Comparison of the Accuracy of Papanicolaou Test Cytology, Visual Inspection With Acetic Acid, and Visual Inspection With Lugol Iodine in Screening for Cervical Neoplasia in Southeast Nigeria

Egede, John Okafor; Ajah, Leonard Ogbonna; Ibekwe, Perpetus; Agwu, UM; Nwizu, Emmanuel; Iyare, FE

doi:10.1200/jgo.17.00127

Cited by 13 publications

(19 citation statements)

References 18 publications

(27 reference statements)

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“…However the absence of randomization in the selection of the study participants in this study may have introduced bias thereby weakening the study. Even though the accuracy of Pap smear cytology is high in this environment [21,37], there can still be errors thereby weakening the validity of the results. Therefore HPV testing, colposcopy and biopsy would have increased the degree of accuracy of the results.…”

Section: Discussionmentioning

confidence: 99%

“…Pap smear cytology screening facilities are available in both urban and rural areas in Nigeria. Though Pap smear cytology is associated with errors, a previous report in South-East Nigeria showed an overall accuracy of Pap smear cytology screening at between 90 and 97% [20,21]. Therefore Pap smear cytology screening will be useful for population studies in this subject matter in Nigeria.…”

Section: Introductionmentioning

confidence: 98%

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Association between obesity and abnormal Papanicolau(Pap) smear cytology results in a resource-poor Nigerian setting

et al. 2020

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Background Though obesity is associated with some malignancies, its association with cervical cancer is still inconclusive. This study was aimed at determining if there was an association between obesity and cervical epithelial cell abnormalities (CEA). Methods This was a cross-sectional comparative study of obese and non-obese women at the Cervical Cancer Screening Clinic, University of Nigeria Teaching Hospital (UNTH), Enugu between January, 2012 and June, 2013. The participants whose body mass index (BMI) were ≥ 30 kg/m2 were classified as obese (200 women) while those whose BMI were < 30 kg/m2 were classified as non-obese (200 women) and the two groups were consecutively recruited at the ratio of 1:1. Pap smear cytology, random blood sugar (RBS) and human immune-deficiency virus (HIV) screening was done for all the participants. Data was analyzed with SPSS version 20. Categorical variables were analyzed using McNemar’s test and Chi-squared test. Logistic regression analysis was used to determine the influence of socio-demographic characteristics on cervical epithelial cell abnormalities. The level of significance was set at ≤0.05. Results Among the obese women, 152(76%) had negative for intra-epithelial lesion or malignancy (NILM) while 48(24%) had cervical epithelial cell abnormalities (CEA). Also 182(91%) non-obese women had NILM while the remaining 18(9%) had CEA. The prevalence of CEA among all the study participants was 16.5%. There was an association between obesity and CEA[OR (95%CI) = 1.353(1.013–1.812); P-value = 0.04].CEA were significantly more common among women who were 40 years and above and single/separated women as well as widows (P-value = < 0.05). Conclusion There was an association between obesity and CEA. This underscores the need for a positive behavioural change among women in order to stem the tide of this public health problem.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Association between obesity and abnormal Papanicolau(Pap) smear cytology results in a resource-poor Nigerian setting

et al. 2020

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“…About 50.33 million Nigerian women are at risk of cervical cancer and the crude incidence rate per 100,000 population is 17.1 while the age standardized incidence per 100,000 population is 29.0 (WHO, 2014). GLOBOCAN 2018 estimate showed 14,943 new cases and 10,403 deaths annually and a woman's lifetime risk of developing and dying as a result of invasive cervical cancer in Nigeria are 2.1% and 1.7%, respectively (GLOBOCAN, 2018;Egede et al, 2018;Garg, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Regular, systematic and organized mass screenings for cervical cancer targeted at all women at risk, as being implemented by developed countries, have resulted in over 80% reduction of morbidity and mortality associated with cervical cancer (Egede et al, 2018;Okunowo et al, 2018;Peirson et al, 2013). Approximately 5% of eligible women in the developing countries undergo screening for cervical cancer (Egede et al, 2018). Therefore majority of those who develop and die of cervical cancer have never been screened.…”

Section: Introductionmentioning

confidence: 99%

Awareness and Attitude of Women towards Cervical Cancer Screening in Abakaliki, South East Nigeria

