1
|
Jain E, Patel A, Parwani AV, Shafi S, Brar Z, Sharma S, Mohanty SK. Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Int J Surg Pathol 2024; 32:433-448. [PMID: 37437093 DOI: 10.1177/10668969231185089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.
Collapse
Affiliation(s)
- Ekta Jain
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Ankush Patel
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Zoya Brar
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Shivani Sharma
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| |
Collapse
|
2
|
Oyibo P, Agbana T, van Lieshout L, Oyibo W, Diehl JC, Vdovine G. An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis. J Microsc 2024; 294:52-61. [PMID: 38291833 DOI: 10.1111/jmi.13269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/01/2024]
Abstract
Traditionally, automated slide scanning involves capturing a rectangular grid of field-of-view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images to determine the best in-focus image. However, these methods can be time-consuming due to the need for X-, Y- and Z-axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best in-focus FoV image, we utilise a hill-climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z-axis. We implemented this procedure to optimise the efficiency of the Schistoscope, an automated digital microscope developed to diagnose urogenital schistosomiasis by imaging Schistosoma haematobium eggs on 13 or 25 mm membrane filters. Our improved method reduces the automated slide scanning time by 63.18% and 72.52% for the respective filter sizes. This advancement greatly supports the practicality of the Schistoscope in large-scale schistosomiasis monitoring and evaluation programs in endemic regions. This will save time, resources and also accelerate generation of data that is critical in achieving the targets for schistosomiasis elimination.
Collapse
Affiliation(s)
- Prosper Oyibo
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Tope Agbana
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Lisette van Lieshout
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | - Wellington Oyibo
- Centre for Transdisciplinary Research for Malaria & Neglected Tropical Diseases, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Jan-Carel Diehl
- Department of Sustainable Design Engineering, Delft University of Technology, Delft, The Netherlands
| | - Gleb Vdovine
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| |
Collapse
|
3
|
Ono D, Kawai H, Kuwahara H, Yokota T. Automated whole slide morphometry of sural nerve biopsy using machine learning. Neuropathol Appl Neurobiol 2024; 50:e12967. [PMID: 38448224 DOI: 10.1111/nan.12967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 02/13/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
AIM The morphometry of sural nerve biopsies, such as fibre diameter and myelin thickness, helps us understand the underlying mechanism of peripheral neuropathies. However, in current clinical practice, only a portion of the specimen is measured manually because of its labour-intensive nature. In this study, we aimed to develop a machine learning-based application that inputs a whole slide image (WSI) of the biopsied sural nerve and automatically performs morphometric analyses. METHODS Our application consists of three supervised learning models: (1) nerve fascicle instance segmentation, (2) myelinated fibre detection and (3) myelin sheath segmentation. We fine-tuned these models using 86 toluidine blue-stained slides from various neuropathies and developed an open-source Python library. RESULTS Performance evaluation showed (1) a mask average precision (AP) of 0.861 for fascicle segmentation, (2) box AP of 0.711 for fibre detection and (3) a mean intersection over union (mIoU) of 0.817 for myelin segmentation. Our software identified 323,298 nerve fibres and 782 fascicles in 70 WSIs. Small and large fibre populations were objectively determined based on clustering analysis. The demyelination group had large fibres with thinner myelin sheaths and higher g-ratios than the vasculitis group. The slope of the regression line from the scatter plots of the diameters and g-ratios was higher in the demyelination group than in the vasculitis group. CONCLUSION We developed an application that performs whole slide morphometry of human biopsy samples. Our open-source software can be used by clinicians and pathologists without specific machine learning skills, which we expect will facilitate data-driven analysis of sural nerve biopsies for a more detailed understanding of these diseases.
Collapse
Affiliation(s)
- Daisuke Ono
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Honami Kawai
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroya Kuwahara
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takanori Yokota
- Department of Neurology and Neurological Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| |
Collapse
|
4
|
Yee J, Rosendahl C, Aoude LG. The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective. Melanoma Res 2024; 34:96-104. [PMID: 38141179 PMCID: PMC10906187 DOI: 10.1097/cmr.0000000000000951] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/29/2023] [Indexed: 12/25/2023]
Abstract
Clinical dermatoscopy and pathological slide assessment are essential in the diagnosis and management of patients with cutaneous melanoma. For those presenting with stage IIC disease and beyond, radiological investigations are often considered. The dermatoscopic, whole slide and radiological images used during clinical care are often stored digitally, enabling artificial intelligence (AI) and convolutional neural networks (CNN) to learn, analyse and contribute to the clinical decision-making. A keyword search of the Medline database was performed to assess the progression, capabilities and limitations of AI and CNN and its use in diagnosis and management of cutaneous melanoma. Full-text articles were reviewed if they related to dermatoscopy, pathological slide assessment or radiology. Through analysis of 95 studies, we demonstrate that diagnostic accuracy of AI/CNN can be superior (or at least equal) to clinicians. However, variability in image acquisition, pre-processing, segmentation, and feature extraction remains challenging. With current technological abilities, AI/CNN and clinicians synergistically working together are better than one another in all subspecialty domains relating to cutaneous melanoma. AI has the potential to enhance the diagnostic capabilities of junior dermatology trainees, primary care skin cancer clinicians and general practitioners. For experienced clinicians, AI provides a cost-efficient second opinion. From a pathological and radiological perspective, CNN has the potential to improve workflow efficiency, allowing clinicians to achieve more in a finite amount of time. Until the challenges of AI/CNN are reliably met, however, they can only remain an adjunct to clinical decision-making.
Collapse
Affiliation(s)
- Joshua Yee
- Faculty of Medicine, University of Queensland, St Lucia
| | - Cliff Rosendahl
- Primary Care Clinical Unit, Medical School, The University of Queensland, Herston
| | - Lauren G. Aoude
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, Australia
| |
Collapse
|
5
|
Sheikh TS, Cho M. Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features. Bioengineering (Basel) 2024; 11:252. [PMID: 38534526 DOI: 10.3390/bioengineering11030252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/10/2024] [Accepted: 02/26/2024] [Indexed: 03/28/2024] Open
Abstract
The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types of nuclei from different organs of the body. The proposed framework improved the segmentation performance for each nuclear type using radiomics. First, we used distinct radiomic features to extract and analyze quantitative information about each type of nucleus and subsequently trained various classifiers based on the best input sub-features of each radiomic feature selected by a LASSO operator. Second, we inputted the outputs of the best classifier to various segmentation models to learn the variants of nuclei. Using the MoNuSAC2020 dataset, we achieved state-of-the-art segmentation performance for each category of nuclei type despite the complexity, overlapping, and obscure regions. The generalized adaptability of the proposed framework was verified by the consistent performance obtained in whole slide images of different organs of the body and radiomic features.
Collapse
Affiliation(s)
- Taimoor Shakeel Sheikh
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
| | - Migyung Cho
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
| |
Collapse
|
6
|
Chen W, Ziebell J, Arole V, Parkinson B, Yu L, Dai H, Frankel WL, Yearsley M, Esnakula A, Sun S, Gamble D, Vazzano J, Mishra M, Schoenfield L, Kneile J, Reuss S, Schumacher M, Satturwar S, Li Z, Parwani A, Lujan G. Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging. J Transl Med 2024; 104:100262. [PMID: 37839639 DOI: 10.1016/j.labinv.2023.100262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 μm in length by 0.5-1 μm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.
Collapse
Affiliation(s)
- Wei Chen
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Ziebell
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Vidya Arole
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Bryce Parkinson
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lianbo Yu
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
| | - Harrison Dai
- Eastern Virginia Medical School, Norfolk, Virginia
| | - Wendy L Frankel
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Martha Yearsley
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Ashwini Esnakula
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Shaoli Sun
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Denise Gamble
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jennifer Vazzano
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Manisha Mishra
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Lynn Schoenfield
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Jeffrey Kneile
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Sarah Reuss
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Melinda Schumacher
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Swati Satturwar
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
| |
Collapse
|
7
|
Vazzano J, Johansson D, Hu K, Eurén K, Elfwing S, Parwani A, Zhou M. Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors. J Transl Med 2023; 103:100257. [PMID: 37813279 DOI: 10.1016/j.labinv.2023.100257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/18/2023] [Accepted: 10/01/2023] [Indexed: 10/11/2023] Open
Abstract
Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histopathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm's annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity, 100%). Preanalytical factors had no significant impact on whole slide imaging and subsequent application of a CADe software. Prediction accuracy could potentially be further improved by processing biopsy specimens in a centralized histology laboratory and training the Algorithm on WSIs from the same laboratory in order to minimize variations in preanalytical factors and optimize the diagnostic performance of the Algorithm.
Collapse
Affiliation(s)
- Jennifer Vazzano
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Dorota Johansson
- Inify Laboratories AB, Stockholm, Sweden (previously part of ContextVision)
| | - Kun Hu
- Department of Pathology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts
| | - Kristian Eurén
- Inify Laboratories AB, Stockholm, Sweden (previously part of ContextVision)
| | - Stefan Elfwing
- Inify Laboratories AB, Stockholm, Sweden (previously part of ContextVision)
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
| | - Ming Zhou
- Department of Pathology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts.
| |
Collapse
|
8
|
Affiliation(s)
- Jonathan Kantor
- Department of Dermatology, Center for Global Health, and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Florida Center for Dermatology, St Augustine, Florida; and Alchemy Labs, Oxford, UK
| |
Collapse
|
9
|
Marletta S, Salatiello M, Pantanowitz L, Bellevicine C, Bongiovanni M, Bonoldi E, De Rezende G, Fadda G, Incardona P, Munari E, Pagni F, Rossi ED, Tallini G, Troncone G, Ugolini C, Vigliar E, Eccher A. Delphi expert consensus for whole slide imaging in thyroid cytopathology. Cytopathology 2023; 34:581-589. [PMID: 37530465 DOI: 10.1111/cyt.13279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/14/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVE Despite an increase in thyroid fine needle aspiration (FNA) and advances in whole slide imaging (WSI) adoption, digital pathology is still considered inadequate for primary diagnosis of these cases. Herein, we aim to validate the utility of WSI in thyroid FNAs employing the Delphi method strategy. METHODS A panel of experts from seven reference cytology centres was recruited. The study consisted of two consecutive rounds: (1) an open-ended, free-response questionnaire generating a list of survey items; and (2) a consensus analysis of 80 selected shared WSIs from 80 cases by six investigators answering six morphological questions utilising a 1 to 5 Likert scale. RESULTS High consensus was achieved for all parameters, with an overall average score of 4.27. The broad majority of items (84%) were ranked either 4 or 5 by each physician. Two badly scanned cases were responsible for more than half of the low-ranked (≤2) values (57%). Good to excellent (≥3) diagnostic confidence was reached in more than 95.2% of cases. For most cases (78%) WSI assessment was not limited by technical issues linked to the image acquisition process. CONCLUSION This systematic Delphi study indicates broad consensus among participating physicians on the application of DP to thyroid cytopathology, supporting expert opinion that WSI is reliable and safe for primary diagnostic purposes.
