MEDICAL IMAGING ANALYSIS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on, claims the benefit of, and incorporates herein by reference in its entirety U.S. Provisional Patent Application Serial No. 61/872,593 filed on August 30, 2013 and entitled "SYSTEM AND METHOD FOR IMPLEMENTING CLINICAL DECISION SUPPORT FOR MEDICAL IMAGING ANALYSIS."

BACKGROUND

[0002] The present disclosure relates to systems and methods for analyzing a medical image. More particularly, the disclosure relates to systems and methods for implementing a standardized specification of a medical image using a decision support algorithm engine.

[0003] Radiologists often recommend additional imaging for patients whose images show, for example, an indeterminate pulmonary nodule or an adrenal lesion. Both pulmonary nodules and adrenal lesions are common incidental findings on abdominal computed tomography (CT] images; however, radiologists commonly recommend follow-up imaging to characterize the nodules or lesions detected in the CT images. While the abdominal radiologist plays an important role in recommending appropriate follow-up, studies show that the many recommendations deviate from best- practice guidelines.

[0004] Several publically-available, nationally agreed-upon consensus guidelines are available to radiologists to provide clinical decision support (CDS] for recommending additional imaging. CDS provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. CDS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support; and contextually relevant reference information, among other tools. However, implementing the guidelines and recommendations provided by standards setting organizations into radiologists' practices is not a requirement. This results in the guidelines and recommendations provided by the standards setting organizations being optionally used with little guidance of how to implement the guidelines and recommendations into a CDS tool.

[0005] In addition to CDS, evidenced-based algorithms exist that guide radiologists to make appropriate report recommendations. Yet, historical data suggests poor compliance with these best practice guidelines. Several algorithms have been created by the American College of Radiology (ACR] Incidental Findings Task Force (IFTF], the Society of Radiologists in Ultrasound (SRU], the Fleischner Society for Thoracic Imaging and Diagnosis, and the American Society of Spine Radiology (ASSR], for example, to assist clinicians in recommending additional imaging for patients. However, some of these algorithms could not be implemented by a CDS tool because of non-specific recommendations. For example, the IFTF acknowledged challenges in the pancreatic cystic mass algorithm as the data does not exist or is too controversial to determine specific guidelines. A further challenge is that no CDS consensus guidelines are readily available for most lesions, leading to lack of consistency in radiologists' recommendations. Additionally, without use of the CDS tool, some radiologists recommended unnecessary additional imaging, inappropriate imaging and sometimes insufficient imaging, leading to inefficient workflow, increased healthcare costs and potential impact on patient outcomes.

[0006] Although national consensus based, best practice imaging recommendation guidelines are becoming increasing available, radiologist compliance is poor. Therefore, a consensus based, best practice guidelines should be developed for most imaging findings which can be incorporated into a CDS tool for image report consistency, accuracy and reliability in order to change how radiologists implement best practice imaging recommendation guidelines into their practices. A point-of-care CDS tool is needed to enable radiologists, who are capable of accurately describing the findings and clinical context, to adhere to these nationally devised guidelines.

SUMMARY

[0007] The present disclosure overcomes the aforementioned drawbacks by providing a point-of-care CDS system that guides clinicians in applying best practices. In particular, the present disclosure can use an integrated, point-of-care CDS system having software integrated with the reporting environment to guide radiologists through relevant algorithms and assist them in consistently applying standards correctly to implement a standardized specification to a medical image. The system employs a hierarchical structure administered with a decision support engine to propagate and maintain consistent and relevant reporting information systems across clinics, hospitals, and other healthcare provider locations.

[0008] In accordance with one aspect of the disclosure, a system for implementing a standardized specification of a medical image is disclosed. The system includes a user-interface configured to receive input data related to the medical image. The system further includes a non-transitory memory having stored thereon a decision support algorithm based on a predetermined guideline. The decision support algorithm may be applied to the input data. In addition, the system includes a decision support engine configured to track the decision support algorithm available on the non- transitory memory, track the input data received by the user-interface, create updated decision support algorithms based on the input data received by the user-interface, and replace the decision support algorithm available on the memory with the updated decision support algorithms. A processor may have access to the memory and the decision support algorithm stored thereon and be configured to compare the input data to the predetermined guideline and process the input data in relation to the decision support algorithm to generate the standardized specification of the medical image. A display may be coupled to the processor and configured to display the standardized specification of the medical image.

