Introduction of the Thin Prep Imaging System (TIS): experience in a high volume academic practice

CervixFormer: A Multi-scale swin transformer-Based cervical pap-Smear WSI classification framework

BACKGROUND AND OBJECTIVES

Cervical cancer affects around 0.5 million women per year, resulting in over 0.3 million fatalities. Therefore, repetitive screening for cervical cancer is of utmost importance. Computer-assisted diagnosis is key for scaling up cervical cancer screening. Current recognition algorithms, however, perform poorly on the whole-slide image (WSI) analysis, fail to generalize for different staining methods and on uneven distribution for subtype imaging, and provide sub-optimal clinical-level interpretations. Herein, we developed CervixFormer-an end-to-end, multi-scale swin transformer-based adversarial ensemble learning framework to assess pre-cancerous and cancer-specific cervical malignant lesions on WSIs.

METHODS

The proposed framework consists of (1) a self-attention generative adversarial network (SAGAN) for generating synthetic images during patch-level training to address the class imbalanced problems; (2) a multi-scale transformer-based ensemble learning method for cell identification at various stages, including atypical squamous cells (ASC) and atypical squamous cells of undetermined significance (ASCUS), which have not been demonstrated in previous studies; and (3) a fusion model for concatenating ensemble-based results and producing final outcomes.

RESULTS

In the evaluation, the proposed method is first evaluated on a private dataset of 717 annotated samples from six classes, obtaining a high recall and precision of 0.940 and 0.934, respectively, in roughly 1.2 minutes. To further examine the generalizability of CervixFormer, we evaluated it on four independent, publicly available datasets, namely, the CRIC cervix, Mendeley LBC, SIPaKMeD Pap Smear, and Cervix93 Extended Depth of Field image datasets. CervixFormer obtained a fairly better performance on two-, three-, four-, and six-class classification of smear- and cell-level datasets. For clinical interpretation, we used GradCAM to visualize a coarse localization map, highlighting important regions in the WSI. Notably, CervixFormer extracts feature mostly from the cell nucleus and partially from the cytoplasm.

CONCLUSIONS

In comparison with the existing state-of-the-art benchmark methods, the CervixFormer outperforms them in terms of recall, accuracy, and computing time.

Beyond the Microscope: A Technological Overture for Cervical Cancer Detection

Cervical cancer is a common and preventable disease that poses a significant threat to women's health and well-being. It is the fourth most prevalent cancer among women worldwide, with approximately 604,000 new cases and 342,000 deaths in 2020, according to the World Health Organization. Early detection and diagnosis of cervical cancer are crucial for reducing mortality and morbidity rates. The Papanicolaou smear test is a widely used screening method that involves the examination of cervical cells under a microscope to identify any abnormalities. However, this method is time-consuming, labor-intensive, subjective, and prone to human errors. Artificial intelligence techniques have emerged as a promising alternative to improve the accuracy and efficiency of Papanicolaou smear diagnosis. Artificial intelligence techniques can automatically analyze Papanicolaou smear images and classify them into normal or abnormal categories, as well as detect the severity and type of lesions. This paper provides a comprehensive review of the recent advances in artificial intelligence diagnostics of the Papanicolaou smear, focusing on the methods, datasets, performance metrics, and challenges. The paper also discusses the potential applications and future directions of artificial intelligence diagnostics of the Papanicolaou smear.

Diagnostic utility of ThinPrep Imaging System® for detecting atypical glandular cells in cervical smear samples

INTRODUCTION

The ThinPrep Imaging System® (TIS) is an automated system that has now been used for over 20 years in the primary screening of ThinPrep liquid-based cervical samples. Although there are a lot of publications about the diagnostic utility of this method in squamous cell lesions, which has advantages such as time-saving and standardization, there are only a few publications on this issue in glandular cell lesions in the literature. We aimed in this study to investigate the diagnostic utility of the system in the detection of premalignant and malignant glandular lesions in cervical smears.

MATERIAL AND METHOD

Our study was conducted retrospectively, and a total of 126 cervical smear samples between 2010 and 2022 that have histological confirmation of endometrial adenocarcinoma (EAC), endocervical adenocarcinoma (ECAC), or adenocarcinoma in situ (AİS), were included. These samples were re-evaluated by manual and TIS by two experienced pathologists, and the results were compared in terms of sensitivity.

RESULTS

We found out that 70 of the 126 smear samples have atypical glandular cells. We detect 48 cases (48/70) (sensitivity 68.5%) in manual examination, however TIS successfully determined 66 cases (66/70) (sensitivity 94.3%). In 4 cases (5.7%) TIS could not detect the atypical cells within the 22 areas.

CONCLUSION

TIS is quite an effective method with a high sensitivity for detecting atypical glandular cells in cervical smears, like detecting squamous cell anomalies. Imposing this system in our laboratory and using them appropriately, save us time and help to ensure standardization. Additionally, it may be a good way to adopt artificial intelligence and digital pathology in today's world.

Deep Feature Engineering in Colposcopy Image Recognition: A Comparative Study

Feature fusion techniques have been proposed and tested for many medical applications to improve diagnostic and classification problems. Specifically, cervical cancer classification can be improved by using such techniques. Feature fusion combines information from different datasets into a single dataset. This dataset contains superior discriminant power that can improve classification accuracy. In this paper, we conduct comparisons among six selected feature fusion techniques to provide the best possible classification accuracy of cervical cancer. The considered techniques are canonical correlation analysis, discriminant correlation analysis, least absolute shrinkage and selection operator, independent component analysis, principal component analysis, and concatenation. We generate ten feature datasets that come from the transfer learning of the most popular pre-trained deep learning models: Alex net, Resnet 18, Resnet 50, Resnet 10, Mobilenet, Shufflenet, Xception, Nasnet, Darknet 19, and VGG Net 16. The main contribution of this paper is to combine these models and then apply them to the six feature fusion techniques to discriminate various classes of cervical cancer. The obtained results are then fed into a support vector machine model to classify four cervical cancer classes (i.e., Negative, HISL, LSIL, and SCC). It has been found that the considered six techniques demand relatively comparable computational complexity when they are run on the same machine. However, the canonical correlation analysis has provided the best performance in classification accuracy among the six considered techniques, at 99.7%. The second-best methods were the independent component analysis, least absolute shrinkage and the selection operator, which were found to have a 98.3% accuracy. On the other hand, the worst-performing technique was the principal component analysis technique, which offered 90% accuracy. Our developed approach of analysis can be applied to other medical diagnosis classification problems, which may demand the reduction of feature dimensions as well as a further enhancement of classification performance.

