Cervical cancer: Automation of Pap test screening

Advances in diagnostic liquid-based cytology

Liquid-based cytology (LBC) has changed the landscape of gynaecological cytology. A growing demand exists for LBC in diagnostic cytology, particularly for ancillary testing, such as immunocytochemistry and molecular testing. Ancillary testing solely based on conventional preparation (CP) methods remains challenging. Recently, the increased demand for specialist testing and minimally invasive techniques, such as endoscopic ultrasonography fine-needle aspiration, to obtain cellular samples has led to an increasing demand for ancillary testing on cytology LBC supernatant, slides and cell block (CB). This facilitates the diagnosis and prognosis in cytology samples enabling personalized treatment. An understanding of the history and future prospects of LBC is crucial for its application in routine diagnostics by cytopathologists and cytotechnologists. In this review, we initiated an internet search using the keyword 'liquid-based cytology', and we conducted a literature review to discuss the usefulness of combined diagnosis of LBC and CP, immunocytochemistry and molecular testing and assessed the quality of nucleic acids in diagnostic LBC. High-quality and cell-rich diagnostic LBC surpassed the CP method alone in terms of reliability and versatility of ancillary testing in cytological diagnosis. Conclusively, diagnostic LBC lends itself to various new technologies and is expected to continue evolving with innovations in the future.

Cost-Effectiveness of Computer-Assisted Cytology in a Primary hrHPV-Based Cervical Cancer Screening Programme

BACKGROUND

Computer-assisted screening (CAS) shows equal performance compared to manual screening, although results are heterogeneous. Furthermore, using CAS may save costs through a potentially increased screening productivity of technicians, therefore also offering a solution for temporary and structural capacity shortage. We evaluated the circumstances under which CAS will be cost-effective compared to manual cytology triage in a primary HPV-based cervical screening programme.

METHODS

Microsimulation model MISCAN-Cervix was used to evaluate 198 different CAS scenarios with varying probabilities to detect cervical intraepithelial neoplasia grade 1 (CIN1) and CIN3 and cost reductions per test, compared to manual cytology triage. Cost-effectiveness was evaluated by costs per (quality-adjusted) life year ((QA)LY) gained.

RESULTS

CAS will be cost-effective in all scenarios, except for the following combinations: (1) no cost reduction and an increased probability of detecting CIN1, (2) a cost reduction of €2 per test and an increased probability of detecting CIN1 from 4% onwards or (3) a cost reduction of €4 per test and an increased probability of detecting CIN1 from 6% onwards, compared to manual cytology triage. All CAS scenarios with any reduction in the probability of detecting CIN1 (i.e., increased CIN2+ specificity), or a reduction in costs from €6 per test onwards suggested a more cost-effective strategy compared to manual cytology triage.

CONCLUSION

As we based our analysis on a realistic range in costs and test performance, the implementation of CAS is likely to be cost-effective. Our results can be used as a guideline to advise when to choose CAS instead of manual cytology triage.

Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners

BACKGROUND

Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance.

METHODS

The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast.

RESULTS

The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners.

CONCLUSIONS

The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X

BACKGROUND

Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting.

METHODS

In this study, the authors report on the development and large-scale validation of a deep-learning tool, AutoParis-X, which can facilitate rapid, semiautonomous examination of urine cytology specimens.

RESULTS

The results of this large-scale, retrospective validation study indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide variety of cell-related and cluster-related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm ratio for cells in these clusters.

CONCLUSIONS

The authors developed a publicly available, open-source, interactive web application that features a simple, easy-to-use display for examining urine cytology whole-slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head-to-head clinical trials.

Development of a Point-of-Care Cervico-Vaginal Sampling/Testing Device for the Colorimetric Detection of Cervical Cancer

This paper reports the colorimetric analysis of cervical-cancer-affected clinical samples by the in situ formation of gold nanoparticles (AuNPs) formed with cervico-vaginal fluids collected from healthy and cancer-affected patients in a clinical setup, termed "C-ColAur". We evaluated the efficacy of the colorimetric technique against the clinical analysis (biopsy/Pap smear) and reported the sensitivity and specificity. We investigated if the aggregation coefficient and size of the nanoparticles responsible for the change in color of the AuNPs (formed with clinical samples) could also be used as a measure of detecting malignancy. We estimated the protein and lipid concentrations in the clinical samples and attempted to investigate if either of these components was solely responsible for the color change, enabling their colorimetric detection. We also propose a self-sampling device, CerviSelf, that could enable the rapid frequency of screening. We discuss two of the designs in detail and demonstrate the 3D-printed prototypes. These devices, in conjugation with the colorimetric technique C-ColAur, have the potential to be self-screening techniques, enabling women to undergo rapid and frequent screening in the comfort and privacy of their homes, allowing a chance at an early diagnosis and improved survival rates.

Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women’s Health Issue—A Review

This review focuses on recent advances and future perspectives in the use of Raman spectroscopy for cervical cancer, a global women’s health issue. Cervical cancer is the fourth most common women’s cancer in the world, and unfortunately mainly affects younger women. However, when detected at the early precancer stage, it is highly treatable. High-quality cervical screening programmes and the introduction of the human papillomavirus (HPV) vaccine are reducing the incidence of cervical cancer in many countries, but screening is still essential for all women. Current gold standard methods include HPV testing and cytology for screening, followed by colposcopy and histopathology for diagnosis. However, these methods are limited in terms of sensitivity/specificity, cost, and time. New methods are required to aid clinicians in the early detection of cervical precancer. Over the past 20 years, the potential of Raman spectroscopy together with multivariate statistical analysis has been shown for the detection of cervical cancer. This review discusses the research to date on Raman spectroscopic approaches for cervical cancer using exfoliated cells, biofluid samples, and tissue ex vivo and in vivo.

Liposome-Loaded Targeted Theranostic Fluorescent Nano-Probes for Diagnosis and Treatment of Cervix Carcinoma

Near-infrared fluorescence imaging, with its high sensitivity, non-invasiveness, and superior real-time feedback properties, has become a powerful skill for accurate diagnosis in the clinic. Nanoparticle-assisted chemotherapy is an effective cure for cancer. Specifically, the combination of near-infrared fluorescence imaging with chemotherapy represents a promising method for precise diagnosis and treatment of cervical cancer. To realize this approach, it is necessary to design and synthesize therapeutic nano-probes with detection abilities. In this work, an organic NIRF emissive heptamethine cyanine dye, IR783, was utilized and encapsulated in biocompatible drug-carrier liposomes). Then, the anticancer drug doxorubicin was loaded, to form LP-IR783-DOX nanoparticles. The LP-IR783-DOX nanoparticles had spherical shapes and were smoothly dispersed in aqueous solutions. Favorable absorption (a peak of 800 nm) and fluorescence (a peak of 896 nm) features were obtained from LP-IR783-DOX nanoparticles in the near-infrared region. Moreover, the specific detection abilities of nanoparticles were confirmed in different cell lines, and nanoparticles exhibited strong detection abilities in human cervix carcinoma cells in particular. To analyze the chemotherapeutic properties of LP-IR783-DOX nanoparticles, live HeLa cells were studied in detail, and the application of these NPs resulted in a chemotherapeutic efficiency of 56.75% based on fluorescein isothiocyanate staining and flow cytometry. The results indicate that nanoparticles have great potential for theranostic application of fluorescence imaging and chemotherapy in cases of cervical cancer.

Cric searchable image database as a public platform for conventional pap smear cytology data

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.