[0009] In accordance with another aspect of the disclosure, a method for implementing a standardized specification of a medical image is disclosed. The method includes acquiring input data related to the medical image from a user-interface and identifying a predetermined guideline. A decision support algorithm is then generated based on the predetermined guideline and applied to the input data. A decision support engine is configured to track the decision support algorithm, track the input data received by the user-interface, create updated decision support algorithms based on the input data received by the user-interface, and replace the decision support algorithm with the updated decision support algorithms. The input data is then compared to the predetermined guideline and processed in relation to the decision support algorithm to generate the standardized specification of the medical image. A report related to the standardized specification of the medical image is then generated and displayed on the user-interface. [0010] The foregoing and other aspects and advantages of the invention will appear from the following description. In the description, reference is made to the accompanying drawings which form a part hereof, and in which there is shown by way of illustration a preferred embodiment of the invention. Such embodiment does not necessarily represent the full scope of the invention, however, and reference is made therefore to the claims and herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a block diagram of a system configured to implement the present disclosure.

[0012] FIG. 2 is a flow chart setting forth exemplary steps of processes for creating a standardized specification of a medical image in accordance with the present disclosure.

[0013] FIG. 3 is a diagram of a predetermined guideline for follow-up determined by a standards body.

[0014] FIG. 4 is a diagram illustrating one implementation of a decision support algorithm used in the process described with respect to FIG. 2.

[0015] FIG. 5 is a screenshot of the decision support algorithm of FIG. 4 in an

XML-based open file format in accordance with the present disclosure.

[0016] FIG. 6 is a screen shot showing an example user-interface configured to receive input data and display the standardized specification of the medical image in accordance with the present disclosure.

DETAILED DESCRIPTION

[0017] Referring particularly now to FIG. 1, a clinical decision support (CDS] system 10 is shown that is configured to integrate a decision support algorithm 12 into a workflow of a radiologist 14. Alternatively, the systems and methods that are described herein may be integrated with dictation software or other systems or subsystems that may be used with or separately from other software and hardware systems, including CDS systems. In general, a standards body 16 develops and updates the decision support algorithm 12 based upon predetermined guidelines determined by standards bodies 26 (shown in FIG. 2], such as the ACR. The standards body 16 may include an imaging expert, specialist expert, a representative of non-specialist referring physicians, and a population care specialist, for example. Additionally or alternatively, other parties, including third parties 17, may be involved in the creation of and/or create guidelines. For example, guidelines may be embodied in whitepapers and other sources of information.

[0018] The decision support algorithm 12, described in further detail below, may then be uploaded to a decision support engine 18. The decision support engine 18 may be configured to create a catalog of decision support algorithms 12, for example, that is available to a reporting system 20 of the radiologist 14. The decision support engine 18 may also be configured to receive standards feedback and updates from standards bodies 26 or standards body 16 and third parties 17 to create updated decision support algorithms 12. To this end, the decision support engine 18 may maintain a variety of definitions that can be used as described hereinafter. Additionally, the decision support engine 18 may enable a local radiologist practice, for example, to make modifications to the decision support algorithms 12 defined by the standards bodies 16 or third parties 17 for their own practice. For example, if a group of local radiologists 14, or an entire institution, wanted to change the threshold at which a recommendation was triggered due to a lesion or nodule type occurring rarely (or frequently] in their practice, local changes can be made to the decision support algorithm 12 that would only affect the language inserted into current reports 24 of the local radiologists' practice. Also, as will be described, custom changes, algorithms, and/or definitions may be made available from repositories 25, which may act as a "store," as will be further described.

[0019] The reporting system 20 is configured to mediate interaction with radiologists 14 to collect input data 114 (shown in FIG. 2} related to patients' medical images (not shown}. The input data 114 may then be forwarded to the decision support engine 18 to generate a standardized specification 22 (shown in FIG. 6} for the medical images. The standardized specification 22 may be a standardized description, recommendation, and/or annotation, for example, of the medical image. An annotation may include, but is not limited to, any written or graphical symbols generated on the medical image by a radiologist, for example, to indicate characteristics (e.g., measurements, points of interest, etc.] of the lesion or nodule. The standardized specification 22 may be sent back to the reporting system 20 to integrate the standardized specification 22 into the current report 24 for the radiologist 14 to view on a display 28.

