(29 days)
TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats).
TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz.
TumorSight Viz is an image processing system designed to assist in the visualization and analysis of breast DCE-MRI studies.
TumorSight reads DICOM magnetic resonance images. TumorSight processes and displays the results on the TumorSight web application.
Available features support:
- Visualization (standard image viewing tools, MIPs, and reformats)
- Analysis (registration, subtractions, kinetic curves, parametric image maps, segmentation and 3D volume rendering)
The TumorSight system consists of proprietary software developed by SimBioSys, Inc. hosted on a cloud-based platform and accessed on an off-the-shelf computer.
Here's a breakdown of the acceptance criteria and the study details for the TumorSight Viz device, based on the provided FDA 510(k) clearance letter:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the reported performance metrics, where the device's performance is deemed "adequate" and "clinically acceptable" if the variability is similar to inter-radiologist variability or differences in error are clinically insignificant.
| Measurement Description | Units | Acceptance Criterion (Implicit) | Reported Device Performance (Mean Abs. Error ± Std. Dev.) |
|---|---|---|---|
| Tumor Volume (n=218) | cubic centimeters (cc) | Similar to inter-radiologist variability | 5.2 ± 12.5 |
| Tumor-to-breast volume ratio (n=218) | % | Clinically acceptable | 0.4 ± 1.2 |
| Tumor longest dimension (n=242) | centimeters (cm) | Similar to inter-radiologist variability (e.g., 1.02 cm ± 1.33 cm) | 1.32 ± 1.65 |
| Tumor-to-nipple distance (n=241) | centimeters (cm) | Similar to inter-radiologist variability (e.g., 0.88 cm ± 1.12 cm) | 1.17 ± 1.55 |
| Tumor-to-skin distance (n=242) | centimeters (cm) | Similar to inter-radiologist variability (e.g., 0.42 cm ± 0.45 cm) | 0.60 ± 0.52 |
| Tumor-to-chest distance (n=242) | centimeters (cm) | Similar to inter-radiologist variability (e.g., 0.79 cm ± 1.14 cm) | 0.86 ± 1.22 |
| Tumor center of mass (n=218) | centimeters (cm) | Clinically acceptable | 0.60 ± 1.47 |
| Segmentation Accuracy | |||
| Volumetric Dice (n=218) | High agreement with reference standard | 0.76 ± 0.26 | |
| Surface Dice (n=218) | High agreement with reference standard (particularly for 3D rendering) | 0.92 ± 0.21 |
The document states: "We found that all tests met the acceptance criteria, demonstrating adequate performance for our intended use." This indicates that the reported performance metrics were considered acceptable by the regulatory body. For measurements where inter-radiologist variability is provided (e.g., longest dimension, tumor-to-skin), the device's error is compared to this variability. For other metrics, the acceptance is based on demonstrating "adequate performance," implying that the reported values themselves were within a predefined acceptable range.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 266 patients (corresponding to 267 samples, accounting for bilateral disease).
- Data Provenance:
- Country of Origin: U.S.
- Retrospective/Prospective: The document does not explicitly state "retrospective" or "prospective." However, the description of "DCE-MRI were obtained from... patients" and establishment of ground truth by reviewing images suggests a retrospective acquisition of data for validation. The mention of "All patients had pathologically confirmed invasive, early stage or locally advanced breast cancer" further supports a retrospective gathering of existing patient data.
- Clinical Sites: More than eight (8) clinical sites in the U.S.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Number of Experts: Three (3) U.S. Board Certified radiologists.
- Qualifications: U.S. Board Certified radiologists. (No specific experience in years is mentioned, but Board Certification implies a high level of expertise.)
4. Adjudication Method for the Test Set
- Adjudication Method: 2+1 (as described in the document).
- For each case, two radiologists independently measured various characteristics and determined if the candidate segmentation was appropriate.
- In cases of disagreement between the first two radiologists ("did not agree on whether the segmentation was appropriate"), a third radiologist provided an additional opinion, and the ground truth was established by majority consensus.
5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done
The document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is directly measured and compared.
Instead, it compares the device's performance to:
- Ground Truth: Radiologist consensus measurements.
- Predicate Device: Its own previous version.
- Inter-radiologist Variability: The inherent variability between human expert readers.
