(112 days)
Not Found
Yes
The device description explicitly states that 'Deep learning' algorithms are applied, which is a subset of machine learning and artificial intelligence.
No
The device is described as a "concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams" to identify suspicious regions and assess their likelihood of malignancy. It provides diagnostic information, but it does not treat or cure any condition.
Yes
Explanation: The device is intended "to identify regions suspicious for breast cancer and assess their likelihood of malignancy" and provides "scores indicating the likelihood that cancer is present," which directly supports making a diagnosis.
Yes
The device description explicitly states "Transpara® is a software only application". While it runs on standard PC hardware or a virtual machine cluster, the device itself, as described and regulated, is the software.
Based on the provided text, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: The Transpara® software analyzes medical images (mammograms and digital breast tomosynthesis exams) to identify suspicious regions. It does not analyze biological samples from the patient.
- Intended Use: The intended use is as a "concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams." This involves image analysis, not laboratory testing of biological specimens.
Therefore, Transpara® 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. The section for "Control Plan Authorized (PCCP) and relevant text" is marked "Not Found."
Intended Use / Indications for Use
Transpara® software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara®.
Product codes
QDQ
Device Description
Transpara® is a software only application designed to be used by physicians to improve interpretation of digital mammography and digital breast tomosynthesis. The system is intended to be used as a concurrent reading aid to help readers with detection and characterization of potential abnormalities suspicious for breast cancer and to improve workflow. 'Deep learning' algorithms are applied to FFDM images and DBT slices for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries). Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
Transpara® offers the following functions which may be used at any time during reading (concurrent use):
a) Computer aided detection (CAD) marks to highlight locations where the device detected suspicious calcifications or soft tissue lesions.
b) Decision support is provided by region scores on a scale ranging from 0-100, with higher scores indicating a higher level of suspicion.
c) Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow.
d) An exam score which categorizes exams on a scale of 1-10 with increasing likelihood of cancer. The score is calibrated in such a way that approximately 10 percent of mammograms in a population of mammograms without cancer falls in each category.
Results of Transpara® are computed in processing server which accepts mammograms or DBT exams in DICOM format as input, processes them, and sends the processing output to a destination using the DICOM protocol in a standardized mammography CAD DICOM format. Common destinations are medical workstations, PACS and RIS. Transpara® is offered as a virtual machine and runs on pre-selected standard PC hardware as well as a dedicated virtual machine cluster. The system can be configured using a service interface. Implementation of a user interface for end users in a medical workstation is to be provided by third parties.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes, 'Deep learning' algorithms are applied to FFDM images and DBT slices for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries).
Input Imaging Modality
full-field digital mammography exams and digital breast tomosynthesis exams
Anatomical Site
breast
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Intended users of Transpara® are physicians qualified to read screening mammography exams and digital breast tomosynthesis exams.
Description of the training set, sample size, data source, and annotation protocol
Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
Description of the test set, sample size, data source, and annotation protocol
For these tests an independent dataset was used, which was acquired from multiple centers and had not been used for development of the algorithms. This testset contained 2D and 3D mammograms acquired with devices from different manufacturers (2D: Hologic, GE, Philips, Siemens and Fujifilm, 3D: Hologic, Siemens and Fujifilm), representative for regular breast cancer screening and asymptomatic patients collected from multiple clinical centers in seven EU countries and the US.
The testset consisted of 7882 non-cancer exams, and 1240 exams with cancer. Of the non-cancer exams 4797 were 2D and 3085 were DBT. Of the exams with cancer 819 and 421 were 2D and DBT, respectively. In total, 61% of the lesions in the exams with cancer in the testset were characterized as a mass, 33% as suspicious calcifications, and 6% as architectural distortions or asymmetries. The three main histological cancer types were invasive ductal carcinoma (60.5%), ductal carcinoma in situ (25.9%), and invasive lobular carcinoma (9.0%). The median lesion extent (defined as maximum diameter in two dimensions) was 16 mm in both 2D data (IQR: 11-24) and 3D data (IQR: 11-25).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device.
Test set sample size: 7882 non-cancer exams, and 1240 exams with cancer (819 2D and 421 DBT with cancer).
Exam based sensitivity was computed by taking the fraction of cancers that were correctly localized in it least one view (MLO or CC). For 2D sensitivity is measured separately for calcifications and soft tissue lesions while for DBT the sensitivity is reported without distinguishing lesion types. False positive rates were computed in exams without cancer, by dividing the number of regions detected per image by the number of images. For 2D, the sensitivity for calcifications is 94.7% (95% CI: 91.7-96.7) at a false positive rate of 0.11 FP/image. The sensitivity for soft tissue lesions is 80.2% (95% Cl: 76.8-83.2) at a false positive rate of 0.02 FP/image and 92.6% (95% Cl: 90.2-94.6) at a false positive rate of 0.17 FP/image. For DBT, sensitivity is 91.3% (95% CI: 88.1-93.6) at a false positive rate of 0.3 FP/volume.