[0020] As shown in FIG. 1, the display 28 may be coupled to a processor 30 of a networked workstation 32 to communicate reports, images, or other information to the radiologist 14. The networked workstation 32 includes a non-transitory memory 34 that can store information, such as the input data and decision support algorithms 12. The processor 30 of the networked workstation 32 may be configured to access the non-transitory memory 34 to compare the input data to the predetermined guideline and process the input data in relation to the decision support algorithm 12. Furthermore, the workstation 32 may be configured to interact with one or more repositories or stores 25 that provides aggregated access to decision support algorithms and/or particular portions or modified versions of definitions. To this end, if a third party 17 creates or translates a white paper into guidelines or creates a specialized decision support algorithm or other resource, such efforts can be made available in the repositories or stores 25 for other parties to access or purchase. As such an application program interface (API] 35 may be provided that enables a given location to upload guidelines or specialized decision support algorithms or other resources to a repository or story 25 for other parties to access or purchase. That is, the API serves to ensure a consistent and understandable format for new or customized resources in a manner appropriate for others to download, purchase, or otherwise access from the repository or story 25.

[0021] Referring now to FIG. 2, a flow chart setting forth exemplary steps 100 for creating and utilizing the standardized specification 22 (shown in FIG. 6} of a medical image is provided. In this regard, the flow chart can be considered as reflecting two sub processes. A first process 101 is used to create the resources for use of the standardized specification 22 and the second process 102 uses the resources to effectuate the standard specification 22. To start the process of creating the resources 101, a predetermined guideline 36, as described above, determined by standards bodies 26 is identified at process block 103. For example, the predetermined guideline 36 could be the Fleischner Society guidelines for follow-up of small lung nodules detected incidentally on CT, as shown in FIG. 3. At process block 104, the relevant features 38 of a nodule or lesion, for example, related to the specific predetermined guideline 36 are identified. The nodule or lesion may include, but is not limited to , pulmonary nodules, cystic renal masses, solid renal masses, liver masses, adrenal masses, cystic pancreatic masses, gallbladder polyps, biliary duct dilations, adnexal masses, abdominal aneurysms, splenic lesions, and the like. [0022] In the exemplary case of the Fleischner Society guidelines for follow-up of small lung nodules detected incidentally on CT, the standards body 16 could include radiologists, pulmonologists, primary care physicians, and public health/cost-benefit analysts. The standards body 16 may define relevant features 38 (shown in FIG. 5} of the nodule or lesion that include, but are not limited to, size (e.g., <8 millimeters], size change (e.g., increase, decreased, stable, no priors], side (i.e., location of nodule or lesion on right or left side], diagnostic feature (e.g., uniformly cystic, macroscopic fat, >50 HU on NCCT] and known malignancy (e.g., yes or no], as shown at process block 106. The standards body 16 may also define a plurality of synonyms 40 (shown in FIG. 5], for each of the relevant features 38 since different radiologists may describe the relevant features 38 using varying, yet still accurate, clinical terminology. For example, one radiologist may use the term "uniformly cystic" to describe the relevant feature 38 of the nodule or lesion, while a different radiologist may use the term "fluid density" or "simple cyst" to describe the same relevant feature 38. While, all of these descriptions may be accurate, they are not consistent for describing the relevant feature 38. Therefore, the standards body 16 or another body may generate the plurality of synonyms 40 for each of the relevant features 38 to ensure consistency when applying the decision support algorithm 12 to the input data 114, as will be described in further detail below.

[0023] At process block 108, the standards body 16 may define the standardized specification 22 language that is integrated into the radiologist's current report 24. For example, referring to FIG. 4, an example of a decision tree that may be used to implement a support algorithm 12 is illustrated. In this example, the standardized specification 22 is generated based on the relevant features 38 of the nodule or lesion, for example, in the medical image and the predetermined guideline 36. In some embodiments, critical test results management (CTRM] systems may be activated by the decision support algorithm 18 to notify the user or radiologist 14 of important results and appropriate follow-up of recommendations.

