Detecting anomalous dose volume histogram information

Abstract

A control circuit accesses (202) a radiotherapy plan for a given patient. The control circuit then generates (203) dose-volume histogram information from the radiotherapy plan, and automatically evaluates (205) the dose-volume histogram information to identify any abnormal results. Generating the information may, at least in part, and for example, include generating at least one dose volume histogram curve. The latter may include generating at least one dose volume histogram curve for each of a plurality of different patient structures, such as one or more treatment volumes and / or one or more organs at risk.

Description

Technical Field

[0001] These teachings generally relate to treating a planned target volume of a patient using energy according to an energy-based treatment plan, and more particularly, to optimizing an energy-based treatment plan. Background Art

[0002] The use of energy to treat disease comprises a known area of ​​the prior art. For example, radiation therapy comprises an important component of many treatment plans for reducing or eliminating unwanted tumors. Unfortunately, the energy applied does not inherently distinguish between unwanted material and adjacent tissues, organs, etc., which are desirable or even critical to the continued survival of the patient. Therefore, energy such as radiation is usually applied in a carefully administered manner to at least attempt to confine the energy to a given target volume. So-called radiation therapy plans often function in the above-described manner.

[0003] A radiation therapy plan typically includes specified values ​​for each of the various treatment platform parameters during each of a plurality of consecutive fields. Treatment plans for a radiation therapy session are often automatically generated through a so-called optimization process. As used herein, "optimization" will be understood to refer to improving a candidate treatment plan without necessarily ensuring that the optimized result is actually the only optimal solution. Such optimization often includes automatically adjusting one or more physical treatment parameters (often while observing one or more corresponding constraints in these aspects) and mathematically calculating the possible corresponding treatment outcomes (such as drug administration levels) to identify a given set of treatment parameters that represents a good compromise between the desired therapy outcome and avoiding undesirable side effects.

[0004] In some application settings, a radiation therapy plan for a given patient may be automatically generated. In at least most application settings, such a radiation therapy plan must be evaluated to determine whether the plan is acceptable for use when administering therapeutic radiation to the patient. Such an evaluation often considers factors such as dosimetry, dose coverage, dose uniformity, and dose consistency. However, applicants have determined that other factors may also be considered to more effectively determine whether a given radiation therapy plan is acceptable. Summary of the invention

[0005] According to a first aspect of the invention, a method as defined in claim 1 is described.

[0006] According to a second aspect of the invention, an apparatus as defined in claim 11 is described.

[0007] Optional features are defined in the dependent claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The above needs are at least partially met by providing an apparatus and method for detecting abnormal dose volume histogram information as described in the following detailed description, particularly when studied in conjunction with the accompanying drawings, in which:

[0009] Figure 1 Included are block diagrams configured in accordance with various embodiments of these teachings;

[0010] Figure 2 Includes flowcharts for configuring various embodiments according to these teachings; and

[0011] Figure 3 Included are graphs of various embodiments configured according to these teachings.

[0012] The elements in the figure are shown for simplicity and clarity, and are not necessarily drawn to scale. For example, the size and / or relative positioning of some elements in the elements in the figure may be exaggerated relative to other elements to help improve the understanding of the various embodiments of this teaching. In addition, common but easy-to-understand elements that are useful or necessary in commercially feasible embodiments are often not depicted, so as to facilitate understanding of these various embodiments of this teaching. Certain actions and / or steps can be described or depicted in a specific order of occurrence, and those skilled in the art will understand that this specificity about the sequence is not actually required. Unless different specific meanings are otherwise set forth in this article, the terms and expressions used herein have the common technical meanings given to such terms and expressions by those skilled in the art of the above-mentioned technical field. Unless otherwise specifically indicated, the word "or" used herein should be interpreted as having a disjunctive structure rather than a conjunction structure. DETAILED DESCRIPTION

[0013] Generally speaking, these various embodiments provide for automatic assessment when a dose-volume histogram corresponding to a particular radiation therapy plan exhibits one or more anomalies.

[0014] According to these teachings, control circuitry accesses a radiation therapy plan for a given patient. The control circuitry then generates dose volume histogram information based on the radiation therapy plan and automatically evaluates the dose volume histogram information to identify any abnormal results. Generating the information may include, at least in part and for example, generating at least one dose volume histogram curve. The latter may include generating at least one dose volume histogram curve for each of a plurality of different patient structures, such as one or more treatment volumes and / or one or more organs at risk.

