(237 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 three-dimensional 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
- Distributed planning configurations (e.g., for conventional linac)
- Adaptive planning capabilities (e.g., for MR-Linac & conventional linac)
- Auto planning features (e.g., for conventional linac)
Monaco basic systems tools, characteristics, and functions:
- Plan review tools
- Manual and automated contouring tools (Segmentation component for MR images)
- 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 Avg)
- Monaco dose and Monitor Unit (MU) calculation
- Dose calculation algorithms for electron, photon, proton planning
Monaco is programmed using C, C++ and C# computer programming languages. Monaco runs on Windows operating system and off-the-shelf computer server/hardware.
The provided FDA 510(k) clearance letter and summary for the Monaco RTP System (6.3) outlines the acceptance criteria and a study supporting the substantial equivalence of the new features. Here's a breakdown of the requested information:
1. A table of acceptance criteria and the reported device performance
| Changed Feature | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Segmentation component for invoking MR auto-segmentation algorithms (AI-based) | Primary metric: Average Hausdorff Distance (AVD) ≤ 3 mm. Secondary metric (value of interest): DICE or AUC ≥ 0.7 for specific structures. Additionally, qualitative analysis based on a 5-point Likert scale to determine if automatically generated structures provide a valuable starting point for clinical delineation. Investigation of any failures to meet the DICE confidence value of 0.7, with findings included as "Limitations." Sub-group analysis based on patient size, pixel size, slice spacing, and number of slices. | For all evaluated structures across all models (Female Pelvis Intact & Hysterectomy, Male Pelvis, and Head & Neck), the mean Absolute Volume Difference (AVD) was less than 3 mm. Structure-specific statistical analyses supported this conclusion. Patterns of failure for any structure failing the DICE confidence value of 0.7 were investigated and included as "Limitations." Qualitative analysis concluded that automatically generated structures provided a valuable starting point for clinical delineation. |
| Auto-planning | All pre-defined acceptance criteria related to workflow performance, protocol management, plan creation, interoperability, and error handling must be met. Plans generated must be clinically acceptable for the intended use and not introduce new safety or effectiveness concerns. | All testing met pre-defined acceptance criteria. Treatment plans generated were reviewed within the clinical workflow and determined to be suitable for clinical use, without introducing new safety or effectiveness concerns. |
| Extending adaptive planning capabilities to EMLA for offline adaptive planning | Correct system behavior during image registration, structure propagation, dose recalculation/re-optimization, offline adaptive plan generation, and workflow execution under representative clinical scenarios. No defects, unexpected behavior, or data integrity issues. Plans generated must be clinically acceptable for the intended use. All predefined acceptance criteria for verification and validation must be met. | No defects, unexpected behavior, or data integrity issues were identified during testing. Validation demonstrated that offline adaptive planning using CT‑to‑CBCT supports the creation of clinically acceptable treatment plans for the intended use. Offline adaptive plans were reviewed within the clinical workflow and determined to be suitable for use. Verification and validation testing met pre-defined acceptance criteria. |
| Interoperability with 3rd party software for image management and contouring | Correct DICOM export functionality, preservation of data integrity, and successful creation of an offline adaptive plan using third-party contouring. Third-party contouring outputs must be clinically acceptable and comparable to reference contours produced by qualified users. All planned Solution Interoperability test cases successfully executed and passed. All verification and validation testing met predefined acceptance criteria. | All verification and validation testing met the predefined acceptance criteria. All planned Solution Interoperability test cases have been successfully executed and passed. |
2. Sample size used for the test set and the data provenance
- AI-based segmentation component:
- Female Pelvis Intact & Hysterectomy models: 529 images (joint image set).
- Male Pelvis model: 250 images.
- Head & Neck model: 1862 images.
- Data Provenance: Not explicitly stated regarding 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 the qualifications of those experts
- AI-based segmentation component: "reference contours produced by qualified users" and "clinical delineation." The exact number or specific qualifications (e.g., radiologist with X years of experience) of these experts are not specified in the provided document.
