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This antibody is intended for in vitro diagnostic (IVD) use.

CONFIRM anti-Estrogen Receptor (ER) (SP1) Rabbit Monoclonal Primary Antibody is intended for laboratory use for the qualitative detection of estrogen receptor (ER) antigen in sections of formalin-fixed, paraffin-embedded breast tissue on a Ventana automated slide stainer with Ventana detection kits and ancillary reagents. CONFIRM anti-ER (SP1) is directed against an epitope present on human ER alpha protein located in the nucleus of ER positive normal and neoplastic cells. CONFIRM anti-ER (SP1) is indicated as an aid in the management, prognosis, and prediction of hormone therapy for breast carcinoma.

This product should be interpreted by a qualified pathologist in conjunction with histological examination, relevant clinical information, and proper controls.

Prescription use only.

CONFIRM anti-ER (SP1) binds to human estrogen receptor alpha (ER) in paraffin embedded tissue sections. The antibody is diluted in 0.05 M Tris-HCl with 2% carrier protein, and 0.10% ProClin 300, a preservative. There is trace (~0.2%) fetal calf serum of U.S. origin from the stock solution. Total protein concentration of the reagent is approximately 20 mg/mL. Specific antibody concentration is approximately 1 ug/mL. CONFIRM anti-ER (SP1) is a rabbit monoclonal antibody produced as a cell culture supernatant.

CONFIRM anti-ER (SP1) is optimized for use on the BenchMark XT and BenchMark ULTRA automated slides stainers using iView DAB and ultraView DAB detection chemistries.

Here's a breakdown of the acceptance criteria and the study details for the CONFIRM anti-Estrogen Receptor (SP1) Rabbit Monoclonal Primary Antibody, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Sample size used for the test set and the data provenance:

   • Method Comparison Study (BenchMark XT vs. BenchMark ULTRA): 120 ER negative and 132 ER positive breast cancer cases (total 252 cases). Data provenance is not explicitly stated in terms of country of origin but is presumed to be retrospective clinical samples. It is a prospective study in terms of evaluating the newly stained slides.
   • Equivalence to Predicate Study (CONFIRM vs. FLEX and LBA): 820 invasive breast cancer cases in the clinical cohort, from which 594 breast cancer cases with primary tumor underwent IHC staining on 1907 tissue microarray cores. The study is described as having "available from the cohort database," suggesting retrospective data for patient outcome and LBA, but the IHC staining and evaluation of the tissue microarrays were performed as part of this study. The origin is implied to be a clinical cohort, but specific country is not mentioned.

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

   • Method Comparison Study: Evaluated by "pathologists" for determining the percentage of stained tumor cells. The exact number of pathologists per case is not specified beyond "multi-reader study", nor are their qualifications.
   • Equivalence to Predicate Study: Evaluated by "three independent pathologists" who determined the percentage of stained tumor cells. Their specific qualifications (e.g., years of experience, subspecialty) are not provided.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • The document implies that pathologists independently evaluated the slides and their interpretations were used to determine ER status. There is no explicit mention of an adjudication method for discordant reads (e.g., 2+1 rule). For the "Equivalence to Predicate Study," three independent pathologists evaluated the slides, but it's not stated how disagreements were resolved for the final ER status used in the analysis.

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:

   • No. This is not a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human readers improvement with or without AI assistance. This study evaluates the performance of an immunohistochemistry (IHC) antibody (CONFIRM anti-ER (SP1)) as a diagnostic reagent, comparing it to an existing predicate device and patient outcomes. The "multi-reader" aspect refers to multiple pathologists reading the slides to generate the initial data, not to an AI-assisted workflow.

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

   • No. This device is an immunohistochemistry antibody reagent, not an algorithm or AI product. Its performance is assessed through the staining of tissue samples, which are then interpreted by human pathologists. Therefore, a "standalone algorithm performance" study is not applicable.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For ER status determination on stained slides: The immediate "ground truth" used for calculating agreement percentages (OPA) was the interpretation by pathologists based on the percentage of stained tumor cells.
   • For comparing against clinical relevance: Progression-free survival outcome data in tamoxifen-treated patients and Ligand Binding Assay (LBA) data were used as ground truth comparators for the "Equivalence to Predicate" study to assess the clinical utility and biological agreement of the IHC assays.

7. The sample size for the training set:

   • This document describes a performance study for an antibody reagent, not a machine learning model. Therefore, there is no "training set" in the context of an algorithm. The antibody itself is "optimized" for use on specific instruments, which implies internal development and testing, but not a dataset-driven training process in the AI sense.

8. How the ground truth for the training set was established:

   • As there is no training set for an AI/algorithm, this question is not applicable. The antibody's specificity and reactivity are inherent to its design and manufacturing process, and its performance is validated in clinical studies like the ones described.

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