ThinPrep® imaging system-assisted vs manual screening of urinary cytology slides in the detection of the "suspicious for high-grade urothelial carcinoma" category

BACKGROUND

The ThinPrep® Imaging System (TIS) is a Food and Drug Administration-approved review system for cervical cytopathology, where it has been shown to increase performance over manually reviewed slides. Application of the TIS to urinary cytology has only been reported in a single study, in 2013.

METHODS

We aimed to compare the agreement of two cytotechnologists' and a pathologist's manual screening (dots) with the fields of view (FOVs) selected by the TIS. We also aimed to track cases in which the TIS could identify missed abnormals and reduce the false-negative fraction. Electronically marked TIS fields (EMTFs) suspicious for high-grade urothelial carcinoma (SHGUC) were controlled by follow-up cystoscopy and histology, where available.

RESULTS

A total of 826 consecutive specimens were studied. Of those, 94 (11.4%) were unreadable by the TIS. There were 710 possible comparisons, of which 380 (53.5%) received no dot after manual screening. Of the 330 remaining slides, 149 (45.1%) had at least one dot matching with the TIS FOVs. After TIS reading, EMTFs were noted in 13 of 636 (2.0%) negative cytology cases. Surveillance showed that 3/13 (23.1%, 0.4% of the 710 possible comparisons) of those cases matched with high grade urothelial carcinoma (HGUC), whereas 6/13 (46.1%, 0.8% of the 710 possible comparisons) had negative follow-up at 24 months, and 4/13 (30.8%) were lost for follow-up.

CONCLUSION

The TIS increases the detection rate of SHGUC cells, potentially leading to a slight decrease in the false-negative fraction, but at the expense of a slight but larger increase in the number of false-positive cases. These findings stress the importance of a careful approach to the evaluation of the FOVs.

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning

Every year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical lesions greatly boosts up the chance of successful treatments of patients, and automated diagnosis and classification of cervical lesions from Papanicolaou (Pap) smear images have become highly demanded. To the authors' best knowledge, this is the first study of fully automated cervical lesions analysis on whole slide images (WSIs) of conventional Pap smear samples. The presented deep learning-based cervical lesions diagnosis system is demonstrated to be able to detect high grade squamous intraepithelial lesions (HSILs) or higher (squamous cell carcinoma; SQCC), which usually immediately indicate patients must be referred to colposcopy, but also to rapidly process WSIs in seconds for practical clinical usage. We evaluate this framework at scale on a dataset of 143 whole slide images, and the proposed method achieves a high precision 0.93, recall 0.90, F-measure 0.88, and Jaccard index 0.84, showing that the proposed system is capable of segmenting HSILs or higher (SQCC) with high precision and reaches sensitivity comparable to the referenced standard produced by pathologists. Based on Fisher's Least Significant Difference (LSD) test (P < 0.0001), the proposed method performs significantly better than the two state-of-the-art benchmark methods (U-Net and SegNet) in precision, F-Measure, Jaccard index. For the run time analysis, the proposed method takes only 210 seconds to process a WSI and is 20 times faster than U-Net and 19 times faster than SegNet, respectively. In summary, the proposed method is demonstrated to be able to both detect HSILs or higher (SQCC), which indicate patients for further treatments, including colposcopy and surgery to remove the lesion, and rapidly processing WSIs in seconds for practical clinical usages.

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.

Cervical cancer: Automation of Pap test screening

BACKGROUND

Cervical cancer progresses slowly, increasing the chance of early detection of pre-neoplastic lesions via Pap exam test and subsequently preventing deaths. However, the exam presents both false-negatives and false-positives results. Therefore, automatic methods (AMs) of reading the Pap test have been used to improve the quality control of the exam. We performed a literature review to evaluate the feasibility of implementing AMs in laboratories.

METHODS

This work reviewed scientific publications regarding automated cytology from the last 15 years. The terms used were "Papanicolaou test" and "Automated cytology screening" in Portuguese, English, and Spanish, in the three scientific databases (SCIELO, PUBMED, MEDLINE).

RESULTS

Of the resulting 787 articles, 34 were selected for a complete review, including three AMs: ThinPrep Imaging System, FocalPoint GS Imaging System and CytoProcessor. In total, 1 317 148 cytopathological slides were evaluated automatically, with 1 308 028 (99.3%) liquid-based cytology slides and 9120 (0.7%) conventional cytology smears. The AM diagnostic performances were statistically equal to or better than those of the manual method. AM use increased the detection of cellular abnormalities and reduced false-negatives. The average sample rejection rate was ≤3.5%.

CONCLUSION

AMs are relevant in quality control during the analytical phase of cervical cancer screening. This technology eliminates slide-handling steps and reduces the sample space, allowing professionals to focus on diagnostic interpretation while maintaining high-level care, which can reduce false-negatives. Further studies with conventional cytology are needed. The use of AM is still not so widespread in cytopathology laboratories.

Computational Cytology: Lessons Learned from Pap Test Computer-Assisted Screening

BACKGROUND

In the face of rapid technological advances in computational cytology including artificial intelligence (AI), optimization of its application to clinical practice would benefit from reflection on the lessons learned from the decades-long journey in the development of computer-assisted Pap test screening.