4. Adjudication method for the test set
- AI-based segmentation component: For the qualitative analysis, it states "a conclusion that the automatically generated structures provided a valuable starting point for clinical delineation." This implies human review and evaluation. However, a formal adjudication method like "2+1" or "3+1" is not explicitly mentioned.
- For other features (Auto-planning, Adaptive planning, Interoperability), reviews mention evaluation within the "clinical workflow" and determination of "suitability for clinical use," but a specific adjudication method beyond internal reviews is not detailed.
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
- A formal MRMC comparative effectiveness study to quantify human reader improvement with AI assistance is not mentioned in the provided text. The AI component was evaluated in a standalone manner for its segmentation accuracy, and qualitatively for its utility as a "starting point for clinical delineation."
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, for the AI-based segmentation component, a standalone algorithm-only performance evaluation was done using metrics like Average Hausdorff Distance (AVD), DICE, and AUC.
7. The type of ground truth used
- For the AI-based segmentation component, the ground truth for the test set involved "reference contours produced by qualified users" and "clinical delineation." This implies expert consensus/delineation rather than pathology or outcomes data.
- For other features, "clinically acceptable treatment plans" and "suitable for clinical use" imply evaluation against accepted clinical standards, likely by qualified personnel.
8. The sample size for the training set
- The training set sample sizes are indicated for the AI-based segmentation models:
- Female Pelvis Intact & Hysterectomy: 529 images (joint image set used for training).
- Male Pelvis: 250 images (used for training).
- Head & Neck: 1862 images (used for training).
9. How the ground truth for the training set was established
- The document implies that the training data for the AI-based segmentation models would have been expertly annotated to establish the ground truth, given the mention of "reference contours produced by qualified users" for evaluation. However, the exact method for establishing ground truth for the training set is not explicitly detailed beyond this inference.
FDA 510(k) Clearance Letter - Monaco RTP System (6.3)
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.08.04
February 19, 2026
Elekta Solutions AB
Elinor Li
Sr. Regulatory Specialist
Hagaplan 4
Stockholm, 113 68
Sweden
Re: K252002
Trade/Device Name: Monaco RTP System (6.3)
Regulation Number: 21 CFR 892.5050
Regulation Name: Medical Charged-Particle Radiation Therapy System
Regulatory Class: Class II
Product Code: MUJ
Dated: June 27, 2025
Received: June 27, 2025
Dear Elinor Li:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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K252002 - Elinor Li Page 2
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 and 21 CFR 820.70) and document changes and approvals in the Medical Device File (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the Quality Management System Regulation (QMSR) (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-
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K252002 - Elinor Li Page 3
assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Lora D. Weidner, Ph.D.
Assistant Director
Radiation Therapy Team
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
Page 4
Indications for Use
Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions.
Please provide the device trade name(s).
Monaco RTP System (6.3)
Please provide your Indications for Use below.
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 three-dimensional 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
Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
Monaco RTP System Page 9 of 46
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Elekta Solutions AB June 2025
Monaco- Traditional 510(k)
Traditional 510(k) Summary (21 CFR § 807.92)
I. Submitter
Address: Elekta Solutions AB
Hagaplan 4
113 68 Stockholm
Sweden
Contact: Elinor Li
Sr. Regulatory Specialist, Regulatory Affairs Software
+1 (224) 6022203 / + 86 18817581406
Elinor.Li@elekta.com
510(k) Number: K252002
Date Prepared: 2025-06-27
II. Device
Trade Name/Brand Name: Monaco RTP System (6.3)
Monaco, Elekta One | Planning, Elekta One | Planning Pro
Product Classification: Class II
Common Name: Monaco Radiotherapy Treatment Planning (RTP)
Classification Name: System, Planning, Radiation Therapy Treatment
Regulation Number: 21 CFR 892.5050
Product Code: MUJ
III. Predicate Device
Predicate #: K223233
Predicate Trade Name: Monaco RTP System (6.2)
Predicate Product Code: MUJ
IV. Device Description Summary
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
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- 3D conformal planning
- Distributed planning configurations (e.g.,for conventional linac)
- Adaptive planning capabilities (e.g.,for MR-Linac & conventional linac)
- Auto planning features (e.g.,for conventional linac)
Monaco basic systems tools, characteristics, and functions:
- Plan review tools
- Manual and automated contouring tools (Segmentation component for MR images)
- 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 Avg)
- Monaco dose and Monitor Unit (MU) calculation
- Dose calculation algorithms for electron, photon, proton planning
Monaco is programmed using C, C++ and C# computer programming languages. Monaco runs on Windows operating system and off-the-shelf computer server/hardware.
