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Intended Use: CORVUS is intended for use as a planning tool for conformal radiation therapy. Using operator-supplied input and patient scans, it creates a plan for treatment delivery systems and generates a set of beam weights that, when applied to a compatible system, facilitates delivery of an intensity-modulated 3D conformal radiation therapy treatment. CORVUS is intended only to suggest a delivery plan. It is the physician's responsibility to verify that the dose distributions which would result from plan implementation are appropriate for a particular patient. The CORVUS system is intended to be used as an integrated system with a modulating device for planning and delivery of conformal radiation therapy. The modulating device can be the NOMOS MIMIC, nomosSTAT MLC, or a supported MLC. CORVUS produces radiation fields which are modulated to conform to the projected tumor volume plus margins. The system tries to achieve target goals while sparing sensitive structures.

Indications for Use: The CORVUS system is a radiation treatment planning package designed to allow medical physicists, dosimetrists, and radiation oncologists to create conformal treatment plans using photon (x-ray) external beam radiation therapy. The treatment plans generated by CORVUS are based upon treatment machine-specific data and are intended to provide a guide to delivering external beam radiation therapy which conforms to the target volume defined by the radiation oncologist. The CORVUS system is valid for use only with external beam photon therapy; calculations for electrons and intracavity sources (Brachytherapy) are NOT supported.

CORVUS is a semi-automatic planning system: rather than simply verifying a userdesigned plan, the system itself suggests a plan. A clinician then reviews and approves the plan. CORVUS is designed to generate plans for treatment delivery systems that can create multiple radiation patterns composed of pencil beams on which the intensity can be individually controlled. The treatment beams are weighted so that when they are projected into the treatment space they superimpose to give the desired dose distribution. Each radiation field is generated using one of several optimization methods provided with the system, including simulated annealing and gradient descent. The treatment beams are set not only to deliver the prescribed dose to the identified target volume, but also to keep the dose to other sensitive volumes below user-defined limits. Planning is done volumetrically: the beam weights for treating the entire target volume are generated simultaneously. The dose matrix is volumetric. The dose to each point is calculated to be that received from all beams and from all gantry angles. Dosage is calculated using a finite size pencil beam (FSPB) algorithm based on the beam characterization of clinically measured data. The degree to which a treatment plan is optimized is determined in part by constraints placed on the planning algorithm. The user has direct control over these constraints, which include dose goals to the target structures, dose limits to the sensitive structures, and the specification of arcs or fixed gantry positions in the treatment plan. CORVUS treatment plans need not have the isocenter located within the target volume. An unlimited number of targets falling within the treatment volume can be planned for at the same time. Dose may be prescribed for up to 32 structures, 29 of them userselectable, any number of which may be separate targets or radiation-sensitive structures. Each structure can have a separate dose prescription.

Here's an analysis of the provided text regarding the acceptance criteria and the study proving the device meets them, structured as requested:

Device Name: CORVUS Radiation Therapy Treatment Planning System (Model: CORVUS 09)

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes the CORVUS 09 as an update to an existing device (CORVUS 5.0M) and focuses on the equivalence of the updated device to its predicate, with specific improvements. Therefore, the "acceptance criteria" are implied to be equivalence to the predicate device in overall treatment planning quality and improved accuracy in specific areas.

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

• Test Set Sample Size: The document does not specify a distinct "test set sample size" in terms of patient data or specific clinical cases.
  • For dosimetric validation, it states: "A total of 53 comparisons with measurement or comparisons with Monte-Carlo were completed." This refers to specific measurements against known standards.
  • For system functionality, it states: "A total of 62 system tests passed the criteria."
• Data Provenance: The document does not specify the country of origin of data or whether it was retrospective or prospective. The validation appears to be primarily focused on physical measurements and comparisons with Monte-Carlo simulations, rather than clinical patient data.

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

• Number of Experts: Not explicitly stated as a specific number.
• Qualifications of Experts: For the LDI algorithm's treatment plan quality validation, it states: "qualified personnel who are familiar with using an inverse treatment planning system in a clinical setting." For other validations, it refers to "medical physicists skilled in the art of conformal radiation therapy." No specific years of experience are mentioned.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method (e.g., 2+1, 3+1) for establishing ground truth, as the testing primarily involves comparing device outputs to physical measurements (film, MOSFETs, ion-chambers) and Monte-Carlo simulations, or through direct comparison of plan quality by qualified personnel.

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

No. The document describes technical performance validation and comparison to a predicate device, not a human reader study in combination with AI assistance. Therefore, no effect size of human readers improving with/without AI assistance is provided. The device (CORVUS) is the planning tool, and ActiveRx (a feature of CORVUS 09) involves a user in the optimization process, but this is not described as an MRMC comparative effectiveness study where human readers interpret output to make diagnostic decisions.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, implicitly. The "performance testing data" section focuses on the algorithm's accuracy (dosimetric validation, leakage calculation, system tests) independent of a human's final clinical interpretation, although a physician approves the plan. The system "suggests a plan" and "generates a set of beam weights" which are then reviewed. The validation studies for dose calculation and plan quality, comparing against physical measurements and Monte-Carlo, are standalone assessments of the algorithm's output.

7. The Type of Ground Truth Used

The ground truth for the performance testing was established using:

• Physical Measurements: Film measurements, MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors), and ion-chambers.
• Computational/Simulated Data: Monte-Carlo calculations.
• Clinical/Expert Criteria: "Clinical criteria which are associated with conformal therapy" for evaluating treatment plan quality, as assessed by "qualified personnel" or "medical physicists."

8. The Sample Size for the Training Set

The document does not provide information on the sample size used for the training set. It describes optimization algorithms (simulated annealing, gradient descent) but not how a training set was used to develop or train the system. This suggests that the system's dose calculation and optimization methods are based on physics-based models and algorithms rather than a machine learning model that requires a distinct "training set" in the conventional sense.

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

As no specific training set is mentioned in the context of machine learning, there is no information on how its ground truth was established. The system's underlying physics models and algorithms would have been developed based on established principles and characterized with machine-specific data mentioned in the "Indications for Use" section.

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