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Cantley RL, Jing X, Smola B, Hao W, Harrington S, Pantanowitz L. Validation of AI-assisted ThinPrep® Pap test screening using the Genius TM Digital Diagnostics System. J Pathol Inform 2024; 15:100391. [PMID: 39114431 PMCID: PMC11304920 DOI: 10.1016/j.jpi.2024.100391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 06/30/2024] [Indexed: 08/10/2024] Open
Abstract
Advances in whole-slide imaging and artificial intelligence present opportunities for improvement in Pap test screening. To date, there have been limited studies published regarding how best to validate newer AI-based digital systems for screening Pap tests in clinical practice. In this study, we validated the Genius™ Digital Diagnostics System (Hologic) by comparing the performance to traditional manual light microscopic diagnosis of ThinPrep® Pap test slides. A total of 319 ThinPrep® Pap test cases were prospectively assessed by six cytologists and three cytopathologists by light microscopy and digital evaluation and the results compared to the original ground truth Pap test diagnosis. Concordance with the original diagnosis was significantly different by digital and manual light microscopy review when comparing across: (i) exact Bethesda System diagnostic categories (62.1% vs 55.8%, respectively, p = 0.014), (ii) condensed diagnostic categories (76.8% vs 71.5%, respectively, p = 0.027), and (iii) condensed diagnoses based on clinical management (71.5% vs 65.2%, respectively, p = 0.017). Time to evaluate cases was shorter for digital (M = 3.2 min, SD = 2.2) compared to manual (M = 5.9 min, SD = 3.1) review (t(352) = 19.44, p < 0.001, Cohen's d = 1.035, 95% CI [0.905, 1.164]). Not only did our validation study demonstrate that AI-based digital Pap test evaluation had improved diagnostic accuracy and reduced screening time compared to light microscopy, but that participants reported a positive experience using this system.
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Affiliation(s)
- Richard L. Cantley
- Department of Pathology, University of Michigan-Michigan Medicine, 2800 Plymouth Rd, Building 35, Ann Arbor, MI 48109, USA
| | - Xin Jing
- Department of Pathology, University of Michigan-Michigan Medicine, 2800 Plymouth Rd, Building 35, Ann Arbor, MI 48109, USA
| | - Brian Smola
- Department of Pathology, University of Michigan-Michigan Medicine, 2800 Plymouth Rd, Building 35, Ann Arbor, MI 48109, USA
| | - Wei Hao
- Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Sarah Harrington
- Scientific Affairs, Hologic, Inc., 250 Campus Drive, Marlborough, MA 01752, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, 5230 Centre Avenue, Pittsburgh, PA 15232, USA
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Maffei E, D'Ardia A, Ciliberti V, Serio B, Sabbatino F, Zeppa P, Caputo A. The Current and Future Impact of Lymph Node Fine-Needle Aspiration Cytology on Patient Care. Surg Pathol Clin 2024; 17:509-519. [PMID: 39129145 DOI: 10.1016/j.path.2024.04.010] [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] [Indexed: 08/13/2024]
Abstract
Lymph node (LN) fine-needle aspiration cytology (FNAC) is a common diagnostic procedure for lymphadenopathies. Despite the qualities and potentialities of LN-FNAC, the number of possible pathologies and the variety of clinical contexts represent a challenge and require a continuous upgrading of the procedure according to the emerging clinical requests and new technologies. This study presents an overview of the current and future impact of LN-FNAC on the care of patients with lymphadenopathy.
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Affiliation(s)
- Elisabetta Maffei
- Pathology Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy
| | - Angela D'Ardia
- Pathology Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy
| | - Valeria Ciliberti
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, Naples, Italy
| | - Bianca Serio
- Haematology Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy
| | - Francesco Sabbatino
- Oncology Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy
| | - Pio Zeppa
- Pathology Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy.
