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Köteles MM, Vigdorovits A, Kumar D, Mihai IM, Jurescu A, Gheju A, Bucur A, Harich OO, Olteanu GE. Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists' Assessment Approach. Diagnostics (Basel) 2023; 13:2326. [PMID: 37510069 PMCID: PMC10377791 DOI: 10.3390/diagnostics13142326] [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: 06/02/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide images (WSIs) of DAC-NOS from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Our model demonstrated high precision, sensitivity, and F1 score across different grading components in about 17.5 h with 19,000 iterations. However, the agreement between the model's grading and that of general pathologists varied, showing the highest agreement for the mitotic count score. These findings suggest that AI has the potential to enhance the accuracy and reproducibility of breast cancer grading, warranting further refinement and validation of this approach.
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Affiliation(s)
| | - Alon Vigdorovits
- Bihor County Clinical Emergency Hospital, Gh. Doja Street No. 65, 410169 Oradea, Romania
- Center for Research and Innovation in Personalized Medicine of Respiratory Diseases, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
- Victor Babes Institute of Pathology-Next Generation Pathology Research Group, Splaiul Independenţei 99-101, 050096 Bucharest, Romania
| | | | - Ioana-Maria Mihai
- Department of Microscopic Morphology-Morphopatology, ANAPATMOL Research Center, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Aura Jurescu
- Department of Microscopic Morphology-Morphopatology, ANAPATMOL Research Center, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Adelina Gheju
- Emergency County Hospital Deva, Bulevardul 22 Decembrie 58, 330032 Deva, Romania
| | - Adeline Bucur
- Department of Microscopic Morphology, Discipline of Histology, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
| | - Octavia Oana Harich
- Department of Functional Sciences, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
| | - Gheorghe-Emilian Olteanu
- Center for Research and Innovation in Personalized Medicine of Respiratory Diseases, "Victor Babes" University of Medicine and Pharmacy, Timisoara Eftimie Murgu Sq. No. 2, 300041 Timisoara, Romania
- Faculty of Pharmacy, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
- Research Center for Pharmaco-Toxicological Evaluations, Faculty of Pharmacy, "Victor Babes" University of Medicine and Pharmacy, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania
- Center of Expertise for Rare Lung Diseases, Clinical Hospital of Infectious Diseases and Pneumophthisiology "Dr. Victor Babes" Timisoara, Gh. Adam Street No. 13, 300310 Timisoara, Romania
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Fernandez G, Prastawa M, Madduri AS, Scott R, Marami B, Shpalensky N, Cascetta K, Sawyer M, Chan M, Koll G, Shtabsky A, Feliz A, Hansen T, Veremis B, Cordon-Cardo C, Zeineh J, Donovan MJ. Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years. Breast Cancer Res 2022; 24:93. [PMID: 36539895 PMCID: PMC9764637 DOI: 10.1186/s13058-022-01592-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence. METHODS In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast. With 3:1 balanced training and validation cohorts, patients were retrospectively followed for a median of 6 years. The main outcome was to validate an automated BC phenotyping system combined with clinical features to produce a binomial risk score predicting BC recurrence at diagnosis. RESULTS The PDxBr training model (n = 1559 patients) had a C-index of 0.78 (95% CI, 0.76-0.81) versus clinical 0.71 (95% CI, 0.67-0.74) and image feature models 0.72 (95% CI, 0.70-0.74). A risk score of 58 (scale 0-100) stratified patients as low or high risk, hazard ratio (HR) 5.5 (95% CI 4.19-7.2, p < 0.001), with a sensitivity 0.71, specificity 0.77, NPV 0.95, and PPV 0.32 for predicting BC recurrence within 6 years. In the validation cohort (n = 516), the C-index was 0.75 (95% CI, 0.72-0.79) versus clinical 0.71 (95% CI 0.66-0.75) versus image feature models 0.67 (95% CI, 0.63-071). The validation cohort had an HR of 4.4 (95% CI 2.7-7.1, p < 0.001), sensitivity of 0.60, specificity 0.77, NPV 0.94, and PPV 0.24 for predicting BC recurrence within 6 years. PDxBr also improved Oncotype Recurrence Score (RS) performance: RS 31 cutoff, C-index of 0.36 (95% CI 0.26-0.45), sensitivity 37%, specificity 48%, HR 0.48, p = 0.04 versus Oncotype RS plus AI-grade C-index 0.72 (95% CI 0.67-0.79), sensitivity 78%, specificity 49%, HR 4.6, p < 0.001 versus Oncotype RS plus PDxBr, C-index 0.76 (95% CI 0.70-0.82), sensitivity 67%, specificity 80%, HR 6.1, p < 0.001. CONCLUSIONS PDxBr is a digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict the risk of early-stage BC recurrence. With future validation studies, we anticipate the PDxBr model will enrich current gene expression assays and enhance treatment decision-making.
