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Jahanifar M, Shephard A, Zamanitajeddin N, Graham S, Raza SEA, Minhas F, Rajpoot N. Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures. Med Image Anal 2024; 94:103132. [PMID: 38442527 DOI: 10.1016/j.media.2024.103132] [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: 11/15/2022] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/07/2024]
Abstract
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive to domain shift often seen in histology images. We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation (Detecting Fast) and candidate refinement (Detecting Slow) stages. The proposed candidate segmentation model, termed EUNet, is fast and accurate due to its architectural design. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. Candidates are then refined using a deeper classifier network, EfficientNet-B7, in the second stage. We make sure both stages are robust against domain shift by incorporating domain generalization methods. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three largest publicly available mitosis datasets, winning the two mitosis domain generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase the utility of the proposed algorithm by processing the TCGA breast cancer cohort (1,124 whole-slide images) to generate and release a repository of more than 620K potential mitotic figures (not exhaustively validated).
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Affiliation(s)
- Mostafa Jahanifar
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK.
| | - Adam Shephard
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK
| | - Neda Zamanitajeddin
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK
| | - Simon Graham
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK; Histofy Ltd, Birmingham, UK
| | - Shan E Ahmed Raza
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK
| | - Fayyaz Minhas
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK
| | - Nasir Rajpoot
- Tissue Image Analytic (TIA) Center, Department of Computer Science, University of Warwick, UK; Histofy Ltd, Birmingham, UK.
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2
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Ibrahim A, Jahanifar M, Wahab N, Toss MS, Makhlouf S, Atallah N, Lashen AG, Katayama A, Graham S, Bilal M, Bhalerao A, Ahmed Raza SE, Snead D, Minhas F, Rajpoot N, Rakha E. Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application. Mod Pathol 2024; 37:100416. [PMID: 38154653 DOI: 10.1016/j.modpat.2023.100416] [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: 06/16/2023] [Revised: 10/27/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023]
Abstract
In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.
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Affiliation(s)
- Asmaa Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Department of Pathology, Faculty of Medicine, Suez Canal University, Egypt
| | - Mostafa Jahanifar
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Noorul Wahab
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Michael S Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Histopathology Department, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Nehal Atallah
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ayat G Lashen
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Ayaka Katayama
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Simon Graham
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Mohsin Bilal
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Abhir Bhalerao
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Shan E Ahmed Raza
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - David Snead
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, United Kingdom
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, University of Warwick, United Kingdom
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, United Kingdom.
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom; Pathology Department, Hamad Medical Corporation, Doha, Qatar.
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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4
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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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Ibrahim A, Toss MS, Makhlouf S, Miligy IM, Minhas F, Rakha EA. Improving mitotic cell counting accuracy and efficiency using phosphohistone-H3 (PHH3) antibody counterstained with haematoxylin and eosin as part of breast cancer grading. Histopathology 2023; 82:393-406. [PMID: 36349500 PMCID: PMC10100421 DOI: 10.1111/his.14837] [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: 08/09/2022] [Revised: 10/08/2022] [Accepted: 11/05/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Mitotic count in breast cancer is an important prognostic marker. Unfortunately, substantial inter- and intraobserver variation exists when pathologists manually count mitotic figures. To alleviate this problem, we developed a new technique incorporating both haematoxylin and eosin (H&E) and phosphorylated histone H3 (PHH3), a marker highly specific to mitotic figures, and compared it to visual scoring of mitotic figures using H&E only. METHODS Two full-face sections from 97 cases were cut, one stained with H&E only, and the other was stained with PHH3 and counterstained with H&E (PHH3-H&E). Counting mitoses using PHH3-H&E was compared to traditional mitoses scoring using H&E in terms of reproducibility, scoring time, and the ability to detect mitosis hotspots. We assessed the agreement between manual and image analysis-assisted scoring of mitotic figures using H&E and PHH3-H&E-stained cells. The diagnostic performance of PHH3 in detecting mitotic figures in terms of sensitivity and specificity was measured. Finally, PHH3 replaced the mitosis score in a multivariate analysis to assess its significance. RESULTS Pathologists detected significantly higher mitotic figures using the PHH3-H&E (median ± SD, 20 ± 33) compared with H&E alone (median ± SD, 16 ± 25), P < 0.001. The concordance between pathologists in identifying mitotic figures was highest when using the dual PHH3-H&E technique; in addition, it highlighted mitotic figures at low power, allowing better agreement on choosing the hotspot area (k = 0.842) in comparison with standard H&E (k = 0.625). A better agreement between image analysis-assisted software and the human eye was observed for PHH3-stained mitotic figures. When the mitosis score was replaced with PHH3 in a Cox regression model with other grade components, PHH3 was an independent predictor of survival (hazard ratio [HR] 5.66, 95% confidence interval [CI] 1.92-16.69; P = 0.002), and even showed a more significant association with breast cancer-specific survival (BCSS) than mitosis (HR 3.63, 95% CI 1.49-8.86; P = 0.005) and Ki67 (P = 0.27). CONCLUSION Using PHH3-H&E-stained slides can reliably be used in routine scoring of mitotic figures and integrating both techniques will compensate for each other's limitations and improve diagnostic accuracy, quality, and precision.
