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Bertram CA, Donovan TA, Bartel A. Mitotic activity: A systematic literature review of the assessment methodology and prognostic value in canine tumors. Vet Pathol 2024:3009858241239565. [PMID: 38533804 DOI: 10.1177/03009858241239565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
One of the most relevant prognostic indices for tumors is cellular proliferation, which is most commonly measured by the mitotic activity in routine tumor sections. The goal of this systematic review was to analyze the methods and prognostic relevance of histologically measuring mitotic activity that have been reported for canine tumors in the literature. A total of 137 articles that correlated the mitotic activity in canine tumors with patient outcome were identified through a systematic (PubMed and Scopus) and nonsystematic (Google Scholar) literature search and eligibility screening process. Mitotic activity methods encompassed the mitotic count (MC, number of mitotic figures per tumor area) in 126 studies, presumably the MC (method not specified) in 6 studies, and the mitotic index (MI, number of mitotic figures per number of tumor cells) in 5 studies. A particularly high risk of bias was identified based on the available details of the MC methods and statistical analyses, which often did not quantify the prognostic discriminative ability of the MC and only reported P values. A significant association of the MC with survival was found in 72 of 109 (66%) studies. However, survival was evaluated by at least 3 studies in only 7 tumor types/groups, of which a prognostic relevance is apparent for mast cell tumors of the skin, cutaneous melanoma, and soft tissue tumor of the skin and subcutis. None of the studies using the MI found a prognostic relevance. This review highlights the need for more studies with standardized methods and appropriate analysis of the discriminative ability to prove the prognostic value of the MC and MI in various tumor types. Future studies are needed to evaluate the influence of the performance of individual pathologists on the appropriateness of prognostic thresholds and investigate methods to improve interobserver reproducibility.
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Bertram CA, Bartel A, Donovan TA, Kiupel M. Atypical Mitotic Figures Are Prognostically Meaningful for Canine Cutaneous Mast Cell Tumors. Vet Sci 2023; 11:5. [PMID: 38275921 PMCID: PMC10821277 DOI: 10.3390/vetsci11010005] [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: 11/28/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Cell division through mitosis (microscopically visible as mitotic figures, MFs) is a highly regulated process. However, neoplastic cells may exhibit errors in chromosome segregation (microscopically visible as atypical mitotic figures, AMFs) resulting in aberrant chromosome structures. AMFs have been shown to be of prognostic relevance for some neoplasms in humans but not in animals. In this study, the prognostic relevance of AMFs was evaluated for canine cutaneous mast cell tumors (ccMCT). Histological examination was conducted by one pathologist in whole slide images of 96 cases of ccMCT with a known survival time. Tumor-related death occurred in 11/18 high-grade and 2/78 low-grade cases (2011 two-tier system). The area under the curve (AUC) was 0.859 for the AMF count and 0.880 for the AMF to MF ratio with regard to tumor-related mortality. In comparison, the AUC for the mitotic count was 0.885. Based on our data, a prognostically meaningful threshold of ≥3 per 2.37 mm2 for the AMF count (sensitivity: 76.9%, specificity: 98.8%) and >7.5% for the AMF:MF ratio (sensitivity: 76.9%, specificity: 100%) is suggested. While the mitotic count of ≥ 6 resulted in six false positive cases, these could be eliminated when combined with the AMF to MF ratio. In conclusion, the results of this study suggests that AMF enumeration is a prognostically valuable test, particularly due to its high specificity with regard to tumor-related mortality. Additional validation and reproducibility studies are needed to further evaluate AMFs as a prognostic criterion for ccMCT and other tumor types.