SUMMARY

The initial driving force for automated screening in cytology was the overwhelming number of Pap tests requiring manual screening, leading to workflow backlogs and incorrect diagnoses. Several companies invested resources to address these concerns utilizing different specimen processing techniques and imaging systems. However, not all companies were commercially prosperous. Successful implementation of this new technology required viable use cases, improved clinical outcomes, and an acceptable means of integration into the daily workflow of cytopathology laboratories. Several factors including supply and demand, Food and Drug Administration (FDA) oversight, reimbursement, overcoming learning curves and workflow changes associated with the adoption of new technology, and cytologist apprehension, played a significant role in either promoting or preventing the widespread adoption of automated screening technologies. Key Messages: Any change in health care, particularly those involving new technology that impacts clinical workflow, is bound to have its successes and failures. However, perseverance through learning curves, optimizing workflow processes, improvements in diagnostic accuracy, and regulatory and financial approval can facilitate widespread adoption of these technologies. Given their history with successfully implementing automated Pap test screening, cytologists are uniquely positioned to not only help with the development of AI technology for other areas of pathology, but also to guide how they are utilized, regulated, and managed.

Comparison of ThinPrep Integrated Imager-Assisted Screening versus Manual Screening of ThinPrep Liquid-Based Cytology Specimens

OBJECTIVE

The aim of this study was to find out whether ThinPrep Integrated Imager (Hologic Inc.) screening is non-inferior to manual screening in the detection of cervical lesion.

STUDY DESIGN

For a total of 4,011 ThinPrep Pap test specimens stained by ThinPrep staining, manual screening (Manual arm) and ThinPrep Integrated Imager screening (Imager arm) were performed so as not to be screened by the same cytotechnologist, and the sensitivity and specificity in the detection of cervical lesion were compared using McNemar's test.

RESULTS

The sensitivity to detect CIN1 or more squamous cell abnormalities or glandular abnormalities was 91.67% (= 374/408, 95% confidence interval [CI]: 88.44-94.08%) for the Manual arm and 92.40% (= 377/408, 95% CI: 89.28-94.70%) for the Imager arm, and the specificity was 88.87% (= 3,113/3,503, 95% CI: 87.77-89.88%) for the Manual arm and 89.55% (= 3,137/3,503, 95% CI: 88.48-90.54%) for the Imager arm. The differences in sensitivity and in specificity, respectively, were 0.74% (95% CI: -3.14-4.61%, McNemar's test, p = 0.8041) and 0.69% (95% CI: -0.13-1.50%, McNemar's test, p = 0.1125). About the equality of sensitivity and specificity between the 2 methods, 95% CIs of the difference between sensitivity and specificity are in the clinical equivalence range of ±5%, so the Imager arm is non-inferior to the Manual arm.

CONCLUSION

The Imager arm was confirmed to have an equivalent and non-inferior capacity in the detection of cervical lesions compared with the Manual arm, suggesting that its practical application in cervical cytology tests is highly possible.

Prevention of Cervical Cancer: Guideline of the DGGG and the DKG (S3 Level, AWMF Register Number 015/027OL, December 2017) - Part 1 with Introduction, Screening and the Pathology of Cervical Dysplasia

Aims Annual opportunistic screening for cervical carcinoma has been carried out in Germany since 1971. The creation of this S3 guideline meets an important need, outlined in the National Cancer Plan, with regard to screening for cervical cancer, as the guideline aims to provide important information and support for planned organized screening for cervical cancer in Germany. Methods With the financial support of German Cancer Aid, 21 professional societies developed evidence-based statements and recommendations (classified using the GRADE system) for the screening, management and treatment of precancerous conditions of the cervix. Two independent scientific institutes compiled systematic reviews for this guideline. Recommendations The first part of this short summary presents the pathological basis and considers various questions related to screening for cervical cancer. As also reported in earlier reviews, the meta-analysis by Kleijnen Systematic Reviews showed that HPV-based screening offers better protection against invasive cervical cancer compared to cytology-based screening. The authors of this guideline therefore recommend - in accordance with the guideline of the Joint National Committee of Germany (Gemeinsamer Bundesauschuss, G-BA) - that women aged 35 and above should be examined at regular intervals (at least every 3 years) and undergo HPV-based screening. Co-testing can also be carried out. Women between the ages of 20 and 35 should have cytological screening every 2 years.

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

BACKGROUND

Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.

METHOD

Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.

RESULTS

The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.

CONCLUSIONS

The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology

Background

Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms.

Aims

This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses.

Methods

Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis.

Results

Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses.

Conclusions

Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.

Updates in Cervical Cytology: The 90-Year-Long Journey from Battle Creek to Today

Ninety years ago, at the Battle Creek conference, Papanicolaou introduced cervical exfoliative cytology. Since then, the "Pap test" has come a long way. The discovery of a causal relationship between cervical carcinoma and HPV infection opened the door for molecular testing and immunomarkers for HPV. The Clinical Laboratory Improvement Amendments, 1988, established quality assurance and quality control programs to monitor performance of cytology laboratories. The Bethesda System for reporting cervical cytology laid the foundations for cervical cytology education, implementation of management guidelines, and further research on cervical carcinogenesis. HPV vaccine penetration in both genders remains 62% or less.

A high throughput approach for analysis of cell nuclear deformability at single cell level

Various physiological and pathological processes, such as cell differentiation, migration, attachment, and metastasis are highly dependent on nuclear elasticity. Nuclear morphology directly reflects the elasticity of the nucleus. We propose that quantification of changes in nuclear morphology on surfaces with defined topography will enable us to assess nuclear elasticity and deformability. Here, we used soft lithography techniques to produce 3 dimensional (3-D) cell culture substrates decorated with micron sized pillar structures of variable aspect ratios and dimensions to induce changes in cellular and nuclear morphology. We developed a high content image analysis algorithm to quantify changes in nuclear morphology at the single-cell level in response to physical cues from the 3-D culture substrate. We present that nuclear stiffness can be used as a physical parameter to evaluate cancer cells based on their lineage and in comparison to non-cancerous cells originating from the same tissue type. This methodology can be exploited for systematic study of mechanical characteristics of large cell populations complementing conventional tools such as atomic force microscopy and nanoindentation.