| | - Alessandro Caputo
- Pathology Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy
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Chatterjee PB, Hingway SR, Hiwale KM. Evolution of Pathological Techniques for the Screening of Cervical Cancer: A Comprehensive Review. Cureus 2024; 16:e60769. [PMID: 38903362 PMCID: PMC11188840 DOI: 10.7759/cureus.60769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 05/21/2024] [Indexed: 06/22/2024] Open
Abstract
The evolutionary journey of cervical cancer screening has been a major medical success story, considering the substantial role it has played in dwindling the disease burden. Through sustained collaborative efforts within the medical community, significant advances have been made from the humble yet path-breaking conventional Pap smear to the current automated screening systems and human papillomavirus (HPV) molecular testing. With the integration of artificial intelligence into screening techniques, we are currently at the precipice of circumventing the pitfalls of manual cytology readings and improving the efficiency of the screening systems by a significant margin. Despite the technological milestones traversed, the high logistics and operational cost, besides the technical know-how of operating the automated systems, can pose a major practical challenge in the widespread adoption of these advanced techniques in cervical cancer screening programs. This would suggest the need to adopt strategies that are tailored to the demands and needs of the different settings keeping their limitations in mind. This review aims to take the reader through the entire evolutionary journey of cervical cancer screening programs, highlight the individual merits and demerits of each technique, and discuss the recommendations from the major global guidelines.
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Affiliation(s)
- Priya B Chatterjee
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Snehlata R Hingway
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Kishor M Hiwale
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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van Diest PJ, Flach RN, van Dooijeweert C, Makineli S, Breimer GE, Stathonikos N, Pham P, Nguyen TQ, Veta M. Pros and cons of artificial intelligence implementation in diagnostic pathology. Histopathology 2024; 84:924-934. [PMID: 38433288 DOI: 10.1111/his.15153] [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: 11/15/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.
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Affiliation(s)
- Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Rachel N Flach
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Seher Makineli
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Paul Pham
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tri Q Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mitko Veta
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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Hang JF, Ou YC, Yang WL, Tsao TY, Yeh CH, Li CB, Hsu EY, Hung PY, Lin MY, Hwang YT, Liu TJ, Tung MC. Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple Z-Plane Scanning. Acta Cytol 2024:1-9. [PMID: 38648759 DOI: 10.1159/000538985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images. METHODS This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ [SurePath]), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total number of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses. RESULTS The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78-0.91 vs. 0.75-0.88, p = 0.451-0.578). Notably, it substantially reduced both scanning time (137.2-635.0 s vs. 332.6-1,278.8 s, p < 0.05) and image file size (0.51-2.10 GB vs. 1.16-3.10 GB, p < 0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%). CONCLUSION We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.
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Affiliation(s)
- Jen-Fan Hang
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Chuan Ou
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
| | | | - Tang-Yi Tsao
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
| | | | - Chi-Bin Li
- AIxMed, Inc., Santa Clara, California, USA
| | - En-Yu Hsu
- AIxMed, Inc., Santa Clara, California, USA
| | | | | | - Yi-Ting Hwang
- Department of Statistics, National Taipei University, Taipei, Taiwan
| | | | - Min-Che Tung
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
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Swanson AA, Pantanowitz L. The evolution of cervical cancer screening. J Am Soc Cytopathol 2024; 13:10-15. [PMID: 37865567 DOI: 10.1016/j.jasc.2023.09.007] [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: 08/27/2023] [Revised: 09/17/2023] [Accepted: 09/20/2023] [Indexed: 10/23/2023]
Abstract
There are few medical success stories in history as significant as the reduction in cervical cancer incidence. Through the collaborative efforts of dedicated scientific pioneers, the past century has witnessed remarkable advancement that began with the detection of exfoliated cancer cells through cytologic examination to widespread implementation of cervical cancer screening programs to the discovery of the link between cervical cancer and human papillomavirus (HPV). Current screening methods apply HPV-based testing, and artificial intelligence-based screening systems utilizing digitalized cytology images are being used in a continuous effort to optimize the accuracy and efficiency of the Papanicolaou test. This review summarizes the major milestones in cervical cancer screening history to emphasize its evolution as the World Health Organization aims for the global elimination of cervical cancer.
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Affiliation(s)
- Amy A Swanson
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, Minnesota.