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Affiliation(s)
- Christof A. Bertram
- Institute of Veterinary Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Alexander Bartel
- Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, 14163 Berlin, Germany;
| | - Taryn A. Donovan
- Department of Anatomic Pathology, The Schwarzman Animal Medical Center, New York, NY 10065, USA;
| | - Matti Kiupel
- Veterinary Diagnostic Laboratory, Michigan State University, Lansing, MI 48910, USA
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van Bergeijk SA, Stathonikos N, ter Hoeve ND, Lafarge MW, Nguyen TQ, van Diest PJ, Veta M. Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow. J Pathol Inform 2023; 14:100316. [PMID: 37273455 PMCID: PMC10238836 DOI: 10.1016/j.jpi.2023.100316] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/13/2023] [Accepted: 04/28/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to subjectivity. This study investigated whether mitoses counting in BC using digital whole slide images (WSI) compares better to light microscopy (LM) when assisted by artificial intelligence (AI), and to which extent differences in digital MC (AI assisted or not) result in BR grade variations. Methods Fifty BC patients with paired core biopsies and resections were randomly selected. Component scores for BR grade were extracted from pathology reports. MC was assessed using LM, WSI, and AI. Different modalities (LM-MC, WSI-MC, and AI-MC) were analyzed for correlation with scatterplots and linear regression, and for agreement in final BR with Cohen's κ. Results MC modalities strongly correlated in both biopsies and resections: LM-MC and WSI-MC (R2 0.85 and 0.83, respectively), LM-MC and AI-MC (R2 0.85 and 0.95), and WSI-MC and AI-MC (R2 0.77 and 0.83). Agreement in BR between modalities was high in both biopsies and resections: LM-MC and WSI-MC (κ 0.93 and 0.83, respectively), LM-MC and AI-MC (κ 0.89 and 0.83), and WSI-MC and AI-MC (κ 0.96 and 0.73). Conclusion This first validation study shows that WSI-MC may compare better to LM-MC when using AI. Agreement between BR grade based on the different mitoses counting modalities was high. These results suggest that mitoses counting on WSI can well be done, and validate the presented AI algorithm for pathologist supervised use in daily practice. Further research is required to advance our knowledge of AI-MC, but it appears at least non-inferior to LM-MC.
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Affiliation(s)
- Stijn A. van Bergeijk
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Natalie D. ter Hoeve
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Maxime W. Lafarge
- Medical Image Analysis Group (IMAG/e), Eindhoven University of Technology, Eindhoven, The Netherlands
- Computational and Translational Pathology Group, Department of Pathology and Molecular Pathology, University Hospital and University of Zürich, Schmelzbergstrasse 12, 8091 Zurich, Switzerland
| | - Tri Q. Nguyen
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Paul J. van Diest
- Department of Pathology, University Medical Center Utrecht, Postal Box 85500, 3508 GA Utrecht, The Netherlands
| | - Mitko Veta
- Medical Image Analysis Group (IMAG/e), Eindhoven University of Technology, Eindhoven, The Netherlands
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Defining the area of mitoses counting in invasive breast cancer using whole slide image. Mod Pathol 2022; 35:739-748. [PMID: 34897279 PMCID: PMC9174050 DOI: 10.1038/s41379-021-00981-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/19/2021] [Indexed: 01/02/2023]
Abstract
Although counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.
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Bertram CA, Aubreville M, Donovan TA, Bartel A, Wilm F, Marzahl C, Assenmacher CA, Becker K, Bennett M, Corner S, Cossic B, Denk D, Dettwiler M, Gonzalez BG, Gurtner C, Haverkamp AK, Heier A, Lehmbecker A, Merz S, Noland EL, Plog S, Schmidt A, Sebastian F, Sledge DG, Smedley RC, Tecilla M, Thaiwong T, Fuchs-Baumgartinger A, Meuten DJ, Breininger K, Kiupel M, Maier A, Klopfleisch R. Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy. Vet Pathol 2021; 59:211-226. [PMID: 34965805 PMCID: PMC8928234 DOI: 10.1177/03009858211067478] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.
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Affiliation(s)
- Christof A. Bertram
- University of Veterinary Medicine, Vienna, Austria
- Freie Universität Berlin, Berlin, Germany
| | | | | | | | - Frauke Wilm
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christian Marzahl
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | - Sophie Merz
- IDEXX Vet Med Labor GmbH, Kornwestheim, Germany
| | | | | | | | | | | | | | - Marco Tecilla
- Roche Pharmaceutical Research and Early Development (pRED), Basel, Switzerland
| | | | | | | | | | | | - Andreas Maier
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Lashen A, Ibrahim A, Katayama A, Ball G, Mihai R, Toss M, Rakha E. Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images. Histopathology 2021; 79:913-925. [PMID: 34455620 DOI: 10.1111/his.14543] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 07/24/2021] [Accepted: 08/15/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND AIMS Visual assessment of mitotic figures in breast cancer (BC) remains a challenge. This is expected to be more pronounced in the digital pathology era. This study aims to refine the criteria of mitotic figure recognition, particularly in whole slide images (WSI). METHOD AND RESULTS Haematoxylin and eosin (H&E)-stained BC sections (n = 506) were examined using light microscopy (LM) and WSI. A set of features for identifying mitosis in WSI and to distinguish true figures from mimickers was developed. Changes in the mitotic count between the two platforms was explored. Morphological features of mitoses were recorded separately, including absence of nuclear membrane, chromatin hairy-like projections, shape, cytoplasmic features, mitotic cell size and relationship to surrounding cells. Each mitotic phase has its own mimickers. Fifty-eight per cent of mitoses showed absent hairy-like projection in WSI; however, 89% retained their ragged nuclear border, which distinguished them from mimickers including apoptotic cells, lymphocytes and dark elongated hyperchromatic structures. Mitosis in WSI showed loss of fine details, and there was a 20% average reduction rate of mitotic counts when compared to the same area on LM. Using refined mitosis recognition criteria in WSI resulted in a twofold improvement of interobserver concordance. However, when compared to LM, 19% of cases were underscored in WSIs. CONCLUSIONS All morphological features of mitosis should be considered to enable recognition and differentiation from their mimickers, particularly in WSI, to ensure reliable BC grading. Refining mitotic cut-offs per specific area when using WSI, based on the degree of reduction and association with outcome, is warranted.
