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The Explorer 4D™ Treatment Planning System software is intended for use in preparing treatment plans for patients who have intracranial diseases where neurological radio surgery has been prescribed. The software is used to electronically import CT and MR images to determine the precise location of the target, to define and visualize treatment beam locations, and to visualize dose to the target and other structures.

The Explorer 4D™ Treatment Planning System (TPS) provides individual treatment plans for patients undergoing gamma radiation therapy treatment. The TPS provides for the import of patient images and selecting a series of relevant patient images to create a treatment plan. Precise calculation of the dose delivery parameters is supported by the registration of fiducial markers as a reference point between patient images. These images are then annotated with the region of interest (ROI) to be exposed to radiation. Several tools are provided to adjust the ROI based on the desired treatment area, within these regions "shots" (radiation sources) and associated treatment dose levels are defined as they related to a specified collimator size. When the plan definition is completed the operator can save or export the treatment plan.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Explorer 4D™ Treatment Planning System:

The document describes a 510(k) submission for the Explorer 4D™ Treatment Planning System (version 2.1), primarily focused on demonstrating its substantial equivalence to a predicate device (Explorer 4D™ Treatment Planning System version 2.0). The key difference in the new version is the ability to import MR images in addition to CT images.

The acceptance criteria are implicitly tied to demonstrating that the new feature (MR image import and subsequent treatment planning) performs equivalently to the existing functionality (CT image import) and that the overall system remains safe and effective.

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" with numerical targets in a typical format. Instead, it frames the performance data in terms of "equivalence," particularly regarding the accuracy of dose calculations when using MR images compared to CT images.

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

• Sample Size: The document does not specify a numerical sample size for the test set used in "accuracy comparison tests" or "functional testing." It only states that "all tests were conducted with comparison tests" and "production" (which seems to be a typo/phrase fragment but implies real-world or production-like data).
• Data Provenance: Not explicitly stated. The document refers to "MR images instead of CT images" for the new functionality and "comparison tests" between MR and CT results. It does not mention the country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: The document states that "Test results have been verified by physicist manual calculations as accurate for CT and MR images." This implies that qualified physicists were involved in verifying the accuracy, but their specific experience or number is not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. The verification by "physicist manual calculations" suggests an independent assessment, but the process (e.g., blinded, consensus-based) is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No, an MRMC comparative effectiveness study was not done or is not reported in this document. The study described focuses on the technical performance and accuracy of the device itself (dose calculations) rather than how human readers' performance with or without AI assistance changes. The device is a treatment planning system, not an AI diagnostic assistant tool for human interpretation.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Standalone Performance: Yes, the described "accuracy comparison tests" and verification by "physicist manual calculations as accurate for CT and MR images" essentially represent a standalone performance evaluation of the algorithms within the treatment planning system. The goal was to ensure the system's calculations were correct on their own, whether using CT or MR input images.

7. The Type of Ground Truth Used

• Ground Truth Type: The ground truth appears to be "physicist manual calculations" of dose, which served as the reference standard against which the device's calculated results (using both CT and MR images) were compared for accuracy.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable/Not specified. This document pertains to a 510(k) submission for a treatment planning system, not a machine learning or AI-based device that typically requires a large training set. The system likely uses established physics-based dose calculation algorithms which are validated rather than "trained" in the machine learning sense.

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

• Training Set Ground Truth Establishment: Not applicable/Not specified, for the same reasons as #8. The "training" of such a system would involve rigorous engineering and physics validation of its algorithms, rather than training on a dataset with established ground truth.

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The Explorer 4DTM Treatment Planning System software is intended for use in preparing treatment plans for patients who have intracranial diseases where neurological radio surgery has been prescribed.

The software is used to electronically import CT images to determine the precise location of the target, to define and visualize treatment beam locations, and to visualize dose to the target and other structures.

The Explorer 4D™ Treatment Planning System (TPS) provides individual treatment plans for patients undergoing gamma radiation therapy treatment. The TPS provides for the import of patient images and selecting a series of relevant patient images to create a treatment plan.

Precise calculation of the dose delivery parameters is supported by the registration of fiducial markers as a reference point between patient images. These images are then annotated with the region of interest (ROI) to be exposed to radiation. Several tools are provided to adjust the ROI based on the desired treatment area, within these regions "shots" (radiation sources) and associated treatment dose levels are defined as they related to a specified collimator size.

Here's a breakdown of the acceptance criteria and study information for the American Radiosurgery Explorer 4D104 Treatment Planning System, based on the provided text:

Acceptance Criteria and Device Performance

Study Information

The document refers to a "Performance Test Report (Attachment 10)" which provides data from ten separate performance tests. However, the provided text does not contain the detailed contents of this attachment. Therefore, much of the requested information cannot be definitively answered from the given excerpt.

Here's what can be inferred or explicitly stated:

1. Sample size used for the test set and the data provenance:

   • Sample Size: The document mentions "data from ten separate performance tests." It does not specify the number of cases or data points within these tests.
   • Data Provenance: Not specified in the provided text (e.g., country of origin, retrospective or prospective).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not specified in the provided text.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not specified in the provided text.

4. 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:

   • A MRMC study is not indicated for this device. The Explorer 4D™ TPS is a treatment planning system, which assists in defining treatment parameters, not an AI or diagnostic tool for human readers to interpret. The stated performance metrics are for the system's accuracy in spatial planning and dose calculation, not for improving human diagnostic accuracy. There's no mention of human readers or AI assistance in the context of comparative effectiveness.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

   • The performance metrics (1mm spatial accuracy, 3% dose delivery accuracy) appear to be for the standalone (algorithm only) performance of the Treatment Planning System. While the system provides tools for operators to adjust the ROI, the core performance measures cited are inherent to the system's calculations.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

   • The document implies that the ground truth for spatial accuracy and dose delivery would be based on physical measurements and validated dose calculation models. For example, phantoms are often used for calibration and testing in radiation therapy. The text mentions "phantom calibration" in the context of the predicate device, suggesting similar methods would be used for the Explorer 4D. However, the exact type of ground truth for each of the "ten separate performance tests" is not detailed.

7. The sample size for the training set:

   • Not specified in the provided text. This device is a treatment planning system, and while it involves algorithms, it's not described as a machine learning/AI diagnostic tool with a distinct "training set" in the conventional sense. Its development would involve software engineering and physics-based modeling rather than typical machine learning training on a large dataset of patient images.

8. How the ground truth for the training set was established:

   • Not applicable/specified, given the nature of the device as a treatment planning system rather than a machine learning model requiring a specific "training set" with established ground truth labels for classification/prediction tasks.

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