(136 days)
Not Found
Yes
The document explicitly states that VBrain uses an "artificial intelligence algorithm (i.e., deep learning neural networks)" and mentions "deep learning neural networks" in the device description.
No
VBrain is a software device intended to assist medical professionals in radiation therapy treatment planning by providing initial contours of known brain tumors; it is not designed to directly treat or diagnose patients. Its purpose is informational and assists in a workflow, not for therapy itself.
No
The "Intended Use" section explicitly states that VBrain is "not intended for replacing their current standard practice of manual contouring process" and "not intended to be used to detect tumors for diagnosis." Its purpose is to assist in radiation therapy treatment planning by providing initial contours of known (diagnosed) brain tumors.
Yes
The device description explicitly states "VBrain is a software device" and describes its components and functionality as purely software-based, operating on a PACS network and processing images to generate DICOM-RT objects. There is no mention of accompanying hardware components.
Based on the provided information, VBrain is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body (like blood, urine, tissue) to provide information for diagnosis, monitoring, or screening.
- VBrain's Function: VBrain analyzes medical images (MRI scans) of the brain. It does not analyze specimens taken from the body.
- Intended Use: The intended use clearly states that VBrain is intended to assist trained medical professionals during radiation therapy treatment planning by providing initial contours of known (diagnosed) brain tumors. It is not intended for diagnosis or detection of tumors.
- Informational Purposes: The description emphasizes that the contours are for informational purposes only and not intended to replace manual contouring or be used for diagnosis.
Therefore, VBrain falls under the category of medical image analysis software, not an In Vitro Diagnostic device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
VBrain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors (i.e., region of interest, ROI) on axial T1 contrast-enhanced brain MRI images.
VBrain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to be used to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas on axial T1 contrast-enhanced MRI images; It is not intended to be used with images of other brain tumors. The user must know the tumor type when they use VBrain. VBrain is intended to be used on adult patients only.
Medical professionals must finalize (confirm or modify) the contours generated by VBrain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.
Product codes
QKB
Device Description
VBrain is a software device indicated for use in the analysis of brain MRI images. The device consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to assist trained medical professionals, during clinical workflows of radiation therapy treatment planning, by highlighting and contouring known (diagnosed) brain tumors on the axial T1 contrast-enhanced MRI images. The software is configured to work on a PACS network. Upon user's request, it will patient scans or users can send corresponding MR images, and the device will utilize deep learning neural networks to generate contours for the detected/diagnosed brain tumors and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT) back to the network. The medical professionals must finalize (confirm and modify) the contours produced by VBrain as necessary using an external platform that supports RT DICOM viewing/editing, such as a treatment planning system.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
axial T1 contrast-enhanced brain MRI images
Anatomical Site
brain
Indicated Patient Age Range
adult patients only
Intended User / Care Setting
Trained medical professionals including, but not limited to, radiologists, oncologists, physicians, medical technologists, dosimetrists, and physicists.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The test dataset was an independent dataset consisting of 116 cases with 238 tumors acquired consecutively and retrospectively from 4 different institutions (3 US and 1 non-US). The ground truth of each tumor contours was generated from the consensus of three board-certified radiation oncologists.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Vysioneer conducted a retrospective, blinded, multicenter, multinational study with the proposed device VBrain with the primary endpoint to evaluate the software's performance on identifying axial T1 contrast-enhanced MRI scans containing brain metastases, acoustic neuromas, or meningiomas.
The test dataset was an independent dataset consisting of 116 cases with 238 tumors acquired consecutively and retrospectively from 4 different institutions (3 US and 1 non-US). The ground truth of each tumor contours was generated from the consensus of three board-certified radiation oncologists. Five metrics are evaluated: (1) lesion-wise sensitivity, (2) false-positive rate, (3) lesion-wise Dice coefficient, (4) average Hausdorff distance, and (5) centroid distance between VBrain's segmentation and ground-truth segmentation. VBrain meets all performance goals.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Lesion-wise sensitivity: 90.3% (95% CI: 86.1-93.7%)
False-positive rate: 0.681 tumors/case (95% CI: 0.500-0.879 tumors/case)
Lesion-wise Dice similarity coefficient (DSC): 0.793 (95% Cl: 0.775-0.811)
Average Hausdorff distance in terms of lesion size: 5.0% (95% CI: 4.4-5.6%)
Centroid distance in terms of lesion size: 5.6% (95% CI: 5.0-6.2%)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
0
March 19, 2021.
