(263 days)
AutoContour is intended to assist radiation treatment planners in contouring structures within medical images in preparation for radiation therapy treatment planning.
AutoContour consists of 3 main components:
- An "agent" service designed to run on the Windows Operating System that is configured by the user to monitor a network storage location for new CT datasets that are to be automatically uploaded to:
- A cloud-based AutoContour automatic contouring service that produces initial contours and
- A web application accessed via web browser which allows the user to perform registration with other image sets as well as review, edit, and export the structure set containing the contours.
The provided text describes the acceptance criteria and study proving the device meets those criteria. Here's a breakdown of the requested information:
1. Table of Acceptance Criteria & Reported Device Performance
The document states that formal acceptance criteria and reported device performance are detailed in "Radformation's AutoContour Complete Test Protocol and Report." However, this specific report is not included in the provided text. The summary only generally states that "Nonclinical tests were performed... which demonstrates that AutoContour performs as intended per its indications for use" and "Verification and validation tests were performed to ensure that the software works as intended and pass/fail criteria were used to verify requirements."
Therefore, a table of acceptance criteria and reported device performance cannot be constructed from the provided text.
2. Sample Size Used for the Test Set and Data Provenance
The document mentions that "tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model." However, the sample size for the test set is not explicitly stated.
Regarding data provenance:
- The document implies the data used was medical image data (specifically CT, and for registration purposes, MR and PET).
- The country of origin is not specified.
- The terms "training and validation sets" and "independent datasets" suggest these were retrospective datasets used for model development and evaluation. There is no mention of prospective data collection.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not provide any information about the number of experts used to establish ground truth for the test set or their qualifications.
4. Adjudication Method for the Test Set
The document does not specify any adjudication method (e.g., 2+1, 3+1, none) used for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance?
The document explicitly states: "As with the Predicate Devices, no clinical trials were performed for AutoContour." This indicates that an MRMC comparative effectiveness study involving human readers and AI assistance was not conducted. Therefore, no effect size for human reader improvement is reported.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document mentions "tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model." This strongly suggests that standalone performance of the algorithm was evaluated. Although specific metrics for this standalone performance are not detailed in the provided text, the validation of a machine learning model against independent datasets implies a standalone evaluation.
7. The Type of Ground Truth Used
The document mentions that AutoContour is intended to "assist radiation treatment planners in contouring structures within medical images." Given this, the ground truth for the contours would typically be expert consensus or expert-annotated contours. However, the document itself does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data).
8. The Sample Size for the Training Set
The document mentions "training and validation sets" but does not provide the sample size for the training set.
9. How the Ground Truth for the Training Set Was Established
The document mentions "training and validation sets" but does not detail how the ground truth for the training set was established. Similar to the test set, it would likely involve expert contouring, but this is not explicitly stated.
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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
October 30, 2020
Radformation, Inc. % Mr. Kurt Sysock Co-founder/CEO 335 Madison Avenue, 16th Floor NEW YORK NY 10017
Re: K200323
Trade/Device Name: AutoContour Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: QKB Dated: September 19, 2020 Received: September 22, 2020
Dear Mr. Sysock:
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
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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,
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
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Indications for Use
510(k) Number (if known)
Device Name AutoContour
Indications for Use (Describe)
AutoContour is intended to assist radiation treatment planners in contouring structures within medical images in preparation for radiation therapy treatment planning.
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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Section 5. 510(k) Summary
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This 510(k) Summary has been created per the requirements of the Safe Medical Device Act (SMDA) of 1990, and the content is provided in conformance with 21 CFR Part 807.92.
