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Panther OIS is an information management system used to manage medical data and clinical workflow in a hospital or clinic. To support radiation oncology users, it allows the user to:

• · Enter or import, modify, store and archive treatment plans and images from treatment planning systems.
• · Import, view, manipulate, enhance, annotate, store, and archive radiological images.
• · Select and provide radiation treatment plans to a radiation treatment delivery system for atment.
• · Store and view treatment records provided by the radiation treatment delivery system.

Panther OIS is an information management system used to organize medical data and facilitate the clinic workflow in a hospital or clinic. It is built on the Windows system as standard thin client-server system with a centralized database.

Panther OlS has incorporated and enhanced the previously cleared Prowess Panther OIS|R&V (K122616) into its framework so the user is able to:

• Record patient related information, especially radiation treatment planning and records. ●
• Schedule patients, medical resources and any type of activity.
• Capture procedure codes that will be used for billing.
• Generate reports for statistics purpose.
• Complete their tasks easily using the new ribbon Ul that supports their workflow.
• Import treatment plans
• Record patient treatment histories
• . Deliver treatment plans on radiation treatment delivery systems that have a DICOM interface for both external and stereotactic plans.
• Import, access, store and archive radiological images and review images to verify delivered treatment.
• The client is updated to a thin client so that it can be installed on more supported devices.
• Support for stereotactic treatment workflow which is similar to the external beam workflow but using a shot concept instead of a beam/arc concept.

Changes within the scope of this 510(k):

• Addition of support for stereotactic planning ●
• Addition of thin-client support, running resources stored on a central server instead of its localized hard drive
• R&V-related functionalities have been removed

Here's a breakdown of the acceptance criteria and study information for the Panther OIS device, based on the provided FDA 510(k) summary:

The Panther OIS is an information management system for radiation oncology. It is not an AI-powered diagnostic device, and therefore, many of the typical AI-related study components (like expert ground truth, MRMC studies, or specific performance metrics like sensitivity/specificity) are not directly applicable or reported in this type of submission.

The acceptance criteria primarily revolve around the system's functionality, safety, and adherence to established software development and quality standards, demonstrating substantial equivalence to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is an Oncology Information System (OIS) focusing on data management and workflow, the "acceptance criteria" are primarily related to its ability to perform its defined functions reliably and safely, matching or exceeding the predicate devices. Performance is demonstrated through successful verification and validation (V&V) testing.

2. Sample Size for the Test Set and Data Provenance

• Test Set Sample Size: The document does not specify a numerical "sample size" in terms of cases or patients for the software testing. Instead, it refers to:
  • "Established test plans and protocol" for in-house V&V.
  • External testing by "OUR United" to verify safety and performance under conditions equivalent to an actual clinical environment.
  • "Our beta-site using clinical cases" for additional in-field testing.
• Data Provenance:
  • External testing by "OUR United": The specific country of origin is not mentioned.
  • Beta-site testing: Not specified.
  • Retrospective or Prospective: Not explicitly stated, but the use of "clinical cases" during beta-site testing implies retrospective data or data collected prospectively during the test period. The functionality of an OIS would typically be tested with existing clinical data or simulated patient data.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

• Ground Truth for OIS Functionality: The concept of "ground truth" for an OIS is different from that for an AI diagnostic algorithm. For an OIS, ground truth relates to the accuracy of data handling, workflow execution, and proper interfacing. This is typically established through:
  • Defined specifications: The software correctly implements its intended functions.
  • Comparison to predicate devices: The functionality matches or improves upon what is already cleared.
  • Clinical expert review: In this case, "clinical physicists contracted by Prowess" were involved in verifying the adequacy of risk mitigation. This implies their expertise was used to validate the safety and functional integrity of the system in a clinical context.
• Number of Experts & Qualifications:
  • The document mentions "clinical physicists contracted by Prowess" were involved in verifying the adequacy of risk mitigation. The specific number of these physicists is not provided.
  • Their qualification is stated as "clinical physicists," implying expertise in the practical application and safety aspects of radiation oncology systems.