Comparative analysis of cervical cytology screening methods and staining protocols for detection rate and accurate interpretation of ASC-H: Data from a high-volume laboratory in Turkey

BACKGROUND

This study evaluated the effectiveness of the ThinPrep(®) Imaging System (TIS) and ThinPrep(®) Pap Stain (TPPS). A comparative analysis was conducted to determine the detection rates of atypical squamous cells, cannot exclude high-grade squamous intraepithelial lesion (ASC-H), the ASC:squamous intraepithelial lesion (SIL) ratio, biopsy follow-up for ASC-H in terms of the screening method used (manual screening [MS] vs. TIS screening [TISS]) and the staining protocol (regular Pap stain [RPS] vs. TPPS).

METHODS

This study was performed over two periods. The RPS period included manually screened slides, whereas the TPPS period included TIS + manually screened slides. All data from the study periods were compared using statistical analysis.

RESULTS

The detection rate of ASC-H was significantly higher during the TPPS period than during the RPS period (0.49% vs. 0.23%); this finding is in contrast to the insignificant difference between the screening method periods. The positive predictive value (PPV) of ASC-H cytodiagnosis for cervical intraepithelial neoplasia of grade 2 or more severe histologies was significantly different between manually screened and TIS slides (22.10% vs. 38.55%), in contrast to an insignificant difference between RPS and TPPS periods (37.14% vs. 29.77%).

CONCLUSION

Implementation of the TIS did not change the ASC-H detection rates appreciably. However, the new technology improved PPV for ASC-H cytodiagnosis and enabled the detection of true disease. Our laboratory statistics indicate that the TPPS is not a superior staining protocol and did not increase our diagnostic accuracy for ASC-H compared with RPS.

A comparison of ThinPrep Imager-assisted with manual screening, and its place in the New Zealand cervical cancer screening program

AIMS

Overseas studies have shown equivalent sensitivity and specificity between computer-assisted screening and manual screening, with increased screener productivity. This study was undertaken to test these in a New Zealand laboratory setting.

METHODS

A total of 9232 slides were read manually alone, and following ThinPrep Imager-assisted screening, and the results compared. Two senior screeners and a cytopathologist reviewed the slides with discordant results.

RESULTS

The detection rate for abnormalities was 7.30% for Imager-assisted screening and 7.83% for manual screening. The concordance in diagnosis of abnormalities ranged from 72.7% to 100% with the lowest concordance for high-grade abnormalities diagnosed by Imager-assisted screening. The rate of unsatisfactory smears with Imager-assisted screening is half that of manual screening. There was a screener productivity increase of 140%. In all but one case, abnormal cells were identified by the Imager but screeners varied in their interpretations.

CONCLUSIONS

Overall, Imager-assisted screening was as sensitive as manual screening, and more sensitive for high-grade lesions, with a halving of the rate of unsatisfactory smears. In the setting of the New Zealand cervical screening program, the initial screen by the Imager removes the need for a second, rapid rescreen required by the program for manual screening.

Imaging systems and urine specimens: a new match made?

The application of standard cytologic examination of the urine specimen can now be augmented by using ThinPrep technology and other automatic screening devices. The technical and preparation adjustments required for these new technologies notwithstanding, the potential for shorter screening times and the possibility of greater diagnostic accuracy may provide great diagnostic promise for this very common specimen.

Application of the ThinPrep imaging system in urine cytology: a prospective study

BACKGROUND

In this prospective study, for the first time, the authors compared the accuracy of reading urine specimens using the ThinPrep Imager System (TIS) with the accuracy of conventional screening for the detection of abnormal urine cells.

METHODS

ThinPrep slides were made from 1455 urine specimens and were read with conventional screening and with TIS. Findings were categorized as "unsatisfactory or failure to read the slide," "benign," or "abnormal." The Cohen κ coefficient was calculated to determine inter-rater agreement between both methods. Urine samples that were followed by biopsies were used to compare the sensitivity, specificity, positive predictive value, and negative predictive value of both methods. From 22 urine specimens, the screening times were measured and compared.

RESULTS

There was substantial agreement between both methods (κ score, 0.77). Of 175 urine specimens that were followed by bladder biopsies, for conventional screening, the sensitivity was 51.3%, specificity was 68.4%, the positive predictive value was 77.2%, and the negative predictive value was 40.2%; for TIS screening, the respective values were 54.6%, 68.4%, 78.3%, and 41.9%. The average time was 5.2 minutes for conventional screening and 3.9 minutes for TIS.

CONCLUSIONS

With a κ score of 0.77, the current study demonstrated a good correlation between reading urine specimens with conventional screening and with TIS. Using TIS resulted in slightly increased sensitivity, positive predictive value, and negative predictive value compared with conventional screening and had the same specificity. These results indicate that reading urine specimens using TIS is equally reliable as conventional cytology.

Effect of Thin Prep(®) imaging system on laboratory rate and relative sensitivity of atypical squamous cells, high-grade squamous intraepithelial lesion not excluded and high-grade squamous intraepithelial lesion interpretations

Introduction:

Automated screening of Thin Prep^® Papanicolaou Tests has become increasingly common in clinical practice. Increased productivity has initiated laboratory use of the Thin Prep^® Imaging System (TIS). Increased sensitivity is a potential additional benefit of TIS. Published studies have shown an increase in discovery of dysplastic cells. This study evaluates the effect of TIS on the incidence of atypical squamous cells high-grade squamous intraepithelial lesion not excluded (ASC-H) and high-grade squamous intraepithelial lesion (HGSIL) results on Thin Prep^® Pap Tests by comparing TIS-assisted and manual screening findings and the diagnoses on subsequent follow-up in a screening population over a 1-year time period.