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Affiliation(s)
- Ayat Lashen
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
| | - Asmaa Ibrahim
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Ayaka Katayama
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Graham Ball
- John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Raluca Mihai
- Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
| | - Michael Toss
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| | - Emad Rakha
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
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Wang M, Aung PP, Prieto VG. Standardized Method for Defining a 1-mm2 Region of Interest for Calculation of Mitotic Rate on Melanoma Whole Slide Images. Arch Pathol Lab Med 2021; 145:1255-1263. [PMID: 33417687 DOI: 10.5858/arpa.2020-0137-oa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Mitotic rate counting is essential in pathologic evaluations in melanoma. The American Joint Committee on Cancer recommends reporting the number of mitotic figures (MFs) in a 1-mm2 area encompassing the "hot spot." There is currently no standard procedure for delineating a 1-mm2 region of interest for MF counting on a digital whole slide image (WSI) of melanoma. OBJECTIVE.— To establish a standardized method to enclose a 1-mm2 region of interest for MF counting in melanoma based on WSIs and assess the method's effectiveness. DESIGN.— Whole slide images were visualized using the ImageScope viewer (Aperio). Different monitors and viewing magnifications were explored and the annotation tools provided by ImageScope were evaluated. For validation, we compared mitotic rates obtained from WSIs with our method and those from glass slides with traditional microscopy with 30 melanoma cases. RESULTS.— Of the monitors we examined, a 32-inch monitor with 3840 × 2160 resolution was optimal for counting MFs within a 1-mm2 region of interest in melanoma. When WSIs were viewed in the ImageScope viewer, ×10 to ×20 magnification during screening could efficiently locate a hot spot and ×20 to ×40 magnification during counting could accurately identify MFs. Fixed-shape annotations with 500 × 500-μm squares or circles can precisely and efficiently enclose a 1-mm2 region of interest. Our method on WSIs was able to produce a higher mitotic rate than with glass slides. CONCLUSIONS.— Whole slide images may be used to efficiently count MFs. We recommend fixed-shape annotation with 500 × 500-μm squares or circles for routine practice in counting MFs for melanoma.
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Affiliation(s)
- Minhua Wang
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Phyu P Aung
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Victor G Prieto
- From the Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
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8
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Donovan TA, Moore FM, Bertram CA, Luong R, Bolfa P, Klopfleisch R, Tvedten H, Salas EN, Whitley DB, Aubreville M, Meuten DJ. Mitotic Figures-Normal, Atypical, and Imposters: A Guide to Identification. Vet Pathol 2020; 58:243-257. [PMID: 33371818 DOI: 10.1177/0300985820980049] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Counting mitotic figures (MF) in hematoxylin and eosin-stained histologic sections is an integral part of the diagnostic pathologist's tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm2). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.
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Affiliation(s)
| | | | | | | | - Pompei Bolfa
- 41635Ross University, Basseterre, Saint Kitts and Nevis
| | | | - Harold Tvedten
- 8095Swedish University of Agricultural Sciences, Uppsala, Sweden
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Kim D, Pantanowitz L, Schüffler P, Yarlagadda DVK, Ardon O, Reuter VE, Hameed M, Klimstra DS, Hanna MG. (Re) Defining the High-Power Field for Digital Pathology. J Pathol Inform 2020; 11:33. [PMID: 33343994 PMCID: PMC7737490 DOI: 10.4103/jpi.jpi_48_20] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/04/2020] [Accepted: 09/01/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). MATERIALS AND METHODS Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at "×40" equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080-4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27" Philips PS27QHDCR, FDA-cleared 24" Dell MR2416, 24" Hewlett Packard Z24n G2, and 28" Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. RESULTS A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). CONCLUSION Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to "×40" digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.