4. Adjudication Method for the Test Set

The document does not describe a traditional adjudication method (like 2+1 or 3+1) because the study is not focused on evaluating human-level diagnostic performance or AI algorithm output agreement. Instead, verification and validation involved:

• In-house testing: By "in-house test engineers."
• External testing: By "OUR United" for performance in equivalent clinical conditions.
• Beta-site testing: By "our beta-site using clinical cases" to confirm safety and effectiveness in a clinical environment.

The "adjudication" is implicitly done by confirming that the software meets its predetermined specifications and functions as intended without errors, as evaluated by these different testing groups.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not done.
  • This type of study is typically conducted for AI-powered diagnostic devices to assess how the AI assists human readers (e.g., radiologists) in their diagnostic tasks.
  • The Panther OIS is an information management system, not an AI diagnostic algorithm, so an MRMC study is not relevant to its clearance.

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done

• Yes, in essence, standalone performance testing was done for the software's functional adherence to specifications.
  • The "verification and validation of the software was performed in-house according to established test plans and protocol." This represents the core "standalone" performance testing of the software's functionalities (e.g., can it import a DICOM plan correctly, can it store a patient record, can it generate a report?).
  • The "functional testing was conducted both in-house and by OUR United."
  • It's important to distinguish that "standalone performance" for an OIS means proving the software itself works as intended according to its functional and safety requirements, not demonstrating a diagnostic accuracy metric.

7. The Type of Ground Truth Used

For an OIS, the "ground truth" is primarily based on:

• Predetermined Specifications: The software is designed to perform specific functions according to a set of requirements. The "ground truth" is that the software correctly executes these requirements.
• Predicate Device Functionality: The Panther OIS demonstrated "substantial equivalence" to predicate devices, meaning its functional behavior aligns with or improves upon established, legally marketed systems.
• Clinical Expert Verification: "Clinical physicists contracted by Prowess" validated risk mitigation, indirectly confirming the system's safe and effective operation within clinical workflows.

There isn't a "pathology" or "outcomes data" type of ground truth in the diagnostic sense, as this device doesn't make diagnostic calls.

8. The Sample Size for the Training Set

• Not applicable / not provided: The Panther OIS is an information management system, not an AI algorithm that undergoes a "training" phase with a dataset. Therefore, there is no "training set sample size" as would be seen for a machine learning model. Its development follows traditional software engineering principles.

9. How the Ground Truth for the Training Set Was Established

• Not applicable: As explained above, this device does not utilize a "training set" in the context of machine learning. The "ground truth" for its development is its functional specifications, compliance with regulatory standards, and established best practices in oncology information systems and software engineering.

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Panther QIS|R&V is an information management system used to manage medical data and clinical workflow in a hospital or clinic. To support radiation oncology users, it allows the user to:

• . Enter or import, modify, store and archive treatment plans and images from treatment planning systems.
• Import, view, manipulate, enhance, annotate, store, and archive radiological . images.
• Select and provide radiation treatment plans to a radiation treatment delivery . system for patient treatment.
• Store and view treatment records provided by the radiation treatment delivery . system.
• Monitor and track treatment progress. +
• When Panther OIS|R&V is acting as an R&V system, verify the delivery device's . settings against the planned parameters, record the treatment parameters settings and prevent treatment if the parameters are out of tolerance.

Panther OIS|R&V is medical device software which allows the user to deliver radiation treatment on linear accelerator to a patient. The software has been developed to be a record and verify system that also manages oncology medical information in order to simplify the oncologist administrative tasks and to optimize the quality of patient treatment. It is a Windows® based system, which uses a single database that can be accessed by any Panther OIS|R&V client station in the treatment facility. Additionally, Panther OIS|R&V is capable of performing such functions as:

