(162 days)
The Monaco system is used to make treatment plans for patients with prescriptions for external beam radiation therapy. The system calculates dose for photon, electron, and proton treatment plans and displays, on-screen and in hard-copy, two- or threedimensional radiation dose distributions inside patients for given treatment plan set-ups.
The Monaco product line is intended for use in radiation treatment planning. It uses generally accepted methods for:
- contouring
- image manipulation
- simulation
- image fusion
- . plan optimization
- QA and plan review
The Monaco RTP System accepts patient diagnostic imaging data from CT and MR scans, and source dosimetry data, typically from a linear accelerator. The system then permits the user to display and define (contour) the target volume to be treated and critical structures which must not receive above a certain level of radiation, on these diagnostic images. Based on the prescribed dose, the user, a Dosimetrist or Medical Physicist, can then create multiple treatment scenarios involving the number, position(s) and energy of radiation beams and the use of a beam modifier (MLC, block, etc.) between the source of radiation and the patient to shape the beam. Monaco RTP system then produces a display of radiation dose distribution within the patient, indicating not only doses to the target volume but to surrounding tissue and structures. The optimal plan satisfying the prescription is then selected, one that maximizes dose to the target volume while minimizing dose to surrounding healthy volumes.
The parameters of the plan are output for later reference and for inclusion in the patient file.
Monaco planning methods and modalities:
- Intensity Modulated Radiation Treatment (IMRT) planning .
- . Electron, photon and proton treatment planning
- . Planning for dynamic delivery methods (e.g. dMLC, dynamic conformal, Volumetric Modulated Arc Therapy (VMAT))
- . Stereotactic planning and support of cone-based stereotactic
- . 3D conformal planning
- . Adaptive planning (e.g. for the Elekta Unity MR-Linac)
Monaco basic systems tools, characteristics, and functions:
- . Plan review tools
- . Manual and automated contouring tools
- DICOM connectivity .
- . Windows operating system
- . Simulation
- . Support for a variety of beam modifiers (e.g. MLCs, blocks, etc.)
- . Standardized uptake value (SUV)
- Specialty Image Creation (MIP, MinIP, and Avq) •
- . Monaco dose and Monitor Unit (MU) calculation:
- Dose calculation algorithms for electron, photon, proton planning .
Monaco is programmed using C and C++ computer programming languages. Monaco runs on Windows operating system and off-the-shelf computer server/hardware.
This document, K213787, is an FDA 510(k) premarket notification for the Elekta Monaco RTP System, Release 6.1. It asserts substantial equivalence to a predicate device, Monaco RTP System (K202789). The document focuses on the non-clinical performance testing of the device, particularly for enhancements in proton therapy functionality.
Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:
1. A table of acceptance criteria and the reported device performance
The document does not provide a specific table with numerical acceptance criteria and corresponding reported device performance values in a format like "Target Value (X%) vs. Observed Value (Y%)". Instead, it describes a more qualitative approach to verifying the enhancements.