Exam-based ROC analysis was performed to compare AUC of the device with the predicate device on the testset, excluding Fujifilm DBT exams because this input was not validated in the predicate device. For 2D, AUC of the device is 0.949, which is higher is non-inferior in comparison to the AUC of 0.929 of the predicate device. The difference is +0.021 (0.013,0.038). For DBT, AUC of the device is 0.931, which is higher is non-inferior in comparison to the AUC of 0.917 of the predicate device. The difference is +0.014 (0.003-0.042).
AUC performance for Fujifilm was 0.952, which is higher is non-inferior in comparison to the AUC of 0.917 of the predicate device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
For 2D, the sensitivity for calcifications is 94.7% (95% CI: 91.7-96.7) at a false positive rate of 0.11 FP/image. The sensitivity for soft tissue lesions is 80.2% (95% Cl: 76.8-83.2) at a false positive rate of 0.02 FP/image and 92.6% (95% Cl: 90.2-94.6) at a false positive rate of 0.17 FP/image. For DBT, sensitivity is 91.3% (95% CI: 88.1-93.6) at a false positive rate of 0.3 FP/volume.
For 2D, AUC of the device is 0.949.
For DBT, AUC of the device is 0.931.
AUC performance for Fujifilm was 0.952.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.2090 Radiological computer-assisted detection and diagnosis software.
(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.
0
Image /page/0/Picture/0 description: The image contains the logos of the Department of Health and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, featuring a stylized human figure. To the right is the FDA logo, with the letters "FDA" in a blue square, followed by "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
July 30, 2021
ScreenPoint Medical B.V. % Umar Waqas Head of Regulatory and Quality Affairs Mercator II, 7th floor, Toernooiveld 300 Nijmegen, Gelderland 6525EC Netherlands
Re: K210404
Trade/Device Name: Transpara 1.7.0 Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection/diagnosis software for lesions suspicious for cancer Regulatory Class: Class II Product Code: QDQ
Dear Umar Waqas:
The Food and Drug Administration (FDA) is sending this letter to notify you of an administrative change related to your previous substantial equivalence (SE) determination letter dated June 2, 2021. Specifically, FDA is updating this SE Letter as an administrative correction in 510K Summary.
Please note that the 510(k) submission was not re-reviewed. For questions regarding this letter please contact Jessica Lamb, OHT7: Office of in vitro Diagnostics and Radiological Health, 307-796-6167, jessica.lamb(@)fda.hhs.gov.
Sincerely,
Hsl. 2. Nils
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
1
Image /page/1/Picture/0 description: The image contains 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.
ScreenPoint Medical B.V. % Umar Waqas, Ph.D. Head of Regulatory and Quality Affairs Mercator II, 7th floor, Toernooiveld 300 Nijmegen, Gelderland 6525EC NETHERLANDS
June 2, 2021
Re: K210404 Trade/Device Name: Transpara 1.7.0 Regulation Number: 21 CFR 892.2090 Regulation Name: Radiological computer assisted detection/diagnosis software for lesions suspicious for cancer Regulatory Class: Class II Product Code: QDQ Dated: May 3, 2021 Received: May 7, 2021
Dear Dr. Waqas:
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 mav, 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
2
requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801 and Part 809); 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 (OS) 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,
Michael D. O'Hara 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
3
DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
510(k) Number (if known) K210404
Device Name
Transpara® 1.7.0
Transpara® software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara®.
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
|---|---|
| ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------ |
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510(k) Summary Transpara® (K210404)
This 510(k) summary of safety and effectiveness information is prepared in accordance with the requirements of 21 CFR § 807.92.
Submitter 1.
Manufacturer:
ScreenPoint Medical B.V.
Mercator II, 7th floor
Toernooiveld 300
6525 EC Nijmegen
Netherlands
Contact person:
Umar Waqas
Office: +31 24 3030045 | +31 24 2020020
Mobile: +31 6 44077104
Mercator II, 7th floor, Toernooiveld 300, 6525 EC Nijmegen, Netherlands
Date:
May 3, 2021
5
2. Device
| Device trade name | Transpara® 1.7.0 |
|---|---|
| Device | Radiological Computer Assisted Detection and |
| Diagnosis Software | |
| Classification regulation | 21 CFR 892.2090 |
| Panel | Radiology |
| Device class | II |
| Product code | QDQ |
| Submission type | Traditional 510(k) |
3. Legally marketed predicate device
| Device trade name | Transpara® 1.6.0 |
|---|---|
| Legal Manufacturer | ScreenPoint Medical B.V. |
| Device | Radiological Computer Assisted Detection and |
| Diagnosis Software | |
| Classification regulation | 21 CFR 892.2090 |
| Panel | Radiology |
| Device class | II |
| Product code | QDQ |
| Clearance number | K193229 |
Device description 4.