• lmporting treatment plans in DICOM or RTP format -
• Downloading of beams contained in the treatment plans to the linear accelerator ﺖ
• Verifying that the beams from the treatment plan are correctly set up on the linear accelerator
• Recording treatments delivered by the linear accelerator -
• Managing patient schedules ・
• Managing schedules for resources such as treatment machines and personnel "
• Maintaining a central repository of treatment plans and records -
• Documenting, monitoring and updating treatments and treatment plan information -
• Performing administrative tasks such as patient management. -
• Data locking or warning to prevent simultaneous access to patient (Tx) data -
• -Reports of patients and treatment history
• Manual Recording of treatment beams ﺖ
• Late Resumption of incomplete treatments -

The provided text describes the Panther OIS|R&V, a medical device software for radiation therapy record and verify systems. The 510(k) summary (K122616) details how the device meets its acceptance criteria through non-clinical testing.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a specific table of quantitative acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, or AUC for the Panther OIS|R&V device. Instead, it discusses meeting predetermined specifications and pass/fail criteria through verification and validation.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated as a number of cases or patients. The document mentions "clinical cases" were used by the beta-site for testing.
• Data Provenance: The beta-site testing was conducted at Community Memorial Hospital. This suggests the data is prospective in nature, as it was part of device development and external testing in a clinical setting. The country of origin would be USA, given Community Memorial Hospital is referenced and the submission is to the FDA.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Number of Experts: Not explicitly stated for the beta-site testing itself.
• Qualifications of Experts: The document mentions "clinical physicists contracted by Prowess" were involved in verifying mitigation strategies identified during risk analysis. While not directly stated as establishing ground truth for the test set, this indicates that qualified clinical experts were involved in the overall safety and effectiveness assessment. For the beta-site, it would be implied that hospital staff (oncologists, radiation therapists, physicists) were the "experts" using the system and providing feedback.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1, none) for the beta-site testing. The nature of testing a Record & Verify (R&V) system is often based on functional validation against expected behavior and safety checks rather than consensus-based ground truth on diagnostic findings.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No MRMC comparative effectiveness study involving human readers and AI assistance is described in this document. The device is not an AI-assisted diagnostic tool but rather an information management and R&V system. The comparison is primarily against existing predicate R&V systems, focusing on functional equivalence and safety, not on improving human reader performance.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, the core of the submission (and the "Summary of Non-clinical Tests") indicates that extensive standalone testing (algorithm only) was performed in-house to verify and validate the software according to its protocol and specifications. The "beta-site" testing, while involving human users in a clinical setting, was also focused on validating the system's intended functions and safety in a real-world scenario, essentially testing the "algorithm only" in its operational environment. The system's primary functions (importing plans, downloading beams, verifying settings, recording treatments) are automated or semi-automated functions of the software itself.

7. The Type of Ground Truth Used

The "ground truth" for verifying this type of software system (an R&V system) would not typically be pathology or outcomes data in the traditional sense, but rather:

• Predetermined Specifications/Expected Behavior: The software's outputs and actions are validated against predefined system requirements, design documents, and expected behavior.
• Functional Accuracy: Verification that the system correctly imports, processes, verifies, and records data according to established radiation therapy protocols and standards. For instance, whether the system accurately verifies that linear accelerator settings match planned parameters.
• Safety Requirements: Validation that the system correctly prevents treatment delivery if parameters are out of tolerance, manages data access securely, and maintains data integrity.
• Comparison to Predicate Devices: Demonstrating that its performance and functionality are equivalent to legally marketed predicate devices.

8. The Sample Size for the Training Set

The document does not provide information about a "training set" in the context of machine learning. The Panther OIS|R&V system, as described, is a record and verify software system, not an AI/ML diagnostic or predictive algorithm that typically requires a large training dataset. Its development would involve traditional software engineering and testing methodologies rather than machine learning training.

9. How the Ground Truth for the Training Set Was Established

Since the device is not described as utilizing machine learning or AI that requires a "training set," this question is not applicable based on the provided information. The "ground truth" for its development and testing would be derived from clinical requirements, software specifications, and established radiation oncology best practices, rather than labeled data for algorithm training.

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