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  • br Conclusions br In conclusion the

    2020-08-06


    5. Conclusions
    In conclusion, the screening barrier most often identified by women was a lack of time (practical barrier) and the screening facilitator most often listed was the low cost of the test. In logistic regression analyses, the number of psychological barriers was related to women's screening status (i.e., overdue vs. up-to-date), whereas the numbers of practical and psychological screening barriers were related to prior screening (i.e., screened vs. never screened), along with the number of practical facilitators. Several individual screening barriers (e.g., embarrassment, lack of time) and screening facilitators (e.g., GP status, low cost of test) were found to be related to women's screening status and prior screening. Taken together, the results suggest that 3X FLAG Peptide psychological and practical screening barriers and practical facilitators may represent the best targets for future intervention to optimize women's cervical cancer screening attendance.
    Declarations of interest
    None.
    References
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    Australian Bureau of Statistics [ABS, 2017. ABS Cat. No. 4839.0 - Patient Experiences in Australia: Summary of Findings, 2016-17. AIHW, Canberra Retrieved on. http:// www.abs.gov.au/ausstats/[email protected]/mf/4839.0, Accessed date: 15 March 2018. Australian Department of Health, 2018. National Cervical Screening Program. For health providers. Retrieved from www.health.gov.au/internet/screening/publishing.nsf/
    Australian Institute of Health & Welfare, 2017. Cervical Screening in Australia 2014-2015. Cancer Screening Series. AIHW, Canberra. https://www.aihw.gov.au/reports/ cancer-screening/cervical-screening-in-australia-2014-2015/contents/table-of-contents.
    accuracy of Pap smear utilization self-report: a methodological consideration in cervical screening research. Health Serv. Res. 26 (1), 97–107.
    Chorley, A.J., Marlow, L.A.V., Forster, A.S., Haddrell, J.B., Waller, J., 2016. Experiences of cervical screening and barriers to participation in the context of an organised programme: a systematic review and thematic synthesis. Psycho Oncol. https://doi. org/10.1002/pon.4126.
    IBM, 2015. IBM SPSS Statistics for Windows, Version 23.0. IBM Corp, New York. International Agency for Research on Cancer, 2014. GLOBOCAN 2012: Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012. IARC, Lyon, France. http:// globocan.iarc.fr/Default.aspx.
    Tabachnick, B.G., Fidell, L.S., 2007. Using Multivariate Statistics, fifth ed. Allyn and Bacon, New York.
    World Health Organization, 2014. Comprehensive Cervical Cancer Control: a Guide to Essential Practice, second ed. Retrieved from. http://www.who.int/ reproductivehealth/publications/cancers/cervical-cancer-guide/en/. Yabroff, K.R., Mangan, P., Mandelblatt, J., 2003. Effectiveness of interventions to increase Papanicolaou smear use. J. Am. Board Fam. Pract. 16 (3), 188–203. https://doi.org/ 10.3122/jabfm.16.3.188.
    402 Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx
    Contents lists available at ScienceDirect
    Journal of King Saud University –
    Computer and Information Sciences
    Autocorrection of lung boundary on 3D CT lung cancer imagesq
    R. Nurfauzi, H.A. Nugroho ⇑, I. Ardiyanto, E.L. Frannita Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Indonesia
    Article history:
    Keywords:
    CADe system
    Lung
    Segmentation
    Boundary correction 
    Lung cancer in men has the highest mortality rate among all types of cancer. Juxta-pleural and juxta-vascular are the most common nodules located on the lung surface. A computer aided detection (CADe) system is effective for assisting radiologists in diagnosing lung nodules. However, the lung seg-mentation step requires sophisticated methods when juxta-pleural and juxta-vascular nodules are pre-sent. Fast computational time and low error in covering nodule areas are the aims of this study. The proposed method consists of five stages, namely ground truth (GT) extraction, data preparation, tracheal extraction, separation of lung fusion and lung border correction. The used data consist of 57 3D CT lung cancer images taken from selected LIDC-IDRI dataset. These nodules are determined as the outer areas labeled by four radiologists. The proposed method achieves the fastest computational time of 0.32 s per slice or 60 times faster than that of conventional adaptive border marching (ABM). Moreover, zone of intolerance pro-duces under segmentation of nodule value as low as 14.6%. It indicates that the proposed method has a potential to be embedded in the lung CADe system to cover pleural juxta and vascular nodule areas in lung segmentation.