(86 days)
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®.
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. Common destinations are medical workstations, PACS and RIS. 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.
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Transpara 1.7.2:
Acceptance Criteria and Device Performance Study for Transpara 1.7.2
The primary study conducted to prove the device meets acceptance criteria was a standalone performance test demonstrating non-inferiority to the predicate device (Transpara 1.7.0).
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the non-inferiority claims to the predicate device in terms of breast cancer detection performance (sensitivity and ROC AUC) at specified false positive rates. While specific pass/fail thresholds for non-inferiority margin are not explicitly given in this document, the statement "non-inferior to the performance of the predicate device Transpara 1.7.0" implies that metrics must meet or exceed the predicate's performance within a defined statistical margin.
| Metric (Implicit Acceptance Criteria) | Reported Device Performance (Transpara 1.7.2) |
|---|---|
| Non-inferiority in Cancer Detection Sensitivity for 2D Mammography compared to predicate device | 2D Sensitivity: 95.0% (93.5-96.4) at 0.30 FP/image |
| Non-inferiority in ROC AUC for 2D Mammography compared to predicate device | 2D AUC: 0.945 (0.935-0.954) |
| Non-inferiority in Cancer Detection Sensitivity for DBT Mammography compared to predicate device | DBT Sensitivity: 93.2% (91.0-95.1) at 0.34 FP/volume |
| Non-inferiority in ROC AUC for DBT Mammography compared to predicate device | DBT AUC: 0.945 (0.936-0.954) |
| Performance metrics for different types of findings (mass, calcifications, architectural distortions, asymmetries, combinations) and histological cancer types (invasive non-specific, DCIS, invasive lobular) | Specific performance breakdowns by finding type and histology are not provided in this summary, but the test set included different types of findings and histological cancers. |
Conclusion: The study explicitly states, "Based on standalone testing it was concluded that Transpara 1.7.2 breast cancer detection performance for 2D and 3D mammograms of compatible devices is non-inferior to the performance of the predicate device Transpara 1.7.0."
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 10,690 exams.
- FFDM: 5,867 exams (4,841 Normal, 149 Benign, 877 Cancer)
- DBT: 4,823 exams (3,988 Normal, 240 Benign, 595 Cancer)
- Data Provenance:
- Acquisition: Acquired from multiple centers, collected from multiple clinical centers.
- Geographic Origin: Seven EU countries and the US.
- Retrospective/Prospective: The document does not explicitly state if the data was retrospective or prospective. However, the mention of "normal follow-up of at least one year" for inclusion of normal exams strongly suggests a retrospective collection of existing patient data.
- Manufacturer Diversity: Included images from different manufacturers (2D: Hologic, GE, Philips, Siemens, Giotto and Fujifilm; 3D: Hologic, Siemens, General Electric and Fujifilm).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not specify the number of experts used to establish the ground truth or their qualifications. It only states that the training algorithms were "trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." For the test set, it mentions "biopsy-proven cancer regions" and "normal follow-up of at least one year" for normal exams, indicating a reliance on clinical outcomes rather than expert consensus for ground truth.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method for establishing ground truth for the test set. The reliance on "biopsy-proven" and "normal follow-up" suggests that ground truth was clinical outcome-based rather than expert-adjudicated review of images for the testing purposes.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- No, a MRMC comparative effectiveness study was not reported as part of this 510(k) summary to directly show human readers improve with AI vs. without AI assistance. The study described is a standalone performance test comparison to a predicate device.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, a standalone performance test was conducted and described in detail. "Standalone performance tests were conducted to demonstrate substantial equivalence with the predicate device."
7. The Type of Ground Truth Used
The ground truth used for the test set appears to be primarily clinical outcome data:
- "biopsy-proven cancer regions" for positive cases.
- "normal follow-up of at least one year" for normal cases.
8. The Sample Size for the Training Set
The document mentions that "Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue," but it does not provide a specific sample size for the training set.
9. How the Ground Truth for the Training Set Was Established
The ground truth for the training set was established through:
- Biopsy-proven examples: For breast cancer and benign abnormalities.
- Clinical outcomes/follow-up: For examples of normal tissue.
This aligns with the ground truth establishment method for the test set, leveraging definitive clinical diagnoses (biopsy) and confirmed negative follow-up for normal cases.
§ 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.