Literature DB >> 18820540

The accuracy of the Papanicolaou smear in the screening and diagnostic settings.

Marylou Cárdenas-Turanzas1, Michele Follen, Graciela M Nogueras-Gonzalez, J L Benedet, J Robert Beck, Scott B Cantor.   

Abstract

OBJECTIVE: We evaluated the performance of the Papanicolaou smear in screening and diagnostic settings. STUDY
DESIGN: We analyzed Papanicolaou smear results of 1,850 women recruited into a clinical trial to evaluate an emerging technology for the detection of cervical cancer. Screening and diagnosis groups were based on the history of previous Papanicolaou smear results. We calculated sensitivities, specificities, positive and negative likelihood ratios (LR+ and LR-), receiver operating characteristic curves, and areas under the receiver operating characteristic curve (AUC).
RESULTS: In the screening group, by defining disease as cervical intraepithelial neoplasia (CIN) 2,3/cancer or worse and using high-grade squamous intraepithelial lesion (HSIL) as the test cutpoint, the AUC was 0.689, and the LR+ and LR- were 39.25 and 0.67, respectively. In the diagnosis group, the AUC was 0.764, and the LR+ and LR- were 3.79 and 0.56, respectively. By defining disease as human papillomavirus/CIN 1 or worse and HSIL as the test cutpoint, the AUC was 0.586, and the LR+ and LR- were 17.01 and 0.92 in the screening group; in the diagnosis group, the AUC was 0.686, and the LR+ and LR- were 2.77 and 0.75, respectively.
CONCLUSIONS: In a screening setting, a Papanicolaou smear result of HSIL or worse is 39 times more likely in a patient with CIN 2,3/cancer than in a patient without it. This compares to 4 times more likely in the diagnostic setting. The magnitude of the positive likelihood ratio observed in the screening group indicated that abnormal Papanicolaou smear results obtained in the screening setting should have more impact on clinical decision making than those from results obtained in the diagnostic setting.

Entities:  

Mesh:

Year:  2008        PMID: 18820540     DOI: 10.1097/LGT.0b013e31816b44bc

Source DB:  PubMed          Journal:  J Low Genit Tract Dis        ISSN: 1089-2591            Impact factor:   1.925


  7 in total

Review 1.  Clinical application of DNA ploidy to cervical cancer screening: A review.

Authors:  David Garner
Journal:  World J Clin Oncol       Date:  2014-12-10

2.  Optical technologies and molecular imaging for cervical neoplasia: a program project update.

Authors:  Timon P H Buys; Scott B Cantor; Martial Guillaud; Karen Adler-Storthz; Dennis D Cox; Clement Okolo; Oyedunni Arulogon; Oladimeji Oladepo; Karen Basen-Engquist; Eileen Shinn; José-Miguel Yamal; J Robert Beck; Michael E Scheurer; Dirk van Niekerk; Anais Malpica; Jasenka Matisic; Gregg Staerkel; Edward Neely Atkinson; Luc Bidaut; Pierre Lane; J Lou Benedet; Dianne Miller; Tom Ehlen; Roderick Price; Isaac F Adewole; Calum MacAulay; Michele Follen
Journal:  Gend Med       Date:  2011-09-22

3.  Differentially expressed proteins among normal cervix, cervical intraepithelial neoplasia and cervical squamous cell carcinoma.

Authors:  Q Zhao; Y He; X-L Wang; Y-X Zhang; Y-M Wu
Journal:  Clin Transl Oncol       Date:  2015-04-18       Impact factor: 3.405

4.  Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia: Testing a device as an adjunct to colposcopy.

Authors:  Scott B Cantor; Jose-Miguel Yamal; Martial Guillaud; Dennis D Cox; E Neely Atkinson; John L Benedet; Dianne Miller; Thomas Ehlen; Jasenka Matisic; Dirk van Niekerk; Monique Bertrand; Andrea Milbourne; Helen Rhodes; Anais Malpica; Gregg Staerkel; Shahla Nader-Eftekhari; Karen Adler-Storthz; Michael E Scheurer; Karen Basen-Engquist; Eileen Shinn; Loyd A West; Anne-Therese Vlastos; Xia Tao; J Robert Beck; Calum Macaulay; Michele Follen
Journal:  Int J Cancer       Date:  2010-11-09       Impact factor: 7.396

5.  Prediction of a miRNA-mRNA functional synergistic network for cervical squamous cell carcinoma.

Authors:  Dan Sun; Lu Han; Rui Cao; Huali Wang; Jiyong Jiang; Yanjie Deng; Xiaohui Yu
Journal:  FEBS Open Bio       Date:  2019-11-17       Impact factor: 2.693

6.  Development of a machine learning model to predict mild cognitive impairment using natural language processing in the absence of screening.

Authors:  Robert B Penfold; David S Carrell; David J Cronkite; Chester Pabiniak; Tammy Dodd; Ashley Mh Glass; Eric Johnson; Ella Thompson; H Michael Arrighi; Paul E Stang
Journal:  BMC Med Inform Decis Mak       Date:  2022-05-12       Impact factor: 3.298

7.  Modelling risk assessment for cervical cancer in symptomatic Saudi women.

Authors:  Wedad Al-Madani; Anwar E Ahmed; Haitham Arabi; Sumaiah Al Khodairy; Nashmia Al Mutairi; Abdul Rahman Jazieh
Journal:  Saudi Med J       Date:  2019-05       Impact factor: 1.484

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.