[0024] For example, if the diagnostic feature of the lesion or nodule is

'uniformly cystic' at decision block 401, the recommendation, based on the predetermined guideline 36, is 'cyst: no recommendation', as shown at end point 414. In another example, if there is no known malignancy at decision block 400, the standards body 16 and the decision support algorithm 12 should consider whether the size of the lesion or nodule has changed, as shown at decision block 402, as a relevant feature 38. If the lesion or nodule has increased in size, for example, the recommendation, based on the predetermined guideline 36, is an adrenal CT, as shown at end point 404. If, for example, at decision block 402, there are no prior images or information regarding the size change in the lesion or nodule in question, the current size may be measured at decision block 406 as the relevant feature 38. If the size of the lesion or nodule, for example, is <4 centimeters, the recommendation, based on the predetermined guideline 36, is a chemical evaluation and an adrenal CT in 6 months, as shown at end point 408. If the size of the lesion or nodule, for example, is >4 centimeters at decision block 406, the recommendation, based on the predetermined guideline 36, is a surgery referral, as shown at end point 410. Each end point 404, 408, 410 and 414 define the standardized specification 22 language to be integrated into a report 42, shown in FIG. 6, that includes a description, impression, and any relevant recommendations for a particular class of described nodules or lesions.

[0025] Returning now to FIG. 2, the decision support algorithm 12 may be generated at process block 110 by the standards body 16. The data described above with respect to FIGS. 3 and 4 may be included, for example, in an XML file that outlines the relevant information (i.e., relevant features 38, plurality of synonyms 40, predetermined guideline 36, standardized specification 22} for the decision support algorithm 12, as shown in FIG. 5.

[0026] The decision support algorithm 12, as shown in the example code in FIG.

5, may include a first section 500 that describes the relevant features 38 related to the nodule or lesion, for example, that are used to determine the output (i.e., the standardized specification 22} of the decision support algorithm 12. The first section 500 of the decision support algorithm 12 may also include the plurality of synonyms 40 that may facilitate natural language processing tools to automatically detect the presence or absence of a relevant feature 38 in non-generated text. For example, as shown in the first section 500, some of the relevant features 38 are a numeric type feature 52, such as the size and density of the nodule or lesion. The numeric features 52 may be entered by the radiologist 14 on a user-interface 50, as shown in FIG. 6, as will be described in further detail below, or the numeric features 52 may be retrieved directly from a picture archiving and communication system (PACS} that is integrated with the system 10. Alternatively, some of the relevant features 38 may be enumeration features 54, such as side (i.e., left or right] of the lesion or nodule, for example, that the radiologist 14 may choose from on the user-interface 50. Further, some of the relevant features 38 may be present or absent features 56, such as diagnostic features (i.e., hypodense, macroscopic fat, uniformly cystic, high density, etc.], for example, that the radiologist 14 may select on the user-interface 50 if the diagnostic feature is present, or leave blank on the user-interface 50 if absent. The decision support algorithm 12 automatically sets the present or absent features 56 to the default value of absent.

[0027] The decision support algorithm 12, as shown in FIG. 5, also includes a second section 502 that describes a decision making process or logic process that determines the output (i.e., the standardized specification 22} of the decision support algorithm 12 based on the relevant features 38 described with respect to the first section 500. The decision making process shown in section 502 may follow the same decision making process previously described with respect to FIG. 4. For example, if the diagnostic feature of the lesion or nodule is 'uniformly cystic' as shown in section 502, the recommendation 514, based on the predetermined guideline 36, is 'cyst_no_recommendation', which is the same recommendation shown at end point 414 in FIG. 4, where each endpoint defines the standardized specification 22 language to be integrated into the report 42, shown in FIG. 6. In addition, the decision making process of the decision support algorithm 12 should include a default or "unknown" value at each branch point so that regardless of how little or how much data the radiologist 14 provides, an end point can be determined.