Therefore, no effect size of how much human readers improve with AI vs. without AI assistance is provided, as this type of MRMC study was not detailed.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, a standalone performance study was done. The sections titled "Performance Tests" and the tables detailing "Validation Testing (Mean Abs. Error ± Std. Dev.)" describe the algorithm's performance in comparison to the established ground truth. This is a standalone evaluation, as it assesses the device's output intrinsically against expert-derived truth without measuring human interaction or improvement. The statement "The measurements generated from the device result directly from the segmentation methodology and are an inferred reflection of the performance of the deep learning algorithm" supports this.
7. The Type of Ground Truth Used
- Type of Ground Truth: Expert Consensus (specifically, pathologist-confirmed lesions measured and evaluated by a consensus of U.S. Board Certified radiologists). The initial diagnosis of early-stage or locally advanced breast cancer for patient selection was based on pathology ("biopsy proven"). However, the ground truth for measurements and segmentation appropriateness for the study was established by radiologists.
8. The Sample Size for the Training Set
- Sample Size for Training Set: One thousand one hundred fifty-six (1156) patients/samples.
9. How the Ground Truth for the Training Set Was Established
The document states: "DCE-MRI were obtained from one thousand one hundred fifty-six (1156) patients from more than fifteen (15) clinical sites in the U.S. for use in training and tuning the device."
However, the document does not explicitly detail how the ground truth for this training set was established. It describes the ground truth establishment method only for the validation dataset (by three U.S. Board Certified radiologists with 2+1 adjudication). For training data, it is common practice to use similar rigorous methods for labeling, but the specifics are not provided in this excerpt.
FDA 510(k) Clearance Letter - TumorSight Viz
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.07.05
July 8, 2025
SimBioSys, Inc.
Kimberly Oleson
Principal Regulatory Consultant
320 N Sangamon Street
Suite 700
Chicago, Illinois 60607
Re: K251766
Trade/Device Name: TumorSight Viz
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH
Dated: June 9, 2025
Received: June 9, 2025
Dear Kimberly Oleson:
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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K251766 - Kimberly Oleson 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 System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (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 systems (QS) regulation (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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K251766 - Kimberly Oleson 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,
Daniel M. Krainak, Ph.D.
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
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.
K251766
Please provide the device trade name(s).
TumorSight Viz
Please provide your Indications for Use below.
TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats).
TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz.
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)
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510(k) Summary
TumorSight Viz 510(k) Summary Page 1 of 9
Submitter Details
SimBioSys, Inc.
320 N Sangamon St, Suite 700, Chicago IL 60607 United States
Contact: Kimberly Oleson
Contact Telephone: (612) 803-2610
Contact Email: kim.oleson@simbiosys.com
Date of Preparation: July 3, 2025
Details of the Submitted Device
Proprietary Name: TumorSight Viz
Common Name: Medical image management and processing system
Classification Name: System, Image Processing, Radiological
Regulation Number: 892.2050
Product Code: QIH
Committee/Panel: Radiology
Device Class: II
Identification of the Legally Marketed Predicate Device
Predicate #: K243189
Predicate Trade Name: TumorSight Viz
Product Code: QIH
Device Description
TumorSight Viz is an image processing system designed to assist in the visualization and analysis of breast DCE-MRI studies.
TumorSight reads DICOM magnetic resonance images. TumorSight processes and displays the results on the TumorSight web application.
Available features support:
- Visualization (standard image viewing tools, MIPs, and reformats)
- Analysis (registration, subtractions, kinetic curves, parametric image maps, segmentation and 3D volume rendering)
The TumorSight system consists of proprietary software developed by SimBioSys, Inc. hosted on a cloud-based platform and accessed on an off-the-shelf computer.
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510(k) Summary
Intended Use and Indications for Use
TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats).
TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz.
Device Description Comparison
TumorSight Viz has the same Device Description as the predicate with the exception that the Communication & Storage feature was reclassified and moved to the TumorSight Platform.
Indications for Use Comparison
TumorSight Viz has the same Indications for Use as the predicate.
