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For in vitro diagnostic use.

FLEX Monoclonal Mouse Anti-Human Progesterone Receptor, Clone PgR 1294, Ready-to-Use (Dako Omnis), is intended for use in immunohistochemistry with the En Vision FLEX visualization system together with the Dako Omnis instrument to qualitatively detect human progesterone receptor in formalin-fixed, paraffin-embedded tissue sections of human breast cancer. The antibody binds progesterone receptor-expressing cells and is useful in the assessment of progesterone receptor status in human breast carcinomas.

The clinical interpretation of any staining or its absence should be complemented by morphological studies using proper controls and should be evaluated within the context of the patient's clinical history and other diagnostic tests by a qualified pathologist. This antibody is intended to be used after the primary diagnosis of tumor has been made by conventional histopathology using nonimmunologic histochemical stains.

FLEX Monoclonal Mouse Anti-Human Progesterone Receptor, Clone PgR 1294, Readyto-Use (Dako Omnis) is utilized to perform a qualitative immunohistochemical (IHC) assay to identify progesterone receptor (PR) expression in formalin fixed human breast cancer tissues routinely processed and paraffin-embedded for histological examination. The proposed device is a variant of FLEX Monoclonal Mouse Anti-Human Progesterone Receptor intended for use with the automated slide staining instrument Dako Omnis.

The design of the proposed device is developed to provide equivalent performance to its predicate FLEX Monoclonal Mouse Anti-Human Progesterone Receptor, Clone PgR 636, Ready-to-Use, (Link) with adaptation of the product configuration, to be used on the Dako Omnis Instrument instead of the Autostainer Link 48 Instrument.

The antibody is provided in liquid form in a buffer containing stabilizing protein and 0.015 mol/L sodium azide. The proposed device is intended for automated immunohistochemical (IHC) slide staining with the Dako Omnis Instrument and the software performing and controlling the automated slide staining process is validated for its intended use.

The provided text is a 510(k) summary for the FLEX Monoclonal Mouse Anti-Human Progesterone Receptor, Clone PgR 1294, Ready-to-Use (Dako Omnis). This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study with acceptance criteria for a new AI/software device. Therefore, a direct response to some of the requested points (e.g., human reader improvement with AI, standalone performance, specific ground truth methods with expert numbers) is not fully achievable from this document.

However, I can extract information related to the device's performance evaluation and its acceptance criteria as presented for regulatory approval.

Acceptance Criteria and Reported Device Performance

The document states that "All results from performance studies confirm that the device meets its acceptance criteria and is substantial equivalent to its predicate device." While specific numerical acceptance criteria are not explicitly tabulated, the performance characteristics evaluated were focused on:

• Analytical specificity for normal tissue: This implies the device should not show staining in normal tissues that do not express progesterone receptor, thus minimizing false positives.
• Reproducibility: This measures the consistency of results within a run, between different lots of reagents, across different laboratories, and among different observers.
• Concordance with the predicate device: This is a key acceptance criterion for substantial equivalence, aiming to show that the new device produces results that agree sufficiently with an already approved device.

Since the document concludes that the device meets its acceptance criteria, the reported device performance is implicitly that these aspects were successfully demonstrated.

Study Details from the Document:

1. Sample size used for the test set and the data provenance: The document mentions a "concordance study" but does not specify the sample size for the test set (number of cases or tissue sections) or the data provenance (e.g., country of origin, retrospective/prospective).
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: The document states that "The clinical interpretation of any staining or its absence... should be evaluated within the context of the patient's clinical history and other diagnostic tests by a qualified pathologist." This indicates that pathologists are involved in interpretation but does not specify the number or specific qualifications (e.g., years of experience) of experts used to establish the ground truth for the test set in the analytical or concordance studies mentioned.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not specified in the provided text.
4. 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: This is not an AI/software device. It is an immunohistochemistry (IHC) reagent used with an automated staining instrument. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and was not performed. The study evaluates the performance of the IHC reagent itself.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: This question is not directly applicable as the device is an IHC reagent and an automated staining platform, not an AI algorithm. The performance evaluation is inherently "standalone" in terms of the reagent's analytical performance, but its interpretation requires a "human-in-the-loop" (a pathologist).
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The ground truth for this type of device, aimed at detecting progesterone receptor expression, is typically established by pathological assessment of the stained tissue sections, often comparing the new device's staining patterns and intensity against a gold standard or the predicate device's results. The interpretation is done by "qualified pathologists."
7. The sample size for the training set: The document does not describe a "training set" in the context of machine learning. For an IHC reagent, "training" would refer to internal development and optimization, not a distinct dataset for algorithm training.
8. How the ground truth for the training set was established: As above, the concept of a "training set" and its ground truth in the AI/ML sense is not applicable to this device. Quality control and optimization during the development of the reagent and staining protocol would rely on expert pathological evaluation.

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