(174 days)
Yes
The summary explicitly states that the algorithm for lobe-based lung ventilation contains an "AI-based component, namely deep-learning based autosegmentation of lung lobes." It also describes the training and testing of this component.
No
The device is described as an "image analysis software" and "image processing and visualization tool" used for "preparation of treatments" and "prior to dosimetric planning in radiation therapy." It does not directly provide therapy but assists in the planning and preparation stages.
Yes
Explanation: The device provides "generation of a synthetic CT based on multiple pre-define MR acquisitions", "segmentation of tumors and organs-at-risk, prior to dosimetric planning in radiation therapy", and "calculates lobe-based lung ventilation". These functionalities are used to extract information from medical images for a clinical purpose (treatment planning and assessment of lung function), which aligns with the definition of a diagnostic device.
Yes
The device description explicitly states "The subject device with the current software version SOMARIS/8 VB70 is an image analysis software". The entire description focuses on software functionalities for image processing, visualization, and contouring. While it interacts with medical images from hardware modalities (CT, PET, etc.), the device itself, as described, is the software application.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The intended use clearly states that the device is a "3D and 4D image visualization, multi-modality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation". It focuses on image processing, visualization, and contouring for treatment planning.
- Device Description: The device description reinforces this by stating it's "an image analysis software for viewing, manipulation, 3D and 4D visualization, comparison of medical images from multiple imaging modalities and for the segmentation of tumors and organs-at-risk, prior to dosimetric planning in radiation therapy".
- Lack of In Vitro Component: An IVD is designed to examine specimens derived from the human body (like blood, urine, tissue) in vitro (outside the body) to provide information for diagnosis, monitoring, or screening. This device operates on medical images acquired in vivo (within the body) and does not involve the analysis of biological specimens.
- Focus on Treatment Planning: The primary function is to aid in the preparation of radiation therapy treatments, which is a clinical procedure performed on the patient, not an in vitro diagnostic test.
While the device uses image processing and AI to analyze medical images, this analysis is for the purpose of aiding in treatment planning and not for performing a diagnostic test on a biological sample.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this device.
Intended Use / Indications for Use
syngo.via RT Image Suite is a 3D and 4D image visualization, multi-modality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation (for example, Brachytherapy, Particle Therapy, External Beam Radiation Therapy). It provides tools to view existing contours, create, edit, modify, copy contours of regions of the body, such as but not limited to, skin outline, targets and organs-at-risk. It also provides functionalities to create simple geometric treatment plans. Contours, images and treatment plans can subsequently be exported to a Treatment Planning System.
The software combines the following digital image processing and visualization tools:
- Multi-modality viewing and contouring of anatomical, and multiparametric images such as but not limited to CT, PET, PET/CT, MRI, Linac CBCT images
- Multiplanar reconstruction (MPR) thick, minimum intensity projection (MIP), volume rendering technique (VRT)
- Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
- Automated Contouring on CT images
- Creation of contours on images supported by the application without prior assignment of a planning CT
- Manual and semi-automatic registration using rigid and deformable registration
- Supports the user in comparing, and adapting contours based on datasets acquired with different imaging modalities and at different time points
- Supports multi-modality image fusion
- Visualization and contouring of moving tumors and organs
- Management of points of interest including but not limited to the isocenter
- Creation of simple geometric treatment plans
- Generation of a synthetic CT based on multiple pre-define MR acquisitions
Product codes (comma separated list FDA assigned to the subject device)
MUJ
Device Description
The subject device with the current software version SOMARIS/8 VB70 is an image analysis software for viewing, manipulation, 3D and 4D visualization, comparison of medical images from multiple imaging modalities and for the segmentation of tumors and organs-at-risk, prior to dosimetric planning in radiation therapy. syngo.via RT Image Suite combines routine and advanced digital image processing and visualization tools for manual and software assisted contouring of volumes of interest, identification of points of interest, sending isocenter points to an external laser system, registering images and exporting final results. syngo.via RT Image Suite supports the medical professional with tools to use during different steps in radiation therapy case preparation.
For the current software version SOMARIS/8 VB70 the following already cleared features have been modified:
- Patient Marking
- Contouring
- 4D Features
- Basic Features of the subject device
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
CT, PET, PET/CT, MRI, Linac CBCT images
Anatomical Site
Regions of the body, such as but not limited to, skin outline, targets and organs-at-risk, lung lobes, thoracic
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Medical professional, radiation therapy departments.