Technological Characteristics
Visualization of dynamic magnetic resonance imaging (MRI) studies is the technological principle for both the subject and predicate devices. It is based on the use of dynamic MRI images in DICOM format which are to be viewed and analyzed by a skilled physician. Both the subject and predicate devices perform the following same technological features:
- Standard Image Viewing Tools (zoom, pan, window/level)
- Image Post Processing (MIPs, reformats, image registration)
- Parametric Maps
- Kinetic Curves
- Automatic Volume Segmentation
- Automatic Linear Measurements (distance to nipple, chest, and closest skin surface)
- Automated DICOM Image Import
The following technological features differ between the subject and predicate devices:
- Updated segmentation model
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510(k) Summary
TumorSight Viz 510(k) Summary Page 3 of 9
Performance Tests
SimBioSys has completed performance testing on an independent dataset to ensure TumorSight Viz meets clinically acceptable levels.
DCE-MRI were obtained from one thousand one hundred fifty-six (1156) patients from more than fifteen (15) clinical sites in the U.S. for use in training and tuning the device. DCE-MRI were obtained for two hundred sixty-six (266) patients(corresponding to 267 samples when accounting for bilateral disease) from more than eight (8) clinical sites in the U.S. for use in validating the device. All patients had pathologically confirmed invasive, early stage or locally advanced breast cancer.
Data was collected to ensure adequate coverage of MRI manufacturer and field strength, and to ensure similarity with the broader population of early-stage and locally advanced breast cancer patients in the U.S. Specifically, patient age at diagnosis, breast cancer subtype, T stage, N stage, histologic subtype, and race/ethnicity all reflect the broader U.S. population.
| Training/Tuning Datasets (n=1156 samples) | Validation Dataset (n=267 samples) | |
|---|---|---|
| Age | ||
| <30 | 28 (2.4%) | 7 (2.6%) |
| 30-39 | 194 (16.8%) | 36 (13.5%) |
| 40-49 | 326 (28.2%) | 64 (24.0%) |
| 50-59 | 352 (30.5%) | 82 (30.7%) |
| 60-69 | 192 (16.6%) | 56 (21.0%) |
| >70 | 63 (5.5%) | 22 (8.2%) |
| Missing | 1 (0.1%) | 0 (0.0%) |
| Race/Ethnicity | ||
| Black† | 169 (14.6%) | 31 (11.6%) |
| Asian and Pacific Islander† | 53 (4.6%) | 22 (8.2%) |
| White† | 717 (62.0%) | 189 (70.8%) |
| American Indian or Alaska Native† | 0 (0.0%) | 0 (0.0%) |
| Other | 2 (0.2%) | 7 (2.6%) |
| Hispanic | 10 (0.9%) | 10 (3.8%) |
| Missing/Unknown | 202 (17.5%) | 8 (3.0%) |
† Non-Hispanic
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510(k) Summary
TumorSight Viz 510(k) Summary Page 4 of 9
The following subgroups present in the dataset were comparable to the U.S. population: cancer subtype, grade, histology, T stage, and N stage.
Images were acquired from sites that utilize standard of care dynamic contrast enhanced MR protocols from GE, Philips, and Siemens scanners with both 1.5T and 3T field strength magnets.
Three (3) U.S. Board Certified radiologists reviewed 267 validation samples to establish the ground truth for the dataset according to predefined guidelines. For each case, two radiologists measured various characteristics about the cancer including longest dimensions along three axes and tumor to landmark (chest, nipple, skin) distances. Each study was reviewed by two radiologists to determine if the candidate segmentation was appropriate. In cases where the two radiologists did not agree on whether the segmentation was appropriate, a third radiologist provided an additional opinion and established a ground truth by majority consensus.
Independence of validation data from training data was ensured by confirming there was no overlap of patients between training/tuning and validation datasets.
The validation samples were tested using both TumorSight Viz and the predicate device.
The measurements generated from the device result directly from the segmentation methodology and are an inferred reflection of the performance of the deep learning algorithm. For example, the distance from chest or skin is calculated after the deep learning segmentation identifies the region of interest and then the resulting measurement is output.
