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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 in a standardized mammography CAD DICOM format. Use of the device is supported for images from the following modality manufacturers: FFDM (Hologic, Siemens, General Electric, Philips, Fujifilm) and DBT (Hologic, Siemens). 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.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document doesn't explicitly present a formal "acceptance criteria" table with numerical cutoffs for specific metrics. Instead, it describes its objectives in terms of "superior" or "non-inferior" performance compared to a baseline (unaided human reading or a previous device version).

2. Sample sizes used for the test set and the data provenance

• Test Set (Pivotal Reader Study for DBT):

  • Sample Size: 240 Siemens Mammomat DBT exams. This included 65 exams with breast cancer, 65 exams with benign abnormalities, and 110 normal exams.
  • Data Provenance: The text states the data were "acquired from multiple centers." It also specifies they were "Siemens Mammomat DBT exams." The country of origin is not explicitly stated, but the manufacturer is based in Germany. The study was retrospective.

• Test Set (Standalone Performance Testing for FFDM):

  • Sample Size: "Independent multi-vendor test-set of mammography and DBT exams." Specific number not provided, but it included exams from five manufacturers: Hologic, GE, Philips, Siemens, and Fujifilm.
  • Data Provenance: The data were "acquired from multiple centers." The study was retrospective.

• Training Set:

  • Sample Size: "Algorithms are trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." No specific number provided.
  • Data Provenance: Not specified, but likely diverse given the mention of a "large database."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document doesn't explicitly detail the process or number of experts used to establish the ground truth for the test set before the reader study. It mentions the reader study itself involved 18 radiologists, but these radiologists were participating in the evaluation of the device, not necessarily establishing an independent ground truth for the test cases prior to the study.

However, the training data used "biopsy-proven examples," which implies ground truth confirmation by pathology. For the reader study, the cases were "enriched," meaning they had known outcomes (cancer, benign, normal). The underlying ground truth for these clinical cases would typically be established by clinical diagnosis, pathology reports from biopsies, and follow-up.

4. Adjudication method for the test set

The document does not explicitly describe an adjudication method (like 2+1 or 3+1 consensus) for establishing the ground truth of the test set cases. The term "enriched sample" suggests that cases with known outcomes (cancer, benign, normal) were selected.

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

• Yes, a multi-reader multi-case (MRMC) comparative effectiveness study was done.

  • Design: "fully-crossed, multi-reader multi-case retrospective study."
  • Participants: 18 MQSA qualified radiologists.

• Effect Size of Human Reader Improvement (with AI vs. without AI assistance):

  • Average AUC: Increased from 0.833 (unaided) to 0.863 (with Transpara™ assistance).
  • P-value: P = 0.0025, indicating statistical significance.
  • Sensitivity: "Superior sensitivity was obtained with Transpara™." (Specific values not provided).
  • Reading Time: "reading time was significantly reduced." (Specific reduction not provided).

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Yes, standalone performance testing was done.
  • Type of Testing: "determining stand-alone performance of the algorithms in Transpara 1.6.0."
  • Context for FFDM: Focused on non-inferiority compared to the predicate device (Transpara 1.3.0) and for new manufacturers (Fujifilm).
  • Context for DBT: It was a secondary objective of the pivotal reader study to "test if standalone AUC performance of Transpara™ was non-inferior to the average AUC performance of the readers." The study results indicated this objective was met. (Specific standalone AUC not provided in the text).

7. The type of ground truth used

• For training data: "biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." This implies pathology and clinical follow-up for normality.
• For the pivotal reader study test set: The "enriched sample" of exams (65 with cancer, 65 benign, 110 normal) suggests ground truth was based on clinical diagnosis, pathology results, and follow-up exams. While not explicitly stated as "expert consensus," these are considered robust forms of ground truth for breast imaging studies.

8. The sample size for the training set

• The training set was described as a "large database." No specific numerical sample size was provided in the document.

9. How the ground truth for the training set was established

• The algorithms were "trained with a large database of biopsy-proven examples of breast cancer, benign abnormalities, and examples of normal tissue." This indicates the ground truth was established through histopathological confirmation (biopsy results) for cancerous and benign cases, and likely clinical follow-up for normal cases to ensure no underlying malignancy was missed.

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