[0015] By one approach, these teachings are adapted to automatically evaluate dose volume histogram information at least in part by determining a distance between the dose volume histogram information and reference information. The reference information may be generated, for example, based on dose volume histogram results corresponding to other radiation therapy plans.

[0016] In response to identifying an abnormal result, the teachings will be adapted to present information about the abnormal result to the user. If desired, the latter may include interactively presenting such information to the user so that the user can modify at least one parameter of the radiation therapy plan optimization process. When no abnormal result is identified, the teachings will support the use of the corresponding optimized radiation therapy plan to apply therapeutic radiation to a given patient.

[0017] So configured, anomalies in a dose volume histogram curve can be automatically detected and such anomalies can then be used for plan evaluation and / or optimization. These teachings are highly flexible in practice and will adapt to use adjustable criteria, such as adjustable distance metrics, when detecting anomalies. These teachings will also adapt to consider whether a particular detected anomaly is potentially problematic, rather than having little or no negative consequences.

[0018] These and other benefits may become more apparent upon a thorough review and study of the following detailed description. Figure 1 , an illustrative device 100 that is compatible with many of these teachings will first be presented.

[0019] In this particular example, enabling device 100 includes control circuit 101. As a "circuit," control circuit 101 thus includes a structure that includes at least one (and typically multiple) conductive paths (such as paths composed of conductive metals such as copper or silver) that transmit electricity in an orderly manner, which path(s) will typically also include corresponding electrical components (both passive (such as resistors and capacitors) and active (such as any of a variety of semiconductor-based devices)) to allow the circuit to affect the control aspects of these teachings.

[0020] Such control circuit 101 may include a fixed-purpose hard-wired hardware platform (including but not limited to an application-specific integrated circuit (ASIC) (which is an integrated circuit that is customized by design for a specific purpose rather than intended for general use), a field programmable gate array (FPGA), etc.), or may include a partially or fully programmable hardware platform (including but not limited to a microcontroller, a microprocessor, etc.). These architectural options for such structures are well known and understood in the art and do not require further description here. The control circuit 101 is configured (e.g., by using corresponding programming that will be well understood by those skilled in the art) to perform one or more of the steps, actions and / or functions described herein.

[0021] The control circuit 101 is operably coupled to a memory 102. The memory 102 may be integrated into the control circuit 101, or may be physically separate (in whole or in part) from the control circuit 101 as desired. The memory 102 may also be local memory relative to the control circuit 101 (where, for example, the two share a common circuit board, chassis, power supply, and / or housing), or may be remote memory, in part or in whole, relative to the control circuit 101 (where, for example, the memory 102 is physically located in another facility, metropolitan area, or even country than the control circuit 101).

[0022] In addition to information such as optimization information for a particular patient and information about a particular radiation therapy platform as described herein, the memory 102 may be used, for example, to non-transitory store computer instructions that, when executed by the control circuit 101, cause the control circuit 101 to operate as described herein. (As used herein, this reference to "non-transitory" will be understood to refer to the non-transitory state of stored content (and thus excluding when the stored content merely constitutes a signal or wave) rather than the volatility of the storage medium itself, and thus includes both non-volatile memory such as read-only memory (ROM) and volatile memory such as dynamic random access memory (DRAM)).

[0023] By an optional approach, the control circuit 101 is also operatively coupled to a user interface 103. The user interface 103 may include any of a variety of user input mechanisms (such as but not limited to a keyboard and keypad, a cursor control device, a touch-sensitive display, a voice recognition interface, a gesture recognition interface, etc.) and / or user output mechanisms (such as but not limited to a visual display, an audio transducer, a printer, etc.) to facilitate receiving information and / or instructions from a user and / or providing information to a user.

[0024] If desired, the control circuit 101 may also be operably coupled to a network interface (not shown). The control circuit 101 so configured may communicate with other components (both internal and external to the device 100) via the network interface. Network interfaces, including both wireless and non-wireless platforms, are well understood in the art and need not be specifically elaborated upon here.

[0025] By one approach, the computed tomography device 106 and / or other imaging device 107 known in the art may derive some or all of any desired patient-related imaging information.

[0026] In this illustrative example, the control circuit 101 is configured to ultimately output an optimized energy-based treatment plan (such as, for example, an optimized radiation treatment plan 113). The energy-based treatment plan generally includes specified values ​​for each of various treatment platform parameters during each of a plurality of consecutive exposure fields. In this case, the energy-based treatment plan is generated by an optimization process, examples of which are further provided herein.