The mean absolute error and variability between the automated measurements (Validation Testing) and ground truth for tumor volume (measured in cubic centimeters; cc) and landmark distances (measured in centimeters; cm) was similar to the variability between device-to-radiologist measurements and inter-radiologist variability. This demonstrates that the error in measurements is consistent to the variability between expert readers. Performance data for the automated measurements is summarized below:
| Measurement Description | Units | Validation Testing (Mean Abs. Error ± Std. Dev.) |
|---|---|---|
| Tumor Volume (n=218) | cubic centimeters (cc) | 5.2 ± 12.5 |
| Tumor-to-breast volume ratio (n=218) | % | 0.4 ± 1.2 |
| Tumor longest dimension (n=242) | centimeters(cm) | 1.32 ± 1.65 |
| Tumor-to-nipple distance (n=241) | centimeters(cm) | 1.17 ± 1.55 |
| Tumor-to-skin distance (n=242) | centimeters(cm) | 0.60 ± 0.52 |
| Tumor-to-chest distance (n=242) | centimeters(cm) | 0.86 ± 1.22 |
| Tumor center of mass (n=218) | centimeters(cm) | 0.60 ± 1.47 |
The tumor segmentation was assessed using the Dice coefficient, utilizing both the volumetric and surface Dice coefficients, which together validate the location, volume, and surface agreement with a reference standard.
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510(k) Summary
The surface Dice coefficient is particularly useful as a proxy for the accuracy of 3D rendering and surface-to-surface distances. Additionally, to further assess the tumor segmentation localization accuracy, we used the distance between the centers of mass of the reference standards and device-generated regions.
Results of Dice and surface Dice are summarized below:
| Performance Measurement | Metric | Validation Testing (Mean ± Std. Dev.) |
|---|---|---|
| Tumor segmentation (n=218) | Volumetric Dice | 0.76 ± 0.26 |
| Surface Dice | 0.92 ± 0.21 |
We found that all tests met the acceptance criteria, demonstrating adequate performance for our intended use.
Risk Management
The device risks were managed and controlled following the requirements of ISO 14971 standard. The device hazards were identified, their risk levels were evaluated, and mitigation measures were taken to reduce the risk levels. The benefits of the TumorSight Viz software outweigh the device residual risks.
Substantial Equivalence
TumorSight Viz is comparable to the predicate in terms of intended use, technological characteristics, and principle of operation.
A table comparing the key features of the subject and predicate devices is provided below:
Predicate Device Comparison
| Predicate Device (TumorSight Viz – version 1.2) | Subject Device (TumorSight Viz – version 1.3) | |
|---|---|---|
| 510(k) | K243189 | K251766 |
| Manufacturer | SimBioSys, Inc. | SimBioSys, Inc. |
| Regulation Number | 892.2050 | 892.2050 |
| Regulation Name | Medical image management and processing system | Medical image management and processing system |
| Classification | 2 | 2 |
| Device Common Name | Image Processing System | Image Processing System |
| Product Code | QIH | QIH |
| Functions | - Extract dynamic contrast enhanced MRI sequence from MRI images for the 3D display and visualization of the anatomy of patient's breast | - Extract dynamic contrast enhanced MRI sequence from MRI images for the 3D display and visualization of the anatomy of patient's breast |
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510(k) Summary
TumorSight Viz 510(k) Summary Page 6 of 9
| Predicate Device | Subject Device | |
|---|---|---|
| Intended Use | TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats). TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz. | TumorSight Viz is intended to be used in the visualization and analysis of breast magnetic resonance imaging (MRI) studies for patients with biopsy proven early-stage or locally advanced breast cancer. TumorSight Viz supports evaluation of dynamic MR data acquired from breast studies during contrast administration. TumorSight Viz performs processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats). TumorSight Viz also includes user-configurable features for visualizing and analyzing findings in breast MRI studies. Patient management decisions should not be made based solely on the results of TumorSight Viz. |
| Data Source (Input) | MRI | MRI |
| Output/Accessibility | Graphic and text results of breast anatomy are accessed via a device with internet connectivity | Graphic and text results of breast anatomy are accessed via a device with internet connectivity |
| Physical Characteristics | "-non-invasive software package -DICOM compatible" | "-non-invasive software package -DICOM compatible" |
