A Comparison of the Acceptance of Female Subjects Between Mammography, Automated Breast Ultrasound and Hand-Held Ultrasound
To investigate the acceptance of automated breast ultrasound (ABUS), mammography (MAM) and handheld ultrasound (HHUS) among Chinese female subjects, and explore how the breast screening experience can be improved. The satisfaction and pain score of 652 female subjects who performed
ABUS, MAM and HHUS examinations were collected via questionnaires. The pain score difference between the screening methods was determined by the Kruskal-Wallis test. Spearman correlation analyses were also performed to assess the association between pain scores across the screening methods
and subject characteristics. Furthermore, we compared the diagnostic consistency of ABUS with between MAM and HHUS. Amongst the three breast screening methods investigated, MAM was rated with the highest pain score, significantly greater than ABUS and HHUS. 92.9% and 94.7% of subjects preferred
ABUS and HHUS examination, respectively, while only 41.0% preferred MAM. MAM pain scores were negatively correlated to age, bra size, and menopausal status, and also positively correlated to the first breastfeeding age and breast density. ABUS pain scores were negatively correlated to BMI
and bra size, and also positively correlated with the cumulative lactation time. Finally, HHUS pain scores were found to be negatively correlated with menarche age, lactation times and the cumulative lactation time, and positively correlated to abortion times and menopausal status. The Cohen's
kappa coefficient revealed significant agreement between ABUS and MAM, and between ABUS and HHUS. The results show that women have a better acceptance of ABUS than MAM.
Keywords: ACCEPTANCE; AUTOMATED BREAST ULTRASOUND SYSTEM; BREAST CANCER; HAND-HELD ULTRASOUND; MAMMOGRAPHY
Document Type: Research Article
Publication date: 01 May 2019
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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