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Affiliation(s)
- Asmaa Ibrahim
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK.,Histopathology department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Michael S Toss
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK
| | - Shorouk Makhlouf
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK.,Department of Pathology, Faculty of Medicine, Assiut University, Assiut, Egypt
| | - Islam M Miligy
- Histopathology department, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt.,Histopathology department, School of Medicine, University of Nottingham, Nottingham, UK
| | - Fayyaz Minhas
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Emad A Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham Biodiscovery Institute, University Park, Nottingham, UK.,Histopathology department, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt.,Histopathology department, School of Medicine, University of Nottingham, Nottingham, UK
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6
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Maier AD. Malignant meningioma. APMIS 2022; 130 Suppl 145:1-58. [DOI: 10.1111/apm.13276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Andrea Daniela Maier
- Department of Neurosurgery, Rigshospitalet Copenhagen University Hospital Copenhagen Denmark
- Department of Pathology, Rigshospitalet Copenhagen University Hospital Copenhagen Denmark
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7
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Mastrosimini MG, Eccher A, Nottegar A, Montin U, Scarpa A, Pantanowitz L, Girolami I. elcome@123WSI validation studies in breast and gynecological pathology. Pathol Res Pract 2022; 240:154191. [DOI: 10.1016/j.prp.2022.154191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022]
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8
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Papparella S, Crescio MI, Baldassarre V, Brunetti B, Burrai GP, Cocumelli C, Grieco V, Iussich S, Maniscalco L, Mariotti F, Millanta F, Paciello O, Rasotto R, Romanucci M, Sfacteria A, Zappulli V. Reproducibility and Feasibility of Classification and National Guidelines for Histological Diagnosis of Canine Mammary Gland Tumours: A Multi-Institutional Ring Study. Vet Sci 2022; 9:vetsci9070357. [PMID: 35878374 PMCID: PMC9325225 DOI: 10.3390/vetsci9070357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Tumours of the mammary gland are common in humans, as in canine species. They are very heterogenous with numerous morphological variants and different biologic behaviours. In the last few decades, several efforts have been made to classify these tumours histologically and establish the level of malignancy by using histologic grading systems. However, reproducibility and diagnostic agreement of such classification and grading have been only rarely assessed. In this study, we tested the variability in diagnoses performed by 15 pathologists using the same classification and grading system. Prior to the study, pathologists agreed on guidelines regarding how to apply these systems. Pathologists worked blindly on 36 digital histologic slides of canine mammary tumours. The agreement was statistically analysed using Cohen’s kappa coefficient that, when equal to 1, indicates perfect agreement. The overall agreement in the identification of hyperplastic-dysplastic/benign/malignant lesions was substantial (kappa 0.76), while outcomes on morphological classification had only a moderate agreement (k = 0.54). Tumour grade assigned by pathologists was the least concordant and kappa could not be calculated. Although promising, the results underline that each diagnostic/grading system should be assessed and optimized for standardization and high diagnostic agreement. Abstract Histological diagnosis of Canine Mammary Tumours (CMTs) provides the basis for proper treatment and follow-up. Nowadays, its accuracy is poorly understood and variable interpretation of histological criteria leads to a lack of standardisation and impossibility to compare studies. This study aimed to quantify the reproducibility of histological diagnosis and grading in CMTs. A blinded ring test on 36 CMTs was performed by 15 veterinary pathologists with different levels of education, after discussion of critical points on the Davis-Thompson Foundation Classification and providing consensus guidelines. Kappa statistics were used to compare the interobserver variability. The overall concordance rate of diagnostic interpretations of WP on identification of hyperplasia-dysplasia/benign/malignant lesions showed a substantial agreement (average k ranging from 0.66 to 0.82, with a k-combined of 0.76). Instead, outcomes on ICD-O-3.2 morphological code /diagnosis of histotype had only a moderate agreement (average k ranging from 0.44 and 0.64, with a k-combined of 0.54). The results demonstrated that standardised classification and consensus guidelines can produce moderate to substantial agreement; however, further efforts are needed to increase this agreement in distinguishing benign versus malignant lesions and in histological grading.