Transpara® is a software only application designed to be used by physicians to improve interpretation of digital mammography and digital breast tomosynthesis. The system is intended to be used as a concurrent reading aid to help readers with detection and characterization of potential abnormalities suspicious for breast cancer and to improve workflow. 'Deep learning' algorithms are applied to FFDM images and DBT slices for recognition of suspicious calcifications and soft tissue lesions (including densities, masses, architectural distortions, and asymmetries). Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue.
Transpara® offers the following functions which may be used at any time during reading (concurrent use):
6
- a) Computer aided detection (CAD) marks to highlight locations where the device detected suspicious calcifications or soft tissue lesions.
- b) Decision support is provided by region scores on a scale ranging from 0-100, with higher scores indicating a higher level of suspicion.
- c) Links between corresponding regions in different views of the breast, which may be utilized to enhance user interfaces and workflow.
- d) An exam score which categorizes exams on a scale of 1-10 with increasing likelihood of cancer. The score is calibrated in such a way that approximately 10 percent of mammograms in a population of mammograms without cancer falls in each category.
Results of Transpara® are computed in processing server which accepts mammograms or DBT exams in DICOM format as input, processes them, and sends the processing output to a destination using the DICOM protocol in a standardized mammography CAD DICOM format. Common destinations are medical workstations, PACS and RIS. Transpara® is offered as a virtual machine and runs on pre-selected standard PC hardware as well as a dedicated virtual machine cluster. The system can be configured using a service interface. Implementation of a user interface for end users in a medical workstation is to be provided by third parties.
Indications for use 5.
Transpara® is a software medical device for use in a healthcare facility or hospital with the following indications for use:
Transpara® software is intended for use as a concurrent reading aid for physicians interpreting screening full-field digital mammography exams and digital breast tomosynthesis exams from compatible FFDM and DBT systems, to identify regions suspicious for breast cancer and assess their likelihood of malignancy. Output of the device includes locations of calcifications groups and soft-tissue regions, with scores indicating the likelihood that cancer is present, and an exam score indicating the likelihood that cancer is present in the exam. Patient management decisions should not be made solely on the basis of analysis by Transpara®.
Intended user population
Intended users of Transpara® are physicians qualified to read screening mammography exams and digital breast tomosynthesis exams.
7
Intended patient population
The device is intended to be used in the population of women undergoing screening mammography and digital breast tomosynthesis.
Warnings and precautions
Transpara® is an adjunct tool and not intended to replace a physicians' own review of a mammogram. Decisions should not be made solely based on analysis by Transpara®.
6. Predicate device comparison
The indication for use of Transpara® 1.7.0 is similar to that of the predicate device. Both devices are intended for concurrent use by physicians interpreting breast images to help them with localizing and characterizing abnormalities. The devices are not intended as a replacement for the review of a physician or their clinical judgement.
The overall design of Transpara® 1.7.0 is the same as that of the predicate device. Both versions detect and characterize findings in radiological breast images and provide information about the presence, location, and characteristics of the findings to the user in a similar way. There are differences in the algorithmic components, which have changed to improve detection accuracy for FFDM and of DBT. Support for Fujifilm DBT has been added.
Changes do not raise different questions of safety and effectiveness of the device when used as labeled.
Summary of non-clinical performance data 7.