V. Intended Use/ Indications for Use
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 three- dimensional 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
VI. Intended Use/ Indications for Use Comparison
The Monaco Indication for Use Statement and Intended use remain unchanged from the Indications for Use Statement and intended use cleared under predicate device K223233.
VII. Comparison to Predicate
| Item | Predicate Device | Subject Device | Substantial Equivalence Discussion |
|---|---|---|---|
| Indications | 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 three-dimensional radiation dose distributions inside the | 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 three-dimensional radiation dose | No change |
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| Item | Predicate Device | Subject Device | Substantial Equivalence Discussion |
|---|---|---|---|
| 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 | distributions inside the 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 | ||
| Use Environment | Access-controlled Healthcare facilities | Access-controlled Healthcare facilities | No change |
| Dose Calculation Algorithms | Monte Carlo - electron & photon Collapsed Cone (photon) Pencil Beam (only used when optimization) GPUMCD for photon (MR linac) GPUMCD for proton Proton Pencil Beam | Monte Carlo - electron & photon Collapsed Cone (photon) Pencil Beam (only used when optimization) GPUMCD for photon (both MR linac and conventional linac) GPUMCD for proton Proton Pencil Beam | Substantially Equivalent GPUMCD extends from MR linac to conventional linac. |
| Proton planning | Yes | Yes | No change |
| Dose calculation for MR-Linac (including magnetic field, coils & cryostat) | Yes | Yes | No change |
| Adaptive Therapy Planning | Adaptive therapy planning for MR linac | Adaptive therapy planning for MR linac & conventional linac | Substantially Equivalent Adaptive Therapy Planning functionalities are extended from MR linac (UNITY) to conventional Linac (EMLA). Enables offline adaptive planning on EMLA. |
| Auto Planning | No | Yes (for conventional linac) | Substantially Equivalent Auto planning feature for iterative modification of optimization cost function parameters introduced in subject device. |
| Contouring | Yes (with traditional algorithm) | Yes (with traditional and machine learning algorithm) | Substantially Equivalent The Segmentation Component utilizes machine-learning based models to automatically segment MR image sets introduced in subject device. |
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Monaco- Traditional 510(k)
| Item | Predicate Device | Subject Device | Substantial Equivalence Discussion |
|---|---|---|---|
| Use Environment | Access-controlled Healthcare facilities | Access-controlled Healthcare facilities | No change |
| Dose Calculation Algorithms | Monte Carlo - electron & photon Collapsed Cone (photon) Pencil Beam (only used when optimization) GPUMCD for photon (MR linac) GPUMCD for proton Proton Pencil Beam | Monte Carlo - electron & photon Collapsed Cone (photon) Pencil Beam (only used when optimization) GPUMCD for photon (both MR linac and conventional linac) GPUMCD for proton Proton Pencil Beam | Substantially Equivalent GPUMCD extends from MR linac to conventional linac. |
| Proton planning | Yes | Yes | No change |
| Dose calculation for MR-Linac (including magnetic field, coils & cryostat) | Yes | Yes | No change |
| Adaptive Therapy Planning | Adaptive therapy planning for MR linac | Adaptive therapy planning for MR linac & conventional linac | Substantially Equivalent Adaptive Therapy Planning functionalities are extended from MR linac (UNITY) to conventional Linac (EMLA). Enables offline adaptive planning on EMLA. |