Description of the training set, sample size, data source, and annotation protocol
The segmentation model was trained and validated on annotated thoracic CT scans from 8721 and 969 patients, respectively.
Description of the test set, sample size, data source, and annotation protocol
It was tested on an independent cohort of 18 radiotherapy patients (6 female, 12 male). Test data was acquired from external clinical collaborations with radiotherapy departments in Europe and the Americas. The clinics used standard radiotherapy equipment and protocols to acquire the CT images. The test set was not made available to the model development team, thus guaranteeing its independence from the training set. Acceptance criterion for the test was unchanged geometric overlap with the annotated ground truth as measured by DICE compared to the predicate device.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software Verification and Validation: Software Documentation for a Major Level of Concern software per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on May 11, 2005 is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. The Risk Analysis was completed, and risk control implemented to mitigate identified hazards. The testing supports that all software specifications have met the acceptance criteria. Testing for verification and validation support the claim of substantial equivalence.
Summary of the Clinical Validation: The subject device employs an algorithm to calculate lobe-based lung ventilation based on two input CT images representing an inspiration and expiration breathing state of the patient. The algorithm contains an AI-based component, namely deep-learning based autosegmentation of lung lobes. The AI-based component is combined with the analytic calculation of a ventilation metric that is well established in scientific literature. The output is segmented lung lobes with an associated ventilation value.
The AI-based component of the algorithm is reused in unmodified form the previously cleared software version VB40A.
The test passed the acceptance criterion for all lung lobes. A mean DICE of 0.92 was achieved for the lung lobes across the test set.
Clinical validation of the entire lobe-based lung ventilation algorithm was performed on 108 CT datasets from 74 individual lung radiotherapy patients (25 female, 49 male; median age: 66 yrs, range 42-87 yrs).
In the first subgroup (4D-CT with normal breathing), the distribution of lung ventilation as produced by the predicate device was evaluated. The five lung lobes showed a similar distribution of lung ventilation with median of about 20%. The investigation demonstrated that the median ventilation is well aligned with the ground truth obtained from literature, confirming the validity of the results returned by the evaluated lung ventilation algorithm.
In the second subgroup (breathhold CT with forced breathing), a proxy for the vital capacity was calculated from the ventilation results as returned from the subject device; and correlated with vital capacity as measured by PFT (spirometry) for the same patient within one week of the CT scan. A significant Pearson correlation of R = 0.63 was observed (p
§ 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.
0
Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo features the letters 'FDA' in a blue square, followed by the words 'U.S. FOOD & DRUG ADMINISTRATION' in blue text.
Siemens Medical Solutions USA Inc. % Clayton Ginn Regulatory Affairs Specialist 810 Innovation Drive KNOXVILLE TN 37932
Re: K220783
Trade/Device Name: syngo.via RT Image Suite Regulation Number: 21 CFR 892.5050 Regulation Name: Medical Charged-Particle Radiation Therapy System Regulatory Class: Class II Product Code: MUJ Dated: August 8, 2022 Received: August 8, 2022
Dear Clayton Ginn:
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 (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 located 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.
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
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 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-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/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-device-advice-comprehensive-regulatoryassistance/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,
for Julie Sullivan Assistant Director 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
2
Indications for Use
510(k) Number (if known)
K220783
Device Name syngo.via RT Image Suite
Indications for Use (Describe)
syngo.via RT Image Suite is a 3D and 4D image visualization, multimodality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation (for example, Brachytherapy, Particle Therapy, External Beam Radiation Therapy). It provides tools to view existing contours, create, edit, modify, copy contours of regions of the body, such as but not limited to, skin outline, targets and organs-at-risk. It also provides functionalities to create simple geometric treatment plans. Contours, images and treatment plans can subsequently be exported to a Treatment Planning System.