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Affiliation(s)
- Serenella Papparella
- Department of Veterinary Medicine and Animal Production, Unit of Pathology, University of Naples Federico II, 80138 Naples, Italy; (S.P.); (V.B.); (O.P.)
| | - Maria Ines Crescio
- National Reference Center for the Veterinary and Comparative Oncology (CEROVEC), Experimental Zooprophylactic Institute of Piedmont, Liguria and Valle d’Aosta, 10154 Turin, Italy;
| | - Valeria Baldassarre
- Department of Veterinary Medicine and Animal Production, Unit of Pathology, University of Naples Federico II, 80138 Naples, Italy; (S.P.); (V.B.); (O.P.)
| | - Barbara Brunetti
- Department of Veterinary Medical Sciences, University of Bologna, 40126 Bologna, Italy;
| | - Giovanni P. Burrai
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy;
- Mediterranean Center for Disease Control (MCDC), University of Sassari, 07100 Sassari, Italy
| | - Cristiano Cocumelli
- Experimental Zooprophylactic Institute of Lazio and Toscana M. Aleandri, 00178 Rome, Italy;
| | - Valeria Grieco
- Department of Veterinary Medicine, University of Milan, 26900 Lodi, Italy;
| | - Selina Iussich
- Department of Veterinary Science, University of Turin, 10095 Turin, Italy; (S.I.); (L.M.)
| | - Lorella Maniscalco
- Department of Veterinary Science, University of Turin, 10095 Turin, Italy; (S.I.); (L.M.)
| | - Francesca Mariotti
- School of Bioscience and Veterinary Medicine, University of Camerino, 62032 Camerino, Italy;
| | - Francesca Millanta
- Department of Veterinary Sciences, University of Pisa, 56124 Pisa, Italy;
| | - Orlando Paciello
- Department of Veterinary Medicine and Animal Production, Unit of Pathology, University of Naples Federico II, 80138 Naples, Italy; (S.P.); (V.B.); (O.P.)
| | - Roberta Rasotto
- Independent Researcher, Via Messer Ottonello 1, 37127 Verona, Italy;
| | | | | | - Valentina Zappulli
- Department of Comparative Biomedicine and Food Science, University of Padua, 35020 Padua, Italy
- Correspondence: ; Tel.: +39-049-8272962
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MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07441-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractMitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a $$68.7\%$$
68.7
%
F1-score and $$49.0\%$$
49.0
%
F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists’ decision.
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10
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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: 3] [Impact Index Per Article: 1.5] [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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11
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Lashen A, Toss MS, Alsaleem M, Green AR, Mongan NP, Rakha E. The characteristics and clinical significance of atypical mitosis in breast cancer. Mod Pathol 2022; 35:1341-1348. [PMID: 35501336 PMCID: PMC9514994 DOI: 10.1038/s41379-022-01080-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 11/09/2022]
Abstract
Atypical mitosis is considered a feature of malignancy, however, its significance in breast cancer (BC) remains elusive. Here, we aimed to assess the clinical value of atypical mitoses in BC and to explore their underlying molecular features. Atypical and typical mitotic figures were quantified and correlated with clinicopathological variables in a large cohort of primary BC tissue sections (n = 846) using digitalized hematoxylin and eosin whole-slide images (WSIs). In addition, atypical mitoses were assessed in The Cancer Genome Atlas (TCGA) BC dataset (n = 1032) and were linked to the genetic alterations and pathways. In this study, the median of typical mitoses was 17 per 3 mm2 (range 0-120 mitoses), while the median of atypical mitoses was 4 (range 0-103 mitoses). High atypical mitoses were significantly associated with parameters characteristic of aggressive tumor behavior. The total number of mitoses, and a high atypical-to-typical mitoses ratio (>0.27) were associated with poor BC specific survival (BCSS), (p = 0.04 and 0.01, respectively). The atypical-to-typical mitoses ratio dichotomized triple negative-BC (TNBC) patients into two distinct groups in terms of the association with the outcome, while the overall number of mitoses was not. Moreover, TNBC patients with high atypical-to-typical mitoses ratio treated with adjuvant chemotherapy were associated with shorter survival (p = 0.003). Transcriptomic analysis of the TCGA-BRCA cohort dichotomized based on atypical mitoses identified 2494 differentially expressed genes. These included genes linked to pathways involved in chromosomal localization and segregation, centrosome assembly, spindle and microtubule formation, regulation of cell cycle and DNA repair. To conclude, the atypical-to-typical mitoses ratio has prognostic value independent of the overall mitotic count in BC patients and could predict the response to chemotherapy in TNBC.
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Affiliation(s)
- Ayat Lashen
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK ,grid.411775.10000 0004 0621 4712Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt
| | - Michael S. Toss
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Mansour Alsaleem
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK ,grid.412602.30000 0000 9421 8094Department of Applied Medical Science, Applied College, Qassim University, Qassim, Saudi Arabia
| | - Andrew R Green
- grid.4563.40000 0004 1936 8868Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK ,grid.4563.40000 0004 1936 8868Nottingham Breast Cancer Research Centre, University of Nottingham, Nottingham, UK
| | - Nigel P. Mongan
- grid.4563.40000 0004 1936 8868School of Veterinary Medicine and Sciences, University of Nottingham, Nottingham, UK ,grid.5386.8000000041936877XDepartment of Pharmacology, Weill Cornell Medicine, New York, NY USA
| | - Emad Rakha
- Academic Unit for Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK. .,Department of Pathology, Faculty of Medicine, Menoufia University, Shebin El Kom, Egypt.
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