[0028] The decision support algorithm 12, as shown in FIG. 5, may also include a third section 504 that provides templates 506 for the standardized specification 22 at each end point that is integrated into the report 42, shown in FIG. 6. The report 42 includes a findings field 44, an impression field 46, and a recommendation field 48 for a particular class of described nodules or lesions that corresponds to a findings field 544, an impression field 546 and a recommendation field 548 in the template 506 of FIG. 5. In this respect, the templates 506 may provide a standardized language that describes the lesion or nodule, for example, and the decision support algorithm 12 simply inserts the numeric features 52, enumeration features 54 and present/absent features 56, determined by the radiologist 14, into the appropriate fields 544, 546, 548 within the template 506. The decision support algorithm 12 can then update the corresponding fields 44, 46, 48, respectively, in the report 42. Additionally, the decision support algorithm 12 may incorporate applicable patient information 66, such as demographic data and clinical context extracted from the radiology information system (RIS}, electronic medical record (EMR} system, the PACS, and voice recognition systems, as shown in FIG. 6.

[0029] The above described decision support algorithm 12 may be a decision support tool delivered to and accessible in the radiologist environment. These decision support algorithms 12 may be loaded into the memory 34 of the decision support engine (DSE} 18, as shown in FIG. 1, and an adjunct software program can maintain a defined catalog of the decision support algorithms 12 generated by the standards body 16. When a client application (i.e., the reporting system 20} submits a radiologist's 14 description of a lesion or nodule, for example, the decision support engine 18 generates the relevant report language (i.e., standardized specification 22} and returns it to the client on the user-interface 50, as shown in FIG. 6. Advantageously, in this architecture, vendors of different reporting software can each generate their own integrated interface by which the relevant lesion features 38 are elicited from the radiologist 14. For example, vendors may choose to incorporate natural language processing to recognize and extract relevant features 38, send these to the DSE 18, and prompt the user whether they wish to incorporate the generated text into their report 42.

[0030] Additionally, the radiologist's 14 description of the lesion or nodule, for example, submitted to the decision support engine 18 is stored into the memory 34 to keep a structured database or catalogue (not shown} of the lesions or nodules described by the radiologist 14. This feature may provide actionable alerts to a referring physician, for example, due to the standardized specifications 22 specified in the decision support algorithm 12. The alerts may be automatically generated through an EMR system (e.g., Epic} or a standalone system, for example. This feature may also provide the capability to track the standardized specification 22 and recommendations made for different patients, for example, and verify that the recommendation has been or will be followed up on. For example, if a chest CT was recommended in six months for a patient, the system 10 may determine if the recommended chest CT happened or not. Lastly, this feature of storing the radiologist's 14 submitted description of the lesion or nodule to the memory 34 of the decision support engine 18 may enable research on a specific lesion in question by automatically identifying a large number or similar cases where lesions with a particular characteristic are identified. [0031] Referring back to FIG. 2, the second process 102 that uses the resources to effectuate the standard specification 22 begins with acquiring the input data 114 at process block 112. The input data 114 may be acquired by the radiologist 14 to assist the radiologist 14 to use structured descriptions and standardized recommendation language (i.e., standardized specification 22} that conforms to the predetermined guideline 36. The input data 114 may correspond to the relevant features 38 (e.g., numeric features 52, enumeration features 54, and present/absent features 56} generated by the standards body 16 and related to the nodule or lesion, for example, that are used to determine the standardized specification 22 of the decision support algorithm 12. The radiologist 14 may enter the input data in the user-interface 50 provided on the display 28, as shown in FIG. 6. In an exemplary embodiment, the radiologist 14 may activate the CDS system 10 by pressing an overlay button (not shown} positioned adjacent to the reporting system 20.

[0032] The reporting system 20 may be a reporting platform such as

PowerScribe 360, that uses voice recognition and transcription for radiology reporting. The reporting system 20 may be integrated with the CDS system 10 using a user- interface scripting tool such as QuickMacros or AutoHotkey, for example, to insert the standardized specification 22 into the findings field 44, the impression field 46, and the recommendation field 48 of the report 42.

[0033] When the user-interface 50 is present on the display 28, the radiologist 14 may select the kind of lesion or nodule (e.g., adrenal nodule or pulmonary nodule} to describe from a toolbox section 58. The radiologist 14 may then enter the input data 114 corresponding to the relevant features 38 (e.g., numeric features 52, enumeration features 54, and present/absent features 56} of the lesion or nodule, for example, in an edit section 60 of the user-interface 50.

