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MOSAIO® is an oncology information system used to manage workflows for treatment planning and delivery. It supports information flow among healthcare facility personnel and can be used wherever radiotherapy and/or chemotherapy are prescribed.

Users can configure MOSAIQ® for Medical Oncology use, Radiation Oncology use, or the two together. It lets users:

• . Assemble electronic patient charts and treatment plans, order diagnostic tests, and prescribe medications.
• Generate and keep medication formulary lists and calculate applicable medication . dosages for medical oncology.
• Import, view, annotate, adjust, enhance, manage and archive images.
• Compare radiation treatment plans and evaluate dose coverage.
• Design leaf plans for operation with radiotherapy treatment machines that have multileaf ● collimators.
• Make sure radiation treatment plans imported from treatment planning systems agree with treatment machine constraints. MOSAIO® reads actual settings from the treatment machine through the machine communication interface. It compares these settings with predefined values. If a mismatch occurs between the planned values and the actual machine settings, the system warns the user.
• . View reference images to setup treatment. MOSAIQ® refers to predefined settings to help treatment machine setup, and communicates patient and machine setup instructions.
• Record actual delivered radiation values in an electronic chart to track treatment. ●
• Use stereotactic localization to calculate set-up coordinates for treatments. .

MOSAIQ® is not intended for use in diagnosis. Medical oncology dose calculation functions are designed for use with patients 18 years or older only.

MOSAIQ is a multi-functional, integrated software suite that forms a comprehensive electronic oncology management system for medical and radiation oncology facilities. For both medical and radiation oncology users, MOSAIQ provides image-enabled electronic patient charting and record management as well as medical transcription and billing functionality. For radiation oncology users, it also includes the ability to import and export radiation treatment plan information, stereotactic localization, treatment plan review, the ability to plan multileaf collimator (MLC) shapes, and verify and record treatment setup and delivery.

This Premarket Notification addresses the addition of the Locate module for Radiation Oncology, which adds stereotactic localization capability to MOSAIQ.

The provided document is a 510(k) premarket notification for the MOSAIQ Oncology Information System. It describes the device's intended use, functionalities, and a comparison with predicate devices to establish substantial equivalence. However, it does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and a specific study proving device performance against those criteria.

Here's a breakdown of what can and cannot be extracted from the document:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria in a table format with corresponding quantitative performance metrics for the MOSAIQ Oncology Information System. Instead, it describes a summary of non-clinical testing where "Verification tests were written and executed to ensure that the system is working as designed. Over 100 test procedures were executed, including tests to verify requirements for new product functionality were met, tests to ensure that risk mitigations function as intended, and regression tests to ensure continued safety and effectiveness of existing functionality. Pass/fail criteria for this testing effort was similar to past testing efforts for the previous versions of MOSAIQ. MOSAIQ passed testing and was deemed safe and effective for its intended use."

While it confirms tests were performed and passed, specific numerical acceptance criteria (e.g., accuracy percentages, error rates, time limits) and the detailed study results meeting those criteria are not provided. The "reported device performance" is a qualitative statement of "passed testing and was deemed safe and effective."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states, "Bench testing was performed...using simulated clinical workflows and ad hoc testing where appropriate, with actual patient data."

• Sample Size for Test Set: The exact number of patient data samples used for testing is not specified. It mentions "actual patient data," but not how many cases or the size of this dataset.
• Data Provenance: The country of origin for the "actual patient data" is not specified. It does not indicate whether the data was retrospective or prospective, though the term "simulated clinical workflows" often implies a retrospective or synthetic approach.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is not provided in the document. The document refers to "actual patient data" and "simulated clinical workflows," but it does not describe the process of establishing ground truth for this data, nor does it mention the involvement or qualifications of experts.

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

This information is not provided in the document. There is no mention of any adjudication process for the test results or the ground truth.

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

The document states, "Clinical trials were not performed as part of the development of this product. Clinical testing on patients is not advantageous in demonstrating substantial equivalence or safety and effectiveness of the device since testing can be performed such that no human subjects are exposed to risk."
Therefore, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader improvement with or without AI assistance was not performed. The device, an Oncology Information System, is not an AI-assisted diagnostic tool in the sense of image interpretation where MRMC studies are typically applicable.

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

The document describes "Verification tests were written and executed to ensure that the system is working as designed." These tests would inherently represent a form of standalone testing of the software's functionality against its requirements.
However, the device is designed to interface with human users ("alerting the user," "warns the user") and is an "Oncology Information System" that "supports information flow among healthcare facility personnel." This implies a human-in-the-loop system where the software provides information and alerts, but human clinicians make final decisions. While individual sub-functions of the software (e.g., dose calculation, mismatch detection) would have been tested in a standalone manner, the overall system is not purely "algorithm only" without human interaction in its intended use.

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

The document uses "actual patient data" in its bench testing but does not specify the type of ground truth established for this data (e.g., whether it was based on pathology, expert consensus, or clinical outcomes). The testing focused on verifying the system's design and functionality rather than diagnostic accuracy against a specific ground truth. For instance, for the "mismatch" warning function, the ground truth would be whether a mismatch truly existed between planned and actual settings, rather than a clinical outcome.

8. The sample size for the training set

This information is not applicable or not provided. The MOSAIQ Oncology Information System, as described, appears to be a rule-based software system for managing workflows and information in oncology, rather than a machine learning or AI system that requires a "training set" in the conventional sense. The document does not mention any machine learning components that would necessitate training data.

9. How the ground truth for the training set was established

This information is not applicable or not provided as there is no mention of a "training set" or machine learning models.

In summary, the provided document focuses on establishing substantial equivalence for an Oncology Information System based on its functionalities and comparison with predicate devices, rather than detailed performance metrics against specific acceptance criteria relevant to diagnostic or AI algorithms. The "study" referenced is a series of "verification tests" performed internally, without the rigorous quantifiable metrics often associated with AI/ML device evaluations.

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