Materials and Methods:

A compilation of all ASC-H and HGSIL cases was prepared by conducting a computerized search over a 1-year period (7/06-6/07). The accumulated cases include Thin Prep Pap tests that were both TIS and manually screened. Follow-up results of cytologic and histologic cervical specimens were obtained for a time period extending to 2010. Interpretation utilizing TIS was in place 10 months prior to the study's initiation.

Results:

During the study period 70,522 Pap tests were performed in our laboratory. One third (33%) of Pap tests were screened with assistance of TIS. Manual screening was performed on 47,380 Pap tests of which 153 (0.32%) were interpreted as ASC-H and 164 (0.35%) were interpreted as HGSIL. During the same time period automated screening (TIS) was performed on 23,111 Pap tests. Interpretation of 62 (0.27%) cases provided an ASC-H result, while 71 (0.31%) were HGSIL. Follow-up cervical dysplasia by colposcopic biopsy and cone biopsy was distributed proportionally between TIS and manual screening for both ASC-H and HGSIL categories. Cervical intraepithelial neoplasia (CIN II/III) was identified on follow-up biopsy of 41% TIS cases and 45% manually screened cases for ASC-H. In the HGSIL subset 71% of TIS cases and 69% manually screened cases showed CIN II/III on follow-up. TIS was 26% less sensitive relative to manual screening for ASC-H cases and 3% less sensitive for HGSIL.

Conclusion:

The similar rate of detection using TIS with an equal percentage of histologic correlation for ASC-H and HGSIL lesions on follow-up histology suggests patients screened by the TIS method are being sent for appropriate follow-up surveillance and treatment. A high-grade or possible high-grade lesion is as likely to be detected by TIS as by a manual screen. The similarities in relative sensitivity and specificity in a direct comparison between manual and TIS screening methodologies indicate that TIS compared to manual screening does not affect detection in patients with high-grade cervical lesions.

Survey of human papillomavirus types and their vertical transmission in pregnant women

Background

The prevalence, genotypes, and vertical transmission characteristics of human papillomavirus (HPV) among pregnant women from Nanjing, China was investigated.

Methods

Cervical cells were collected from healthy pregnant women (n = 3139; stage of gestation, 24.6 ± 2.1 weeks) for cytological evaluation and determination of HPV infection status. Exfoliated oral and genital cells were collected from neonates (<1-day-old, n = 233) whose mothers were positive for HPV DNA. We used HPV Gene Chip technology with 23 HPV genotype probes to conduct our analysis.

Results

Overall prevalence of HPV DNA among pregnant women was 13.4% (422/3139). The most frequently detected HPV genotypes were HPV-16 (29.6%, 125/422), -18 (14.7%, 62/422), and -58 (14.2%, 60/422). The rate of concordance for HPV DNA in maternal-neonatal pairs was 23.6% (55/233), with HPV type-specific concordance occurring in 26 cases. A higher prevalence of HPV DNA was apparent in female neonates compared with males (17.7 vs. 11.6%).

Conclusions

The prevalence of cervical HPV DNA in pregnant women from Nanjing was low, with vertical transmission rates slightly higher. From our findings, we concluded that there was efficient vertical transmission of three HPV genotypes, with HPV-16 the most prevalent type in pregnant women and newborn babies.

Prospective parallel randomized trial of the MultiCyte™ ThinPrep(®) imaging system: the Scottish experience

BACKGROUND

Computer-assisted screening of cervical liquid-based cytology (LBC) preparations using the ThinPrep® Imaging System (TIS) has shown improved qualitative and quantitative gains. The use of Multicyte™ has not been described in a well-established national screening programme with a low incidence of high-grade dyskaryosis.

OBJECTIVES

To assess the impact of computer-assisted screening within the Scottish Cervical Screening Programme (SCSP).

METHODS

Two groups of three laboratories, each sharing a ThinPrep® Imager, screened 79 366 slides randomized to test and 90 551 to control arms by laboratory accession. Screeners were not blinded. Standard laboratory reporting profiles of the SCSP, sensitivity, specificity and false-negative rates of all grades of LBC abnormalities with respect to final cytology reports, predictive value for cervical intraepithelial neoplasia grade 2 or worse (CIN2+) on histology; and screening rates were compared for both arms.

RESULTS

Inadequate and negative reporting rates were significantly lower and low-grade reporting rates significantly higher in the imager arm. Imager-assisted screening showed significantly better specificity than manual screening with respect to the final cytology result. There was no evidence of a significant difference in the detection of CIN2+ or CIN3 +. Positive, abnormal and total predictive values (high-grade, low-grade and all abnormal cytology found to be CIN2+, respectively) were similar in both arms. Productivity was significantly higher in the imager arm.

CONCLUSION

  Computer-assisted screening in a well established screening programme showed significantly improved productivity without loss of quality. These findings should inform future policy for cervical screening programmes.

C-path: a Watson-like visit to the pathology lab

Computer-based quantification of tumor morphology has arguably solved the problem of standardized tumor grading (Beck et al., this issue).

The Positive Impact of Simultaneous Implementation of the BD FocalPoint GS Imaging System and Lean Principles on the Operation of Gynecologic Cytology

Context.—Our cytology laboratory, like many others, is under pressure to improve quality and provide test results faster while decreasing costs. We sought to address these issues by introducing new technology and lean principles.

Objective.—To determine the combined impact of the FocalPoint Guided Screener (GS) Imaging System (BD Diagnostics–TriPath, Burlington, North Carolina) and lean manufacturing principles on the turnaround time (TAT) and productivity of the gynecologic cytology operation.

Design.—We established a baseline measure of the TAT for Papanicolaou tests. We then compared that to the performance after implementing the FocalPoint GS Imaging System and lean principles. The latter included value-stream mapping, workflow modification, and a first in–first out policy.