Image /page/0/Picture/1 description: The image shows 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 is in blue and includes the letters "FDA" in a square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION".
Vysioneer Inc % Chiu S. Lin Consultant 33 Rogers Street, # 308 CAMBRIDGE MA 02142
Re: K203235
Trade/Device Name: VBrain Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: QKB Dated: February 9, 2021 Received: February 10, 2021
Dear Chiu Lin:
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/cfpmp/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 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
1
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 medical devices and radiation-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
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K203235
Device Name V Brain
Indications for Use (Describe)
V Brain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors (i.e., region of interest, ROI) on axial T1 contrast-enhanced brain MRI images.
V Brain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to be used to detect tumors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas on axial T1 contrast-enhanced MRI images; It is not intended to be used with images of other brain tumors. The user must know the tumor type when they use VBrain. VBrain is intended to be used on adult patients only.
Medical professionals must finalize (confirm or modify) the contours generated by VBrain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.
Type of Use (Select one or both, as applicable)
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
|---|---|
| --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
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Image /page/3/Picture/1 description: The image features the logo for Vysioneer, a company name displayed in a bold, sans-serif font. Above the name is a stylized 'V' shape, rendered in a shade of purple. Below the name is a horizontal purple line, and underneath that is the alphanumeric code 'K203235'.
Section 5 510(k) Summary
5.1 Submitter
Vysioneer Inc.
33 Rogers St. #308, Cambridge, MA 02142
| Contact Person: | Jen-Tang Lu, PhD (Chief Executive Officer) |
|---|---|
| Phone: | 609-865-8659 |
| Email: | jt@vysioneer.com |
| Date Summary Prepared: | February 09, 2021 |
5.2 Device Name
| Trade Name: | VBrain |
|---|---|
| Common Name: | Radiological Image Processing Software for |
| Radiation Therapy | |
| Regulation Number / Product Code: | 21 CFR 892.2050 / QKB |
5.3 PREDICATE DEVICE
Primary Predicate #1: AccuContour™, K191928, Xiamen Manteia Technology LTD Primary Predicate #2: MIM - MRT Dosimetry, K182624, MIM Software Inc.
Intended Use / Indications for Use 5.4
VBrain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain MRI images.
4
Image /page/4/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized "V" shape in purple, with the company name "VYSIONEER" written in a sans-serif font below it, also in purple. The logo is simple and modern.
VBrain uses an artificial intelligence algorithm (i.e., deep learning neural networks) to contour (segment) brain tumor on MRI images for trained medical professionals' attention, which is meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. VBrain does not alter the original MRI image, nor does it intend to be used to detect turnors for diagnosis. VBrain is intended only for generating Gross Tumor Volume (GTV) contours of brain metastases, meningiomas, and acoustic neuromas on axial T1 contrast-enhanced MRI images; It is not intended to be used with images of other brain tumors. The user must know the tumor type when they use VBrain is intended to be used on adult patients only.
Medical professionals must finalize (confirm or modify) the contours generated by VBrain, as necessary, using an external platform available at the facility that supports DICOM-RT viewing/editing functions, such as image visualization software and treatment planning system.
Device Description ર્ રંડ
VBrain is a software device indicated for use in the analysis of brain MRI images. The device consists of image preprocessing, deep learning neural networks, and postprocessing components, and is intended to assist trained medical professionals, during clinical workflows of radiation therapy treatment planning, by highlighting and contouring known (diagnosed) brain tumors on the axial T1 contrast-enhanced MRI images. The software is configured to work on a PACS network. Upon user's request, it will patient scans or users can send corresponding MR images, and the device will utilize deep learning neural networks to generate contours for the detected/diagnosed brain tumors and export the results as DICOM-RT objects (using the RT Structure Set ROI Contour attribute, RTSTRUCT) back to the network. The medical professionals must finalize (confirm and modify) the contours produced by VBrain as necessary using an external platform that supports RT DICOM viewing/editing, such as a treatment planning system.
