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      Assessment of repeated reference measurements to inform the validity of optical breast spectroscopy

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          Abstract

          Mammographic breast density is a strong breast cancer risk factor, and its routine clinical measurement could potentially be used to identify women at higher risk of breast cancer and/or monitor primary prevention strategies. Previous reports of optical breast spectroscopy (OBS), a novel approach to measuring breast density, demonstrated that it is safe (no ionizing radiation), portable, low-cost, and does not require image interpretation but have been limited to small, single-center studies. Reference measurements taken on a phantom breast prior to and after each woman’s OBS assessment are required for the calibration of the system transfer function as a part of processing participant data. To inform the validity of participant data, a detailed description of the reference measurements and a repeatability analysis of these measurements taken before and after participant assessment is presented. Reference measurements for OBS from 539 women aged 18–40 years were obtained as a part of a high-throughput epidemiological pilot study. Of these, measurements from 20 women with no useable data due to device failure (3.7%) were excluded and from another 12 women due to user error. The intra-class correlation (ICC) within complete pairs of reference data (taken before and after assessment) was high (all ICC > 0.84). The analysis presented here confirms the OBS participant data as valid for use in ongoing epidemiological research, providing further supporting evidence of OBS as a measure of breast density. A novel method of measuring breast density is needed to bridge large gaps in the knowledge of breast density in younger women and its relation to later-life breast cancer risk.

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
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            Understanding Bland Altman analysis

            In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods.
In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement.
The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot.
The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals.
The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.
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              Measuring agreement in method comparison studies

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                Author and article information

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                Journal
                Review of Scientific Instruments
                Review of Scientific Instruments
                AIP Publishing
                0034-6748
                1089-7623
                April 01 2022
                April 01 2022
                : 93
                : 4
                : 044101
                Affiliations
                [1 ]Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, 35 Stirling Highway M431, Crawley, Western Australia 6009, Australia
                [2 ]University Health Network, Toronto, Ontario M5G 2M9, Canada
                [3 ]Department of Obstetrics and Gynaecology, University of Melbourne and the Royal Women’s Hospital, Melbourne, Victoria 3052, Australia
                [4 ]Surry Biophotonics, Advanced Technology Institute and School of Biosciences and Medicine, The University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
                [5 ]Optical and Biomedical Engineering Laboratory School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, Western Australia 6009, Australia
                [6 ]School of Human Sciences, The University of Western Australia, Crawley 6009, Western Australia, Australia
                [7 ]Medical School, The University of Western Australia, Crawley, Western Australia 6009, Australia
                [8 ]Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A4, Canada
                Article
                10.1063/5.0072223
                35489887
                28c03a39-9cb5-407a-be5e-7fb47490ae99
                © 2022

                https://publishing.aip.org/authors/rights-and-permissions

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