Collapse
Affiliation(s)
- Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
- Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
| | - Maria Salatiello
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Claudio Bellevicine
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | | | | | | | - Guido Fadda
- Department of Human Pathology of the Adulthood and of the Developing Age "Gaetano Barresi", Faculty of Medicine and Surgery, University of Messina, Messina, Italy
| | - Paolo Incardona
- Complex Structure of Anatomic Pathology, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Enrico Munari
- Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, IRCCS (Scientific Institute for Research, Hospitalization and Healthcare) Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Esther Diana Rossi
- Division of Anatomic Pathology and Histology, Fondazione Policlinico Universitario A.Gemelli-IRCCS, Rome, Italy
| | - Giovanni Tallini
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), University of Bologna, Bologna, Italy
- Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Giancarlo Troncone
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Clara Ugolini
- Department of Surgical, Medical, Molecular Pathology and Critical Area, University of Pisa, Pisa, Italy
| | - Elena Vigliar
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| |
Collapse
|
10
|
Terada K, Yoshizawa A, Liu X, Ito H, Hamaji M, Menju T, Date H, Bise R, Haga H. Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non-Small Cell Lung Carcinomas With Histologic Images. Mod Pathol 2023; 36:100302. [PMID: 37580019 DOI: 10.1016/j.modpat.2023.100302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/23/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023]
Abstract
Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using resected specimens. Major pathological response (MPR) has recently been used for treatment evaluation and as an economical survival surrogate; however, interobserver variability and poor reproducibility are often noted. The aim of this study was to develop a deep learning (DL) model to predict MPR from hematoxylin and eosin-stained tissue images and to validate its utility for clinical use. We collected data on 125 primary non-small cell lung carcinoma cases that were resected after neoadjuvant therapy. The cases were randomly divided into 55 for training/validation and 70 for testing. A total of 261 hematoxylin and eosin-stained slides were obtained from the maximum tumor beds, and whole slide images were prepared. We used a multiscale patch model that can adaptively weight multiple convolutional neural networks trained with different field-of-view images. We performed 3-fold cross-validation to evaluate the model. During testing, we compared the percentages of viable tumor evaluated by annotator pathologists (reviewed data), those evaluated by nonannotator pathologists (primary data), and those predicted by the DL-based model using 2-class confusion matrices and receiver operating characteristic curves and performed a survival analysis between MPR-achieved and non-MPR cases. In cross-validation, accuracy and mean F1 score were 0.859 and 0.805, respectively. During testing, accuracy and mean F1 score with reviewed data and those with primary data were 0.986, 0.985, 0.943, and 0.943, respectively. The areas under the receiver operating characteristic curve with reviewed and primary data were 0.999 and 0.978, respectively. The disease-free survival of MPR-achieved cases with reviewed and primary data was significantly better than that of the non-MPR cases (P<.001 and P=.001), and that predicted by the DL-based model was almost identical (P=.005). The DL model may support pathologist evaluations and can offer accurate determinations of MPR in patients.
Collapse
Affiliation(s)
- Kazuhiro Terada
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Akihiko Yoshizawa
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.
| | - Xiaoqing Liu
- Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Hiroaki Ito
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Masatsugu Hamaji
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Toshi Menju
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Hiroshi Date
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Hironori Haga
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| |
Collapse
|
11
|
Grevot A, Boisclair J, Guffroy M, Hall P, Pohlmeyer-Esch G, Jacobsen M, Bach U, Frisk AL, Dybdal N, Palazzi X. Toxicologic Pathology Forum Opinion Piece: Use of Virtual Control Groups in Nonclinical Toxicity Studies: The Anatomic Pathology Perspective. Toxicol Pathol 2023; 51:390-396. [PMID: 38293937 DOI: 10.1177/01926233231224805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
In the last decade, numerous initiatives have emerged worldwide to reduce the use of animals in drug development, including more recently the introduction of Virtual Control Groups (VCGs) concept for nonclinical toxicity studies. Although replacement of concurrent controls (CCs) by virtual controls (VCs) represents an exciting opportunity, there are associated challenges that will be discussed in this paper with a more specific focus on anatomic pathology. Coordinated efforts will be needed from toxicologists, clinical and anatomic pathologists, and regulators to support approaches that will facilitate a staggered implementation of VCGs in nonclinical toxicity studies. Notably, the authors believe that a validated database for VC animals will need to include histopathology (digital) slides for microscopic assessment. Ultimately, the most important step lies in the validation of the concept by performing VCG and the full control group in parallel for studies of varying duration over a reasonable timespan to confirm there are no differences in outcomes (dual study design). The authors also discuss a hybrid approach, whereby control groups comprised both concurrent and VCs to demonstrate proof-of-concept. Once confidence is established by sponsors and regulators, VCs have the potential to replace some or all CC animals.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Noel Dybdal
- Genentech Inc., South San Francisco, California, USA
| | | |
Collapse
|
12
|
Doyle J, Green BF, Eminizer M, Jimenez-Sanchez D, Lu S, Engle EL, Xu H, Ogurtsova A, Lai J, Soto-Diaz S, Roskes JS, Deutsch JS, Taube JM, Sunshine JC, Szalay AS. Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence. J Transl Med 2023; 103:100175. [PMID: 37196983 PMCID: PMC10527458 DOI: 10.1016/j.labinv.2023.100175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/24/2023] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF) is a developing technology that facilitates the evaluation of multiple, simultaneous protein expressions at single-cell resolution while preserving tissue architecture. These approaches have shown great potential for biomarker discovery, yet many challenges remain. Importantly, streamlined cross-registration of multiplex immunofluorescence images with additional imaging modalities and immunohistochemistry (IHC) can help increase the plex and/or improve the quality of the data generated by potentiating downstream processes such as cell segmentation. To address this problem, a fully automated process was designed to perform a hierarchical, parallelizable, and deformable registration of multiplexed digital whole-slide images (WSIs). We generalized the calculation of mutual information as a registration criterion to an arbitrary number of dimensions, making it well suited for multiplexed imaging. We also used the self-information of a given IF channel as a criterion to select the optimal channels to use for registration. Additionally, as precise labeling of cellular membranes in situ is essential for robust cell segmentation, a pan-membrane immunohistochemical staining method was developed for incorporation into mIF panels or for use as an IHC followed by cross-registration. In this study, we demonstrate this process by registering whole-slide 6-plex/7-color mIF images with whole-slide brightfield mIHC images, including a CD3 and a pan-membrane stain. Our algorithm, WSI, mutual information registration (WSIMIR), performed highly accurate registration allowing the retrospective generation of an 8-plex/9-color, WSI, and outperformed 2 alternative automated methods for cross-registration by Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, P < .01 and P < .01, respectively, vs HALO + transformix, P = .083 and P = .049, respectively). Furthermore, the addition of a pan-membrane IHC stain cross-registered to an mIF panel facilitated improved automated cell segmentation across mIF WSIs, as measured by significantly increased correct detections, Jaccard index (0.78 vs 0.65), and Dice similarity coefficient (0.88 vs 0.79).
Collapse
Affiliation(s)
- Joshua Doyle
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland
| | - Benjamin F Green
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Margaret Eminizer
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, Maryland
| | - Daniel Jimenez-Sanchez
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Steve Lu
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Elizabeth L Engle
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Haiying Xu
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Aleksandra Ogurtsova
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Jonathan Lai
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sigfredo Soto-Diaz
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jeffrey S Roskes
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, Maryland
| | - Julie S Deutsch
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Janis M Taube
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joel C Sunshine
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland; Johns Hopkins Center for Translational Immunoengineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Alexander S Szalay
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, Maryland
| |
Collapse
|
13
|
Esselman AB, Patterson NH, Migas LG, Dufresne M, Djambazova KV, Colley ME, Van de Plas R, Spraggins JM. Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli. J Am Soc Mass Spectrom 2023. [PMID: 37319264 DOI: 10.1021/jasms.3c00033] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.
Collapse
Affiliation(s)
- Allison B Esselman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Lukasz G Migas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, The Netherlands
| | - Martin Dufresne
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Katerina V Djambazova
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Madeline E Colley
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, The Netherlands
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| |
Collapse
|
14
|
Abstract
An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.
