Comparison of Sampling Devices for Endocervical Curetting.

Introduction: Endocervical curetting (ECC) is mandatory when colposcopy is inadequate or when the Pap smear suggests glandular lesion. When the curette is used, ECC is painful; this necessitated the development of the endocervical brush. There is no consensus on which device yields more sample, detects true cervical precancer (CIN2+) better or highlights the effects of age and parity on ECC yield. Objective: To compare ECC yield and the ability to pick up CIN2+ by the different devices and effect of parity and age on yield. Method: Three hundred women referred for colposcopy following positive cervical high-risk HPV DNA test who had inadequate colposcopic examination were randomly allocated to curette, brush and curette and brush groups for ECC. All samples were sent for histology, and the results were compared. Result: Of the 300 women, 103, 100 and 97 had ECC with curette, brush and curette and brush, respectively. Samples were adequate in 92 (89.3%) of the curette, 69 (69.0%) of the brush and 78 (80.4%) of the curette and brush groups. The curette and curette and brush yielded more samples (p = 0.00) and (p = 0.04), respectively, compared with the brush, but there was no difference in yield between curette and curette and brush (p = 0.06). However, there was no difference in the yield of CIN2+ between the sampling devices. Age and parity had no effect on the sample adequacy by the different devices. Conclusion: Curette and the curette and brush yielded more samples compared with the brush alone. However, CIN2+ pick-up was similar across all sampling devices.

The Predictors for Non-Adherence to Colposcopy among Women in an Underserved Community in Rural Southern Western Nigeria.

Background: Cervical cancer is the fourth most common cancer in women. It is a major public health problem in developing countries. Effective cervical cancer screening requires that women adhere to the screening program. The factors that influence adherence to colposcopy in rural areas of Nigeria are unknown. The objective of the study was to determine the factors that 0determine adherence and the sexual and reproductive factors that are associated with non-adherence of women to colposcopy. Methods: This is a cross-sectional study of a project undertaken to determine the age- specific incidence of Human Papillomavirus (HPV) infection in Irun Akoko, a rural town in Ondo state of Nigeria. A total of 492 women with abnormal results from 1420 women that were screened were recalled for colposcopy examination. Results: The non-adherence rate for colposcopy in this study was 25.8%. Women younger than 40years (p=0.0011) and those with number of living children ≤2 (p=0.04) are more likely to be non-adherent to colposcopy. Conclusion: The non-adherence rate to colposcopy was high. Younger women and those with fewer children were more likely not to adhere to colposcopy.

Deep multiple-instance learning for abnormal cell detection in cervical histopathology images.

Cervical cancer is a disease of significant concern affecting women's health worldwide. Early detection of and treatment at the precancerous stage can help reduce mortality. High-grade cervical abnormalities and precancer are confirmed using microscopic analysis of cervical histopathology. However, manual analysis of cervical biopsy slides is time-consuming, needs expert pathologists, and suffers from reader variability errors. Prior work in the literature has suggested using automated image analysis algorithms for analyzing cervical histopathology images captured with the whole slide digital scanners (e.g., Aperio, Hamamatsu, etc.). However, whole-slide digital tissue scanners with good optical magnification and acceptable imaging quality are cost-prohibitive and difficult to acquire in low and middle-resource regions. Hence, the development of low-cost imaging systems and automated image analysis algorithms are of critical importance. Motivated by this, we conduct an experimental study to assess the feasibility of developing a low-cost diagnostic system with the H&E stained cervical tissue image analysis algorithm. In our imaging system, the image acquisition is performed by a smartphone affixing it on the top of a commonly available light microscope which magnifies the cervical tissues. The images are not captured in a constant optical magnification, and, unlike whole-slide scanners, our imaging system is unable to record the magnification. The images are mega-pixel images and are labeled based on the presence of abnormal cells. In our dataset, there are total 1331 (train: 846, validation: 116 test: 369) images. We formulate the classification task as a deep multiple instance learning problem and quantitatively evaluate the classification performance of four different types of multiple instance learning algorithms trained with five different architectures designed with varying instance sizes. Finally, we designed a sparse attention-based multiple instance learning framework that can produce a maximum of 84.55% classification accuracy on the test set.

Deep learning-based image evaluation for cervical precancer screening with a smartphone targeting low resource settings - Engineering approach.

Cervical cancer is the fourth most common cancer among women and still one of the major causes of women's death around the world. Early screening of high grade Cervical Intraepithelial Neoplasia (CIN), precursors to cervical cancer, is vital to efforts aimed at improving survival rate and eventually eliminating cervical cancer. Visual Inspection with Acetic acid (VIA) is an assessment method which can inspect the cervix and potentially detect lesions caused by human papillomavirus (HPV), which is a major cause of cervical cancer. VIA has the potential to be an effective screening method in low resource settings when triaged with HPV test, but it has the drawback that it depends on the subjective evaluation of health workers with varying levels of training. A new deep learning algorithm called Automated Visual Evaluation (AVE) for analyzing cervigram images has been recently reported that can automatically detect cervical precancer better than human experts. In this paper, we address the question of whether mobile phone-based cervical cancer screening is feasible. We consider the capabilities of two key components of a mobile phone platform for cervical cancer screening: (1) the core AVE algorithm and (2) an image quality algorithm. We consider both accuracy and speed in our assessment. We show that the core AVE algorithm, by refactoring to a new deep learning detection framework, can run in ~30 seconds on a low-end smartphone (i.e. Samsung J8), with equivalent accuracy. We developed an image quality algorithm that can localize the cervix and assess image quality in ~1 second on a low-end smartphone, achieving an area under the ROC curve (AUC) of 0.95. Field validation of the mobile phone platform for cervical cancer screening is in progress.