5.1. Submitter's Information
| Table 1 : Submitter's Information | |
|---|---|
| Submitter's Name: | Kurt Sysock |
| Company: | Radformation, Inc. |
| Address: | 335 Madison Avenue, 16th FloorNew York, NY 10017 |
| Contact Person: | Alan NelsonChief Science Officer, Radformation |
| Phone: | 518-888-5727 |
| Fax: | ---------- |
| Email: | anelson@radformation.com |
| Date of Summary Preparation | 9/16/2020 |
5.2. Device Information
| Table 2 : Device Information | |
|---|---|
| Trade Name: | AutoContour |
| Common Name: | AutoContour, AutoContouring, AutoContour Agent,AutoContour Web Application |
| Classification Name: | Class II |
| Classification: | Picture archiving and communications system |
| Regulation Number: | 892.2050 |
| Product Code: | QKB |
| Classification Panel: | Radiology |
5.3. Predicate Devices Information
Mirada RTx (K130393) & Workflow Box (K181572)
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5.4. Device Description
AutoContour consists of 3 main components:
-
- An "agent" service designed to run on the Windows Operating System that is configured by the user to monitor a network storage location for new CT datasets that are to be automatically uploaded to:
-
- A cloud-based AutoContour automatic contouring service that produces initial contours and
-
- A web application accessed via web browser which allows the user to perform registration with other image sets as well as review, edit, and export the structure set containing the contours.
5.5. Indications for Use
AutoContour is intended to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning.
5.6. Technological Characteristics
AutoContour (Subject Device) makes use of a Predicate Devices, Mirada RTx (K130393) & Workflow Box (K181572) for substantial equivalence comparison. Note that Workflow Box utilizes Mirada RTx as its predicate device.
5.6.1. AutoContour vs. Mirada RTx (K130393) & Workflow Box (K181572)
RTx is "intended to display and visualize 2D & 3D multi-modal medical image data" and "supports the loading and saving of DICOM RT objects and allows the user to define, import, display, transform, store and export such objects including regions of interest structure and dose volumes to radiation therapy planning systems" (https://www.accessdata.fda.gov/cdrh docs/pdf13/K130393.pdf, accessed 1/20/2020).
Workflow Box "is a system designed to allow users to route DICOM-compliant data to and from automated processing components... [including] processing components for automatically contouring imaging data using … machine learning based algorithms."
(https://www.accessdata.fda.gov/cdrh docs/pdf18/K181572.pdf, accessed 1/20/2020)
| Table 3: Substantial Equivalence AutoContour vs. RTx & Workflow Box | |||
|---|---|---|---|
| Characteristic | Subject Device:AutoContourRadformation | Predicate Device:Workflow Box (K181572) | Predicate Device: MiradaRTx (K130393) |
| TargetPopulation | Any patient type forwhom relevant modalityscan data is available. | Any patient type forwhom relevantmodality scan data is | Any patient type for whomrelevant modality scan datais available. |
| (SubstantiallyEquivalent) | available. | ||
| Energy Usedand/orDelivered | None - software onlyapplication. The softwareapplication does notdeliver or depend onenergy delivered to orfrom patients(SubstantiallyEquivalent) | None - software onlyapplication. Thesoftware applicationdoes not deliver or | None - software onlyapplication. The softwareapplication does notdeliver or depend onenergy delivered to or frompatients |
| Intended users | Trained radiationoncology personnel(SubstantiallyEquivalent) | Designed to be used bytrained clinicians | Designed to be used bytrained clinicians |
| Design: DataVisualisation/Graphical UserInterface | Contains both anautomated processingcomponent and DataVisualisation / GraphicalUser Interface(SubstantiallyEquivalent) | None - the proposeddevice has no datavisualizationfunctionality. All dataprocessing isautomated and doesnot require userinteraction. A controlinterface is provided forsystem administrationand configuration only | Yes. |
| Design: Viewmanipulationand Volumerendering | Window and level, pan,zoom, cross-hairs, slicenavigation, fused views.(Subject Devicefunctionality is a subsetof Predicate Devices) | None - Not applicable | Window and level, pan,zoom, cross-hairs, slicenavigation. Maximum orminimum intensity projection(MIP), volume rendering,color rendering, surfacerendering, multi-planarreconstruction (MPR), fusedviews, gallery views. |