Collapse
Affiliation(s)
- Taher Dehkharghanian
- Department of Nephrology, University Health Network, Toronto, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Youqing Mu
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Hamid R Tizhoosh
- Rhazes Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Clinton J V Campbell
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
| |
Collapse
|
15
|
Shi L, Shen L, Jian J, Xia W, Yang KD, Tian Y, Huang J, Yuan B, Shen L, Liu Z, Zhang J, Zhang R, Wu K, Jing D, Gao X. Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology. Brain Pathol 2023:e13160. [PMID: 37186490 DOI: 10.1111/bpa.13160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocytoma on intraoperative frozen section (IFS) and hematoxylin-eosin (HE)-staining section directly determines patients' treatment options, but it is a difficult task for pathologists. We aimed to investigate whether whole-slide imaging (WSI)-based deep learning can contribute new precision to the diagnosis of IG, oligodendroglioma, and low-grade astrocytoma. Two types of WSIs (500 IFSs and 832 HE-staining sections) were collected from 379 patients at multiple medical centers. Patients at Center 1 were split into the training, testing, and internal validation sets (3:1:1), while the other centers were the external validation sets. First, we subdivided WSIs into small tiles and selected tissue tiles using a tissue tile selection model. Then a tile-level classification model was established, and the majority voting method was used to determine the final diagnoses. Color jitter was applied to the tiles so that the deep learning (DL) models could adapt to the variations in the staining. Last, we investigated the effectiveness of model assistance. The internal validation accuracies of the IFS and HE models were 93.9% and 95.3%, respectively. The external validation accuracies of the IFS and HE models were 82.0% and 76.9%, respectively. Furthermore, the IFS and HE models can predict Ki-67 positive cell areas with R2 of 0.81 and 0.86, respectively. With model assistance, the IFS and HE diagnosis accuracy of pathologists improved from 54.6%-69.7% and 53.5%-83.7% to 87.9%-93.9% and 86.0%-90.7%, respectively. Both the IFS model and the HE model can differentiate the three tumors, predict the expression of Ki-67, and improve the diagnostic accuracy of pathologists. The use of our model can assist clinicians in providing patients with optimal and timely treatment options.
Collapse
Affiliation(s)
- Liting Shi
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Lin Shen
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Junming Jian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Wei Xia
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan, China
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Ke-Da Yang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Yifu Tian
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Jianghai Huang
- Department of Pathology, the Second Xiangya Hospital, Central South University, Changsha, China
| | - Bowen Yuan
- Department of Pathology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Liangfang Shen
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhengzheng Liu
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jiayi Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan, China
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Rui Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Keqing Wu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Di Jing
- Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Gao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Jinan Guoke Medical Engineering and Technology Development Co., Ltd., Jinan, China
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| |
Collapse
|
16
|
Winkelmaier G, Koch B, Bogardus S, Borowsky AD, Parvin B. Biomarkers of Tumor Heterogeneity in Glioblastoma Multiforme Cohort of TCGA. Cancers (Basel) 2023; 15:cancers15082387. [PMID: 37190318 DOI: 10.3390/cancers15082387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/06/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Tumor Whole Slide Images (WSI) are often heterogeneous, which hinders the discovery of biomarkers in the presence of confounding clinical factors. In this study, we present a pipeline for identifying biomarkers from the Glioblastoma Multiforme (GBM) cohort of WSIs from TCGA archive. The GBM cohort endures many technical artifacts while the discovery of GBM biomarkers is challenged because "age" is the single most confounding factor for predicting outcomes. The proposed approach relies on interpretable features (e.g., nuclear morphometric indices), effective similarity metrics for heterogeneity analysis, and robust statistics for identifying biomarkers. The pipeline first removes artifacts (e.g., pen marks) and partitions each WSI into patches for nuclear segmentation via an extended U-Net for subsequent quantitative representation. Given the variations in fixation and staining that can artificially modulate hematoxylin optical density (HOD), we extended Navab's Lab method to normalize images and reduce the impact of batch effects. The heterogeneity of each WSI is then represented either as probability density functions (PDF) per patient or as the composition of a dictionary predicted from the entire cohort of WSIs. For PDF- or dictionary-based methods, morphometric subtypes are constructed based on distances computed from optimal transport and linkage analysis or consensus clustering with Euclidean distances, respectively. For each inferred subtype, Kaplan-Meier and/or the Cox regression model are used to regress the survival time. Since age is the single most important confounder for predicting survival in GBM and there is an observed violation of the proportionality assumption in the Cox model, we use both age and age-squared coupled with the Likelihood ratio test and forest plots for evaluating competing statistics. Next, the PDF- and dictionary-based methods are combined to identify biomarkers that are predictive of survival. The combined model has the advantage of integrating global (e.g., cohort scale) and local (e.g., patient scale) attributes of morphometric heterogeneity, coupled with robust statistics, to reveal stable biomarkers. The results indicate that, after normalization of the GBM cohort, mean HOD, eccentricity, and cellularity are predictive of survival. Finally, we also stratified the GBM cohort as a function of EGFR expression and published genomic subtypes to reveal genomic-dependent morphometric biomarkers.
Collapse
Affiliation(s)
- Garrett Winkelmaier
- Department of Electrical and Biomedical Engineering, College of Engineering, University of Nevada Reno, 1664 N. Virginia St., Reno, NV 89509, USA
| | - Brandon Koch
- Department of Biostatics, College of Public Health, Ohio State University, 281 W. Lane Ave., Columbus, OH 43210, USA
| | - Skylar Bogardus
- Department of Electrical and Biomedical Engineering, College of Engineering, University of Nevada Reno, 1664 N. Virginia St., Reno, NV 89509, USA
| | - Alexander D Borowsky
- Department of Pathology, UC Davis Comprehensive Cancer Center, University of California Davis, 1 Shields Ave, Davis, CA 95616, USA
| | - Bahram Parvin
- Department of Electrical and Biomedical Engineering, College of Engineering, University of Nevada Reno, 1664 N. Virginia St., Reno, NV 89509, USA
- Pennington Cancer Institute, Renown Health, Reno, NV 89502, USA
| |
Collapse
|
17
|
Saini T, Bansal B, Dey P. Digital cytology: Current status and future prospects. Diagn Cytopathol 2023; 51:211-218. [PMID: 36594526 DOI: 10.1002/dc.25099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 12/17/2022] [Accepted: 12/22/2022] [Indexed: 01/04/2023]
Abstract
AIMS In this paper, we reviewed the basic principle and the currentstatus of digital cytopathology. MATERIALS AND METHODS We reviewed the published papers on digitalcytology and analysed its future prospects. RESULTS Virtualcytology using digital platform is being increasingly used to render diagnosisrather than conventional glass slide microscopy. Whole slide imaging (WSI)offers the prospect of true virtual microscopy and in the near future, may evenreplace glass slides in routine practice. It may be pivotal in diagnosing andtraining pathology graduates faster and more accurately. CONCLUSION The digital cytopathology is a promising field and may have great impact indiagnosis, research and teaching.
Collapse
Affiliation(s)
- Tarunpreet Saini
- Department of Cytology and Gynecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Baneet Bansal
- Department of Cytology and Gynecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Pranab Dey
- Department of Cytology and Gynecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| |
Collapse
|
18
|
Zhang X, Gleber‐Netto FO, Wang S, Martins‐Chaves RR, Gomez RS, Vigneswaran N, Sarkar A, William WN, Papadimitrakopoulou V, Williams M, Bell D, Palsgrove D, Bishop J, Heymach JV, Gillenwater AM, Myers JN, Ferrarotto R, Lippman SM, Pickering CR, Xiao G. Deep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia. Cancer Med 2023; 12:7508-7518. [PMID: 36721313 PMCID: PMC10067069 DOI: 10.1002/cam4.5478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/15/2022] [Accepted: 11/14/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Oral leukoplakia (OL) is associated with an increased risk for oral cancer (OC) development. Prediction of OL cancer progression may contribute to decreased OC morbidity and mortality by favoring early intervention. Current OL progression risk assessment approaches face large interobserver variability and is weakly prognostic. We hypothesized that convolutional neural networks (CNN)-based histology image analyses could accelerate the discovery of better OC progression risk models. METHODS Our CNN-based oral mucosa risk stratification model (OMRS) was trained to classify a set of nondysplastic oral mucosa (OM) and a set of OC H&E slides. As a result, the OMRS model could identify abnormal morphological features of the oral epithelium. By applying this model to OL slides, we hypothesized that the extent of OC-like features identified in the OL epithelium would correlate with its progression risk. The OMRS model scored and categorized the OL cohort (n = 62) into high- and low-risk groups. RESULTS OL patients classified as high-risk (n = 31) were 3.98 (95% CI 1.36-11.7) times more likely to develop OC than low-risk ones (n = 31). Time-to-progression significantly differed between high- and low-risk groups (p = 0.003). The 5-year OC development probability was 21.3% for low-risk and 52.5% for high-risk patients. The predictive power of the OMRS model was sustained even after adjustment for age, OL site, and OL dysplasia grading (HR = 4.52, 1.5-13.7). CONCLUSION The ORMS model successfully identified OL patients with a high risk of OC development and can potentially benefit OC early diagnosis and prevention policies.
Collapse
Affiliation(s)
- Xinyi Zhang
- Quantitative Biomedical Research Center, Department of Population and Data SciencesUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | | | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data SciencesUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | | | - Ricardo Santiago Gomez
- Department of Oral Surgery and Pathology, School of DentistryUniversidade Federal de Minas GeraisBelo HorizonteBrazil
| | - Nadarajah Vigneswaran
- Department of Diagnostic and Biomedical SciencesThe University of Texas Health Science Center at Houston School of DentistryHoustonTexasUSA
| | - Arunangshu Sarkar
- Department of Head & Neck SurgeryThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - William N. William
- Department of Thoracic‐Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Hospital BPA Beneficência Portuguesa de São PauloSao PaoloBrazil
| | - Vassiliki Papadimitrakopoulou
- Department of Thoracic‐Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Global Product DevelopmentOncology, Pfizer, Inc.New YorkNew YorkUSA
| | - Michelle Williams
- Department of Anatomical PathologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Diana Bell
- Department of Anatomical PathologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of PathologyCity of HopeDuarteCaliforniaUSA
| | - Doreen Palsgrove
- Department of PathologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Justin Bishop
- Department of PathologyUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - John V. Heymach
- Department of Thoracic‐Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Ann M. Gillenwater
- Department of Head & Neck SurgeryThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Jeffrey N. Myers
- Department of Head & Neck SurgeryThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Renata Ferrarotto
- Department of Thoracic‐Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Scott M. Lippman
- Department of Thoracic‐Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of MedicineUniversity of California San DiegoSan DiegoCaliforniaUSA
| | - Curtis Rg Pickering
- Department of Head & Neck SurgeryThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data SciencesUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Department of BioinformaticsUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| |
Collapse
|
19
|
Baidoshvili A, Khacheishvili M, van der Laak JAWM, van Diest PJ. A whole-slide imaging based workflow reduces the reading time of pathologists. Pathol Int 2023; 73:127-134. [PMID: 36692113 DOI: 10.1111/pin.13309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 12/24/2022] [Indexed: 01/25/2023]
Abstract
Even though entirely digitized microscopic tissue sections (whole slide images, WSIs) are increasingly being used in histopathology diagnostics, little data is still available on the effect of this technique on pathologists' reading time. This study aimed to compare the time required to perform the microscopic assessment by pathologists between a conventional workflow (an optical microscope) and digitized WSIs. WSI was used in primary diagnostics at the Laboratory for Pathology Eastern Netherlands for several years (LabPON, Hengelo, The Netherlands). Cases were read either in a traditional workflow, with the pathologist recording the time required for diagnostics and reporting, or entirely digitally. Reading times were extracted from image management system log files, and the digitized workflow was fully integrated into the laboratory information system. The digital workflow saved time in the majority of case categories, with prostate biopsies saving the most (68% time gain). Taking into account case distribution, the digital workflow produced an average gain of 12.3%. Using WSI instead of conventional microscopy significantly reduces pathologists' reading times. Pathologists must work in a fully integrated environment to fully reap the benefits of a digital workflow.