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Affiliation(s)
- David Kim
- Department of Pathology and Laboratory Medicine, New York-Presbyterian Hospital, Weill Cornell Medical College, New York, NY, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Peter Schüffler
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Warren Alpert Center for Digital and Computational Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E. Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Warren Alpert Center for Digital and Computational Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Warren Alpert Center for Digital and Computational Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David S. Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Warren Alpert Center for Digital and Computational Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Veccia A, Antonelli A, Martini A, Falagario U, Carrieri G, Grob MB, Guruli G, Simeone C, Wiklund P, Porpiglia F, Autorino R. Ureteral location is associated with survival outcomes in upper tract urothelial carcinoma: A population‐based analysis. Int J Urol 2020; 27:966-972. [DOI: 10.1111/iju.14336] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 06/30/2020] [Indexed: 02/02/2023]
Affiliation(s)
- Alessandro Veccia
- Division of Urology Department of Surgery VCU Health System Richmond Virginia USA
- Urology Unit ASST Spedali Civili Hospital, Department of Medical and Surgical Specialties, Radiological Science and Public Health, University of Brescia Brescia Italy
| | - Alessandro Antonelli
- Urology Unit AUOI Verona Department of Surgery, Dentistry, Pediatrics and Gynecology University of Verona Verona Italy
| | - Alberto Martini
- Department of Urology Vita Salute San Raffaele University Milan Italy
- Department of Urology Icahn School of Medicine at Mount Sinai New York City New York USA
| | - Ugo Falagario
- Division of Urology Department of Surgery VCU Health System Richmond Virginia USA
- Department of Urology Icahn School of Medicine at Mount Sinai New York City New York USA
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences University of Foggia Foggia Italy
| | - Giuseppe Carrieri
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences University of Foggia Foggia Italy
| | - Mayer B Grob
- Division of Urology Department of Surgery VCU Health System Richmond Virginia USA
| | - Georgi Guruli
- Division of Urology Department of Surgery VCU Health System Richmond Virginia USA
| | - Claudio Simeone
- Urology Unit ASST Spedali Civili Hospital, Department of Medical and Surgical Specialties, Radiological Science and Public Health, University of Brescia Brescia Italy
| | - Peter Wiklund
- Department of Urology Icahn School of Medicine at Mount Sinai New York City New York USA
| | - Francesco Porpiglia
- Department of Urology “San Luigi Gonzaga” Hospital University of Turin Turin Italy
| | - Riccardo Autorino
- Division of Urology Department of Surgery VCU Health System Richmond Virginia USA
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11
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Pantanowitz L, Hartman D, Qi Y, Cho EY, Suh B, Paeng K, Dhir R, Michelow P, Hazelhurst S, Song SY, Cho SY. Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses. Diagn Pathol 2020; 15:80. [PMID: 32622359 PMCID: PMC7335442 DOI: 10.1186/s13000-020-00995-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 06/25/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation exists when pathologists manually count mitotic figures. Artificial intelligence (AI) coupled with whole slide imaging offers a potential solution to this problem. The aim of this study was to accordingly critique an AI tool developed to quantify mitotic figures in whole slide images of invasive breast ductal carcinoma. METHODS A representative H&E slide from 320 breast invasive ductal carcinoma cases was scanned at 40x magnification. Ten expert pathologists from two academic medical centers labeled mitotic figures in whole slide images to train and validate an AI algorithm to detect and count mitoses. Thereafter, 24 readers of varying expertise were asked to count mitotic figures with and without AI support in 140 high-power fields derived from a separate dataset. Their accuracy and efficiency of performing these tasks were calculated and statistical comparisons performed. RESULTS For each experience level the accuracy, precision and sensitivity of counting mitoses by users improved with AI support. There were 21 readers (87.5%) that identified more mitoses using AI support and 13 reviewers (54.2%) that decreased the quantity of falsely flagged mitoses with AI. More time was spent on this task for most participants when not provided with AI support. AI assistance resulted in an overall time savings of 27.8%. CONCLUSIONS This study demonstrates that pathology end-users were more accurate and efficient at quantifying mitotic figures in digital images of invasive breast carcinoma with the aid of AI. Higher inter-pathologist agreement with AI assistance suggests that such algorithms can also help standardize practice. Not surprisingly, there is much enthusiasm in pathology regarding the prospect of using AI in routine practice to perform mundane tasks such as counting mitoses.