Results.—The mean (SD) TAT for Papanicolaou tests before and after the implementation of FocalPoint GS Imaging System and lean principles was 4.38 (1.28) days and 3.20 (1.32) days, respectively. This represented a 27% improvement in the average TAT, which was statistically significant (P &lt; .001). In addition, the productivity of staff improved 17%, as evidenced by the increase in slides screened from 8.85/h to 10.38/h. The false-negative fraction decreased from 1.4% to 0.9%, representing a 36% improvement.

Conclusions.—In our laboratory, the implementation of FocalPoint GS Imaging System in conjunction with lean principles resulted in a significant decrease in the average TAT for Papanicolaou tests and a substantial increase in the productivity of cytotechnologists while maintaining the diagnostic quality of gynecologic cytology.

Digital imaging in cytopathology

Rapid advances are occurring in the field of cytopathology, particularly in the field of digital imaging. Today, digital images are used in a variety of settings including education (E-education), as a substitute to multiheaded sessions, multisite conferences, publications, cytopathology web pages, cytology proficiency testing, telecytology, consultation through telecytology, and automated screening of Pap test slides. The accessibility provided by digital imaging in cytopathology can improve the quality and efficiency of cytopathology services, primarily by getting the expert cytopathologist to remotely look at the slide. This improved accessibility saves time and alleviates the need to ship slides, wait for glass slides, or transport pathologists. Whole slide imaging (WSI) is a digital imaging modality that uses computerized technology to scan and convert pathology and cytology glass slides into digital images (digital slides) that can be viewed remotely on a workstation using viewing software. In spite of the many advances, challenges remain such as the expensive initial set-up costs, workflow interruption, length of time to scan whole slides, large storage size for WSI, bandwidth restrictions, undefined legal implications, professional reluctance, and lack of standardization in the imaging process.

Comparison of the sensitivity of conventional cytology and the ThinPrep Imaging System for 1,083 biopsy confirmed high-grade squamous lesions

Liquid-based cytology continues to be utilized as an adjunct to conventional cytology in most Australian laboratories, even though a direct-to-vial ThinPrep protocol has been introduced in many countries with established cervical screening programs. Manual screening of ThinPrep slides has been widely practiced for more than 10 years and the recent introduction of the ThinPrep Imaging System (TPI) has been reported as being more sensitive than the conventional smear (CS) in the identification of high-grade cervical disease.We report our experience with ThinPrep Imaging since its introduction into our routine gynecological cytology service. 87,284 split sample pairs reported using the Imaging System demonstrated a decrease in unsatisfactory reports (3.65% for CS and 0.87% for TPI) and an increase in possible high grade and definite high-grade squamous reports (1.57% for CS and 1.62% for TPI).For 1,083 biopsy confirmed high-grade lesions, the correct diagnosis of high grade or possible high-grade squamous disease was made on the ThinPrep imaged slide in 61.0% (661/1,083) of cases and on the CS in 59.4% (643/1,083). This was not statistically significant. When all abnormalities identified on cytology were considered, including possible low grade and definite low-grade abnormalities, the difference in sensitivity for Thinprep imaged slides of 96.0% (1,040/1,083) and CSs of 91.6% (992/1,083) was statistically significant.

Controls in quality assessment in gynecologic cytology: A rational approach to workload limits for the ThinPrep imaging system

Major components of good quality assurance (QA) in cytology laboratories are measuring the screening accuracy of cytotechnologists (CTs) and determining appropriate workload limits for them. Currently, the FDA approved workload limits for image assisted Paps, however, are too high. In addition, the CLIA 88 mandated QA measures, by themselves, are insufficient to accurately measure the screening performance of CTs. Although the use of "controls" is fundamental to good practice in the clinical laboratory; this has not been emphasized in gynecologic cytology. In this review, we underscore the importance of using controls, such as monitoring morning and afternoon abnormal rates, in assessing CT performance and assigning reasonable workload limits. Using controls, however, requires significant alteration in the laboratory workflow, and much lower and variable workload limits for CTs, compared to those approved by FDA.

Image-guided ThinPrep Papanicolaou tests and cotesting with high-risk human papillomavirus in women aged 30 years and older in a low-risk private practice population

BACKGROUND

Screening for cervical cancer precursors has evolved considerably with the introduction of new technologies to improve the early detection of disease. The objective of this study was to analyze the accuracy and effectiveness of combined screening with cytology and high-risk human papillomavirus (HR-HPV) testing in a low-risk population of women aged >or=30 years.

METHODS

Consecutive unselected samples from a group of 1871 women aged >or=30 years were screened with image-guided ThinPrep tests and HR-HPV tests during a 6-month period. Histologic follow-up was reviewed among women with positive HR-HPV tests.

RESULTS

A total of 85 (4.5%) women had positive HR-HPV tests. In 48 HR-HPV-positive women with follow-up biopsies, 41 (85%) were found to have histologic abnormalities. Thirty-three (1.9%) women with cytologically normal Papanicolaou (Pap) tests harbored HR-HPV, and a cervical intraepithelial neoplasia (CIN) 2+ lesion was detected in 1 (16%) of 6 women with histologic follow-up. Conversely, 2 (28%) of 7 women with high-grade intraepithelial lesion on cytology tested negative for HR-HPV during the same period. A case of serous carcinoma with atypical glandular cells on cytology was also negative for HR-HPV, as expected.

CONCLUSIONS

In this low-risk population of women aged >or=30 years, histology-confirmed CIN2+ lesions were identified in women with negative cytology and positive HR-HPV tests, as well as in those with positive cytology and negative HR-HPV tests. Because both cytology and HPV testing alone missed significant lesions, cotesting with Pap and HR-HPV in women aged >or=30 years appears to be a reasonable option in a low-risk population.