| Safety | Clinician review and assessment of analysis prior to use in pre-operative planning. | Clinician review and assessment of analysis prior to use in pre-operative planning. |
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510(k) Summary
TumorSight Viz 510(k) Summary Page 7 of 9
Predicate Device Feature Comparison
| Feature | Predicate Device (TumorSight Viz – version 1.2) | Subject Device (TumorSight Viz – version 1.3) |
|---|---|---|
| Standard image viewing tools | Yes | Yes |
| MIPs | Yes | Yes |
| Reformats | Yes | Yes |
| Registration | Yes | Yes |
| Subtraction series | Yes | Yes |
| View 3D volume rendering | Yes | Yes |
| Kinetic curves | Yes | Yes |
| Parametric image maps | Yes | Yes |
| Manual DICOM import | Yes | Yes |
| Automated DICOM image import | Yes | Yes |
| Updated segmentation model | Yes | Yes |
| View finding volume | Yes | Yes |
| View finding location | Yes | Yes |
| View finding size | Yes | Yes |
| View kinetic curve with highest uptake | Yes | Yes |
| View finding distance to nipple | Yes | Yes |
| View finding distance to skin | Yes | Yes |
| View finding distance to chest | Yes | Yes |
| View adjusted finding size | No - Segmentation is not editable, but surgical margins are editable | No - Segmentation is not editable, but surgical margins are editable |
| Interactive rotation of 3D volume rendering | Yes | Yes |
Performance of TumorSight Viz was directly compared to that of the predicate for measurements including tumor longest dimension, tumor to skin distance, tumor to chest distance, and tumor to nipple distance. As summarized in the following table, these were comparable to inter-radiologist variability in the same measurements:
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510(k) Summary
TumorSight Viz 510(k) Summary Page 8 of 9
| Performance Measurement | N | Metric | Predicate/TumorSight Viz | TumorSight Viz/Ground Truth | Predicate/Ground Truth | Inter-radiologist Variability |
|---|---|---|---|---|---|---|
| (Mean ± Std. Dev.) | (Mean ± Std. Dev.) | (Mean ± Std. Dev.) | (Mean ± Std. Dev.) | |||
| Longest Dimension | 192 | Abs. Distance Error | 0.80 cm ± 1.69 cm | 1.27 cm ± 1.71 cm | 1.63 cm ± 2.01 cm | 1.02 cm ± 1.33 cm |
| Tumor to Skin | 192 | Abs. Distance Error | 0.20 cm ± 0.42 cm | 0.58 cm ± 0.50 cm | 0.61 cm ± 0.60 cm | 0.42 cm ± 0.45 cm |
| Tumor to Chest | 192 | Abs. Distance Error | 0.40 cm ± 0.86 cm | 0.89 cm ± 1.13 cm | 0.98 cm ± 1.16 cm | 0.79 cm ± 1.14 cm |
| Tumor to Nipple | 190 | Abs. Distance Error | 0.61 cm ± 1.47 cm | 1.06 cm ± 1.33 cm | 1.15 cm ± 1.40 cm | 0.88 cm ± 1.12 cm |
| Tumor Volume | 170 | Abs. Volume Error | 2.59 cc ± 7.56 cc | 4.21 cc ± 13.06 cc | 5.23 cc ± 16.95 cc | NA |
The differences in error between the mean absolute errors (MAE) for the predicate and subject device are clinically acceptable because they are on the order of one to two voxels for the mean voxel size in the dataset. These differences are clinically insignificant.
Substantial Equivalence Conclusion
The comparison of the features and non-clinical bench performance testing described above demonstrates that TumorSight Viz is substantially equivalent to the predicate device in function. Furthermore, performance testing in an independent dataset of radiologist measurement ground truth demonstrates adequate performance for the intended use.
Additionally, TumorSight Viz measurement outputs were compared directly to the predicate device output for 192 cases, and both sets of measurements were directly compared to radiologist measurements. TumorSight Viz compared equivalently to the predicate device on all measurements including tumor longest dimension, tumor to skin distance, tumor to chest distance, and tumor to nipple distance.
Non-clinical bench testing, an independent assessment of device performance to radiologist ground truth, and a direct comparison to the predicate device demonstrate that TumorSight Viz is substantially equivalent to the predicate device.
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510(k) Summary
TumorSight Viz 510(k) Summary Page 9 of 9
Functions Not Subject to FDA Premarket Review
This medical device product has functions subject to FDA premarket review as well as functions that are not subject to FDA premarket review. For this application, if the product has functions that are not subject to FDA premarket review, FDA assessed those functions only to the extent that they either could adversely impact the safety and effectiveness of the functions subject to FDA premarket review or they are included as a labeled positive impact that was considered in the assessment of the functions subject to FDA premarket review.
§ 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).