Comparison with Predicate Devices 5.6
VBrain is substantially equivalent to a combination of the primary predicate devices AccuContour™ (K191928) and MIM - MRT Dosimetry (K182624).
The proposed device, VBrain, and the primary predicates, AccuContour™ (K191928) and K182624 (MIM - MRT Dosimetry), are all software devices intended to be used in the workflow of radiation therapy by providing tools of segmenting (contouring) of tumors and/or organs on MRI and/or CT images. Both the proposed device and AccuContour™ (K191928) are AI-based (deep learning) software regulated under the Product Code QKB (Radiological Image Processing Software For Radiation Therapy). On the other hand, both the proposed device and K182624 (MIM - MRT Dosimetry) provide tools for tumor contouring. The only difference is that VBrain uses deep learning (neural networks) to automatically generate tumor contours as a starting point for user's review and edit, while K182624 (MIM - MRT Dosimetry) provides a semi-automatic
5
Image /page/5/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter "V" formed by two diagonal lines in a purple color. Below the symbol, the word "VYSIONEER" is written in a sans-serif font, also in purple. A thin purple line is located below the word.
tool (propagation tool) that requires user's input to start the image segmentation (contouring) process.
Although the proposed new device. VBrain, uses a data-driven deep learning-based algorithm for contouring of known brain tumors, the primary predicate MIM - MRT Dosimetry (K182624) uses a semi-automatic algorithm that requires user's input to start the contouring process. The specific design of the proposed device does not raise different questions of safety and effectiveness, because the new device only provides initial object contours of known (diagnosed) brain tumors for the medical professionals' attention, which are meant for informational purposes only and not intended for replacing their current standard practice of manual contouring process. Medical professionals must use VBrain in conjunction with appropriate software to review and edit results generated automatically by VBrain. In addition, VBrain does not alter the original MRI image, nor does it intend to be used to detect tumors for diagnosis. The medical professionals must know the tumor type when they use VBrain. Consequently, the new device does not change any medical professionals' workflow planning procedure and therefore does not raise different questions of safety and effectiveness.
Please see Table 5-1 comparing the intended use and key technological characteristics of the proposed device and the predicate devices.
| Company | Vysioneer Inc. | Xiamen Manteia Technology LTD. (Primary) | MIM Software Inc. (Primary) |
|---|---|---|---|
| Device Name | VBrain | AccuContour™ | MIM - MRT Dosimetry |
| 510k Number | Pending | K191928 | K182624 |
| Regulation No. | 21CFR 892.2050 | 21CFR 892.2050 | 21CFR 892.2050 |
| Classification | II | II | II |
| Product Code | QKB | QKB | LLZ |
| Intended Use/Indication for Use | VBrain is a software device intended to assist trained medical professionals, during their clinical workflows of radiation therapy treatment planning, by providing initial object contours of known (diagnosed) brain tumors (i.e., the region of interest, ROI) on axial T1 contrast-enhanced brain MRI images. | It is used by radiation oncology department to register multimodality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation. The product has two image process functions: (1) Deep learning contouring: it can automatically contour | MIM software is used by trained medical professionals as a tool to aid in evaluation and information management of digital medical images. The medical image modalities include, but are not limited to, CT, MRI, CR, DX, MG, US, SPECT, PET and XA as supported by ACR/NEMA DICOM 3.0. |
| VBrain uses an artificial | |||
| intelligence algorithm | |||
| (i.e., deep learning | |||
| neural networks) to | |||
| contour (segment) brain | |||
| tumor on MRI images | |||
| for trained medical | |||
| professionals' attention, | |||
| which is meant for | |||
| informational purposes | |||
| only and not intended | |||
| for replacing their | |||
| current standard practice | |||
| of manual contouring | |||
| process. VBrain does not | |||
| alter the original MRI | |||
| image, nor does it intend | |||
| to be used to detect | |||
| tumors for diagnosis. | |||
| VBrain is intended only | |||
| for generating Gross | |||
| Tumor Volume (GTV) | |||
| contours of brain | |||
| metastases, | |||
| meningiomas, and | |||
| acoustic neuromas on | |||