[0027] By one approach, the control circuit 101 can be operably coupled to an energy-based treatment platform 114 that is configured to deliver treatment energy 112 to a corresponding patient 104 according to an optimized energy-based treatment plan 113. These teachings are generally applicable to any of a variety of energy-based treatment platforms / devices. In a typical application setting, the energy-based treatment platform 114 will include an energy source such as a radiation source 115 of ionizing radiation 116.

[0028] By one approach, the radiation source 115 can be selectively moved along an arcuate path via a gantry (where the path includes, at least to some extent, the patient himself during the administration of treatment). The arcuate path can include a complete or nearly complete circle as desired. By one approach, the control circuit 101 controls the movement of the radiation source 115 along the arcuate path and can accordingly control when the radiation source 115 starts moving, stops moving, accelerates, decelerates, and / or the speed at which the radiation source 115 travels along the arcuate path.

[0029] As an illustrative example, radiation source 115 may include, for example, an x-ray source based on a radio frequency (RF) linear particle accelerator (linac-based). A linac is a particle accelerator that greatly increases the kinetic energy of charged subatomic particles or ions by subjecting the charged particles to a series of oscillating electric potentials along a linear beamline, which can be used to generate ionizing radiation (e.g., x-rays) 116 and high-energy electrons.

[0030] A typical energy-based treatment platform 114 may also include one or more support devices 110 (e.g., a couch) to support the patient 104 during a treatment session, one or more patient fixation devices 111, a gantry, or other movable mechanism to allow selective movement of the radiation source 115, and one or more energy shaping devices (e.g., beam shaping devices 117, such as jaws, multi-leaf collimators, etc.) to provide selective energy shaping and / or energy modulation as desired.

[0031] In a typical application setting, it is assumed herein that the patient support device 110 is selectively controllable by the control circuit 101 to move in any direction (i.e., any X, Y, or Z direction) during an energy-based therapy session. Because the above-described elements and systems are well understood in the art, further elaboration in these aspects is not provided herein unless otherwise relevant to the description.

[0032] Reference now Figure 2 , a process 200 will be described that may be performed, for example, in conjunction with the above-described application settings (and more specifically via the aforementioned control circuit 101). Generally speaking, the process 200 is used to facilitate generating an optimized radiation therapy plan 113, thereby facilitating treating a specific patient with therapeutic radiation using a specific radiation therapy platform according to the optimized radiation therapy plan.

[0033] At block 201, the process 200 provides for optionally automatically generating a radiation therapy plan for a given patient. Various methods in these aspects are well understood. Since these teachings are not sensitive to any particular selection in these aspects, further elaboration is not provided here for the sake of brevity.

[0034] The process 200 provides access to a radiation therapy plan for a given patient at block 202. The accessed radiation therapy plan may be the automatically generated plan mentioned above.

[0035] At block 203, the process provides for generating dose volume histogram information based on the accessed radiation therapy plan. This may include, for example, generating at least one dose volume histogram curve. In many application settings, it would be beneficial to generate at least one dose volume histogram curve for each of a plurality of different patient structures (including but not limited to a target treatment volume and any associated organs at risk).

[0036] Dose volume histograms ("DVHs") are known in the art and typically represent three-dimensional dose distributions (e.g., three-dimensional dose distributions created in computerized radiation therapy planning systems based on three-dimensional reconstructions of X-ray computed tomography scans and studies) in a graphical two-dimensional format. The "volume" referred to in DVH analysis may be, for example, a radiation therapy target, a healthy organ located near such a target, an arbitrary structure, etc.

[0037] DVHs are often visualized in either of two ways: as a differential DVH or as a cumulative DVH. For a differential DVH, the column height of a given dose bin corresponds to the volume of the structure that received that dose. The bin dose typically extends along the horizontal axis, while the structure volume (percentage or absolute volume) extends along the vertical axis.

[0038] The cumulative DVH is typically plotted with bin doses along the horizontal axis, but the bar height of the first bin represents the volume of the structure(s) that received greater than or equal to that dose. Then, the bar height of the second bin represents the volume of the structure that received greater than or equal to that dose, and so on. For high granularity, the cumulative DVH is often presented as a smooth line graph. For many application settings, the cumulative DVH is preferred over the differential DVH, but this process 200 can be adapted to either approach.

[0039] At optional block 204, the process 200 may be adapted to generate reference information based on the dose volume histogram results corresponding to other radiation therapy plans. It may be helpful if those other radiation therapy plans all correspond to plans for treating a common treatment region within a corresponding patient. For example, all of the other radiation therapy plans may involve a treatment region that includes (and / or is limited to) a particular organ, tissue, or body region (such as the chest or neck).

