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Abe H, Kawahara A, Akiba J, Yamaguchi R. Advances in diagnostic liquid-based cytology. Cytopathology 2024; 35:682-694. [PMID: 38837293 DOI: 10.1111/cyt.13405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/09/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024]
Abstract
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.
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Affiliation(s)
- Hideyuki Abe
- Department of Diagnostic Pathology, Kurume University Hospital, Kurume, Japan
| | - Akihiko Kawahara
- Department of Diagnostic Pathology, Kurume University Hospital, Kurume, Japan
| | - Jun Akiba
- Department of Diagnostic Pathology, Kurume University Hospital, Kurume, Japan
| | - Rin Yamaguchi
- Department of Diagnostic Pathology, Nagasaki University Hospital, Nagasaki, Japan
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Olthof E, Kaljouw S, van Kemenade F, Uyterlinde A, de Kok I. Cost-Effectiveness of Computer-Assisted Cytology in a Primary hrHPV-Based Cervical Cancer Screening Programme. Cancer Med 2024; 13:e70299. [PMID: 39400537 PMCID: PMC11472647 DOI: 10.1002/cam4.70299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 08/28/2024] [Accepted: 09/12/2024] [Indexed: 10/15/2024] Open
Abstract
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.
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Affiliation(s)
- Ellen M. G. Olthof
- Department of Public HealthErasmus MC University Medical Center RotterdamRotterdamThe Netherlands
| | - S. Kaljouw
- Department of Public HealthErasmus MC University Medical Center RotterdamRotterdamThe Netherlands
| | - Folkert J. van Kemenade
- Department of PathologyErasmus MC University Medical Center RotterdamRotterdamThe Netherlands
| | - Anne M. Uyterlinde
- Department of PathologyAmsterdam University Medical CenterAmsterdamThe Netherlands
| | - Inge M. C. M. de Kok
- Department of Public HealthErasmus MC University Medical Center RotterdamRotterdamThe Netherlands
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Greenberg A, Samueli B, Farkash S, Zohar Y, Ish-Shalom S, Hagege RR, Hershkovitz D. Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners. Diagn Pathol 2024; 19:26. [PMID: 38321431 PMCID: PMC10845737 DOI: 10.1186/s13000-024-01452-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
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.
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Affiliation(s)
- Ariel Greenberg
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
| | - Benzion Samueli
- Department of Pathology, Soroka University Medical Center, 76 Wingate Street, 8486614, Be'er Sheva, Israel
| | - Shai Farkash
- Department of Pathology, Emek Medical Center, Yitshak Rabin Boulevard 21, 1834111, Afula, Israel
| | - Yaniv Zohar
- Department of Pathology, Rambam Medical Center, 8 Haalia Hashnia, 3525408, Haifa, Israel
| | - Shahar Ish-Shalom
- Department of Pathology, Kaplan Medical Center, Pasternak St. P.O.B. 1, 76100, Rehovot, Israel
| | - Rami R Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69978, Tel-Aviv, Israel
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Levy JJ, Chan N, Marotti JD, Kerr DA, Gutmann EJ, Glass RE, Dodge CP, Suriawinata AA, Christensen B, Liu X, Vaickus LJ. Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X. Cancer Cytopathol 2023; 131:637-654. [PMID: 37377320 PMCID: PMC11251731 DOI: 10.1002/cncy.22732] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 06/29/2023]
Abstract
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.
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Affiliation(s)
- Joshua J. Levy
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Department of Dermatology, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
- Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Natt Chan
- Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Jonathan D. Marotti
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Darcy A. Kerr
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Edward J. Gutmann
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | | | | | - Arief A. Suriawinata
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Brock Christensen
- Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
- Department of Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
- Department of Community and Family Medicine, Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Xiaoying Liu
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
| | - Louis J. Vaickus
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, 03766
- Dartmouth College Geisel School of Medicine, Hanover, NH, 03756
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Appidi T, Vakada M, Buddhiraju HS, Chinchulkar SA, Kota A, Yadav DN, Kodandapani S, Simhabhatla SK, Rengan AK. Development of a Point-of-Care Cervico-Vaginal Sampling/Testing Device for the Colorimetric Detection of Cervical Cancer. Diagnostics (Basel) 2023; 13:1382. [PMID: 37189483 PMCID: PMC10137237 DOI: 10.3390/diagnostics13081382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/27/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
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.
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Affiliation(s)
- Tejaswini Appidi
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Murali Vakada
- Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Hima Sree Buddhiraju
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Shubham A. Chinchulkar
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Akshar Kota
- Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Dokkari Nagalaxmi Yadav
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Suseela Kodandapani
- Department of Pathology, Basavatarakam Indo-American Cancer Hospital & Research Institute, Hyderabad 500034, India
| | - Surya Kumar Simhabhatla
- Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
| | - Aravind Kumar Rengan
- Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Kandi 502284, India
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Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women’s Health Issue—A Review. Molecules 2023; 28:molecules28062502. [PMID: 36985474 PMCID: PMC10056388 DOI: 10.3390/molecules28062502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023] Open
Abstract
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.
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Zhu L, Yan T, Alimu G, Zhang L, Ma R, Alifu N, Zhang X, Wang D. Liposome-Loaded Targeted Theranostic Fluorescent Nano-Probes for Diagnosis and Treatment of Cervix Carcinoma. J Biomed Nanotechnol 2022. [DOI: 10.1166/jbn.2022.3332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
- Lijun Zhu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
| | - Ting Yan
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
| | - Gulinigaer Alimu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
| | - Linxue Zhang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
| | - Rong Ma
- Department of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Nuernisha Alifu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
| | - Xueliang Zhang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
| | - Duoqiang Wang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830054, China
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Cric searchable image database as a public platform for conventional pap smear cytology data. Sci Data 2021; 8:151. [PMID: 34112812 PMCID: PMC8192784 DOI: 10.1038/s41597-021-00933-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 05/11/2021] [Indexed: 01/02/2023] Open
Abstract
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.
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