| axial T1 contrast- | |||
| enhanced MRI images; it | |||
| is not intended to be | |||
| used with images of | |||
| other brain tumors. The | |||
| user must know the | |||
| tumor type when they | |||
| use VBrain. VBrain is | |||
| intended to be used on | |||
| adult patients only. | |||
| Medical professionals | |||
| must finalize (confirm or | |||
| modify) the contours | |||
| generated by VBrain, as | |||
| necessary, using an | |||
| external platform | |||
| available at the facility | |||
| that supports DICOM- | |||
| RT viewing/editing | |||
| functions, such as image | |||
| visualization software | |||
| and treatment planning | |||
| system | the organ-at-risk, | ||
| including head and neck, | |||
| thorax, abdomen and | |||
| pelvis (for both male and | |||
| female), | |||
| (2) Automatic | |||
| Registration, and | |||
| (3) Manual Contour. |
It also has the following
general functions:
(1) Receive,
add/edit/delete, transmit,
input/export, medical
images and DICOM
data;
(2) Patient management;
(3) Review of processed
images;
(4) Open and Save of
files. | MIM assists in the
following indications:
• Receive, transmit,
store, retrieve,
display, print, and
process medical
images and DICOM
objects.
• Create, display and
print reports from
medical images.
• Registration, fusion
display, and
review of medical
images for diagnosis,
treatment evaluation,
and treatment planning.
• Evaluation of cardiac
left ventricular end-
diastolic volume,
end-systolic volume, and
ejection
fraction.
• Localization and
definition of objects,
such as tumors and
normal tissues in
medical images.
• Creation,
transformation, and
modification of contours
for applications
including, but not
limited to, quantitative
analysis, aiding adaptive
therapy, transferring
contours to radiation
therapy treatment
planning systems, and
archiving contours for
patient follow-up and
management.
• Quantitative and
statistical analysis of
PET/SPECT brain scans
by comparing to other
registered PET/SPECT
brain scans | |
| | | | • Planning and
evaluation of permanent
implant brachytherapy
procedures (not
including
radioactive
microspheres).
• Calculating absorbed
radiation dose as a result
of administering a
radionuclide. |
| | | | When using device
clinically, the user
should only use FDA
approved
radiopharmaceuticals. If
using with unapproved
ones, this device should
only be used for research
purposes. |
| | | | Lossy compressed
mammographic images
and digitized film screen
images must not be
reviewed for
primary image
interpretations.
Images that are printed
to film must be printed
using an FDA-approved
printer for the diagnosis
of digital mammography
images. |
| | | | Mammographic images
must be viewed on a
display system that has
been cleared by the FDA
for the diagnosis of
digital mammography
images. The software is
not to be used for
mammography CAD. |
| Segmentation
(Contouring)
Technology | Deep learning | Deep learning | Atlas-based algorithm
and propagation tools
(requiring user's input to |
| | | | start the image
segmentation process) |
| Operating System | Linux operating system | Microsoft Windows | Microsoft Windows and
Apple macOS operating
systems |
| User Population | Trained medical
professionals including,
but not limited to,
radiologists, oncologists,
physicians, medical
technologists,
dosimetrists, and
physicists. | It is used by radiation
oncology department. | Trained medical
professionals |
| Supported Modalities | Axial T1 contrast-
enhanced MRI images | Segmentation Features:
Non-Contrast CT
Registration Features:
CT, MRI, PET | CT, MRI, CR, DX, MG,
US, SPECT, PET and
XA as supported by
ACR/NEMA DICOM
3.0. |
| Localization and
Definition of Objects
(ROI) | Qualified brain tumors -
brain metastases,
meningiomas, and
acoustic neuromas | Organ-at-risk, including
head and neck, thorax,
abdomen and pelvis (for
both male and female) | Tumors and normal
tissues |
| Performance Testing &
Software V & V | To support the intended
use of the VBrain AI
software for brain tumor
contouring
(segmentation)
performance, Vysioneer
conducted a
retrospective, blinded,
multicenter,
multinational study with
the VBrain software. The
test data sets consisted of
116 cases acquired from
4 different institutions (3
US and 1 non-US). Five
metrics are evaluated: (1)
lesion-wise sensitivity,
(2) false-positive rate, (3)
lesion-wise Dice
coefficient, (4) average
Hausdorff distance, and
(5) average centroid
distance between | Segmentation
performance test
The segmentation
performance test was
performed on proposed
device and predicate
device to evaluate the
automated segmentation
accuracy. Two separate
tests were performed.