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Affiliation(s)
- Gerardo Fernandez
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcel Prastawa
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | - Abishek Sainath Madduri
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Richard Scott
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | - Bahram Marami
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | - Nina Shpalensky
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | | | - Mary Sawyer
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Monica Chan
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Giovanni Koll
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | - Alexander Shtabsky
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | - Aaron Feliz
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | | | | | | | - Jack Zeineh
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA
| | - Michael J Donovan
- PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA.
- Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Pathology, University of Miami, Miami, FL, USA.
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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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Rizzo PC, Girolami I, Marletta S, Pantanowitz L, Antonini P, Brunelli M, Santonicco N, Vacca P, Tumino N, Moretta L, Parwani A, Satturwar S, Eccher A, Munari E. Technical and Diagnostic Issues in Whole Slide Imaging Published Validation Studies. Front Oncol 2022; 12:918580. [PMID: 35785212 PMCID: PMC9246412 DOI: 10.3389/fonc.2022.918580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/24/2022] [Indexed: 01/07/2023] Open
Abstract
ObjectiveDigital pathology with whole-slide imaging (WSI) has many potential clinical and non-clinical applications. In the past two decades, despite significant advances in WSI technology adoption remains slow for primary diagnosis. The aim of this study was to identify common pitfalls of WSI reported in validation studies and offer measures to overcome these challenges.MethodsA systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all validation studies designed to evaluate the feasibility of WSI for diagnostic clinical use in pathology. Technical and diagnostic problems encountered with WSI in these studies were recorded.ResultsA total of 45 studies were identified in which technical issues were reported in 15 (33%), diagnostic issues in 8 (18%), and 22 (49%) reported both. Key technical problems encompassed slide scan failure, prolonged time for pathologists to review cases, and a need for higher image resolution. Diagnostic challenges encountered were concerned with grading dysplasia, reliable assessment of mitoses, identification of microorganisms, and clearly defining the invasive front of tumors.ConclusionDespite technical advances with WSI technology, some critical concerns remain that need to be addressed to ensure trustworthy clinical diagnostic use. More focus on the quality of the pre-scanning phase and training of pathologists could help reduce the negative impact of WSI technical difficulties. WSI also seems to exacerbate specific diagnostic tasks that are already challenging among pathologists even when examining glass slides with conventional light microscopy.
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Affiliation(s)
- Paola Chiara Rizzo
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | | | - Stefano Marletta
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
- Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Liron Pantanowitz
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, United States
| | - Pietro Antonini
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Matteo Brunelli
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Nicola Santonicco
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Paola Vacca
- Bambino Gesù Children’s Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Nicola Tumino
- Bambino Gesù Children’s Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Lorenzo Moretta
- Bambino Gesù Children’s Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Rome, Italy
| | - Anil Parwani
- Department of Pathology, Ohio State University Medical Center, Columbus, OH, United States
| | - Swati Satturwar
- Department of Pathology, Ohio State University Medical Center, Columbus, OH, United States
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
- *Correspondence: Albino Eccher,
| | - Enrico Munari
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
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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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Salama AM, Hanna MG, Giri D, Kezlarian B, Jean MH, Lin O, Vallejo C, Brogi E, Edelweiss M. Digital validation of breast biomarkers (ER, PR, AR, and HER2) in cytology specimens using three different scanners. Mod Pathol 2022; 35:52-59. [PMID: 34518629 PMCID: PMC8702445 DOI: 10.1038/s41379-021-00908-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 11/30/2022]
Abstract
Progression in digital pathology has yielded new opportunities for a remote work environment. We evaluated the utility of digital review of breast cancer immunohistochemical prognostic markers (IHC) using whole slide images (WSI) from formalin fixed paraffin embedded (FFPE) cytology cell block specimens (CB) using three different scanners.CB from 20 patients with breast cancer diagnosis and available IHC were included. Glass slides including 20 Hematoxylin and eosin (H&E), 20 Estrogen Receptor (ER), 20 Progesterone Receptor (PR), 16 Androgen Receptor (AR), and 20 Human Epidermal Growth Factor Receptor 2 (HER2) were scanned on 3 different scanners. Four breast pathologists reviewed the WSI and recorded their semi-quantitative scoring for each marker. Kappa concordance was defined as complete agreement between glass/digital pairs. Discordances between microscopic and digital reads were classified as a major when a clinically relevant change was seen. Minor discordances were defined as differences in scoring percentages/staining pattern that would not have resulted in a clinical implication. Scanner precision was tabulated according to the success rate of each scan on all three scanners.In total, we had 228 paired glass/digital IHC reads on all 3 scanners. There was strong concordance kappa ≥0.85 for all pathologists when comparing paired microscopic/digital reads. Strong concordance (kappa ≥0.86) was also seen when comparing reads between scanners.Twenty-three percent of the WSI required rescanning due to barcode detection failures, 14% due to tissue detection failures, and 2% due to focus issues. Scanner 1 had the best average precision of 92%. HER2 IHC had the lowest intra-scanner precision (64%) among all stains.This study is the first to address the utility of WSI in breast cancer IHC in CB and to validate its reporting using 3 different scanners. Digital images are reliable for breast IHC assessment in CB and offer similar reproducibility to microscope reads.