[0034] Referring again to FIG. 2, at process block 116 the input data 114 may be compared to the relevant features 38 and synonyms 40 defined by the standards body 16 to ensure consistency when applying the decision support algorithm 12 to the input data 114 at process block 118. Once the decision support algorithm is applied to the input data 114, the standardized specification 22 is generated at process block 120.

[0035] At process block 122, the radiologist 14 can manipulate and update the input data 114 related to the lesion or nodule in the edit section 60 shown in FIG. 6. If the radiologist 14 is satisfied with the input data 114 entered in the edit section 60, the report 42 including the standardized specification 22 is generated by the decision support algorithm 12, as shown at process block 124, and displayed in a report section 62.

[0036] However, the radiologist 14 may manipulate and update the input data

114 at process block 122. When the input data 114 is updated, the decision support engine 18 compares the new input data 114 to the relevant features 38 at process block 116 determined by the standards body 16. The decision support engine 18 may track the updated input data 114 at process block 122 to create updated decision support algorithms 12. The updated decision support algorithms 12 may be applied to the updated input data 114 at process block 118 and the standardized specification 22 may be generated at process block 120. In one non-limiting example, if the radiologist 14 is satisfied with the input data 114, the decision support engine 18 and/or API 35 may be configured to detect when unstructured report data might contain an opportunity to use the decision support algorithm 12 to generate a more standardized report 42. As described above, such may be then communicated or made available via the repository/store 25.

[0037] In either case, whether the radiologist 14 updates the input data 114 or not, the standardized specification 22 generated by the decision support algorithm 12 may interactively be shown in real-time in the report section 62 on the user-interface 50 that includes the findings, impression, and recommendation fields 44, 46, 48. For example, as previously described with respect to FIG. 5, the template 506 includes corresponding findings, impression, and recommendation fields 544, 546, 548 so that when the radiologist 14 enters or updates the input data 114 related to the relevant features 38, the decision support algorithm 12 updates the corresponding fields 44, 46, 48 included in the report section 62 of the user-interface 50 with the standardized specification 22. In this respect, when the radiologist 14 is satisfied with the standardized specification 22 shown on the report 42 in the report section 62, a button 64 may be activated by the radiologist to copy the report 42 into the reporting system 20 to be incorporated into the current report 24 for a particular patient and medical image.

[0038] The standardized specification 22 in the report section 62 may be continually updated in real-time as the radiologist 14 updates the input data 114 by implementing a client layer (not shown] that runs within a web browser control on the display 28 to mediate the radiologist 14 interaction with the CDS system 10. The client layer may be implemented in JavaScript, for example, using jQuery and Knockout libraries. As the radiologist 14 enters the input data 114, the client layer communicates asynchronously with the decision support engine 18 and retrieves updated standardized specifications 22 that can be displayed to the radiologist 14 on the user- interface 50.

[0039] As previously described, the radiologist's 14 description of the lesion or nodule, for example, may be submitted to the decision support engine 18 and stored into the memory 34 to keep a structured database or catalogue (not shown] of the lesions or nodules described by the radiologist 14. At the discretion of individual radiologists 14, the decision support engine 18 may collect anonymized versions of the input data 114 and quantitative data using a locally installed software application. The software application may be downloaded and/or purchased from repository or store 25 for use on the user's networked workstation 32, or a mobile device (not shown] for example. The software application stored on the networked workstation 32, or a mobile device, allows the decision support engine 18 to access a central data repository capable of tracking outcome and other longitudinal data resulting from the characterization of particular lesions. The above-described API 35 may then facilitate uploading of further resources to from the workstation 32 to the respository/store 25.

[0040] Thus, a system and method for point-of-care CDS is provided that guides clinicians in applying best practices. In particular, the present disclosure can use an integrated, point-of-care CDS system having software integrated with the reporting environment to guide radiologists through relevant algorithms and assist them in consistently applying standards correctly to implement a standardized specification to a medical image. The system employs a hierarchical structure administered with a decision support engine to propagate and maintain consistent and relevant reporting information systems across clinics, hospitals, and other healthcare provider locations.

[0041] The present invention has been described in terms of one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

[0042] As used in the claims, the phrase "at least one of A, B, and C" means at least one of A, at least one of B, and/or at least one of C, or any one of A, B, or C or combination of A, B, or C. A, B, and C are elements of a list, and A, B, and C may be anything contained in the Specification.