . As shown in  FIG.  8   , computing device  210  includes one or more processor(s)  402  configured to execute computer-executable instructions such as instructions composing base dose calculation tool  300 , registration engine  510 , and recurrent therapy engine  610 . Such computer-executable instructions can be stored on one or more computer-readable media including non-transitory, computer-readable storage media such as storage  404 . For instance, storage  404  can include non-volatile storage to persistently store base dose calculation tool  300 , registration engine  510 , recurrent therapy engine  610 , and/or data  410  (e.g., parameters, contours, BED maps, feature information, working data, etc.). Storage  404  can also include volatile storage that stores base dose calculation tool  300 , registration engine  510 , recurrent therapy engine  610 , and other data  410  (or portions thereof) during execution by processor  402 . 
     Computing device  210  includes a communication interface  406  to couple computing device  210  to various remote systems (e.g. treatment planning system, etc.). Communication interface  406  can be a wired or wireless interface including, but not limited to, a WiFi interface, an Ethernet interface, a fiber optic interface, a cellular radio interface, a satellite interface, etc. An I/O interface  408  is also provided to couple computing device  210  to various input and output devices such as displays, touch screens, keyboards, mice, touchpads, etc. By way of example, I/O interface  408  can include wired or wireless interfaces such as, but not limited to, a USB interface, a serial interface, a WiFi interface, a short-range RF interface (Bluetooth), an infrared interface, a near-field communication (NFC) interface, etc. 
     The word “exemplary” is used herein to mean serving as an example, instance or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Further, at least one of A and B and/or the like generally means A or B or both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. 
     In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes,” “having,” “has,” “with,” or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.” 
     The implementations have been described, hereinabove. It will be apparent to those skilled in the art that the above methods and apparatuses may incorporate changes and modifications without departing from the general scope of this invention. It is intended to include all such modifications and alterations in so far as they come within the scope of the appended claims or the equivalents thereof