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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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The NOMOS Slit Collimator (BEAK™) is intended for use in Radiosurgery/Radiation Therapy to secondarily collimate the width of the radiation beam after it passes through the MIMiC® Multileaf Intensity Modulating Collimator (K940412). Several sizes of collimators will be provided with a slit width, at isocenter, ranging from 1 mm to 8 mm.

The Slit Collimator (BEAK) is tungsten block whose purpose is to provide secondary collimation for the radiation beam exiting from the NOMOS MIMiC (K940412). The MIMiC has the capability of delivering treatment slices whose width is either 8 mm or 16 mm. The Slit collimator, by providing secondary collimation of the 8 mm beam, allows slices to be delivered that are 1 - 8 mm in thickness without the need to modify the control mechanisms built into the MIMiC that ensure that leaf (vane) movement is functioning correctly. In this sense, the Slit Collimator serves the same purpose as the secondary collimators provided with radiosurgical systems or the secondary collimators, commonly called blocks, that are used with radiation therapy linear accelerators.

This document does not contain a description of acceptance criteria or a study proving device performance against such criteria. The provided text is a 510(k) summary for the NOMOS Slit Collimator (BEAK™), which focuses on demonstrating substantial equivalence to predicate devices rather than a detailed performance study with acceptance criteria.

Therefore, I cannot populate the table or answer the questions as the required information is not present in the provided text. The document primarily describes the device, its intended use, and argues for its substantial equivalence to existing devices based on design, materials, and intended purpose without detailing specific performance metrics or clinical study results.

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This device is intended to be used as an integrated system for the planning and delivery of dynamic conformal radiation therapy to produce a radiation field which is modulated to conform to the projected tumor volume plus margins by means of a dynamic (moving) multileaf collimator, while simultaneously moving the accelerator gantry.

Integrated radiation therapy treatment planning and delivery system

The provided text is a Premarket Notification (K953258) for the NOMOS PEACOCK System, dated November 18, 1995. This document is a summary of safety and effectiveness, primarily focused on establishing substantial equivalence to predicate devices, as per the regulations in place at the time (pre-1996 for 510(k) submissions).

Crucially, this type of document from 1995 does NOT align with the modern understanding and requirements for demonstrating device performance with specific acceptance criteria and detailed study results as you've requested. The regulatory landscape and expectations for evidence of performance were significantly different then.

Therefore, I cannot extract the information you've requested because it is not present in this 1995 premarket notification. Here's a breakdown of why and what information is available:

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

• Not Available. The document does not define explicit "acceptance criteria" in the modern sense (e.g., sensitivity, specificity, accuracy targets). Instead, it relies on the concept of "substantial equivalence" to predicate devices. The "reported device performance" is broadly stated as "Physics and clinical testing has demonstrated that the Peacock System performs as intended," without specific metrics or comparisons to acceptance criteria.

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

• Not Available. The document mentions "Physics and clinical testing" but does not provide any details about sample sizes, the nature of the studies (retrospective/prospective), or data provenance.

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):

• Not Available. Ground truth establishment, expert involvement, and their qualifications are not mentioned.

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

• Not Available. Adjudication methods are not discussed.

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:

• Not Available and Not Applicable. This device is a radiation therapy treatment planning and delivery system, not an AI-assisted diagnostic tool for "human readers." Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this device's intended use in the context of this document.

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

• Not Applicable in the modern AI sense. The "algorithm" here is the treatment planning software, and it is inherently designed to be used by human oncology professionals. The concept of "standalone" algorithm performance as understood for AI-based diagnostic tools isn't directly applicable in the same way. The document doesn't detail specific performance studies of the software in isolation.

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

• Not Available. The document refers to "clinical testing" generally, but does not specify how "ground truth" was established for any performance evaluations. For a treatment planning system, ground truth would typically relate to dose distribution accuracy, tumor targeting, patient outcomes, etc., but these details are not provided.

8. The sample size for the training set:

• Not Applicable / Not Available. The "Peacock System" is described as an integrated treatment planning and delivery system. In 1995, this would involve computational models and algorithms for dose calculation and beam shaping, not machine learning or AI models that require specific "training sets" in the modern sense.

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

• Not Applicable / Not Available. See point 8.

Summary of Information Available from the Provided Text:

• Device Name: NOMOS PEACOCK System (consisting of CORVUS and MIMiC)
• Proprietary Name: PEACOCK™ System
• Common Name: Integrated radiation therapy treatment planning and delivery system
• Applicant: NOMOS Corporation
• Predicate Devices: Varian Varisource HDR Afterloader with integrated planning system (K852842), GE Target Planning System (K896353) & Arc Therapy (K802809)
• Classification: Class II (under 21 CFR 892.5050 for simulation systems, 21 CFR 892.5710 for beam-shaping blocks)
• Intended Use: Integrated system for planning and delivery of dynamic conformal radiation therapy to modulate a radiation field to conform to the projected tumor volume plus margins, by means of a dynamic multileaf collimator while simultaneously moving the accelerator gantry.
• Performance Claim: "Physics and clinical testing has demonstrated that the Peacock System performs as intended." (No specific data provided in this summary.)
• Substantial Equivalence Claim: Similar in design, construction, materials, intended use, and performance characteristics to predicate treatment planning systems and multileaf collimators. No new issues of safety or effectiveness introduced.

Conclusion:

The provided document is a very old (1995) 510(k) summary focused on substantial equivalence. It predates modern regulatory expectations for detailed performance studies, particularly for AI/machine learning devices. Therefore, it does not contain the specific information regarding acceptance criteria, study designs, sample sizes, ground truth establishment, or expert involvement that you have requested.

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