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania
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Caputo A, Macrì L, Gibilisco F, Vatrano S, Taranto C, Occhipinti E, Santamaria F, Arcoria A, Scillieri R, Fraggetta F. Validation of full-remote reporting for cervicovaginal cytology: the Caltagirone-Acireale distributed lab. J Am Soc Cytopathol 2023; 12:378-385. [PMID: 37482510 DOI: 10.1016/j.jasc.2023.06.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/04/2023] [Accepted: 06/01/2023] [Indexed: 07/25/2023]
Abstract
INTRODUCTION Cervical cancer is the fourth most common cancer in women, and its prevention is based on vaccination and screening. Screening consists of molecular human papillomavirus (HPV) testing and cytologic analysis of cervical smears, which require expensive equipment and the interaction of numerous professionals such as biologists, cytologists, laboratory technicians, and pathologists. MATERIALS AND METHODS We centralize the cervical samples from more than 51 clinics in 1 main laboratory, where automated HPV testing is performed. HPV-positive cases are collected and used to prepare a liquid-based cytology slide, which is stained and immediately scanned. The resulting whole-slide images (WSIs) are immediately available in a remote laboratory where they are examined by experienced cytologists using virtual microscopy. This setup was validated by making each of the 3 readers independently diagnose 506 specimens in random order, using both conventional light microscopy (CLM) and WSIs, with a minimum wash-out period of 3 weeks and with a final discussion for all cases. RESULTS Intraobserver agreement among CLM and WSI ranged from 0.71 to 0.79, and interobserver agreement for the 3 readers compared with the consensus diagnosis was similar for the 2 modes of assessment. Readers subjectively felt confident in their WSI diagnosis for inadequate and negative cases, but less so in other cases. The perceived difficulty was slightly higher in WSI readings. CONCLUSIONS Interobserver agreement in cervicovaginal cytology is moderate and does not vary if the slides are examined conventionally or digitally. Despite higher reported subjective difficulty and lower confidence in the WSI diagnosis, we did not observe a deterioration in diagnostic performance using WSI compared with CLM.
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Affiliation(s)
- Alessandro Caputo
- Department of Pathology, University Hospital "San Giovanni di Dio e Ruggi D'Aragona", Salerno, Italy; Department of Pathology, Hospital "Gravina e Santo Pietro", Caltagirone, Italy
| | - Luigia Macrì
- Central Cervicovaginal Screening Unit and Center for Cancer Epidemiology and Prevention, Turin, Italy
| | - Fabio Gibilisco
- Department of Pathology, Hospital "Gravina e Santo Pietro", Caltagirone, Italy; Department of Medical and Surgical Sciences and Advanced Technologies, "G. F. Ingrassia", University of Catania, Catania, Italy
| | - Simona Vatrano
- Department of Pathology, Hospital "Gravina e Santo Pietro", Caltagirone, Italy
| | - Chiara Taranto
- Department of Pathology, Hospital "Gravina e Santo Pietro", Caltagirone, Italy
| | | | | | - Angela Arcoria
- Department of Pathology, Hospital "Gravina e Santo Pietro", Caltagirone, Italy
| | | | - Filippo Fraggetta
- Department of Pathology, Hospital "Gravina e Santo Pietro", Caltagirone, Italy.
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Ikeda K, Sakabe N, Maruyama S, Ito C, Shimoyama Y, Oboshi W, Komene T, Yamaguchi Y, Sato S, Nagata K. Relationship between a deep learning model and liquid-based cytological processing techniques. Cytopathology 2023; 34:308-317. [PMID: 37051774 DOI: 10.1111/cyt.13235] [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: 01/06/2023] [Revised: 02/28/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023]
Abstract
OBJECTIVE Artificial intelligence (AI)-based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid-based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies according to the LBC processing technique used. In this study, we elucidated the relationship between two LBC techniques and cell detection and classification using a deep learning model. METHODS Cytological specimens were prepared using the ThinPrep and SurePath methods. The accuracy of cell detection and cell classification was examined using the one- and five-cell models, which were trained with one and five cell types, respectively. RESULTS When the same LBC processing techniques were used for the training and detection preparations, the cell detection and classification rates were high. The model trained on ThinPrep preparations was more accurate than that trained on SurePath. When the preparation types used for training and detection were different, the accuracy of cell detection and classification was significantly reduced (P < 0.01). The model trained on both ThinPrep and SurePath preparations exhibited slightly reduced cell detection and classification rates but was highly accurate. CONCLUSIONS For the two LBC processing techniques, cytomorphology varied according to cell type; this difference affects the accuracy of cell detection and classification by deep learning. Therefore, for highly accurate cell detection and classification using AI, the same processing technique must be used for both training and detection. Our assessment also suggests that a deep learning model should be constructed using specimens prepared via a variety of processing techniques to construct a globally applicable AI model.