| Auto Planning | No | Yes (for conventional linac) | Substantially Equivalent Auto planning feature for iterative modification of optimization cost function parameters introduced in subject device. |
| Contouring | Yes (with traditional algorithm) | Yes (with traditional and machine learning algorithm) | Substantially Equivalent The Segmentation Component utilizes machine-learning based models to automatically segment MR image sets introduced in subject device. |
| Distributed planning deployment | Yes (for conventional linac) | Yes (for conventional linac) | No change |
| Local Biological Measure Optimization | Hypertion Optimizer (Constrained Optimization) | Next Generation Optimizer (GPU-accelerated Hypertion Optimizer) | Substantially Equivalent Next Generation Optimizer in the subject device is the GPU based optimizer which uses Pseudo-Gradient Descent algorithm. |
| Operating System | Windows 10 | Windows 10 | No change |
| DICOM RT Support | Yes | Yes | No change |
| Programming Language | C, C++, C# | C, C++, C# | No change |
| Modalities Supported | Photon, Electron, Proton | Photon, Electron, Proton | No change |
| Beam modelling | Beam modeling is performed by Elekta personnel. Standardized beam models are provided for some Elekta linac energy options. | Beam modeling is performed by Elekta personnel. Standardized beam models are provided for some Elekta linac energy options. | No change |
| Scripting | UI based scripting | Both UI based and non-UI based scripting | Substantially Equivalent |
| Archive/Retrieve | Yes | Yes | No change |
| Standards Compliance | ISO 13485 ISO 14971 IEC 62304 IEC 62083 IEC 82304-1 IEC 61217 IEC 62366-1 ISO 15223-1 | ISO 13485 ISO 14971 IEC 62304 IEC 62083 IEC 82304-1 IEC 61217 IEC 62366-1 IEC 81001-5-1 ISO 15223-1 ISO 20417 | Substantially Equivalent |
| Compatibility with Connected Systems | • EMLA • MR-linac Unity • MOSAIQ • Smart flow | • EMLA • MR-linac Unity • MOSAIQ • Third party contouring tools • Smart flow | Substantially Equivalent |
VIII Technological Comparison
At a high level, both the predicate device and the subject device are based on the same characteristics:
Monaco RTP System version 6.3 is an updated version of the predicate device and has identical intended use and technological characteristics (identical designs, principles of operation, and use environments) as well as the same indications for use as the predicate device cleared per K223233.
Any difference in the technological characteristics do not raise new questions of safety and effectiveness as proven by established methods of verification and validation.
The new features introduced into the subject device are described below:
Monaco now provides, through software system modification,
- auto planning feature for iterative modification of optimization cost function parameters,
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- segmentation component for invoking MR auto-segmentation algorithms,
- interoperability with 3rd party software for image management and contouring and
- extending the adaptive planning capabilities to EMLA for offline adaptive planning.
IX. Summary of Performance Testing (Non-Clinical)
Testing in the form of manual and automated verification were performed to evaluate the performance and functionality of the new features against requirement specification.
Regression test of unchanged functionalities in the subject device was done to ensure that new and updated functionalities did not introduce any undesirable effects.
Design validation of the device have been performed by competent and professionally qualified personnel to ensure that the product fulfils the intended use and user needs. The design validation also ensured that the risk control measures associated with functions related to safety for the affected functionality were effective.