| Design: Imageregistration | Manual Rigidregistration.(Subject Devicefunctionality is a subsetof Predicate Devices) | Registration for thepurposes ofreplanning/re-contouringand atlas basedcontouring. Thealgorithms used forimage registration arethe same for bothpredicate and proposeddevices. | Manual and LandmarkRigid. Automatic multi-modalrigid. Mono-modal andmulti-modal deformableregistration. Motion correctionin hybrid scans and gatedscans. Registration for thepurposes ofre-planning/recontouring andatlas based contouring. |
| Regions andVolumes ofinterest (ROI) | Machine learning basedcontouring and manualROI manipulation.(Subject Device | Atlas Based contouring,registration basedre-contouring, machine | 2D and 3D ROIs,semi-automatic ROIdefinition, isocontour ROIsusing threshold and |
| functionality is a subsetof Predicate Devices) | learning basedcontouring | percentage of maximum,one-click seed-pointingcontouring, manual ROImanipulation , ROItransformation, Atlas-basedcontouring. | |
| Design:Region/volumeof interestmeasurementsand sizemeasurements | None - not applicable(SubstantiallyEquivalent) | None - not applicable | Intensity, Hounsfield units,activity and SUVmeasurements including min,max, mean, peak, standarddeviation, total glycolyticactivity, median, histogram,max and mean ratio toreference region. Gray for RTDose. Size measurementsinclude 2D and 3Dmeasurements includingrulers and volume, lineprofile. |
| Design:Region/VolumeQuantification | None - not applicable(SubstantiallyEquivalent) | None - not applicable | Regions table with chartingsupports analysis ofmeasurement over multiplestudies using standardprotocols such as RECIST,PERCIST and WHO |
| Design:Supportedmodalities | CT input for contouringor manualregistration/fusion.MR, PET input formanualregistration/fusion only.DICOM RTSTRUCT foroutput(Minor differences) | CT, MR, DICOMRTSTRUCT for imageprocessing. Any validDICOM data for datarouting | Static and gated CT andPET , and static MR, SPECT,NM, DICOM RT |
| Design:Reporting anddata routing | No built-in reporting,supports exportingDICOM RTSTRUCT file.(Minor differences) | Supports routing anddistribution of images toother DICOM nodesincluding to customexecutables determinedby the user. | Yes- Distribution of DICOMcompliant Images into otherDICOM compliant systems.Built-in basic reporting |
| Compatibilitywith theenvironmentand otherdevices | Compatible with datafrom any DICOMcompliant scanners forthe applicable modalities.Agent Uploadercomponent compatiblewith Microsoft Windows. | Compatible with datafrom any DICOMcompliant scanners forthe applicablemodalities. Compatiblewith MicrosoftWindows . Integrationwith Mirada DBx | Compatible with data fromany DICOM compliantscanners for the applicablemodalities. Compatiblewith Microsoft Windows .Integration with Mirada DBxapplication launcher and databrowser. |
| Cloud-based automaticcontouring servicecompatible with Linux.Web application Serverbased applicationcompatible with Linuxwith frontend compatiblewith all modern webbrowsers..(Minor differences) | application launcher anddata browser | ||
| Communications/Networking | TCP/IP(Subject Devicefunctionality is a subsetof Predicate Devices) | TCP/IP and SCP | TCP/IP and SCP |
| Computerplatform &operatingsystem | Agent Uploadersupporting MicrosoftWindows 10 (64-bit) andMicrosoft WindowsServer 2016.Cloud-based Serverbased automaticcontouring applicationcompatible with Linux.Web application Serverbased applicationcompatible with Linuxwith frontend compatiblewith all modern webbrowsers.(Minor differences) | Server based applicationsupporting MicrosoftWindows 10 (64-bit) andMicrosoft WindowsServer 2016. | Workstation and Serverbased application supportingWindows Server 2008 R2,SP1 and Windows 7 (64-bit) |
| Support forradiationtreatmentplanning | AutoContour is intendedto assist radiationtreatment planners incontouring and reviewingstructures within medicalimages in preparation forradiation therapytreatment planning(Subject Device intendeduse is a subset ofpredicate devices) | "Contours generated byWorkflow Box may beused as an input toclinical workflowsincluding, but not limitedto, radiation therapytreatment planning" | "RTx supports the loadingand saving of DICOM RTobjects and allows the user todefine, import, display,transform, store and exportsuch objects includingregions of interest structuresand dose volumes toradiation therapy planningsystems." |
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Source for RTx & Workflow Box: (https://www.accessdata.fda.gov/cdrh_docs/pdf18/[K181572](https://510k.innolitics.com/search/K181572).pdf, accessed 1/20/2020)
As shown in Table 3, almost all technological characteristics are either substantially equivalent or a subset of the predicate devices' technological characteristics.