In the design and development of Transpara® 1.7.0, ScreenPoint applied the following voluntary FDA recognized standards and quidelines:
| Standard ID | Standard Title | FDA Recognition # |
|---|---|---|
| IEC 62366-1 | ||
| Edition 1.1 2020-06 | Medical devices - Part 1: Application of | |
| usability engineering to medical devices | 5-129 | |
| IEC 62366-1 | ||
| Edition 1.0 2015-02 | Medical devices - Part 1: Application of | |
| usability engineering to medical devices | ||
| [Including CORRIGENDUM 1 (2016)] | 5-114 |
8
| ISO, 14155 Third
edition 2020-07 | Clinical investigation of medical devices
for human subjects - Good clinical
practice | 2-282 |
|-------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|-------|
| ISO,
14155
Second edition
2011-02-01, | Clinical investigation of medical devices
for human subjects - Good clinical
practice | 2-205 |
| ISO 14971:2019 | Medical Devices - Application Of Risk
Management To Medical Devices | 5-125 |
| IEC 62304:2015 | Medical Device Software - Software Life
Cycle Processes | 13-79 |
| ISO,
15223-1
Third Edition
2016-11-01, | Medical devices - Symbols to be used
with medical device labels labelling and
information to be supplied - Part 1:
General requirements | 5-117 |
| DEN180005 | Decision summary with special controls
for class II radiology device | |
The following guidance documents were used to support this submission:
- . Guidance for Industry and FDA Staff - Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (Issued on May 11, 2005)
- · Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data – Premarket Notification [510(k)] Submissions (Issued on July 3, 2012)
- Guidance for Industry and FDA Staff Clinical Performance Assessment: . Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket notification [510(k)] Submissions (Issued on January 2020)
- · The 510(k) Program: Evaluating Substantial Equivalence in Premarket Notifications [510(k)] (Issued on July 28 2014)
Transpara®1.7.0 is a software-only device. The level of concern for the device is determined as Moderate Level of Concern.
Non-clinical performance tests
Verification testing was conducted, which consisted of software unit testing, software integration testing and software system testing. The verification tests showed that the software application satisfied the software requirements.
Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device. For these tests an independent dataset was used, which
9
was acquired from multiple centers and had not been used for development of the algorithms. This testset contained 2D and 3D mammograms acquired with devices from different manufacturers (2D: Hologic, GE, Philips, Siemens and Fujifilm, 3D: Hologic, Siemens and Fujifilm), representative for regular breast cancer screening and asymptomatic patients collected from multiple clinical centers in seven EU countries and the US.
The testset consisted of 7882 non-cancer exams, and 1240 exams with cancer. Of the non-cancer exams 4797 were 2D and 3085 were DBT. Of the exams with cancer 819 and 421 were 2D and DBT, respectively. In total, 61% of the lesions in the exams with cancer in the testset were characterized as a mass, 33% as suspicious calcifications, and 6% as architectural distortions or asymmetries. The three main histological cancer types were invasive ductal carcinoma (60.5%), ductal carcinoma in situ (25.9%), and invasive lobular carcinoma (9.0%). The median lesion extent (defined as maximum diameter in two dimensions) was 16 mm in both 2D data (IQR: 11-24) and 3D data (IQR: 11-25).
Exam based sensitivity was computed by taking the fraction of cancers that were correctly localized in it least one view (MLO or CC). For 2D sensitivity is measured separately for calcifications and soft tissue lesions while for DBT the sensitivity is reported without distinguishing lesion types. False positive rates were computed in exams without cancer, by dividing the number of regions detected per image by the number of images. For 2D, the sensitivity for calcifications is 94.7% (95% CI: 91.7-96.7) at a false positive rate of 0.11 FP/image. The sensitivity for soft tissue lesions is 80.2% (95% Cl: 76.8-83.2) at a false positive rate of 0.02 FP/image and 92.6% (95% Cl: 90.2-94.6) at a false positive rate of 0.17 FP/image. For DBT, sensitivity is 91.3% (95% CI: 88.1-93.6) at a false positive rate of 0.3 FP/volume.
Exam-based ROC analysis was performed to compare AUC of the device with the predicate device on the testset, excluding Fujifilm DBT exams because this input was not validated in the predicate device. For 2D, AUC of the device is 0.949, which is higher is non-inferior in comparison to the AUC of 0.929 of the predicate device. The difference is +0.021 (0.013,0.038). For DBT, AUC of the device is 0.931, which is higher is non-inferior in comparison to the AUC of 0.917 of the predicate device. The difference is +0.014 (0.003-0.042).
AUC performance for Fujifilm was 0.952, which is higher is non-inferior in comparison to the AUC of 0.917 of the predicate device.
Based on results of verification and validation tests it is concluded that Transpara® 1.7.0 is effective in the detection of soft lesions and calcifications at an appropriate safety level.
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Conclusions 8.
The data presented in this 510(k) includes all required information to support the review by FDA. Standalone performance tests with FFDM and DBT demonstrate that Transpara® 1.7.0 achieves non-inferior detection performance compared to the predicate device.
ScreenPoint has applied a risk management process in accordance with FDA recognized standards to identify, evaluate, and mitigate all known hazards related to Transpara® 1.7.0. These hazards may occur when accuracy of diagnosis is potentially affected, causing either false-positives or false-negatives. All identified risks are effectively mitigated and it can be concluded that the residual risk is outweighed by the benefits.
Considering all data in this submission, the data provided in this 510(k) application supports the safe and effective use of Transpara® 1.7.0 for its indications for use and substantial equivalence to the predicate device.