The following testing was performed to establish substantial equivalence for the changes in scope of this 510(k):
| Changes on scope of Monaco 6.3 | Testing Performed to establish substantial equivalence |
|---|---|
| Segmentation component for invoking MR auto-segmentation algorithms | For the AI-based segmentation component, performance testing was conducted for the models – Female Pelvis Intact & Hysterectomy (trained on a joint image set of 529 images), Male Pelvis (trained on an image set of 250 images) and Head& Neck trained on an image set of 1862 images). The primary metric for evaluating the model's performance is the Average Hausdorff Distance (AVD). The acceptance threshold is set at 3 mm. A DICE or AUC value of 0.7 is also taken to represent a value of interest that might indicate model performance with respect to a structure or set of structures, however the DICE and AUC results were not explicitly used as the pass/fail metric. Quantitative performance evaluation demonstrated that, for all evaluated structures across all models, the mean Absolute Volume Difference (AVD) was less than 3 mm. Structure specific statistical analyses supported this conclusion. In addition, for all structures and models, the patterns of failure for any structure that did not meet the Dice Similarity Coefficient (DICE) confidence value of interest of 0.7 were investigated and any findings were included as "Limitations". Sub-group analysis is carried on the assessment of performance in the patient size, pixel size, slice spacing and number of slices subgroups. Additionally, qualitative analysis has also been executed based on a 5-point Likert scale with a conclusion that the automatically generated structures provided a valuable starting point for clinical delineation. |
| Auto-planning | Verification testing was performed to evaluate the Auto‑Planning functionality, including workflow performance, protocol management, plan creation, interoperability, and error handling. Validation testing demonstrated that the Auto‑Planning functionality supports the creation of clinically acceptable treatment plans for the intended use. Treatment plans generated using Auto‑Planning were reviewed within the clinical workflow and determined to be suitable for clinical use, without introducing new safety or |
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| Changes on scope of Monaco 6.3 | Testing Performed to establish substantial equivalence |
|---|---|
| effectiveness concerns. All testing met pre-defined acceptance criteria. | |
| Extending the adaptive planning capabilities to EMLA for offline adaptive planning | Verification testing was performed to evaluate the offline adaptive planning functionality using Monaco. Testing assessed correct system behavior during image registration, structure propagation, dose recalculation/re-optimization, offline adaptive plan generation, and workflow execution under representative clinical scenarios. The purpose of testing was to confirm that offline adaptive planning functions operate as intended and support the creation of an updated treatment plan based on CBCT imaging without compromising data integrity or workflow performance. No defects, unexpected behavior, or data integrity issues were identified during testing. Validation testing demonstrated that offline adaptive planning using CT‑to‑CBCT supports creation of clinically acceptable treatment plans for the intended use. The offline adaptive plans generated using CBCT imaging were reviewed within the clinical workflow and determined to be suitable for use. Verification and validation testing met pre-defined acceptance criteria. |
| Interoperability with 3rd party software for image management and contouring | The device incorporates or interfaces with third‑party contouring functionality intended to support radiotherapy treatment planning. Verification and validation testing were conducted to confirm that the third‑party contouring performs as intended and does not adversely impact the safety or effectiveness of the overall system. Verification testing was conducted to confirm correct DICOM export functionality, preservation of data integrity, and successful creation of an offline adaptive plan when using the third‑party contouring functionality. Validation testing was conducted to verify the treatment planning workflows in a Treatment Planning System (TPS) and treatment preparation in Record and Verify (R&V) system. Acceptance criteria were defined to ensure that third‑party contouring outputs are clinically acceptable and comparable to reference contours produced by qualified users. All verification and validation testing met the predefined acceptance criteria. All planned Solution Interoperability test cases have been successfully executed and passed. |
Results from verification and validation testing demonstrate that conformance to applicable technical requirement specification and user needs have been met and that the device functions as intended.
X. Summary of Performance Testing (Clinical)
No animal or clinical tests were performed to establish substantial equivalence with the predicate device. The performance data demonstrate that the subject device is as safe and effective and performs as well as the predicate devices (K223233).
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XI. Substantial Equivalence Conclusion
The subject device, Monaco 6.3 is substantially equivalent (SE) to the predicate Monaco 6.2 (K223233). The intended use and indications for use are identical to the predicate device and the principles of operation remain unchanged.
The technological characteristics are substantially equivalent to the predicate device. The device safety and performance have been established by non-clinical testing in conformance with predetermined performance criteria, FDA guidance, and recognized consensus standards.
The result of verification and validation as well as conformance to relevant safety standards demonstrate that Monaco 6.3 meets the established safety and performance criteria and is substantially equivalent to the predicate device.
§ 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.