The software combines the following digital image processing and visualization tools:
- · Multi-modality viewing and contouring of anatomical, and multiparametric images such as but not limited to CT, PET, PET/CT, MRI, Linac CBCT images
- · Multiplanar reconstruction (MPR) thick, minimum intensity projection (MIP), volume rendering technique (VRT)
- · Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
- · Automated Contouring on CT images
- · Creation of contours on images supported by the application without prior assignment of a planning CT
- Manual and semi-automatic registration using rigid and deformable registration
- · Supports the user in comparing, and adapting contours based on datasets acquired with different imaging modalities and at different time points
- Supports multi-modality image fusion
- · Visualization and contouring of moving tumors and organs
- · Management of points of interest including but not limited to the isocenter
- · Creation of simple geometric treatment plans
- · Generation of a synthetic CT based on multiple pre-define MR acquisitions
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ------------------------------------------------- | -- |
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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3
510(K) SUMMARY FOR SYNGO.VIA RT IMAGE SUITE
Identification of the Submitter I.
Importer/Distributor
Siemens Medical Solutions USA, Inc. 40 Liberty Boulevard Malvern, PA 19355
Establishment Registration Number 2240869
Manufacturing Site
Siemens Healthcare GmbH Siemensstr 1 D-91301 Forchheim, Germany
Establishment Registration Number
3004977335
Submitter Contact Person:
Clayton Ginn Regulatory Affairs Specialist 2501 North Barrington Road Hoffman Estates, IL 60192-2061 Phone: (865) 898-2692 Email: clayton.ginn@siemens-healthineers.com
II. Device Name and Classification
Product Name: syngo.via RT Image Suite Propriety Trade Name: syngo.via RT Image Suite Classification Name: Medical charged-particle radiation therapy system Classification Panel: Radiology 21 CFR §892.5050 CFR Section: Device Class: Class II Product Code: MUJ
III. Predicate Device
Predicate Device:
| Trade Name: | syngo.via RT Image Suite |
|---|---|
| 510(k) Number: | K211379 |
| Clearance Date: | 07/30/2021 |
| Classification Name: | Medical charged-particle radiation therapy system |
| Classification Panel: | Radiology |
4
| CFR Section: | 21 CFR §892.5050 |
|---|---|
| Device Class: | Class II |
| Product Code: | MUJ |
Reference Device:
| Trade Name: | syngo.CT Bone Reading |
|---|---|
| 510(k) Number: | K123584 |
| Clearance Date: | 03/12/2013 |
| Classification Name: | System, X-Ray, Tomography, Computed |
| Classification Panel: | Radiology |
| CFR Section: | 21 CFR §892.1750 |
| Device Class: | Class II |
| Product Code: | JAK |
IV. Device Description
The subject device with the current software version SOMARIS/8 VB70 is an image analysis software for viewing, manipulation, 3D and 4D visualization, comparison of medical images from multiple imaging modalities and for the segmentation of tumors and organs-at-risk, prior to dosimetric planning in radiation therapy. syngo.via RT Image Suite combines routine and advanced digital image processing and visualization tools for manual and software assisted contouring of volumes of interest, identification of points of interest, sending isocenter points to an external laser system, registering images and exporting final results. syngo.via RT Image Suite supports the medical professional with tools to use during different steps in radiation therapy case preparation.
For the current software version SOMARIS/8 VB70 the following already cleared features have been modified:
- Patient Marking
- Contouring
- 4D Features ●
- Basic Features of the subject device ●
V. Indications for Use
syngo.via RT Image Suite is a 3D and 4D image visualization, multi-modality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation (for example, Brachytherapy, Particle Therapy, External Beam Radiation Therapy).
It provides tools to view existing contours, create, edit, modify, copy contours of regions of the body, such as but not limited to, skin outline, targets and organs-at-risk. It also provides functionalities to create simple geometric treatment plans. Contours, images and treatment plans can subsequently be exported to a Treatment Planning System.
The software combines the following digital image processing and visualization tools:
- . Multi-modality viewing and contouring of anatomical, and multi-parametric images such as but not limited to CT, PET, PET/CT, MRI, Linac CBCT images
- Multiplanar reconstruction (MPR) thin/thick, minimum intensity projection (MIP), volume rendering technique (VRT)
- . Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
- Automated Contouring on CT images
5
- . Creation of contours on images supported by the application without prior assignment of a planning CT
- Manual and semi-automatic registration using rigid and deformable registration ●
- . Supports the user in comparing, contouring, and adapting contours based on datasets acquired with different imaging modalities and at different time points
- . Supports multi-modality image fusion
- . Visualization and contouring of moving tumors and organs
- Management of points of interest including but not limited to the isocenter ●
- . Creation of simple geometric treatment plans
- Generation of a synthetic CT based on multiple pre-define MR acquisitions ●
The Indications for Use is unchanged from that of the predicate device.