[0040] In a typical application setting, much or all of this reference information will be generated or otherwise accessed from appropriate external sources prior to performing the other steps of process 200. However, there may be situations where already available reference information can be supplemented with newly generated content developed as needed, or even situations where an entire set of reference information is developed as needed.

[0041] At block 205, the process 200 provides for automatically evaluating the dose volume histogram information to identify abnormal results, if any. By one approach, the automatic evaluation includes determining a distance between the dose volume histogram information and reference information, such as but not limited to the reference information generated above.

[0042] When at least one anomaly is detected at block 206, the teachings may optionally be adapted to responsively present information about the anomaly result to the user via the aforementioned user interface 103 at block 207. By one approach, this may include interactively presenting such information so that the user may modify at least one parameter of the radiation therapy plan optimization process that produced the aforementioned accessed radiation therapy plan. Specifically, the user may generate a modified radiation therapy plan, which may then be evaluated again according to the process 200. For example, the user may adjust the target position, beam geometry, or some other optimization parameter of choice, and then rerun the optimization process. This optimization, plan generation, and anomaly detection process may be repeated until a satisfactory result is achieved.

[0043] By one illustrative approach, the user will be presented with information showing clearly highlighted abnormal dose volume histograms. For example, a color code may be used to indicate the portion of the dose volume histogram curve that caused it to be classified as abnormal. By another approach, in lieu of or in combination with the above, the user may click or otherwise select the depicted button (or the like) to open an additional window presenting additional relevant digital information (e.g., information about the reference distribution, abnormality criteria, and / or relative magnitude of the abnormality in a given case). If desired, a separate display or window may be shown for each abnormal dose volume histogram curve.

[0044] When no abnormality is detected, at block 208 , the process 200 may provide for responsively using a corresponding optimized radiation therapy plan to apply therapeutic radiation to the given patient.

[0045] So configured, these teachings provide anomaly-based detection that is fast, requires little computational overhead, and is numerically interpretable. These teachings are both practical and flexible in practice. For example, if desired, these teachings can be deployed in a workflow where re-optimization can be appropriately triggered without human supervision or intervention.

[0046] Some further examples and details will now be provided in the above aspects. It should be understood that these examples and details are intended for illustrative purposes and are not intended to imply any particular limitations regarding these teachings.

[0047] These teachings will be adapted to generate and / or select the aforementioned reference information in a selectively parsed manner. For example, a given anatomical part can be divided into multiple subgroups. The criteria for such division may include, for example, tumor type, tumor location, relative proximity of structure to other organs, selected organ-at-risk trade-offs, treatment modalities and beam placement, treatment regimens, and achieved clinical goals. Such subdivisions vary within a given subgroup so that anomaly detection may more meaningfully discern anomalies without confusion and / or noise introduced by other parts of the anatomical part.

[0048] The aforementioned distance metric for dose volume histogram anomaly detection can be based on the overall shape of the dose volume histogram curve. The dose volume histogram curves in this example are defined as monotonically decreasing histograms. They can be presented as a one-dimensional function that maps relative volume y in [0,1] to relative dose d in [0,d_max / d_goal]. This method produces a numerical relationship d=DVH(y).

[0049] One method of determining the distance between a current dose-volume histogram curve and a previously observed dose-volume histogram curve (which includes the aforementioned reference information) includes performing dimensionality reduction on the dose-volume histogram curve:

[0050] DVH curve → {z_i} (Eq. 1)

[0051] where each z_i is a scalar value and i=0...n_i.

[0052] An example of dimensionality reduction involves integrating the dose-volume histogram with appropriate weighting function(s):

[0053] z_i=Integral w_i(y)DVH(y)dy, where the integral over y goes from 0 to 1.

[0054] In this example, the weighting function w_i(y) may be adjusted to control how different parts of the dose volume histogram curve are considered. This approach may be particularly useful for emphasizing some parts of a dose volume histogram curve over other parts of the curve.

[0055] These teachings will accommodate a variety of ways to choose such a weighting function. A first choice in these aspects will provide w_i(y)=1 for all y. This will mean the area under the dimensionless dose volume histogram curve. Thus, larger z values ​​indicate greater radiation to the overall structure in the background.

[0056] A second option in these aspects would be to provide a step function (or a sum of two or more step functions) to emphasize a certain portion of the dose volume histogram. For example, another weight function could be defined as wi(y) = 1 when y < 0.2, and wi(y) = 0 otherwise. This approach would emphasize only the portion of the higher dose received by the structure of interest.