Collapse
Affiliation(s)
- Alexi Baidoshvili
- Laboratory of Pathology East Netherlands (LabPON), Hengelo, The Netherlands
- David Tvildiani Medical University, Tbilisi, Georgia
| | | | | | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
20
|
Li B, Nelson MS, Chacko JV, Cudworth N, Eliceiri KW. Hardware-software co-design of an open-source automatic multimodal whole slide histopathology imaging system. J Biomed Opt 2023; 28:026501. [PMID: 36761254 PMCID: PMC9905038 DOI: 10.1117/1.jbo.28.2.026501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Significance Advanced digital control of microscopes and programmable data acquisition workflows have become increasingly important for improving the throughput and reproducibility of optical imaging experiments. Combinations of imaging modalities have enabled a more comprehensive understanding of tissue biology and tumor microenvironments in histopathological studies. However, insufficient imaging throughput and complicated workflows still limit the scalability of multimodal histopathology imaging. Aim We present a hardware-software co-design of a whole slide scanning system for high-throughput multimodal tissue imaging, including brightfield (BF) and laser scanning microscopy. Approach The system can automatically detect regions of interest using deep neural networks in a low-magnification rapid BF scan of the tissue slide and then conduct high-resolution BF scanning and laser scanning imaging on targeted regions with deep learning-based run-time denoising and resolution enhancement. The acquisition workflow is built using Pycro-Manager, a Python package that bridges hardware control libraries of the Java-based open-source microscopy software Micro-Manager in a Python environment. Results The system can achieve optimized imaging settings for both modalities with minimized human intervention and speed up the laser scanning by an order of magnitude with run-time image processing. Conclusions The system integrates the acquisition pipeline and data analysis pipeline into a single workflow that improves the throughput and reproducibility of multimodal histopathological imaging.
Collapse
Affiliation(s)
- Bin Li
- University of Wisconsin–Madison, Center for Quantitative Cell Imaging, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
- Morgridge Institute for Research, Madison, Wisconsin, United States
| | - Michael S. Nelson
- University of Wisconsin–Madison, Center for Quantitative Cell Imaging, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | - Jenu V. Chacko
- University of Wisconsin–Madison, Center for Quantitative Cell Imaging, Madison, Wisconsin, United States
| | - Nathan Cudworth
- University of Wisconsin–Madison, Center for Quantitative Cell Imaging, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
| | - Kevin W. Eliceiri
- University of Wisconsin–Madison, Center for Quantitative Cell Imaging, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
- Morgridge Institute for Research, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
| |
Collapse
|
21
|
Marletta S, Pantanowitz L, Santonicco N, Caputo A, Bragantini E, Brunelli M, Girolami I, Eccher A. Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta. Pediatr Dev Pathol 2023; 26:5-12. [PMID: 36448447 DOI: 10.1177/10935266221137953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study was to therefore review the literature regarding digital pathology of the placenta. A systematic literature search was conducted in several electronic databases. Studies involving the application of digital imaging and artificial intelligence techniques to human placental samples were retrieved and analyzed. Relevant articles were categorized by digital image technique and their relevance to studying normal and diseased placenta. Of 2008 retrieved articles, 279 were included. Digital imaging research related to the placenta was often coupled with immunohistochemistry, confocal microscopy, 3D reconstruction, and/or deep learning algorithms. By significantly increasing pathologists' ability to recognize potentially prognostic relevant features and by lessening inter-observer variability, published data overall indicate that the application of digital pathology to placental and perinatal diseases, along with clinical and radiology correlation, has great potential to improve fetal and maternal health care including the selection of targeted therapy in high-risk pregnancy.
Collapse
Affiliation(s)
- Stefano Marletta
- Department of Pathology and Diagnostics, Section of Pathology, University Hospital of Verona, Verona, Italy
| | | | - Nicola Santonicco
- Department of Pathology and Diagnostics, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Alessandro Caputo
- Department of Medicine and Surgery, University of Salerno, Salerno, Italy
| | - Emma Bragantini
- Department of Pathology, Santa Chiara Hospital, Trento, Italy
| | - Matteo Brunelli
- Department of Pathology and Diagnostics, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Ilaria Girolami
- Department of Pathology & Clinical Labs, University of Michigan, Ann Arbor, MI, USA
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| |
Collapse
|
22
|
Rizzo PC, Caputo A, Maddalena E, Caldonazzi N, Girolami I, Dei Tos AP, Scarpa A, Sbaraglia M, Brunelli M, Gobbo S, Marletta S, Pantanowitz L, Della Mea V, Eccher A. Digital pathology world tour. Digit Health 2023; 9:20552076231194551. [PMID: 37654717 PMCID: PMC10467307 DOI: 10.1177/20552076231194551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 07/27/2023] [Indexed: 09/02/2023] Open
Abstract
Objective Digital pathology (DP) is currently in the spotlight and is rapidly gaining ground, even though the history of this field spans decades. Despite great technological progress, the adoption of DP for routine clinical diagnostic use remains limited. Methods A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all published studies that encompassed any application of DP. Results Of 4888 articles retrieved, 4041 were included. Relevant articles were categorized as "diagnostic" (147/4041, 4%) where DP was utilized for routine diagnostic workflow and "non-diagnostic" (3894/4041, 96%) for all other applications. The "non-diagnostic" articles were further categorized according to DP application including "artificial intelligence" (33%), "education" (5%), "narrative" (17%) for reviews and editorials, and "technical" (45%) for pure research publications. Conclusion This manuscript provided temporal and geographical insight into the global adoption of DP by analyzing the published scientific literature.
Collapse
Affiliation(s)
- Paola Chiara Rizzo
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Alessandro Caputo
- Department of Medicine and Surgery, University of Salerno, Fisciano, Italy
| | - Eddy Maddalena
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Nicolò Caldonazzi
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Ilaria Girolami
- Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy
- Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität, Bolzano, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy
| | - Aldo Scarpa
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Marta Sbaraglia
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy
| | - Matteo Brunelli
- Department of Pathology and Diagnostics and Public Health, Section of Pathology, University Hospital of Verona, Verona, Italy
| | - Stefano Gobbo
- Department of Translational Medicine, University of Ferrara, Ferrara, Italy
| | - Stefano Marletta
- Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Verona, Italy
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | | | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| |
Collapse
|
23
|
Abdel-Nasser M, Singh VK, Mohamed EM. Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network. Diagnostics (Basel) 2022; 12. [PMID: 36553031 DOI: 10.3390/diagnostics12123024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Existing nuclei segmentation methods face challenges with hematoxylin and eosin (H&E) whole slide imaging (WSI) due to the variations in staining methods and nuclei shapes and sizes. Most existing approaches require a stain normalization step that may cause losing source information and fail to handle the inter-scanner feature instability problem. To mitigate these issues, this article proposes an efficient staining-invariant nuclei segmentation method based on self-supervised contrastive learning and an effective weighted hybrid dilated convolution (WHDC) block. In particular, we propose a staining-invariant encoder (SIE) that includes convolution and transformers blocks. We also propose the WHDC block allowing the network to learn multi-scale nuclei-relevant features to handle the variation in the sizes and shapes of nuclei. The SIE network is trained on five unlabeled WSIs datasets using self-supervised contrastive learning and then used as a backbone for the downstream nuclei segmentation network. Our method outperforms existing approaches in challenging multiple WSI datasets without stain color normalization.
Collapse
|
24
|
Zheng Q, Yang R, Ni X, Yang S, Xiong L, Yan D, Xia L, Yuan J, Wang J, Jiao P, Wu J, Hao Y, Wang J, Guo L, Jiang Z, Wang L, Chen Z, Liu X. Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides. Cancers (Basel) 2022; 14:cancers14235807. [PMID: 36497289 PMCID: PMC9737237 DOI: 10.3390/cancers14235807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.
Collapse
Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lin Xiong
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Dandan Yan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lingli Xia
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingsong Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yiqun Hao
- Division of Nephrology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jianguo Wang
- Department of Hepatic-Biliary-Pancreatic Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Liantao Guo
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhengyu Jiang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
| |
Collapse
|
25
|
Abstract
Simple Summary Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising. Abstract Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.