Atypical glandular cells (AGC): ThinPrep Imaging System (TIS), manual screening (MS), and correlation with Hybrid Capture 2 (HC2) HPV DNA testing

OBJECTIVE

The aim of the study was to determine if the ThinPrep Imaging System (T1S) improves the positive predictive value (PPV) of atypical glandular cell (AGC) diagnosis for identifying HPV-related squamous and/or glandular lesions over manual screening (MS), and if human papilloma virus (HPV)-DNA testing improves the diagnostic yield.

MATERIALS AND METHODS

85 ThinPrep cervical cytology specimens with a diagnosis of AGC by TIS (n = 51) and MS (n = 34) were retrieved. The diagnoses were correlated with corresponding histologic follow-up and high risk (HR)-HPV testing results.

RESULTS

The PPV of AGC by TIS and MS for HPV-related squamous lesions were similar. In the MS group, more cases of glandular pathology were identified, however only three represented adenocarcinoma in-situ (AIS), and the remaining ten were endometrial carcinomas (EMCA).

CONCLUSIONS

TIS and MS are comparable in the detection of AGC representing squamous histology and the addition of HPV DNA testing does not differentially improve performance. Although the MS group harbored more glandular pathology, the differences in the detection of AIS were not statistically significant.

Implementation of the ThinPrep Imaging System in a tertiary military medical center

BACKGROUND

The ThinPrep Imaging System (TIS) was implemented at Brooke Army Medical Center (BAMC) in February 2006 and has been a crucial part of the ability of the Department of Pathology and Laboratory Services ability to improve efficiency and turnaround times for Papanicolaou (Pap) test reporting. The increased detection rate of squamous abnormalities, specifically high-grade squamous intraepithelial lesions (HSIL), has been well documented by many studies. In addition, the TIS has increased productivity for many laboratories. The objective of this study was to evaluate the effects of implementing the TIS at BAMC, a tertiary military medical center. Specifically, the following were assessed: 1) whether the detection of squamous abnormalities was increased with the TIS, 2) how the rate of high-risk human papillomavirus (HR-HPV) detection in atypical squamous cells (ASC) of undetermined significance (ASC-US) cases changed (or did not change) before and after implementation of the TIS, and 3) how the TIS influenced productivity.

METHODS

All gynecologic cytology at BAMC has been collected and processed using the ThinPrep system since 2002. Before February 2006 and before implementation of the TIS, Pap tests were screened manually by the cytotechnologists. Detection rates of squamous abnormalities were compared between the period from February 2005 to December 2005 (manual screening) and the period from February 2006 to December 2006 (image-assisted screening). Squamous abnormalities included ASC-US; ASC, cannot rule out HSIL (ASC-H); low-grade squamous intraepithelial lesion (LSIL); HSIL; glandular abnormalities; and malignancies (squamous or glandular). In addition, the rates of HR-HPV-positive, HR-HPV-negative, and HR-HPV-quantity not sufficient were compared for the same periods. During both periods, testing for HR-HPV was performed only on ASC-US Pap tests. HR-HPV was tested with Digene Hybrid Capture 2 methodology. Productivity was calculated as the change in average slides screened per hour before and after imager implementation.

RESULTS

In total, 107,647 Pap tests were analyzed in the 2005 (54,438 Pap tests) and 2006 (53,209 Pap tests) timeframes. Increases in the detection of ASC-H, atypical glandular cells (AGC), LSIL, and HSIL were statistically significant. The proportion of negative for intraepithelial lesion or malignancy (NILM) and unsatisfactory cases decreased significantly with implementation of the TIS. The ASC to squamous intraepithelial lesion (ASC:SIL) ratio decreased from 1.5 to 1.0 after TIS implementation. Decreases in the ASC-US HR-HPV-positive proportion and increases in the ASC-US HR-HPV-negative proportion after implementation of the TIS were statistically significant. In our laboratory, a 60% increase in productivity was noted with use of the TIS.

CONCLUSIONS

Implementation of the TIS at BAMC significantly increased the detection of ASC-H, AGC, LSIL, and HSIL but had no significant impact on the ASC-US detection rate. Although the ASC-US rate did not change, both the HR-HPV-positive rate and the ASC:SIL ratio decreased. The data from the current study suggested that, at least initially, the use of imager-directed screening may increase the number of clinically insignificant ASC-US Pap tests.

Does the ThinPrep Imaging System increase the detection of high-risk HPV-positive ASC-US and AGUS? The Women and Infants Hospital experience with over 200,000 cervical cytology cases

Background:

Published reports have demonstrated that introduction of the ThinPrep Imaging System (Imager) to the cytology screening services has increased the detection rate of high-grade squamous intraepithelial lesions (HSILs). In accordance with recent clinical treatment guidelines, patients with atypical squamous or glandular cells of undetermined significance (ASC-US or AGUS) are often tested for high-risk HPV infection using the Hybrid Capture HPV DNA test. We took the opportunity to investigate whether the Imager had resulted in any significant differences in our diagnostic categories, as well as whether the Imager increased the detection of high-risk HPV-DNA-positive (HRHPV+) ASC-US or AGUS.

Materials and Methods:

Cytology cases with the diagnosis of ASC-US and AGUS were retrieved from the archival files of our institution during periods of 11 months prior to and 11 months after the introduction of the Imager. The total number of cases in each category was correlated with results of reflex high-risk HPV DNA testing when the latter were available. All AGUS diagnoses were correlated with subsequent biopsy follow-up. Statistical analyses were performed using the chi-Square test with Yate's Correction and Fisher's Exact test.