[0057] These teachings will also adapt the inclusion of dose volume histogram point metrics by selecting the weighting function to be a delta distribution (or a sum of delta distributions).

[0058] Dimensionality reduction of the dose volume histogram curve can also be based on a known reference dose volume histogram. For example, given a set of selected structures, a dose volume histogram curve can first be calculated from a set of reference plans. It is assumed here that the set of reference plans describes the reference distribution and does not contain anomalies. (If this is not the case, the reference group can be filtered to eliminate all anomalies before adopting the remaining information described herein). If the set of reference plans is sufficiently uniform, the reference dose volume histogram curve for each structure of interest can be simply calculated or aggregated. As an example of these aspects, this can include taking the average of all dose volume histogram curves for the structure from all plans in the group. If there are too many differences within the set of reference plans, the differences can be first predicted as much as possible by modeling the correlation between the patient geometry and the shape of the dose volume histogram curve.

[0059] Using the reference dose volume histogram curve, the dose volume histogram curve for a given structure from a new patient can be scored. This can include, for example, penalizing areas where the new dose volume histogram is above the reference dose volume histogram for organs at risk and below the reference dose volume histogram for target volumes, respectively. For example, the penalty can be the area between the two curves (a smaller score is generated if the curves are close to each other, and a larger score is generated when the curves differ greatly - the latter can be determined by, for example, evaluating proximity using a first threshold, and evaluating greater distances using a second threshold).

[0060] These teachings will be adapted to take into account applicable clinical scenarios, for example, by adding an additional penalty for each clinical goal not achieved by the dose-volume histogram curve being evaluated. Figure 3 Illustrative examples of components that may contribute to the calculation of such a score are provided.For example, the additional penalty may be the distance between the clinical goal not achieved and a point from curve 301 corresponding to the same dose level multiplied by a weight value.

[0061] Continue to refer Figure 3 , the second curve 302 is a reference dose-volume histogram calculated for a given organ at risk from a reference plan group, while the first dose-volume histogram curve 301 corresponds to a radiotherapy plan evaluated for a new patient. The clinical target for the organ at risk is indicated by a plus sign "+". With respect to the graph on the left, the score of the new dose-volume histogram 301 is calculated as the area between the two curves, selecting only the area where the first curve 301 is above the second dose-volume histogram 302. Since all clinical targets were achieved in this example, there are no anomalies and no additional penalties need to be added. With respect to the example graph on the right, for this patient, the score of the first dose-volume histogram curve 301 is calculated as the area between the first curve and the second curve in the area where the first curve 301 is above the second dose-volume histogram curve 302 plus a penalty for not achieving the leftmost clinical target.

[0062] If desired, two or more mappings {z_i}_1 and {z_i}_2 may also be defined that are adjusted to monitor different dose volume histogram curves. For example, {z_i}_1 may monitor a certain organ and {z_i}_2 may monitor another organ (or patient target volume). This approach may be useful when a single value is not sufficient to characterize complex situations for anomaly detection. The approach also provides a way to monitor certain trade-offs of interest or concern.

[0063] With regard to forming a reference distribution of a mapping representation, in the case of a single mapping, these teachings will adapt the computation of the mapping representation z_i to obtain a reference distribution Z = {z_i}, i.e. for all patients in a selected subset of similar reference plans. In the case of considering two or more mappings, one may have two or more independent reference distributions Z_1, Z_2, etc., where Z_1 = {Z_i} and Z_2 = {Z_j}, etc., or a joint reference distribution may be formed (e.g. Z_joined = {Z_i, Z_j}).

[0064] These teachings will be adapted to various methods for implementing the aforementioned anomaly detection, ranging from classical statistical methods to modern outlier detection. The choice of method can be based on clinical and / or statistical (i.e., data volume) considerations and / or preliminary studies. As a simple illustration, if there is a considerable amount of reference data, the value z_i can be considered to be normally distributed. It can then be checked whether the new data point deviates from the distribution mean by an amount of n sigma, which is considered an anomaly. If the distribution of the value z_i is unknown, or the distribution is non-normal, there are also other corresponding methods to estimate the anomaly (e.g., the nearest neighbor method).

[0065] If a joint reference distribution is considered, there are similarly various ways to set up anomaly detection (e.g., clustering-based methods).