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Affiliation(s)
- Katsuhide Ikeda
- Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Nanako Sakabe
- Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sayumi Maruyama
- Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Chihiro Ito
- Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuka Shimoyama
- Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Wataru Oboshi
- Department of Medical Technology and Sciences, School of Health Sciences at Narita, International University of Health and Welfare, Narita, Japan
| | - Tetsuya Komene
- Department of Medical Technology and Sciences, School of Health Sciences at Narita, International University of Health and Welfare, Narita, Japan
| | - Yoshitaka Yamaguchi
- Department of Medical Technology and Sciences, School of Health Sciences at Narita, International University of Health and Welfare, Narita, Japan
| | - Shouichi Sato
- Clinical Engineering, Faculty of medical sciences, Juntendo University, Urayasu, Japan
| | - Kohzo Nagata
- Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
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Kim T, Rao J. "SMART" cytology: The next generation cytology for precision diagnosis. Semin Diagn Pathol 2023; 40:95-99. [PMID: 36639316 DOI: 10.1053/j.semdp.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/22/2022] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.
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Affiliation(s)
- Teresa Kim
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA, 90095, United States of America
| | - Jianyu Rao
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA, 90095, United States of America.
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Cervical cell classification with deep-learning algorithms. Med Biol Eng Comput 2023; 61:821-833. [PMID: 36626113 DOI: 10.1007/s11517-022-02745-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 12/18/2022] [Indexed: 01/11/2023]
Abstract
Cervical cancer is a serious threat to the lives and health of women. The accurate analysis of cervical cell smear images is an important diagnostic basis for cancer identification. However, pathological data are often complex and difficult to analyze accurately because pathology images contain a wide variety of cells. To improve the recognition accuracy of cervical cell smear images, we propose a novel deep-learning model based on the improved Faster R-CNN, shallow feature enhancement networks, and generative adversarial networks. First, we used a global average pooling layer to enhance the robustness of the data feature transformation. Second, we designed a shallow feature enhancement network to improve the localization and recognition of weak cells. Finally, we established a data augmentation network to improve the detection capability of the model. The experimental results demonstrate that our proposed methods are superior to CenterNet, YOLOv5, and Faster R-CNN algorithms in some aspects, such as shorter time consumption, higher recognition precision, and stronger adaptive ability. Its maximum accuracy is 99.81%, and the overall mean average precision is 89.4% for the SIPaKMeD and Herlev datasets. Our method provides a useful reference for cervical cell smear image analysis. The missed diagnosis rate and false diagnosis rate are relatively high for cervical cell smear images of different pathologies and stages. Therefore, our algorithms need to be further improved to achieve a better balance. We will use a hyperspectral microscope to obtain more spectral data of cervical cells and input them into deep-learning models for data processing and classification research. First, we sent training samples of cervical cells into our proposed deep-learning model. Then, we used the proposed model to train eight types of cervical cells. Finally, we utilized the trained classifier to test the untrained samples and obtained the classification results. Fig 1. Deep-learning cervical cell classification framework.