Collapse
Affiliation(s)
- Hwa-Yen Chiu
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Division of Internal Medicine, Hsinchu Branch, Taipei Veterans General Hospital, Hsinchu 310, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| |
Collapse
|
26
|
Wilson PC, Messias N. How Whole Slide Imaging and Machine Learning Can Partner with Renal Pathology. Kidney360 2022; 3:413-415. [PMID: 35582192 PMCID: PMC9034807 DOI: 10.34067/kid.0007982021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 01/10/2023]
Affiliation(s)
- Parker C. Wilson
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri
| | - Nidia Messias
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri
| |
Collapse
|
27
|
Haghighi M, Tolley J, Schito AN, Kwan R, Garcia C, Prince S, Harpaz N, Thung SN, Craven CK, Cordon-Cardo C, Westra WH. Whole Slide Imaging for Teleconsultation: The Mount Sinai Hospital, Labcorp Dianon, and Philips Collaborative Experience. J Pathol Inform 2022; 12:53. [PMID: 35070482 PMCID: PMC8721867 DOI: 10.4103/jpi.jpi_74_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/18/2021] [Accepted: 12/02/2021] [Indexed: 11/14/2022] Open
Abstract
Background: With the emergence of whole slide imaging (WSI) and widespread access to high-speed Internet, pathology labs are now poised to implement digital pathology as a way to access diagnostic pathology expertise. This paper describes a collaborative partnership between a high-volume reference diagnostic laboratory (Labcorp) and an academic pathology department (Mount Sinai Hospital) in the transition from a traditional glass slide service to a digital platform. Using the standard framework of implementation science, we evaluate the consistency and quality of the Philips IntelliSite Pathology Solution (PIPS) in delivering save and efficient diagnostic services. Materials and Methods: Digital and glass slide diagnoses of all consult cases were documented over a 12-month period. The Proctor guideline was used to quantitatively and qualitatively measure (e.g., focus group studies, field notes, and administrative data) implementation success. Lean techniques (e.g., value stream mapping) were applied to measure changes in efficiency with the transition to a digital platform. Results: Our study supports the acceptability, high adoption, appropriateness, feasibility, fidelity, and sustainability of the digital pathology platform. The digital portal also improved the quality of patient care by increasing efficiency, effectiveness, safety, and timeliness. The intraobserver concordance rate was 100%. The digital transition resulted in a reduction in turnaround time from 86 h to an average 35 min and a 20-fold increase in efficiency of the consultation process. Conclusion: As the pathology community contemplates digital pathology as a transformational tool in providing broad access to diagnostic expertise across time and space, our study provides an implementation strategy along with evidence that the digital platform is safe, effective, and efficient.
Collapse
Affiliation(s)
- Mehrvash Haghighi
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Jay Tolley
- Laboratory Corporation of America Holdings, Burlington, NC, USA
| | | | - Ricky Kwan
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Chris Garcia
- Laboratory Corporation of America Holdings, Burlington, NC, USA
| | - Shakira Prince
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Noam Harpaz
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Swan N Thung
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Catherine K Craven
- Department of Population Health Sciences, Joe R. & Teresa Lozano Long School of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
| | - Carlos Cordon-Cardo
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - William H Westra
- Department of Pathology, Molecular and Cell-Based Medicine, The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| |
Collapse
|
28
|
Hoenerhoff MJ, Keane KA. Whole Slide Imaging (WSI) in Toxicologic Pathology. Toxicol Pathol 2022; 50:166. [PMID: 35001752 DOI: 10.1177/01926233211068874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
29
|
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: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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.
| |
Collapse
|
30
|
Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021; 12:42. [PMID: 34881097 PMCID: PMC8609289 DOI: 10.4103/jpi.jpi_36_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open
Abstract
Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
Collapse
Affiliation(s)
- Shima Mehrvar
- Preclinical Safety, AbbVie Inc., North Chicago, IL, USA
| | | | - Pradeep Babburi
- Business Technology Solutions, AbbVie Inc., North Chicago, IL, USA
| | | | | | | | | | | |
Collapse
|
31
|
Patel A, Balis UGJ, Cheng J, Li Z, Lujan G, McClintock DS, Pantanowitz L, Parwani A. Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review. J Pathol Inform 2021; 12:50. [PMID: 35070479 PMCID: PMC8721869 DOI: 10.4103/jpi.jpi_66_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 12/21/2022] Open
Abstract
Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.
Collapse
Affiliation(s)
- Ankush Patel
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Zaibo Li
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | - Giovanni Lujan
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| | | | - Liron Pantanowitz
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University, Columbus, Ohio, USA
| |
Collapse
|
32
|
Fraggetta F, L’Imperio V, Ameisen D, Carvalho R, Leh S, Kiehl TR, Serbanescu M, Racoceanu D, Della Mea V, Polonia A, Zerbe N, Eloy C. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics (Basel) 2021; 11:2167. [PMID: 34829514 PMCID: PMC8623219 DOI: 10.3390/diagnostics11112167] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/12/2022] Open
Abstract
The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.
Collapse
Affiliation(s)
- Filippo Fraggetta
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Pathology Unit, “Gravina” Hospital, Caltagirone, ASP Catania, Via Portosalvo 1, 95041 Caltagirone, Italy
| | - Vincenzo L’Imperio
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Medicine and Surgery, Pathology, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, 20900 Monza, Italy
| | - David Ameisen
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Imginit SAS, 152 Boulevard du Montparnasse, 75014 Paris, France
| | - Rita Carvalho
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Sabine Leh
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Pathology, Haukeland University Hospital, Jonas Lies Vei 65, 5021 Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Jonas Lies Vei 87, 5021 Bergen, Norway
| | - Tim-Rasmus Kiehl
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Mircea Serbanescu
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Daniel Racoceanu
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, Inserm, CNRS, APHP, Inria Team “Aramis”, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
| | - Vincenzo Della Mea
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
| | - Antonio Polonia
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology of Porto University (Ipatimup), 4200-804 Porto, Portugal
- Medical Faculty, University of Porto, 4200-319 Porto, Portugal
| | - Norman Zerbe
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Catarina Eloy
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology of Porto University (Ipatimup), 4200-804 Porto, Portugal
- Medical Faculty, University of Porto, 4200-319 Porto, Portugal
| |
Collapse
|
33
|
Medeiros Savi F, Mieszczanek P, Revert S, Wille ML, Bray LJ. A New Automated Histomorphometric MATLAB Algorithm for Immunohistochemistry Analysis Using Whole Slide Imaging. Tissue Eng Part C Methods 2021; 26:462-474. [PMID: 32729382 DOI: 10.1089/ten.tec.2020.0153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
The use of animal models along with the employment of advanced and sophisticated stereological methods for assessing bone quality combined with the use of statistical methods to evaluate the effectiveness of bone therapies has made it possible to investigate the pathways that regulate bone responses to medical devices. Image analysis of histomorphometric measurements remains a time-consuming task, as the image analysis software currently available does not allow for automated image segmentation. Such a feature is usually obtained by machine learning and with software platforms that provide image-processing tools such as MATLAB. In this study, we introduce a new MATLAB algorithm to quantify immunohistochemically stained critical-sized bone defect samples and compare the results with the commonly available Aperio Image Scope Positive Pixel Count (PPC) algorithm. Bland and Altman analysis and Pearson correlation showed that the measurements acquired with the new MATLAB algorithm were in excellent agreement with the measurements obtained with the Aperio PPC algorithm, and no significant differences were found within the histomorphometric measurements. The ability to segment whole slide images, as well as defining the size and the number of regions of interest to be quantified, makes this MATLAB algorithm a potential histomorphometric tool for obtaining more objective, precise, and reproducible quantitative assessments of entire critical-sized bone defect image data sets in an efficient and manageable workflow.
Collapse
Affiliation(s)
- Flavia Medeiros Savi
- Centre in Regenerative Medicine, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.,School of Mechanical, Medical and Process Engineering, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Pawel Mieszczanek
- Centre in Regenerative Medicine, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Sophia Revert
- Centre in Regenerative Medicine, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Marie-Luise Wille
- Centre in Regenerative Medicine, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.,School of Mechanical, Medical and Process Engineering, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.,ARC ITTC for Multiscale 3D Imaging, Modelling, and Manufacturing, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Laura Jane Bray
- Centre in Regenerative Medicine, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.,School of Mechanical, Medical and Process Engineering, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| |
Collapse
|
34
|
Shi W, Georgiou P, Akram A, Proute MC, Serhiyenia T, Kerolos ME, Pradeep R, Kothur NR, Khan S. Diagnostic Pitfalls of Digital Microscopy Versus Light Microscopy in Gastrointestinal Pathology: A Systematic Review. Cureus 2021; 13:e17116. [PMID: 34548958 PMCID: PMC8437006 DOI: 10.7759/cureus.17116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
Digital microscopy (DM) is one of the cutting-edge advances in pathology, which entails improved efficiency, diagnostic advantages, and potential application in virtual diagnosis, particularly in the current era of the coronavirus disease (COVID-19) pandemic. However, the diagnostic challenges are the remaining concerns for its wider adoption by pathologists, and these concerns should be addressed in a specific subspecialty. We aim to identify the common diagnostic pitfalls of whole slide imaging (WSI), one modality of DM, in gastrointestinal (GI) pathology. From validating studies of primary diagnosis performance, we included 16 records with features on GI cases involved, at least two weeks wash-out periods, and more than 60 case study designs. A tailored quality appraisal assessment was utilized to evaluate the risks of bias for these diagnostic accuracy studies. Furthermore, due to the highly heterogeneous studies and unstandardized definition of discordance, we extract the discordant cases in GI pathology and calculate the discrepant rate, resulting from 0.5% to 64.28%. Targeting discrepancy cases between digital microscopy and light microscopy, we demonstrate five main diagnostic pitfalls regarding WSI as follows: additional time to review slides in WSI, hard to identify dysplasia nucleus, missed organisms like Helicobacter pylori (H. pylori), specific cell recognitions, and technical issues. After detailed reviews and analysis, we generate two essential suggestions for further GI cases signing out by DM. One is to use systematized 20x scans for diagnostic workouts and requesting 40x or even 60x scans for challenging cases; another is that a high-volume slides training should be set before the real clinical application of WSI for primary diagnosis, particularly in GI pathology.