One test involved
images generated in
healthcare institutions in
China using scanner
models available in
China covering three
major vendors. The other
involved images
generated in healthcare
institutions in US using
scanner models available
in US covering three
major vendors. The three
major vendors were GE, | MIM Software Inc. has
conducted performance
and integration testing
on MIM - MRT
Dosimetry software with
a comparison to a
commercially available
solution for internal
radionuclide dosimetry.
Standard quality control
phantoms, simulated
phantoms based on the
NEMA IEC Body
Phantom, simulated
phantoms based on
patient data, and clinical
patient data were used
for verification testing.
All tests were performed
using standard clinical
acquisition and
reconstruction protocols.
The accuracy of planar
corrections for |
| VBrain's segmentation
and clinicians' segmentation. All the
metrics were
demonstrated to pass the
performance goals.
Software verification
and validation testing
were conducted, and
documentation was
provided as
recommended by FDA's
Guidance for Industry
and FDA Staff,
"Guidance for the
Content of Premarket
Submissions for
Software Contained in
Medical Devices" for
software devices
identified as Major
Level of Concern related
to radiation therapy
treatment planning. | Siemens and Philips. For
each body parts, all
intended organs were
included in images of the
US and China. Ground
truthing of each image
was generated from the
consensus of at least
three licensed
physicians. DICE
similarity coefficients
(DSC) was used for
evaluation. DSC values
were calculated on two
sets of images for test
group and control group,
respectively. According
to the results, it could be
concluded that the DSC
of proposed device was
non-inferiority compared
with that of the predicate
device.
Registration
performance test
The registration
performance test was
performed on proposed
device and predicate
device to evaluate the
automated registration
accuracy. Two separate
tests were performed.
One test involved
images generated in
healthcare institutions in
China using scanner
models available in
China covering three
major vendors. And the
image registration
feature is tested on
multi-modality image
sets from same patients.
The other involved most
images generated in
healthcare institutions in | attenuation, scatter, and
background were
verified in simulated
phantoms. The average
errors were less than
12% for all regions
except for the smallest
region (2.6 cm) with
21% error for Lu-177
and 17% error for I-131
where the partial volume
effect lowered accuracy
as expected. In all cases,
the software passed its
performance
requirements and met
specifications
The accuracy of area-
under-the curve (AUC)
calculations were
verified for different
fitting options using
simulated data with
differences less than
3.1% compared to
manual AUC
calculations which met
predefined acceptance
criteria when
considering the presence
of Poison noise in the
image data.