However, based on the "SUMMARY OF PERFORMACE TESTING (NON-CLINICAL)" section, we can infer the following:
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| General Device Performance & Functionality | "Development, verification, and validation activities for the modified system were carried out in accordance with design controls... applicable ISO 13485 Quality Management System requirements, ISO 14971 Risk Management requirements, and IEC 62304 requirements for software life-cycle processes. Non-clinical testing was performed to evaluate device performance and functionality in accordance with design and risk management requirements at subsystem, integration and system levels including interoperability." |
| LET Calculation Accuracy (Monoenergetic Spots) | "The LET to water and medium were calculated for monoenergetic spots in Monaco and Geant4 for a range of energies and materials. The results were quantitatively compared to each other as well as qualitatively compared to other published results." (Implies the results were acceptably close to reference values). |
| LET Distribution Accuracy (Complex Spot/Beam Arrangements) | "LET distributions for complex multiple spot and multiple beam arrangements as well as plan summations were calculated in Monaco and compared to expected values as obtained through manual summation of individual spots according to the design equations." (Implies that Monaco's calculations matched the expected values). |
| Clinical Workflow Validation | "Formal validation of the clinical workflows has been performed on a clinically representative production equivalent system by competent and professionally qualified personnel." (Implies successful validation). |
| Safety and Performance (Overall) | "The device safety and performance have been addressed by non-clinical testing in conformance with predetermined performance criteria, FDA guidance, and recognized consensus standards. The results of verification and validation as well as conformance to relevant safety standards demonstrate that the Monaco RTP System meets the established safety and performance criteria and is substantially equivalent to the predicate device." |
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 does not explicitly state a "sample size" in terms of number of patient cases for the non-clinical testing. The tests described are computational comparisons (Monaco vs. Geant4, Monaco vs. manual summation) for various energies, materials, spot arrangements, and beam arrangements. These are not patient-specific data sets but rather simulated or theoretical scenarios designed to evaluate the computational accuracy of the new features.
There is no mention of "country of origin" or whether it was "retrospective or prospective" as these terms typically apply to studies involving patient data, which this non-clinical testing does not appear to use.
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)
For the non-clinical tests described:
- LET calculation accuracy: The ground truth for monoenergetic spots was established by comparison to Geant4 (a Monte Carlo simulation toolkit) and "other published results". This implies a reliance on established physics models and potentially peer-reviewed literature rather than human expert interpretation of images.
- LET distribution accuracy: Ground truth for complex arrangements was established by "expected values as obtained through manual summation of individual spots according to the design equations." This suggests a mathematical derivation as the ground truth.
- Clinical workflows: "Formal validation... by competent and professionally qualified personnel." No specific number or detailed qualifications (e.g., "radiologist with 10 years of experience") are provided for these personnel.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Given that the non-clinical tests involve comparisons to established algorithms (Geant4), published results, and mathematical derivations, there is no mention or indication of an adjudication method like "2+1" or "3+1", which are typically used for establishing consensus among human interpreters. The comparisons are to objective, established computational or theoretical benchmarks.
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, a multi-reader, multi-case (MRMC) comparative effectiveness study was not done. The document explicitly states: "No animal or clinical tests were performed to establish substantial equivalence with the predicate device." Therefore, there is no information on how much human readers improve with or without AI assistance, as the changes are to the dose calculation algorithms themselves (new proton functionalities like robust optimization, robust evaluation, and LET calculation) rather than an AI-assisted diagnostic or contouring tool that directly impacts human reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, the described performance testing is primarily a standalone (algorithm only) evaluation. The comparisons are:
- Monaco vs. Geant4 (algorithm vs. algorithm/physics model)
- Monaco vs. manual summation based on design equations (algorithm vs. mathematical derivation)
The "clinical workflow validation" does involve "competent and professionally qualified personnel" interacting with the system, but the core performance evaluation of the new proton features (LET, robustness) is algorithmic.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The ground truth used for the non-clinical testing appears to be a combination of:
- Established physics models/simulations: Geant4 for monoenergetic LET calculations.
- Mathematical derivations/design equations: For complex LET distributions.
- "Published results": For qualitative comparison of monoenergetic LET.
There is no mention of expert consensus (for image interpretation), pathology, or outcomes data being used as ground truth for these specific non-clinical tests.
8. The sample size for the training set
The document does not mention a "training set" or "training data". This is because the Monaco RTP System (as described in this submission) uses deterministic algorithms for dose calculation (e.g., Monte Carlo algorithm) rather than machine learning or AI models that require a separate training phase. The "development" mentioned refers to software engineering and algorithm implementation, not machine learning model training.
9. How the ground truth for the training set was established
Since no training set is mentioned or implied for the deterministic algorithms described, this question is not applicable based on the provided document.
§ 892.5050 Medical charged-particle radiation therapy system.
(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.