Collapse
Affiliation(s)
- Wangpan Shi
- Pathology, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Petros Georgiou
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA.,Department of Oncology, Oxford University, Oxford, GBR
| | - Aqsa Akram
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Matthew C Proute
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Tatsiana Serhiyenia
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Mina E Kerolos
- General Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Roshini Pradeep
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Nageshwar R Kothur
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Safeera Khan
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| |
Collapse
|
35
|
Schüffler PJ, Geneslaw L, Yarlagadda DVK, Hanna MG, Samboy J, Stamelos E, Vanderbilt C, Philip J, Jean MH, Corsale L, Manzo A, Paramasivam NHG, Ziegler JS, Gao J, Perin JC, Kim YS, Bhanot UK, Roehrl MHA, Ardon O, Chiang S, Giri DD, Sigel CS, Tan LK, Murray M, Virgo C, England C, Yagi Y, Sirintrapun SJ, Klimstra D, Hameed M, Reuter VE, Fuchs TJ. Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center. J Am Med Inform Assoc 2021; 28:1874-1884. [PMID: 34260720 PMCID: PMC8344580 DOI: 10.1093/jamia/ocab085] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/25/2021] [Accepted: 05/04/2021] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes. MATERIALS AND METHODS We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent. RESULTS The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence-driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases. CONCLUSIONS We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
Collapse
Affiliation(s)
- Peter J Schüffler
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Luke Geneslaw
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - D Vijay K Yarlagadda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Matthew G Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jennifer Samboy
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Evangelos Stamelos
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Chad Vanderbilt
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - John Philip
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Health Informatics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Marc-Henri Jean
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Lorraine Corsale
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Allyne Manzo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Neeraj H G Paramasivam
- Department of Information Systems, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - John S Ziegler
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jianjiong Gao
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Juan C Perin
- Department of Information Systems, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Young Suk Kim
- School of Medicine, Stanford University, Stanford, California, USA
| | - Umeshkumar K Bhanot
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Michael H A Roehrl
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Sarah Chiang
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Dilip D Giri
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Carlie S Sigel
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Lee K Tan
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Melissa Murray
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christina Virgo
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christine England
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yukako Yagi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - S Joseph Sirintrapun
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - David Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Meera Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Victor E Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Thomas J Fuchs
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| |
Collapse
|
36
|
Betmouni S. Diagnostic digital pathology implementation: Learning from the digital health experience. Digit Health 2021; 7:20552076211020240. [PMID: 34211723 PMCID: PMC8216403 DOI: 10.1177/20552076211020240] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 05/04/2021] [Indexed: 01/18/2023] Open
Abstract
Digital Pathology (also referred to as Telepathology and Whole Slide Imaging) is the process of producing high resolution digital images from tissue sections on glass slides. These glass slides are normally examined under a microscope by a pathologist as part of the diagnostic process. The emergence of digital pathology now means that digital images are stored on secure servers and can be viewed on computer monitors; enabling pathologists to work remotely and to collaborate with other colleagues when second opinions are needed. The implementation of digital pathology into clinical practice has many potential benefits. Although this has been long recognised, its adoption as a diagnostic tool remains low and pathologists’ projections about its future deployment are cautious. Notable early digital pathology adopters have led the way. The challenge now is to scale-up digital pathology beyond the relatively few large networks and centres of excellence. Many other areas of healthcare have accumulated experience about optimising approaches to digital health/healthcare technology deployment and sustainability. This has been done in a multi-disciplinary context and has applied theoretical/conceptual frameworks. Thus far there has been little use of similar frameworks in the planning of digital pathology deployment in clinical practice. In this essay, I will explore the scope of digital pathology implementation approaches that have been deployed in clinical practice and examine what can be learned from the wider healthcare experience of adopting, scaling-up and sustaining innovative healthcare solutions.
Collapse
Affiliation(s)
- Samar Betmouni
- Digital Health Enterprise Zone, University of Bradford, Bradford, UK.,Digital Health Enterprise Zone, University of Bradford, Bradford, UK
| |
Collapse
|
37
|
Raas MWD, Silva TP, Freitas JCO, Campos LM, Fabri RL, Melo RCN. Whole slide imaging is a high-throughput method to assess Candida biofilm formation. Microbiol Res 2021; 250:126806. [PMID: 34157481 DOI: 10.1016/j.micres.2021.126806] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 06/04/2021] [Accepted: 06/08/2021] [Indexed: 01/11/2023]
Abstract
New strategies that enable fast and accurate visualization of Candida biofilms are necessary to better study their structure and response to antifungals agents. Here, we applied whole slide imaging (WSI) to study biofilm formation of Candida species. Three relevant biofilm-forming Candida species (C. albicans ATCC 10231, C. glabrata ATCC 2001, and C. tropicalis ATCC 750) were cultivated on glass coverslips both in presence and absence of widely used antifungals. Accumulated biofilms were stained with fluorescent markers and scanned in both bright-field and fluorescence modes using a WSI digital scanner. WSI enabled clear assessment of both size and structural features of Candida biofilms. Quantitative analyses readily detected reductions in biofilm-covered surface area upon antifungal exposure. Furthermore, we show that the overall biofilm growth can be adequately assessed across both bright-field and fluorescence modes. At the single-cell level, WSI proved adequate, as morphometric parameters evaluated with WSI did not differ significantly from those obtained with scanning electron microscopy, considered as golden standard at single-cell resolution. Thus, WSI allows for reliable visualization of Candida biofilms enabling both large-scale growth assessment and morphometric characterization of single-cell features, making it an important addition to the available microscopic toolset to image and analyse fungal biofilm growth.
Collapse
Affiliation(s)
- Maximilian W D Raas
- Laboratory of Cellular Biology, Department of Biology, ICB, Federal University of Juiz de Fora, UFJF, Juiz de Fora, MG, Brazil; Faculty of Medical Sciences, Radboud University, Nijmegen, the Netherlands
| | - Thiago P Silva
- Laboratory of Cellular Biology, Department of Biology, ICB, Federal University of Juiz de Fora, UFJF, Juiz de Fora, MG, Brazil
| | - Jhamine C O Freitas
- Bioactive Natural Products Laboratory, Department of Biochemistry, Institute of Biological Sciences, Federal University of Juiz de Fora, UFJF, Juiz de Fora, MG, Brazil
| | - Lara M Campos
- Bioactive Natural Products Laboratory, Department of Biochemistry, Institute of Biological Sciences, Federal University of Juiz de Fora, UFJF, Juiz de Fora, MG, Brazil
| | - Rodrigo L Fabri
- Bioactive Natural Products Laboratory, Department of Biochemistry, Institute of Biological Sciences, Federal University of Juiz de Fora, UFJF, Juiz de Fora, MG, Brazil
| | - Rossana C N Melo
- Laboratory of Cellular Biology, Department of Biology, ICB, Federal University of Juiz de Fora, UFJF, Juiz de Fora, MG, Brazil.
| |
Collapse
|
38
|
Li N, Lv T, Sun Y, Liu X, Zeng S, Lv X. High throughput slanted scanning whole slide imaging system for digital pathology. J Biophotonics 2021; 14:e202000499. [PMID: 33638313 DOI: 10.1002/jbio.202000499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 06/12/2023]
Abstract
In whole slide imaging (WSI), normally only a one layer imaging of the slide is performed. Autofocus at multiple positions is usually required. But defocus blur still exists due to tissue folding or specimen thickness. Repeated Z-stack scan be applied here, which, however, is too time consuming. Here, a high throughput slanted scanning WSI system is reported. In this system, the slide surface was slanted 1° relative to the focal plane. Thus, the focal plane spanned multiple layers of the sample. By moving the slide, multi-layer image data of the sample can be acquired simultaneously at a time frame comparable to conventional 1-layer imaging. With image fusion, defocus blur can be avoided. High quality and fast imaging of both cytological and histological slide specimens was demonstrated without applying aberration correction. The system can be a highly efficient way for the application of WSI in digital pathology.
Collapse
Affiliation(s)
- Ning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Science, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Science, Huazhong University of Science and Technology, Wuhan, China
| | - Yulin Sun
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Science, Huazhong University of Science and Technology, Wuhan, China
| | - Xiuli Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Science, Huazhong University of Science and Technology, Wuhan, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Science, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohua Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Science, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
39
|
Raja N, Naikodi S, Govindarajan A, Palanisamy K. Toluidine blue staining of murine mast cells and quantitation by a novel, automated image analysis method using whole slide skin images. J Histotechnol 2021; 44:190-195. [PMID: 33998401 DOI: 10.1080/01478885.2021.1915934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Mast cells are immune cells of myeloid lineage, characterized by the presence of cytoplasmic granules. These cells play a significant role in multiple pathologies, such as urticaria and type 1 hypersensitivity reactions. Mast cells in tissue sections can be demonstrated with metachromatic stains, such as toluidine blue. Metachromatic staining of mast cells is influenced by the type of mast cell, pH of the staining solution and fixative employed. The objective of this study is to identify a simple, consistent, and reproducible toluidine blue staining method to quantify the mast cells in whole slide skin images using automated image analysis. Skin sections from naive mice and atopic dermatitis mice were stained with toluidine blue methods by Churukian-Schenk and Luna. Luna's toluidine blue staining method without the eosin counterstaining provided optimal staining of mast cells with a greater contrast to the background. The Churukian-Schenk toluidine blue method resulted in slight background staining which confounded the accurate detection and quantification of mast cells in mouse skin sections using an automated image analysis algorithm. Using the Luna toluidine blue stain, a 5-fold increase in the total number of mast cells was observed in atopic dermatitis skin samples as compared to naive mice. In summary, a simple, conventional, and reproducible toluidine blue method was identified to quantify the mast cells in mouse skin sections using an automated image analysis algorithm.