[0066] The outlier detection criterion can be chosen such that it allows to distinguish positive outliers from negative outliers. For example, it is clear from the definition of the mapping in a particular example that higher values ​​represented by the mapping mean more dose to the critical organ. In this case, a lateral outlier detection can be simply performed, i.e. testing whether the mapped values ​​are significantly higher than the reference distribution (positive outliers). In this case, the test for the lower side can be omitted, since such cases are negative outliers and the dose volume histogram curve is certainly clinically satisfactory.

[0067] When a reference dose-volume histogram curve is used for anomaly detection, the dose-volume histogram curve of a new patient may also be compared with the reference dose-volume histogram curve using a nonparametric Kolmogorov-Smirnov test.

[0068] As yet another example, based on the reference information, a mean + / - 2 standard deviation dose volume histogram curve of that information may be calculated, thereby defining a region bounded by the two result curves. The dose volume histogram curve being evaluated may be compared to this region to identify and detect points where the curve exceeds the region on either side thereof.

[0069] Those skilled in the art will appreciate that various modifications, changes and combinations may be made to the above-described embodiments without departing from the scope of the present invention, and such modifications, changes and combinations should be considered to be within the scope of the concept of the present invention.

Claims

1. A method comprising: By the control circuit: access the radiation therapy plan used for a given patient; generating dose volume histogram information according to the radiotherapy plan; The dose volume histogram information is automatically evaluated to identify abnormal results.

2. The method of claim 1 , wherein generating the dose volume histogram information comprises at least in part: At least one dose volume histogram curve is generated.

3. The method of claim 1 or claim 2, wherein generating the dose volume histogram information comprises at least in part: At least one dose volume histogram curve is generated for each patient structure of a plurality of different patient structures.

4. The method of any preceding claim, wherein automatically evaluating the dose volume histogram information to identify abnormal results comprises at least in part: A distance between the dose volume histogram information and reference information is determined.

5. The method according to claim 4, further comprising: The reference information is generated according to the dose volume histogram results corresponding to other radiotherapy plans.

6. The method of claim 5, wherein the other radiation therapy plans each correspond to a plan for treating a common treatment region in a corresponding patient.

7. The method according to any preceding claim, further comprising: In response to identifying an anomalous result, information regarding the anomalous result is presented to a user.

8. The method of claim 7, wherein presenting the information about the abnormal result to the user comprises: The information regarding the abnormal result is interactively presented to a user so that the user can modify at least one parameter of a radiation therapy planning optimization process.

9. The method according to any preceding claim, further comprising: In response to assessing that the dose-volume histogram information does not identify an abnormal result, therapeutic radiation is applied to the given patient using a corresponding optimized radiation therapy plan.

10. The method according to any preceding claim, further comprising: The radiation therapy plan for the given patient is automatically generated.

11. An apparatus comprising: The control circuit is configured as follows: access the radiation therapy plan used for a given patient; generating dose volume histogram information according to the radiotherapy plan; The dose volume histogram information is automatically evaluated to identify abnormal results.

12. The apparatus of claim 11, wherein the control circuit is configured to generate the dose volume histogram information at least in part by generating at least one dose volume histogram curve.

13. The apparatus of claim 11 or claim 12, wherein the control circuit is configured to generate the dose volume histogram information at least in part by generating at least one dose volume histogram curve for each patient structure of a plurality of different patient structures.

14. The apparatus of any one of claims 11 to 13, wherein the control circuit is configured to automatically evaluate the dose volume histogram information to identify abnormal results at least in part by determining a distance between the dose volume histogram information and reference information.

15. The apparatus of claim 14, wherein the control circuit is further configured to: The reference information is generated according to the dose volume histogram results corresponding to other radiotherapy plans.

16. The apparatus of claim 15, wherein the other radiation therapy plans each correspond to a plan for treating a common treatment region in a corresponding patient.

17. The apparatus according to any one of claims 11 to 16, wherein the control circuit is further configured to: In response to identifying an anomalous result, information regarding the anomalous result is presented to a user.

18. The apparatus of claim 17, wherein the control circuit is configured to present the information about the abnormal result to a user by interactively presenting the information about the abnormal result to the user, such that the user can modify at least one parameter of a radiation therapy planning optimization process.

19. The apparatus according to any one of claims 11 to 18, wherein the control circuit is further configured to: In response to assessing that the dose-volume histogram information does not identify an abnormal result, therapeutic radiation is applied to the given patient using a corresponding optimized radiation therapy plan.

20. The apparatus according to any one of claims 11 to 19, wherein the control circuit is further configured to: The radiation therapy plan for the given patient is automatically generated.

Images