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Chantziantoniou N. BestCyte® primary screening of 500 ThinPrep Pap Test thin-layers: 3 Cytologists' Interobserver diagnostic concordance with predicate manual microscopy relative to Truth Reference diagnoses defining NILM, ASCUS+, LSIL+, and ASCH+ thresholds for specificity, sensitivity, and equivalency grading. J Pathol Inform 2023; 14:100182. [PMID: 36747889 PMCID: PMC9898738 DOI: 10.1016/j.jpi.2022.100182] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/08/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Background The BestCyte® Cell Sorter Imaging System (BestCyte) facilitates algorithmic discrimination of clinically relevant cells in Pap test cytopathology by classifying and projecting images of cells in galleries based on cytomorphology. Warranted is awareness of potential BestCyte advantages as measured through 3 cytologists' interobserver diagnostic concordance, specificity and sensitivity differentials, and equivalency grading relative to manual microscopy (MM). Objectives Using 500 MM-reported ThinPrep thin-layers, analyze: (1) cytologists' blinded BestCyte screening to raise Bethesda diagnoses; (2) correlate BestCyte and MM diagnoses (i.e., predicate) to establish Truth Reference Diagnoses (TRDx) from concordance between 4 possible diagnoses; (3) analyze cytologists' and MM predicate diagnoses through 4 diagnostic thresholds defined by TRDx: NILM (Negative) for specificity, and ASCUS+, LSIL+, and ASCH+ (Positive) for graded sensitivity (with abnormal cells decreasing in size with increasing dysplasia); and, (4) statistically determine cytologists' equivalency grading to MM using 95% Confidence Interval (CI) ranges. Results 500 TRDx breakdown (n/%): NILM (241/48.2), ASCUS (79/15.8), ASCH (9/1.80), AGUS (2/0.40), LSIL (86/17.2), HSIL (68/13.6), CA (2/0.40), UNSAT (13/2.60). TRDx breakdown (n/%) per 4 of 4, 3 of 4, 2 of 4 diagnostic concordances: 264 (52.8%), 182 (36.4%), 54 (10.8%), respectively. No cases of discordant diagnoses were recorded. HSIL TRDx were established from 66.2% of 4 of 4 concordances, followed by NILM (59.3%), LSIL (46.5%), ASCUS (41.8%); antithetically, from 4.40% of 2 of 4 concordances. Specificity for MM predicate (NILM): 67.08%; for Cytologists 1, 2, and 3: 89.71%, 82.30%, 97.53%, respectively. For NILM threshold, cytologists revealed Significantly Superior equivalency to MM. Sensitivity for ASCUS+, LSIL+, and ASCH+ thresholds: MM (91.36%, 86.67%, 74.36%); Cytologist 1 (95.88%, 96.97%, 94.87%); Cytologist 2 (95.47%, 95.76%, 93.59%), Cytologist 3 (94.65%, 95.15%, 98.72%), respectively. Cytologists revealed Significantly Superior equivalency to MM for graded Positive thresholds; with Cytologist 3 for ASCUS+ being: Superior. Conclusions BestCyte detects and efficiently displays abnormal cells in strategic galleries standardizing objectivity by systematizing mosaics of cell-types for cytologists' consideration. BestCyte fosters consistent, enhanced cytologists' sensitivity values for the ASCUS+, LSIL+, and ASCH+ Positive thresholds relative to MM. Also, BestCyte facilitates improved specificity and superior equivalency grading to MM reflecting efficient screening, and reduced labor. Confident interpretations of small dysplastic epithelial cells characteristic of HSIL led to exceptional interobserver diagnostic concordance inferring BestCyte is primed for effective cervical cancer screening practice.
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Chantziantoniou N. BestCyte® Cell Sorter Imaging System: Primary and Adjudicative Whole Slide Image Rescreening Review Times of 500 ThinPrep Pap Test Thin-layers - An Intra-observer, Time-Surrogate Analysis of Diagnostic Confidence Potentialities. J Pathol Inform 2022; 13:100095. [PMID: 36268084 PMCID: PMC9576977 DOI: 10.1016/j.jpi.2022.100095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/10/2022] [Indexed: 11/17/2022] Open
Abstract