Collapse
Affiliation(s)
- Nivethitha Raja
- Discovery Toxicology, Biocon-Bristol Myers Squibb R&D Center, Syngene International Limited, Bangalore, India
| | - Sangmesh Naikodi
- Discovery Toxicology, Biocon-Bristol Myers Squibb R&D Center, Syngene International Limited, Bangalore, India
| | - Arunprasath Govindarajan
- Discovery Toxicology, Biocon-Bristol Myers Squibb R&D Center, Syngene International Limited, Bangalore, India
| | - Kamalavenkatesh Palanisamy
- Discovery Toxicology, Biocon-Bristol Myers Squibb R&D Center, Syngene International Limited, Bangalore, India
| |
Collapse
|
40
|
Wilbur DC, Smith ML, Cornell LD, Andryushkin A, Pettus JR. Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross-institutional study. Histopathology 2021; 79:499-508. [PMID: 33813779 DOI: 10.1111/his.14376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/11/2021] [Accepted: 03/29/2021] [Indexed: 11/30/2022]
Abstract
AIMS Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on slides from multiple institutions. We used machine learning to identify glomeruli on renal biopsies and compared performance between single and multiple institutions. METHODS AND RESULTS Randomly selected, adequately sampled renal core biopsy cases (71) consisting of four stains each (haematoxylin and eosin, trichrome, silver, periodic acid Schiff) from three institutions were digitised at ×40. Glomeruli were manually annotated by three renal pathologists using a digital tool. Cases were divided into training/validation (n = 52) and evaluation (n = 19) cohorts. An algorithm was trained to develop three convolutional neural network (CNN) models which tested case cohorts intra- and inter-institutionally. Raw CNN search data from each of the four slides per case were merged into composite regions of interest containing putative glomeruli. The sensitivity and modified specificity of glomerulus detection (versus annotated truth) were calculated for each model/cohort. Intra-institutional (3) sensitivity ranged from 90 to 93%, with modified specificity from 86 to 98%. Interinstitutional (1) sensitivity was 77%, with modified specificity 97%. Combined intra- and inter-institutional (1) sensitivity was 86%, with modified specificity 92%. CONCLUSIONS Feature detection sensitivity degrades when training and test material originate from different sites. Training using a combined set of digital slides from three institutions improves performance. Differing histology methods probably account for algorithm performance contrasts. Our data highlight the need for diverse training sets for the development of generalisable machine learning histology algorithms.
Collapse
Affiliation(s)
| | - Maxwell L Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic - Scottsdale, Phoenix, AZ, USA
| | - Lynn D Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic - Rochester, Rochester, MN, USA
| | | | - Jason R Pettus
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, MH, USA
| |
Collapse
|
41
|
Ginter PS, Idress R, D'Alfonso TM, Fineberg S, Jaffer S, Sattar AK, Chagpar A, Wilson P, Harigopal M. Histologic grading of breast carcinoma: a multi-institution study of interobserver variation using virtual microscopy. Mod Pathol 2021; 34:701-9. [PMID: 33077923 DOI: 10.1038/s41379-020-00698-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 12/14/2022]
Abstract
Breast carcinoma grading is an important prognostic feature recently incorporated into the AJCC Cancer Staging Manual. There is increased interest in applying virtual microscopy (VM) using digital whole slide imaging (WSI) more broadly. Little is known regarding concordance in grading using VM and how such variability might affect AJCC prognostic staging (PS). We evaluated interobserver variability amongst a multi-institutional group of breast pathologists using digital WSI and how discrepancies in grading would affect PS. A digitally scanned slide from 143 invasive carcinomas was independently reviewed by 6 pathologists and assigned grades based on established criteria for tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). Statistical analysis was performed. Interobserver agreement for grade was moderate (κ = 0.497). Agreement was fair (κ = 0.375), moderate (κ = 0.491), and good (κ = 0.705) for grades 2, 3, and 1, respectively. Observer pair concordance ranged from fair to good (κ = 0.354-0.684) Perfect agreement was observed in 43 cases (30%). Interobserver agreement for the individual components was best for TF (κ = 0.503) and worst for MC (κ = 0.281). Seventeen of 86 (19.8%) discrepant cases would have resulted in changes in PS and discrepancies most frequently resulted in a PS change from IA to IB (n = 9). For two of these nine cases, Oncotype DX results would have led to a PS of 1A regardless of grade. Using VM, a multi-institutional cohort of pathologists showed moderate concordance for breast cancer grading, similar to studies using light microscopy. Agreement was the best at the extremes of grade and for evaluation of TF. Whether the higher variability noted for MC is a consequence of VM grading warrants further investigation. Discordance in grading infrequently leads to clinically meaningful changes in the prognostic stage.
Collapse
|
42
|
Pantanowitz L, Harrington S. Experience Reviewing Digital Pap Tests using a Gallery of Images. J Pathol Inform 2021; 12:7. [PMID: 34012711 PMCID: PMC8112346 DOI: 10.4103/jpi.jpi_96_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/03/2020] [Accepted: 12/16/2020] [Indexed: 11/05/2022] Open
Abstract
Introduction: Hologic is developing a digital cytology platform. An educational website was launched for users to review these digitized Pap test cases. The aim of this study was to analyze data captured from this website. Materials and Methods: ThinPrep® Pap test slides were scanned at ×40 using a volumetric (14 focal plane) technique. Website cases consisted of an image gallery and whole slide image (WSI). Over a 13 month period data were recorded including diagnoses, time participants spent online, and number of clicks on the gallery and WSI. Results: 51,289 cases were reviewed by 918 reviewers. Cytotechnologists spent less time (M [Median] = 65.0 s) than pathologists (M = 82.2 s) reviewing cases (P < 0.001). Longer times were associated with incorrect diagnoses and cases with organisms. Cytotechnologists matched the reference diagnoses in 85% of cases compared to pathologists who matched in 79.8%. While in 62% of cases reviewers only examined the gallery, they attained the correct diagnosis 92.7% of the time. Pathologists made more clicks on the gallery and WSI than cytotechnologists (P < 0.001). Diagnostic accuracy decreased with increasing clicks. Conclusions: Website participation provided feedback about how cytologists interact with a digital platform when reviewing cases. These data suggest that digital Pap test review when comprised of an image gallery displaying diagnostically relevant objects is quick and easy to interpret. The high diagnostic concordance of digital Pap tests with reference diagnoses can be attributed to high image quality with volumetric scanning, image gallery format, and ability for users to freely navigate the entire digital slide.
Collapse
|
43
|
Bédard A, Westerling-Bui T, Zuraw A. Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis. Toxicol Pathol 2021; 49:897-904. [PMID: 33576323 DOI: 10.1177/0192623320987804] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Inflammatory bowel disease (IBD) is a complex disease which leads to life-threatening complications and decreased quality of life. The dextran sulfate sodium (DSS) colitis model in mice is known for rapid screening of candidate compounds. Efficacy assessment in this model relies partly on microscopic semiquantitative scoring, which is time-consuming and subjective. We hypothesized that deep learning artificial intelligence (AI) could be used to identify acute inflammation in H&E-stained sections in a consistent and quantitative manner. Training sets were established using ×20 whole slide images of the entire colon. Supervised training of a Convolutional Neural Network (CNN) was performed using a commercial AI platform to detect the entire colon tissue, the muscle and mucosa layers, and 2 categories within the mucosa (normal and acute inflammation E1). The training sets included slides of naive, vehicle-DSS and cyclosporine A-DSS mice. The trained CNN was able to segment, with a high level of concordance, the different tissue compartments in the 3 groups of mice. The segmented areas were used to determine the ratio of E1-affected mucosa to total mucosa. This proof-of-concept work shows promise to increase efficiency and decrease variability of microscopic scoring of DSS colitis when screening candidate compounds for IBD.
Collapse
Affiliation(s)
- Agathe Bédard
- Pathology Department, 25913Charles River, Senneville, Quebec, Canada
| | | | - Aleksandra Zuraw
- Pathology Department, 25913Charles River, Senneville, Quebec, Canada
| |
Collapse
|
44
|
Jhun I, Levy D, Lim H, Herrera Q, Dobo E, Burns D, Hetherington W, Macasaet R, Young AJ, Kong CS, Folkins AK, Yang EJ. Implementation of Collodion Bag Protocol to Improve Whole-slide Imaging of Scant Gynecologic Curettage Specimens. J Pathol Inform 2021; 12:2. [PMID: 34012706 PMCID: PMC8112341 DOI: 10.4103/jpi.jpi_82_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/20/2020] [Accepted: 10/27/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Digital pathology has been increasingly implemented for primary surgical pathology diagnosis. In our institution, digital pathology was recently deployed in the gynecologic (GYN) pathology practice. A notable challenge encountered in the digital evaluation of GYN specimens was high rates of scanning failure of specimens with fragmented as well as scant tissue. To improve tissue detection failure rates, we implemented a novel use of the collodion bag cell block preparation method. Materials and Methods: In this study, we reviewed 108 endocervical curettage (ECC) specimens, representing specimens processed with and without the collodion bag cell block method (n = 56 without collodion bag, n = 52 with collodion bag). Results: Tissue detection failure rates were reduced from 77% (43/56) in noncollodion bag cases to 23/52 (44%) of collodion bag cases, representing a 42% reduction. The median total area of tissue detection failure per level was 0.35 mm2 (interquartile range [IQR]: 0.14, 0.70 mm2) for noncollodion bag cases and 0.08 mm2 (IQR: 0.03, 0.20 mm2) for collodion bag cases. This represents a greater than fourfold reduction in the total area of tissue detection failure per level (P < 0.001). In addition, there were no out-of-focus levels among collodion bag cases, compared to 6/56 (11%) of noncollodion bag cases (median total area = 4.9 mm2). Conclusions: The collodion bag method significantly improved the digital image quality of fragmented/scant GYN curettage specimens, increased efficiency and accuracy of diagnostic evaluation, and enhanced identification of tissue contamination during processing. The logistical challenges and labor cost of deploying the collodion bag protocol are important considerations for feasibility assessment at an institutional level.
Collapse
Affiliation(s)
- Iny Jhun
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - David Levy
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Harumi Lim
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Quintina Herrera
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Erika Dobo
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dominique Burns
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - William Hetherington
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ronald Macasaet
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - April J Young
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Christina S Kong
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ann K Folkins
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Eric Joon Yang
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| |
Collapse
|
45
|
Smith MA, Westerling-Bui T, Wilcox A, Schwartz J. Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models. Toxicol Pathol 2021; 49:905-911. [PMID: 33397208 DOI: 10.1177/0192623320981560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Many compounds affect the cellularity of hematolymphoid organs including bone marrow. Toxicologic pathologists are tasked with their evaluation as part of safety studies. An artificial intelligence (AI) tool could provide diagnostic support for the pathologist. We looked at the ability of a deep-learning AI model to evaluate whole slide images of macaque sternebrae to identify and enumerate bone marrow hematopoietic cells. The AI model was trained and able to differentiate the hematopoietic cells from the other sternebrae tissues. We compared the model to severity scores in a study with decreased hematopoietic cellularity. The mean cells/mm2 from the model was lower for each increase in severity score. The AI model was trained by 1 pathologist, providing proof of concept that AI model generation can be fast and agile, without the need of a cross disciplinary team and significant effort. We see great potential for the role of AI-based bone marrow screening.