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5.7. Discussion of differences
Subset of the Predicate Device
The comparison table above shows that several features of AutoContour are a subset of the features of the predicate device. This is because AutoContour's indication for use is narrower in scope than the predicate device in that it is intended only "to assist radiation treatment planners in contouring and reviewing structures within medical images in preparation for radiation therapy treatment planning". The design of AutoContour reflects that narrower scope and the features not shared between AutoContour are such that they can operate independently of the features that are not shared with the predicate devices, and therefore these differences do not create new questions regarding the safety and effectiveness of the device relative to the predicate devices.
Minor differences
The following minor differences exist, but do not represent any significant additional risks or decreased effectiveness for the device for its intended use:
- . Design: Supported modalities: AutoContour only supports CT image sets as inputs specifically for the contouring feature, while MR and PET image set inputs are only supported for registration purposes. In the predicate devices, all modalities are available for the workflows. This does not affect the safety and effectiveness of the device as it relates to its indications for use as the vast majority of radiation treatment planning is performed on CT images, and where MR or PET modalities are necessary for localization, AutoContour does support manual registration with those modalities which can be used to guide the contouring of the CT image set.
- . Design: Reporting and data routing: AutoContour does not include reporting features or direct data-routing with DICOM compliant systems instead AutoContour simply produces a DICOM compliant RT Structure Set file that the user may import into a DICOM compliant treatment planning system. This does not raise new questions regarding the safety and effectiveness of the device since the scope of AutoContour's indication for use is limited to making the contoured structures available for radiation treatment planning purposes and does not include managing or routing of the image data to various systems. The feature of managing or routing of the image data in this way is not a necessary component to achieve this goal, so not having this feature does not affect the safety or effectiveness of the device.
- Compatibility with the environment and other devices / Computer platform & operating system: AutoContour utilizes different computer platform & operating system for its 3 components, but this does not raise new questions regarding the safety and effectiveness of the device relative to the predicate device.
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5.7. Performance Data
As with the Predicate Devices, no clinical trials were performed for AutoContour. Nonclinical tests were performed according to Radformation's AutoContour Complete Test Protocol and Report, which demonstrates that AutoContour performs as intended per its indications for use. Further tests were performed on independent datasets from those included in training and validation sets in order to validate the generalizability of the machine learning model.
Verification and validation tests were performed to ensure that the software works as intended and pass/fail criteria were used to verify requirements.
5.8. Conclusion
AutoContour is deemed substantially equivalent to the Predicate Devices, Mirada RTx (K130393) & Workflow Box (K181572). Verification and Validation testing and Hazard Analysis demonstrate that AutoContour is as safe and effective as the Predicate Devices. The minor technological differences between AutoContour and the Predicate Devices with regard to the intended use do not raise any questions on the safety and effectiveness of the Subject Device.
§ 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).