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Affiliation(s)
- Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center Cancer Pavilion, Suite 201, 5150 Centre Ave, Pittsburgh, PA, 15232, USA.
- Department of Anatomical Pathology, University of the Witwatersrand and National Health Laboratory Services, Johannesburg, South Africa.
| | - Douglas Hartman
- Department of Pathology, University of Pittsburgh Medical Center Cancer Pavilion, Suite 201, 5150 Centre Ave, Pittsburgh, PA, 15232, USA
| | - Yan Qi
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Eun Yoon Cho
- Department of Pathology, Samsung Medical Center, Seoul, South Korea
| | | | | | - Rajiv Dhir
- Department of Pathology, University of Pittsburgh Medical Center Cancer Pavilion, Suite 201, 5150 Centre Ave, Pittsburgh, PA, 15232, USA
| | - Pamela Michelow
- Department of Anatomical Pathology, University of the Witwatersrand and National Health Laboratory Services, Johannesburg, South Africa
| | - Scott Hazelhurst
- School of Electrical & Information Engineering and Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Sang Yong Song
- Department of Pathology, Samsung Medical Center, Seoul, South Korea
| | - Soo Youn Cho
- Department of Pathology, Samsung Medical Center, Seoul, South Korea
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Tabata K, Uraoka N, Benhamida J, Hanna MG, Sirintrapun SJ, Gallas BD, Gong Q, Aly RG, Emoto K, Matsuda KM, Hameed MR, Klimstra DS, Yagi Y. Validation of mitotic cell quantification via microscopy and multiple whole-slide scanners. Diagn Pathol 2019; 14:65. [PMID: 31238983 PMCID: PMC6593538 DOI: 10.1186/s13000-019-0839-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 06/11/2019] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The establishment of whole-slide imaging (WSI) as a medical diagnostic device allows that pathologists may evaluate mitotic activity with this new technology. Furthermore, the image digitalization provides an opportunity to develop algorithms for automatic quantifications, ideally leading to improved reproducibility as compared to the naked eye examination by pathologists. In order to implement them effectively, accuracy of mitotic figure detection using WSI should be investigated. In this study, we aimed to measure pathologist performance in detecting mitotic figures (MFs) using multiple platforms (multiple scanners) and compare the results with those obtained using a brightfield microscope. METHODS Four slides of canine oral melanoma were prepared and digitized using 4 WSI scanners. In these slides, 40 regions of interest (ROIs) were demarcated, and five observers identified the MFs using different viewing modes: microscopy and WSI. We evaluated the inter- and intra-observer agreements between modes with Cohen's Kappa and determined "true" MFs with a consensus panel. We then assessed the accuracy (agreement with truth) using the average of sensitivity and specificity. RESULTS In the 40 ROIs, 155 candidate MFs were detected by five pathologists; 74 of them were determined to be true MFs. Inter- and intra-observer agreement was mostly "substantial" or greater (Kappa = 0.594-0.939). Accuracy was between 0.632 and 0.843 across all readers and modes. After averaging over readers for each modality, we found that mitosis detection accuracy for 3 of the 4 WSI scanners was significantly less than that of the microscope (p = 0.002, 0.012, and 0.001). CONCLUSIONS This study is the first to compare WSIs and microscopy in detecting MFs at the level of individual cells. Our results suggest that WSI can be used for mitotic cell detection and offers similar reproducibility to the microscope, with slightly less accuracy.
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Affiliation(s)
- Kazuhiro Tabata
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
- Department of Pathology, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki, Nagasaki 8528501 Japan
| | - Naohiro Uraoka
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
| | - Jamal Benhamida
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
| | | | - Brandon D. Gallas
- Center For Devices and Radiological Health, Office of Science and Engineering Laboratories, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993 USA
| | - Qi Gong
- Center For Devices and Radiological Health, Office of Science and Engineering Laboratories, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993 USA
| | - Rania G. Aly
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
- Department of Pathology, Faculty of Medicine, Alexandria university, 22 El-Guish Road, El-Shatby, Alexandria, 21526 Egypt
| | - Katsura Emoto
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, 10065 NY USA
| | - Kant M. Matsuda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
| | - Meera R. Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
| | - David S. Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
| | - Yukako Yagi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065 USA
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