Collapse
|
46
|
Jha A, Yang H, Deng R, Kapp ME, Fogo AB, Huo Y. Instance segmentation for whole slide imaging: end-to-end or detect-then-segment. J Med Imaging (Bellingham) 2021; 8:014001. [PMID: 33426152 PMCID: PMC7790159 DOI: 10.1117/1.jmi.8.1.014001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 12/11/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose: Automatic instance segmentation of glomeruli within kidney whole slide imaging (WSI) is essential for clinical research in renal pathology. In computer vision, the end-to-end instance segmentation methods (e.g., Mask-RCNN) have shown their advantages relative to detect-then-segment approaches by performing complementary detection and segmentation tasks simultaneously. As a result, the end-to-end Mask-RCNN approach has been the de facto standard method in recent glomerular segmentation studies, where downsampling and patch-based techniques are used to properly evaluate the high-resolution images from WSI (e.g., > 10,000 × 10,000 pixels on 40 × ). However, in high-resolution WSI, a single glomerulus itself can be more than 1000 × 1000 pixels in original resolution which yields significant information loss when the corresponding features maps are downsampled to the 28 × 28 resolution via the end-to-end Mask-RCNN pipeline. Approach: We assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework. Beyond such a comparison, we also comprehensively evaluate the performance of our detect-then-segment pipeline through: (1) two of the most prevalent segmentation backbones (U-Net and DeepLab_v3); (2) six different image resolutions ( 512 × 512 , 256 × 256 , 128 × 128 , 64 × 64 , 32 × 32 , and 28 × 28 ); and (3) two different color spaces (RGB and LAB). Results: Our detect-then-segment pipeline, with the DeepLab_v3 segmentation framework operating on previously detected glomeruli of 512 × 512 resolution, achieved a 0.953 Dice similarity coefficient (DSC), compared with a 0.902 DSC from the end-to-end Mask-RCNN pipeline. Further, we found that neither RGB nor LAB color spaces yield better performance when compared against each other in the context of a detect-then-segment framework. Conclusions: The detect-then-segment pipeline achieved better segmentation performance compared with the end-to-end method. Our study provides an extensive quantitative reference for other researchers to select the optimized and most accurate segmentation approach for glomeruli, or other biological objects of similar character, on high-resolution WSI.
Collapse
Affiliation(s)
- Aadarsh Jha
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States
| | - Haichun Yang
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States
| | - Ruining Deng
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States
| | - Meghan E. Kapp
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States
| | - Agnes B. Fogo
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States
| |
Collapse
|
47
|
Schumacher VL, Aeffner F, Barale-Thomas E, Botteron C, Carter J, Elies L, Engelhardt JA, Fant P, Forest T, Hall P, Hildebrand D, Klopfleisch R, Lucotte T, Marxfeld H, Mckinney L, Moulin P, Neyens E, Palazzi X, Piton A, Riccardi E, Roth DR, Rousselle S, Vidal JD, Williams B. The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019). Toxicol Pathol 2020; 49:720-737. [PMID: 33297858 DOI: 10.1177/0192623320975841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.
Collapse
Affiliation(s)
- Vanessa L Schumacher
- 1529Roche Innovation Center Basel, Pharma Research and Early Development, F. Hoffmann-La Roche, Ltd, Basel, Switzerland
| | - Famke Aeffner
- Amgen Inc, Amgen Research, Translational Safety and Bioanalytical Sciences, South San Francisco, CA, USA
| | | | | | | | - Laëtitia Elies
- 72810Bayer Crop Science Division, Sophia Antipolis, France.,25913Charles River Laboratories, Lyon, France
| | | | | | | | | | | | - Robert Klopfleisch
- 9166Freie Universitaet Berlin, Institute of Veterinary Pathology, Berlin, Germany
| | - Thomas Lucotte
- 56511Agence nationale de sécurité du médicament et des produits de santé (ANSM), Saint-Denis, France
| | | | - LuAnn Mckinney
- 4137US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Elizabeth Neyens
- Elizabethtoxpath Consulting Inc, Vancouver, British Columbia, Canada
| | | | - Alain Piton
- ALP Quality Systems, Sophia Antipolis, France
| | | | | | | | | | - Bethany Williams
- 572272Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.,Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom
| |
Collapse
|
48
|
Kobayashi S, Saio M, Fujimori M, Hirato J, Oyama T, Fukuda T. Macrophages in Giemsa-stained cerebrospinal fluid specimens predict carcinomatous meningitis. Oncol Lett 2020; 20:352. [PMID: 33123263 PMCID: PMC7586284 DOI: 10.3892/ol.2020.12217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 09/15/2020] [Indexed: 12/02/2022] Open
Abstract
Carcinomatous meningitis is a condition in which tumor cells spread to the subarachnoid space. Leukocyte counting and typing of cerebrospinal fluid (CSF) cell components are performed manually or using flow cytometry. However, a detailed analysis of these variables using cytological specimens has not yet been reported. The present study analyzed cytological specimens using Giemsa staining and whole slide imaging with computer-assisted image analysis (CAIA) to clarify the characteristics of the leukocyte population in CSF, especially in carcinomatous meningitis. Manual evaluation was performed using 280 Giemsa-stained cytological CSF specimens. For 49 samples, CAIA was used for the whole area of Papanicolaou (Pap) staining, and Giemsa-stained specimens of the same samples were imaged using a virtual slide scanner. The nuclear morphology of the leukocytes was assessed, and the total leukocyte and leukocyte subset (lymphocytes, neutrophils and macrophages) counts were evaluated. Then, the number and percentage of each leukocyte subset population were evaluated. The total leukocyte count was significantly higher in Giemsa-stained specimens compared with in Pap-stained specimens. The percentage of macrophages was significantly higher in samples from patients with non-hematological tumors compared with in samples from patients without tumors, which was confirmed by manual evaluation of the specimens. In addition, the cut-off value of the percentage of macrophages that could discriminate between the tumor history negative cases and cytologically tumor positive cases was determined, revealing that a higher proportion of macrophages reflected the existence of atypical/malignant epithelial tumor cells in CSF samples. Thus, atypical cell screening and analysis of the background characteristics of the leukocyte population should be the focus of cytological specimen screening, especially not to miss carcinomatous meningitis.
Collapse
Affiliation(s)
- Sayaka Kobayashi
- Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Gunma University Graduate School of Health Sciences, Maebashi, Gunma 371-8514, Japan
| | - Masanao Saio
- Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Gunma University Graduate School of Health Sciences, Maebashi, Gunma 371-8514, Japan
| | - Misa Fujimori
- Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Gunma University Graduate School of Health Sciences, Maebashi, Gunma 371-8514, Japan
| | - Junko Hirato
- Clinical Department of Pathology, Gunma University Hospital, Maebashi, Gunma 371-8511, Japan
- Department of Pathology, Public Tomioka General Hospital, Tomioka, Gunma, 370-2316, Japan
| | - Tetsunari Oyama
- Clinical Department of Pathology, Gunma University Hospital, Maebashi, Gunma 371-8511, Japan
- Department of Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan
| | - Toshio Fukuda
- Laboratory of Histopathology and Cytopathology, Department of Laboratory Sciences, Gunma University Graduate School of Health Sciences, Maebashi, Gunma 371-8514, Japan
| |
Collapse
|
49
|
Bian Z, Guo C, Jiang S, Zhu J, Wang R, Song P, Zhang Z, Hoshino K, Zheng G. Autofocusing technologies for whole slide imaging and automated microscopy. J Biophotonics 2020; 13:e202000227. [PMID: 32844560 DOI: 10.1002/jbio.202000227] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/14/2020] [Accepted: 08/20/2020] [Indexed: 06/11/2023]
Abstract
Whole slide imaging (WSI) has moved digital pathology closer to diagnostic practice in recent years. Due to the inherent tissue topography variability, accurate autofocusing remains a critical challenge for WSI and automated microscopy systems. The traditional focus map surveying method is limited in its ability to acquire a high degree of focus points while still maintaining high throughput. Real-time approaches decouple image acquisition from focusing, thus allowing for rapid scanning while maintaining continuous accurate focus. This work reviews the traditional focus map approach and discusses the choice of focus measure for focal plane determination. It also discusses various real-time autofocusing approaches including reflective-based triangulation, confocal pinhole detection, low-coherence interferometry, tilted sensor approach, independent dual sensor scanning, beam splitter array, phase detection, dual-LED illumination and deep-learning approaches. The technical concepts, merits and limitations of these methods are explained and compared to those of a traditional WSI system. This review may provide new insights for the development of high-throughput automated microscopy imaging systems that can be made broadly available and utilizable without loss of capacity.
Collapse
Affiliation(s)
- Zichao Bian
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Chengfei Guo
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Shaowei Jiang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Jiakai Zhu
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Ruihai Wang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Pengming Song
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Zibang Zhang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Kazunori Hoshino
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, USA
| |
Collapse
|
50
|
Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020; 9:E3697. [PMID: 33217963 PMCID: PMC7698715 DOI: 10.3390/jcm9113697] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/27/2020] [Accepted: 11/13/2020] [Indexed: 12/11/2022] Open
Abstract
Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist's profession.
Collapse
Affiliation(s)
- Stephan W. Jahn
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
| | - Markus Plass
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
| | - Farid Moinfar
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; (M.P.); (F.M.)
- Department of Pathology, Ordensklinikum/Hospital of the Sisters of Charity, Seilerstätte 4, 4010 Linz, Austria
| |
Collapse
|