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Affiliation(s)
- Abeer M Salama
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Dilip Giri
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Brie Kezlarian
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Marc-Henri Jean
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Oscar Lin
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Christina Vallejo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Edi Brogi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Marcia Edelweiss
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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7
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Ibrahim A, Lashen A, Toss M, Mihai R, Rakha E. Assessment of mitotic activity in breast cancer: revisited in the digital pathology era. J Clin Pathol 2021; 75:365-372. [PMID: 34556501 DOI: 10.1136/jclinpath-2021-207742] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/06/2021] [Indexed: 11/04/2022]
Abstract
The assessment of cell proliferation is a key morphological feature for diagnosing various pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic figures in routine histological sections remains the gold-standard method to evaluate the proliferative activity and grading of cancer. Despite the apparent simplicity of such a well-established method, visual assessment of mitotic figures in breast cancer (BC) remains a challenging task with low concordance among pathologists which can lead to under or overestimation of tumour grade and hence affects management. Guideline recommendations for counting mitoses in BC have been published to standardise methodology and improve concordance; however, the results remain less satisfactory. Alternative approaches such as the use of the proliferation marker Ki67 have been recommended but these did not show better performance in terms of concordance or prognostic stratification. The advent of whole slide image technology has brought the issue of mitotic counting in BC into the light again with more challenges to develop objective criteria for identifying and scoring mitotic figures in digitalised images. Using reliable and reproducible morphological criteria can provide the highest degree of concordance among pathologists and could even benefit the further application of artificial intelligence (AI) in breast pathology, and this relies mainly on the explicit description of these figures. In this review, we highlight the morphology of mitotic figures and their mimickers, address the current caveats in counting mitoses in breast pathology and describe how to strictly apply the morphological criteria for accurate and reliable histological grade and AI models.
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Affiliation(s)
- Asmaa Ibrahim
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK.,Department of Pathology, Suez Canal University, Ismailia, Egypt
| | - Ayat Lashen
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK.,Department of Pathology, Menoufia University, Shebin El-Kom, Egypt
| | - Michael Toss
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK
| | - Raluca Mihai
- Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
| | - Emad Rakha
- Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK .,Department of Pathology, Menoufia University, Shebin El-Kom, Egypt
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8
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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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Samuelson MI, Chen SJ, Boukhar SA, Schnieders EM, Walhof ML, Bellizzi AM, Robinson RA, Rajan K D A. Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis. Am J Clin Pathol 2021; 155:638-648. [PMID: 33511392 PMCID: PMC7929400 DOI: 10.1093/ajcp/aqaa280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The ongoing global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitates adaptations in the practice of surgical pathology at scale. Primary diagnosis by whole-slide imaging (WSI) is a key component that would aid departments in providing uninterrupted histopathology diagnosis and maintaining revenue streams from disruption. We sought to perform rapid validation of the use of WSI in primary diagnosis meeting recommendations of the College of American Pathologists guidelines. METHODS Glass slides from clinically reported cases from 5 participating pathologists with a preset washout period were digitally scanned and reviewed in settings identical to typical reporting. Cases were classified as concordant or with minor or major disagreement with the original diagnosis. Randomized subsampling was performed, and mean concordance rates were calculated. RESULTS In total, 171 cases were included and distributed equally among participants. For the group as a whole, the mean concordance rate in sampled cases (n = 90) was 83.6% counting all discrepancies and 94.6% counting only major disagreements. The mean pathologist concordance rate in sampled cases (n = 18) ranged from 90.49% to 97%. CONCLUSIONS We describe a novel double-blinded method for rapid validation of WSI for primary diagnosis. Our findings highlight the occurrence of a range of diagnostic reproducibility when deploying digital methods.
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Affiliation(s)
- Megan I Samuelson
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Stephanie J Chen
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Sarag A Boukhar
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Eric M Schnieders
- Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Mackenzie L Walhof
- Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Andrew M Bellizzi
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Robert A Robinson
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Anand Rajan K D
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
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10
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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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Chang MC, Mrkonjic M. Review of the current state of digital image analysis in breast pathology. Breast J 2020; 26:1208-1212. [PMID: 32342590 DOI: 10.1111/tbj.13858] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 11/05/2019] [Indexed: 01/10/2023]
Abstract
Advances in digital image analysis have the potential to transform the practice of breast pathology. In the near future, a move to a digital workflow offers improvements in efficiency. Coupled with artificial intelligence (AI), digital pathology can assist pathologist interpretation, automate time-consuming tasks, and discover novel morphologic patterns. Opportunities for digital enhancements abound in breast pathology, from increasing reproducibility in grading and biomarker interpretation, to discovering features that correlate with patient outcome and treatment. Our objective is to review the most recent developments in digital pathology with clear impact to breast pathology practice. Although breast pathologists currently undertake limited adoption of digital methods, the field is rapidly evolving. Care is needed to validate emerging technologies for effective patient care.
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Affiliation(s)
- Martin C Chang
- University of Vermont Cancer Center, Burlington, VT, USA.,Department of Pathology and Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Miralem Mrkonjic
- Sinai Health System, Toronto, ON, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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