The accuracy of the
generation of CT-derived
physical density maps
were verified in clinical
patient data and
compared to published
results with less than 5%
difference for soft tissue
regions and less than
10% difference for bone
regions. The difference
for lung density fell
within the range of
expected density values. | |
| | most moving images | - MRT Dosimetry was | |
| | came from U.S and a | verified in simulated | |
| | small amount of moving | phantoms and clinical | |
| | images adopted from | patient data for I-131 | |
| | online database were | and Lu-177. The | |
| | originally from non-US | acceptance criterion for | |
| | sources. All the scanner | MIM - MRT Dosimetry | |
| | models covered three | is a difference of mean | |
| | major vendors. And the | dose of smaller or equal | |
| | image registration | to 20% in comparison to | |
| | feature is only tested on | a commercially available | |
| | multi-modality image | solution after correction | |
| | sets from different | of the standard phantoms | |
| | patients. Both tests | in the commercial | |
| | covered various | solution to match the | |
| | modalities, including | mass of the patient data. | |
| | CT/CT, CT/MR and | Additionally, | |
| | CT/PET. The | comparison of the Voxel | |
| | Normalized Mutual | S Value method in MIM | |
| | Information (NMI) was | - MRT Dosimetry to | |
| | used for evaluation. NMI | Local Deposition Model | |
| | values were calculated | values for Lu-177 | |
| | on two sets of images for | showed a difference less | |
| | both the proposed device | than 1% for all organs | |
| | and predicate device, | tested. In all cases the | |
| | respectively. The NMI | software demonstrated | |
| | value of proposed device | acceptable agreement | |
| | was compared with that | between the different | |
| | of the predicate device. | dose methods. | |
| | According to the results, | | |
| | it could be concluded | | |
| | that the NMI of | | |
| | proposed device was | | |
| | non-inferiority compared | | |
| | with that of the predicate | | |
| | device. | | |
| | | | |
Table 5-1. Comparison with the Predicate Devices.
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Image /page/6/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a stylized "V" shape above it. The "V" shape is made up of two diagonal lines that do not connect at the bottom. The color of the logo is a dark purple.
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Image /page/7/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a horizontal line underneath. Above the word is a stylized "V" shape, with a small gap in the middle.
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Image /page/8/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter V above the word "VYSIONEER". The letter V is made up of two diagonal lines that do not connect at the bottom. The logo is purple.
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Image /page/9/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of the word "VYSIONEER" in a sans-serif font, with a horizontal line underneath the word. Above the word is a stylized "V" shape, which is made up of two separate lines that do not connect. The color of the text, line, and "V" shape is a dark purple.
U.S. All fixed image and | calculations using MIM
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Image /page/10/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter "V" in purple, with the word "VYSIONEER" in a sans-serif font below it. A horizontal purple line is located below the word "VYSIONEER".
Performance Data 5.7
Vysioneer conducted a retrospective, blinded, multicenter, multinational study with the proposed device VBrain with the primary endpoint to evaluate the software's performance on identifying axial T1 contrast-enhanced MRI scans containing brain metastases, acoustic neuromas, or meningiomas. The test dataset was an independent dataset consisting of 116 cases with 238 tumors acquired consecutively and retrospectively from 4 different institutions (3 US and 1 non-US). The ground truth of each tumor contours was generated from the consensus of three board-certified radiation oncologists. Five metrics are evaluated: (1) lesion-wise sensitivity, (2) false-positive rate,
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Image /page/11/Picture/1 description: The image shows the logo for Vysioneer. The logo consists of a stylized letter V in purple, with the word "VYSIONEER" written in purple below it. The letter V is made up of two diagonal lines that do not quite meet at the top.
(3) lesion-wise Dice coefficient, (4) average Hausdorff distance, and (5) centroid distance between VBrain's segmentation and ground-truth segmentation. VBrain meets all performance goals.
Specifically, lesion-wise sensitivity of VBrain was observed to be 90.3% (95% CI: 86.1-93.7%) and the false-positive rate was observed to be 0.681 tumors/case (95% CI: 0.500-0.879) tumors/case). In addition, segmentation performance was measured with the lesion-wise Dice similarity coefficient (DSC) and average Hausdorff distance between VBrain's segmentation and ground-truth segmentation in terms of lesion size. They were observed to be lesion-wise DSC: 0.793 (95% Cl: 0.775-0.811) and average Hausdorff distance in terms of lesion size: 5.0% (95% CI: 4.4-5.6%). Centroid distance between VBrain's segmentation and ground-truth segmentation was measured in terms of lesion size and was observed to be 5.6% (95% CI: 5.0-6.2%).
Software Verification and Validation 5.8
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" for software devices identified as Major Level of Concern related to radiation therapy treatment planning.
Conclusion રું તે તે
In conclusion, Vysioneer Inc. has conducted performance testing on VBrain. In all the cases, the software passed its requirements for safety and effectiveness and does not introduce any new potential safety risks. It demonstrates that VBrain is substantially equivalent to and performs at least as safely and effectively as the listed predicate devices.