Egede¹,

Ajah²,

Agwu³

et al. 2021

GJHS

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INTRODUCTION: Cervical cancer screening has significantly contributed to the detection of premalignant and malignant lesions of the cervix and prevention of the morbidity and mortality associated with the disease. In the developing countries, especially sub-Saharan Africa where the burden of cervical cancer is highest, the high-risk population may not know the screening schedules or be able to pay for the services, and so fail to benefit. OBJECTIVE: To determine the level of awareness of cervical cancer screening schedule and willingness to pay for cervical screening services among women in Abakaliki, southeast Nigeria. METHOD: The study design was a descriptive cross-sectional questionnaire-based and the population comprised 800 participants who came for free cervical cancer screening at well women centre, Alex Ekwueme Federal University Teaching Hospital, Abakaliki between January and December 2017. Data were analyzed using the Statistical Package for Sciences version 20.0. RESULT: Of the 756 (94.5%) questionnaires analyzed, the mean age was 41.4 years, modal parity 4; 83.6% had prior knowledge of cervical cancer while 81.0% knew that cervical cancer screening is a diagnostic tool. Surprisingly, only 32% of those aware of cervical cancer had previously done cervical cancer screening, while 10.8% knew the interval for cervical cancer screening. On screening for cervical cancer in future, 89.2% of the respondents were willing to repeat the test while 54.2% would be willing to pay for the screening services. Being 40 years of age or less, married, educated, of high socio-economic class and having first sexual intercourse at 18 years or less were associated with willingness to pay for screening. CONCLUSION: Although the knowledge of cervical cancer is high, cervical cancer screening uptake is low, many women are aware of the interval for cervical cancer screening, and many will not be willing to pay for cervical cancer screening out of their pockets. 

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“…However, not all AW lesions are indicative of cervical intraepithelial neoplasia (CIN), and not all CIN turns white with acetic acid application. This results in relatively poor quality assessments using VIA, where the typical sensitivity and specificity of VIA are in the range from ~70–80% as reported in [ 2 , 3 , 4 ]; but, high subjectivity has also been reported in [ 5 ] with only a 56.8% complete decision agreement rate among 20 gynecologists over 919 cases. A breakthrough deep learning method, called Automated Visual Evaluation (AVE) [ 6 ], was shown to successfully classify digitized cervix outperforming human experts on a test dataset especially for a key age group in women.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening

et al. 2020

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Automated Visual Examination (AVE) is a deep learning algorithm that aims to improve the effectiveness of cervical precancer screening, particularly in low- and medium-resource regions. It was trained on data from a large longitudinal study conducted by the National Cancer Institute (NCI) and has been shown to accurately identify cervices with early stages of cervical neoplasia for clinical evaluation and treatment. The algorithm processes images of the uterine cervix taken with a digital camera and alerts the user if the woman is a candidate for further evaluation. This requires that the algorithm be presented with images of the cervix, which is the object of interest, of acceptable quality, i.e., in sharp focus, with good illumination, without shadows or other occlusions, and showing the entire squamo-columnar transformation zone. Our prior work has addressed some of these constraints to help discard images that do not meet these criteria. In this work, we present a novel algorithm that determines that the image contains the cervix to a sufficient extent. Non-cervix or other inadequate images could lead to suboptimal or wrong results. Manual removal of such images is labor intensive and time-consuming, particularly in working with large retrospective collections acquired with inadequate quality control. In this work, we present a novel ensemble deep learning method to identify cervix images and non-cervix images in a smartphone-acquired cervical image dataset. The ensemble method combined the assessment of three deep learning architectures, RetinaNet, Deep SVDD, and a customized CNN (Convolutional Neural Network), each using a different strategy to arrive at its decision, i.e., object detection, one-class classification, and binary classification. We examined the performance of each individual architecture and an ensemble of all three architectures. An average accuracy and F-1 score of 91.6% and 0.890, respectively, were achieved on a separate test dataset consisting of more than 30,000 smartphone-captured images.

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Comparison of the Accuracy of Papanicolaou Test Cytology, Visual Inspection With Acetic Acid, and Visual Inspection With Lugol Iodine in Screening for Cervical Neoplasia in Southeast Nigeria

Cited by 13 publications

References 18 publications

Association between obesity and abnormal Papanicolau(Pap) smear cytology results in a resource-poor Nigerian setting

Association between obesity and abnormal Papanicolau(Pap) smear cytology results in a resource-poor Nigerian setting

Awareness and Attitude of Women towards Cervical Cancer Screening in Abakaliki, South East Nigeria

Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening

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