Results:

A total of 108,371 and 104,555 of ThinPrep® Pap Test (TPPT) cases were reviewed during 11 months pre- and post-imager introduction. The ASC-US rate was 5.4% in the pre-Imager and 5.3% in the post-Imager period. The HPV reflex test was 38% and 34% positive respectively in the pre- and post-Imager period (P>0.124). Similarly, 0.14% and 0.12% AGUS were found in the pre- and post-Imager period. The positive HPV reflex test was 14% versus 23% (P = 0.1690). The abnormal biopsy follow-up rate in the AGUS category was increased from 20.9% in the pre-Imager period to 31% in the post-Imager period (P = 0.1471). The ASCUS/SIL ratios were 1.9 and 1.6 respectively.

Conclusions:

The ASC-US and AGUS rates did not change statistically before and after the introduction of the Imager in our cytology laboratory. Although use of the Imager did not increase detection of HPV+ ASC-US, it did appear to increase the detection rate of HPV+ AGUS and subsequent abnormal biopsy follow-up rates in all categories. However, the increase in the detection rate did not reach the point of statistical significance

The impact of digital imaging in the field of cytopathology

With the introduction of digital imaging, pathology is undergoing a digital transformation. In the field of cytology, digital images are being used for telecytology, automated screening of Pap test slides, training and education (e.g. online digital atlases), and proficiency testing. To date, there has been no systematic review on the impact of digital imaging on the practice of cytopathology. This article critically addresses the emerging role of computer-assisted screening and the application of digital imaging to the field of cytology, including telecytology, virtual microscopy, and the impact of online cytology resources. The role of novel diagnostic techniques like image cytometry is also reviewed.

Use of the ThinPrep Imaging System does not alter the frequency of interpreting Papanicolaou tests as atypical squamous cells of undetermined significance

BACKGROUND

Automated screening of Papanicolaou tests (Pap tests) improves the productivity of cytopathology laboratories. The ThinPrep Imaging System (TIS) has been widely adopted primarily for this reason for use on ThinPrep Pap tests (TPPT). However, TIS may also influence the interpretation of Pap tests, leading to changes in the frequency of various interpretive categories. The effect of the TIS on rates of TPPT interpretation as atypical squamous cells of undetermined significance (ASC-US) is of concern because any shift in the frequency of ASC-US will alter the sensitivity and specificity of the Pap test. We have sought to determine whether automated screening of TPPT has altered ASC-US rates in our institution when compared with manual screening (MS) of TPPT.

METHODS

A computerized search for all ASC-US with reflex Human Papillomavirus (HPV) testing over a one-year-period (7/1/06 to 6/30/07) was conducted. Cases included both TPPT screened utilizing TIS and screened manually. HPV test results for both groups were recorded. Pertinent follow-up cervical cytology and histology results were retrieved for the period extending to 11/30/07. Automated screening was in clinical use for 10 months prior to the start of the study.

RESULTS

Automated screening was performed on 23,103 TPPT, of which 977 (4.23%) were interpreted as ASC-US. Over the same period, MS was performed on 45,789 TPPT, of which 1924 (4.20%) were interpreted as ASC-US. Reflex HPV testing was positive for high risk (HR) types in 47.4% of the TIS cases and 50.2% of MS cases. Follow-up cervical dysplasia found by colposcopy was also distributed proportionally between the two groups. Cervical intraepithelial neoplasia (CIN) was found on follow-up biopsy of 20.1% of the TIS cases (5.2% CIN 2/3) and 21.2% of MS cases (5.1% CIN 2/3). None of these differences were statistically significant.

CONCLUSION

Use of the ThinPrep Imaging System did not appreciably change ASC-US rates or follow-up reflex HPV test results in our laboratory. This demonstrates that the benefits of automated screening may be obtained without increasing the rate of referral to colposcopy for ASC-US follow-up.

Papanicolaou smear history in women with low-grade cytology before cervical cancer diagnosis

BACKGROUND

The purpose of the current study was to examine the screening histories of women diagnosed with invasive cervical cancer (ICC) in 2000 who had previous Papanicolaou (Pap) smears deemed to be unsatisfactory or with low-grade findings that did not lead to biopsy.

METHODS

A total of 252 Pap smears from 47 women taken between 1992 and 2000 were included in the study; 247 smears were reexamined at the laboratory of origin before the study and all 252 were then reexamined independently by 2 experienced cytotechnicians and 2 cytopathologists.

RESULTS

Of the 47 cases of ICC, 35 were squamous cell carcinoma, 10 were adenocarcinoma, and 2 were other types. On reexamination at the laboratory of origin, 24 cases were upgraded and in the study group 27 cases were upgraded to diagnoses requiring biopsy. On reexamination at the laboratory of origin, it was found that the first high-grade squamous intraepithelial lesion (HSIL) could have been diagnosed on average 4.2 years earlier than it was originally (95% confidence interval [95% CI], 3.3-5.1 years). On reexamination by the study group the first diagnosis of HSIL was made in smears dating from 5.4 years before the diagnosis of ICC (95% CI, 4.5-6.2 years).

CONCLUSIONS

The study confirms that unsatisfactory and low-grade Pap smears imply a risk of developing high-grade lesions at a later date and shows that in a screening program a subgroup of smears may be diagnosed as unsatisfactory or low grade despite the presence of high-grade findings that are detectable on reexamination.

Automated screening versus manual screening: A comparison of the ThinPrep® imaging system and manual screening in a time study

The ThinPrep Imaging System (TIS) is an automated system that assists cytotechnologists in the primary screening of ThinPrep liquid based cervical samples. Between June 1, 2004, and April 1, 2005, four experienced cytotechnologists participated in the study in which the duration of the screening procedure was timed for each of the 11,354 slides included. In every slide 22 fields of view were reviewed, and the samples that contained potentially abnormal cells were fully screened. The screening time was reduced by 42% (mean) (p < 0.001). By manual rescreening of the negative TIS samples, abnormal cells were found in 10 samples (false negative rate 0.14%). In every case the abnormal cells had been identified by the scanner, but misinterpreted by the cytotechnologist. These findings stressed the importance of carefulness in the interpretation of the marked fields and beyond that helped the cytotechnologists and pathologists to have more confidence in the automated system.