Background The novel Artificial Intelligence-driven BestCyte® Cell Sorter Imaging System (BestCyte) enables hybrid digital screening through classification and sorting of tiles depicting cells in 8 galleries or whole slide image (WSI) reviews. Objectives (1) Analyze expenditures of time (minutes) for primary BestCyte cell sorter screening and adjudicative WSI rescreening of 500 blinded, randomized ThinPrep thin-layers to determine review times per Bethesda nomenclature; (2) Analyze review times for NILM qualifier diagnoses reflecting increasing interpretive complexity (i.e., Inflammation, Reactive/Repair, Bacterial cytolysis, Bacterial vaginosis, Atrophy, and Atrophic vaginitis); (3) Challenge accuracy of primary diagnoses (Downgraded, Upheld, and Upgraded) following adjudicative WSI rescreening to assess correlated review times as surrogate indicators of diagnostic confidence in BestCyte functionality (i.e., learning curve); and (4) Correlate primary and adjudicative diagnoses to calculate intra-observer reproducibility Kappa coefficients per Bethesda nomenclature. Results Of 500 thin-layers, the mean [primary/adjudicative rescreening review times (minutes)] were: Overall study [1.38/3.94], NILM [1.23/3.02], ASCUS [1.18/2.53], ASC-H [1.73/4.86], AGUS [1.84/6.34], LSIL [1.49/4.16], HSIL [1.52/4.10], CA [0.65/2.57]. Of 500 primary Bethesda diagnoses: 2 (0.40%) downgraded; 483 (96.6%) upheld; 15 (3.00%) upgraded after adjudicative WSI rescreening. Of 354 NILM diagnoses: 0 downgraded; 344 (97.2%) upheld; 10 (2.82%) upgraded. Of 34 ASCUS diagnoses: 2 (5.88%) downgraded; 28 (82.4%) upheld; 4 (11.8%) upgraded. Of 17 ASC-H diagnoses: 0 downgraded; 16 (94.1%) upheld; 1 (5.88%) upgraded. Of AGUS (n=1), LSIL (n=24), HSIL (n=52), CA (n=1), UNSAT (n=17): 100% upheld. Kappa coefficients with 95% (Confidence Intervals): Overall study 0.9305 (0.8983–0.9627), NILM 0.9429 (0.9110–0.9748), ASCUS 0.8378 (0.7393–0.9363), ASC-H 0.9112 (0.8113–0.9999), AGUS 1.0 (1.0–1.0), LSIL 0.9189 (0.8400–0.9978), HSIL 0.9894 (0.9685–0.9999), CA 1.0 (1.0–1.0), UNSAT 1.0 (1.0–1.0). Primary BestCyte cell image review time trends for NILM, ASCUS, LSIL, and HSIL, revealed plateaus relative to decreasing respective adjudicative WSI rescreening times. Conclusions Given innovative robustness, BestCyte accommodates interpretive fundamentals, enabling shorter ThinPrep thin-layer review times with optimal intra-observer concordance per Bethesda nomenclature through classifying, ranking, sorting, and displaying clinically relevant cells efficiently in galleries. BestCyte fosters continuously optimizing diagnostic confidence learning curves; may supplant manual microscopy for primary screening.
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Eloy C, Russ G, Suciu V, Johnson SJ, Rossi ED, Pantanowitz L, Vielh P. Preoperative diagnosis of thyroid nodules: An integrated multidisciplinary approach. Cancer Cytopathol 2022; 130:320-325. [PMID: 35020978 DOI: 10.1002/cncy.22546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/22/2021] [Accepted: 11/29/2021] [Indexed: 11/08/2022]
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Lau RP, Kim TH, Rao J. Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology. Front Med (Lausanne) 2021; 8:689954. [PMID: 34277664 PMCID: PMC8282905 DOI: 10.3389/fmed.2021.689954] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/08/2021] [Indexed: 12/14/2022] Open
Abstract
Several advances in recent decades in digital imaging, artificial intelligence, and multiplex modalities have improved our ability to automatically analyze and interpret imaging data. Imaging technologies such as optical coherence tomography, optical projection tomography, and quantitative phase microscopy allow analysis of tissues and cells in 3-dimensions and with subcellular granularity. Improvements in computer vision and machine learning have made algorithms more successful in automatically identifying important features to diagnose disease. Many new automated multiplex modalities such as antibody barcoding with cleavable DNA (ABCD), single cell analysis for tumor phenotyping (SCANT), fast analytical screening technique fine needle aspiration (FAST-FNA), and portable fluorescence-based image cytometry analyzer (CytoPAN) are under investigation. These have shown great promise in their ability to automatically analyze several biomarkers concurrently with high sensitivity, even in paucicellular samples, lending themselves well as tools in FNA. Not yet widely adopted for clinical use, many have successfully been applied to human samples. Once clinically validated, some of these technologies are poised to change the routine practice of cytopathology.
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Affiliation(s)
- Ryan P. Lau
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at the University of California, Los Angeles, CA, United States
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Iftikhar MS, Talha GM, Aleem M, Shamim A. Bioinformatics–computer programming. NANOTECHNOLOGY IN CANCER MANAGEMENT 2021:125-148. [DOI: 10.1016/b978-0-12-818154-6.00009-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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