6
VI. Comparison of Technological Characteristics with the Predicate Device
As with the predicate device syngo.via RT Image Suite SOMARIS/8 VB70, the subject device supports viewing, manipulation, 3D and 4D visualization, comparison of medical images from multiple imaging modalities and the segmentation of tumors and organs-at-risk, prior to dosimetric planning and response assessment in radiation therapy.
At a high-level a tabular summary of the subject and predicate device's technological differences is provided as Table 4 below for the software version SOMARIS/8 VB70:
| Feature | Description and Comparison of the Subject Device to the Predicate Device |
|---|---|
| Patient Marking | General Description: |
| Transmission of reference points with the offset details to a movable laser system for patient marking. |
Modification:
• Simplified workflow to place isocenters in the spine
• Consistent user-configured default names for POIs throughout the application |
| Contouring | General Description:
With Contouring, the user can create, delete, and edit Volumes of Interest (VOIs).
Modification:
• Tool to convert isodose lines from a DICOM RT dose file to contours
• Minor usability improvement in the structure display in the image segment |
| 4D Features | General Description:
The subject device provides features to handle 4D image data, such as the display of a cine loop of images acquired through gated CT.
Modification:
• Contouring on 4D Image Data improved
• Lobe-based Lung Ventilation implemented |
| Basic Feature of
syngo.via RT
Image Suite | General Description:
The set provides basic feature of the subject device.
Modification:
A software interface was added to provide additional advanced visualization and measurement tools to extend the subject device |
7
VII. Performance Data
The following performance data were provided in support of the substantial equivalence determination.
Software Verification and Validation
Software Documentation for a Major Level of Concern software per FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" issued on May 11, 2005 is also included as part of this submission. The performance data demonstrates continued conformance with special controls for medical devices containing software. The Risk Analysis was completed, and risk control implemented to mitigate identified hazards. The testing supports that all software specifications have met the acceptance criteria. Testing for verification and validation support the claim of substantial equivalence.
Summary of the Clinical Validation
The subject device employs an algorithm to calculate lobe-based lung ventilation based on two input CT images representing an inspiration and expiration breathing state of the patient. The algorithm contains an AI-based component, namely deep-learning based autosegmentation of lung lobes. The AIbased component is combined with the analytic calculation of a ventilation metric that is well established in scientific literature. The output is segmented lung lobes with an associated ventilation value.
The AI-based component of the algorithm is reused in unmodified form the previously cleared software version VB40A. The segmentation model was trained and validated on annotated thoracic CT scans from 8721 and 969 patients, respectively. It was tested on an independent cohort of 18 radiotherapy patients (6 female, 12 male). Test data was acquired from external clinical collaborations with radiotherapy departments in Europe and the Americas. The clinics used standard radiotherapy equipment and protocols to acquire the CT images. The test set was not made available to the model development team, thus guaranteeing its independence from the training set. Acceptance criterion for the test was unchanged geometric overlap with the annotated ground truth as measured by DICE compared to the predicate device. The test passed the acceptance criterion for all lung lobes. A mean DICE of 0.92 was achieved for the lung lobes across the test set.
In addition to the validation of the AI-based segmentation component, a clinical validation of the entire lobe-based lung ventilation algorithm was performed on 108 CT datasets from 74 individual lung radiotherapy patients (25 female, 49 male; median age: 66 yrs, range 42-87 yrs). Two subgroups were formed based on the CT acquisition technique and respective breathing mode of the patient.
In the first subgroup (4D-CT with normal breathing), the distribution of lung ventilation as produced by the predicate device was evaluated. The five lung lobes showed a similar distribution of lung ventilation with median of about 20%. The investigation demonstrated that the median ventilation is well aligned with the ground truth obtained from literature, confirming the validity of the results returned by the evaluated lung ventilation algorithm.
In the second subgroup (breathhold CT with forced breathing), a proxy for the vital capacity was calculated from the ventilation results as returned from the subject device; and correlated with vital capacity as measured by PFT (spirometry) for the same patient within one week of the CT scan. A significant Pearson correlation of R = 0.63 was observed (p