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Domen A, Deben C, Verswyvel J, Flieswasser T, Prenen H, Peeters M, Lardon F, Wouters A. Cellular senescence in cancer: clinical detection and prognostic implications. J Exp Clin Cancer Res 2022; 41:360. [PMID: 36575462 PMCID: PMC9793681 DOI: 10.1186/s13046-022-02555-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/30/2022] [Indexed: 12/28/2022] Open
Abstract
Cellular senescence is a state of stable cell-cycle arrest with secretory features in response to cellular stress. Historically, it has been considered as an endogenous evolutionary homeostatic mechanism to eliminate damaged cells, including damaged cells which are at risk of malignant transformation, thereby protecting against cancer. However, accumulation of senescent cells can cause long-term detrimental effects, mainly through the senescence-associated secretory phenotype, and paradoxically contribute to age-related diseases including cancer. Besides its role as tumor suppressor, cellular senescence is increasingly being recognized as an in vivo response in cancer patients to various anticancer therapies. Its role in cancer is ambiguous and even controversial, and senescence has recently been promoted as an emerging hallmark of cancer because of its hallmark-promoting capabilities. In addition, the prognostic implications of cellular senescence have been underappreciated due to the challenging detection and sparse in and ex vivo evidence of cellular senescence in cancer patients, which is only now catching up. In this review, we highlight the approaches and current challenges of in and ex vivo detection of cellular senescence in cancer patients, and we discuss the prognostic implications of cellular senescence based on in and ex vivo evidence in cancer patients.
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Affiliation(s)
- Andreas Domen
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium.
- Department of Oncology, Antwerp University Hospital (UZA), 2650, Edegem (Antwerp), Belgium.
| | - Christophe Deben
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
| | - Jasper Verswyvel
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
| | - Tal Flieswasser
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
| | - Hans Prenen
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
- Department of Oncology, Antwerp University Hospital (UZA), 2650, Edegem (Antwerp), Belgium
| | - Marc Peeters
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
- Department of Oncology, Antwerp University Hospital (UZA), 2650, Edegem (Antwerp), Belgium
| | - Filip Lardon
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
| | - An Wouters
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, 2610, Wilrijk (Antwerp), Belgium
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Frank SJ. Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets. J Pathol Inform 2022; 14:100174. [PMID: 36687530 PMCID: PMC9852683 DOI: 10.1016/j.jpi.2022.100174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/01/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose To provide a flexible, end-to-end platform for visually distinguishing diseased from undiseased tissue in a medical image, in particular pathology slides, and classifying diseased regions by subtype. Highly accurate results are obtained using small training datasets and reduced-scale source images that can be easily shared. Approach An ensemble of lightweight convolutional neural networks (CNNs) is trained on different subsets of images derived from a relatively small number of annotated whole-slide histopathology images (WSIs). The WSIs are first reduced in scale in a manner that preserves anatomic features critical to analysis while also facilitating convenient handling and storage. The segmentation and subtyping tasks are performed sequentially on the reduced-scale images using the same basic workflow: generating and sifting tiles from the image, then classifying each tile with an ensemble of appropriately trained CNNs. For segmentation, the CNN predictions are combined using a function to favor a selected similarity metric, and a mask or map for a a candidate image is produced from tiles whose combined predictions exceed a decision boundary. For subtyping, the resulting mask is applied to the candidate image, and new tiles are derived from the unoccluded regions. These are classified by the subtyping CNNs to produce an overall subtype prediction. Results and conclusion This approach was applied successfully to two very different datasets of large WSIs, one (PAIP2020) involving multiple subtypes of colorectal cancer and the other (CAMELYON16) single-type breast cancer metastases. Scored using standard similarity metrics, the segmentations outperformed more complex models typifying the state of the art.
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103
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Iqbal MS, Ahmad W, Alizadehsani R, Hussain S, Rehman R. Breast Cancer Dataset, Classification and Detection Using Deep Learning. Healthcare (Basel) 2022; 10:2395. [PMID: 36553919 PMCID: PMC9778593 DOI: 10.3390/healthcare10122395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University AJK, Bagh 12500, Pakistan
| | - Waqas Ahmad
- Higher Education Department Govt, AJK, Mirpur 10250, Pakistan
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC 3216, Australia
| | - Sadiq Hussain
- Examination Branch, Dibrugarh University, Dibrugarh 786004, India
| | - Rizwan Rehman
- Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh 786004, India
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104
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Kawazoe Y, Shimamoto K, Yamaguchi R, Nakamura I, Yoneda K, Shinohara E, Shintani-Domoto Y, Ushiku T, Tsukamoto T, Ohe K. Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy. Diagnostics (Basel) 2022; 12:diagnostics12122955. [PMID: 36552963 PMCID: PMC9776670 DOI: 10.3390/diagnostics12122955] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/20/2022] [Accepted: 11/20/2022] [Indexed: 11/29/2022] Open
Abstract
The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman's space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.
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Affiliation(s)
- Yoshimasa Kawazoe
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Correspondence: ; Tel.: +81-3-5800-9077
| | - Kiminori Shimamoto
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Ryohei Yamaguchi
- Ohshima Memorial Kisen Hospital, 3-5-15, Misaki, Chiba 274-0812, Japan
| | - Issei Nakamura
- NTT DOCOMO, Inc., Sanno Park Tower, 2-11-1, Nagata-cho, Chiyoda-ku, Tokyo 100-6150, Japan
| | - Kota Yoneda
- Department of Reproductive, Developmental, and Aging Sciences, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Emiko Shinohara
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yukako Shintani-Domoto
- Department of Diagnostic Pathology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo 113-8602, Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tatsuo Tsukamoto
- Department of Nephrology and Dialysis, Tazuke Kofukai Medical Research Institute, Kitano Hospital, 2-4-20, Ohgimachi, Kita-ku, Osaka 530-8480, Japan
| | - Kazuhiko Ohe
- Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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105
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Nielsen PS, Georgsen JB, Vinding MS, Østergaard LR, Steiniche T. Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14327. [PMID: 36361209 PMCID: PMC9654525 DOI: 10.3390/ijerph192114327] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/07/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Deep learning for the analysis of H&E stains requires a large annotated training set. This may form a labor-intensive task involving highly skilled pathologists. We aimed to optimize and evaluate computer-assisted annotation based on digital dual stains of the same tissue section. H&E stains of primary and metastatic melanoma (N = 77) were digitized, re-stained with SOX10, and re-scanned. Because images were aligned, annotations of SOX10 image analysis were directly transferred to H&E stains of the training set. Based on 1,221,367 annotated nuclei, a convolutional neural network for calculating tumor burden (CNNTB) was developed. For primary melanomas, precision of annotation was 100% (95%CI, 99% to 100%) for tumor cells and 99% (95%CI, 98% to 100%) for normal cells. Due to low or missing tumor-cell SOX10 positivity, precision for normal cells was markedly reduced in lymph-node and organ metastases compared with primary melanomas (p < 0.001). Compared with stereological counts within skin lesions, mean difference in tumor burden was 6% (95%CI, -1% to 13%, p = 0.10) for CNNTB and 16% (95%CI, 4% to 28%, p = 0.02) for pathologists. Conclusively, the technique produced a large annotated H&E training set with high quality within a reasonable timeframe for primary melanomas and subcutaneous metastases. For these lesion types, the training set generated a high-performing CNNTB, which was superior to the routine assessments of pathologists.
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Affiliation(s)
- Patricia Switten Nielsen
- Department of Pathology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, DK-8200 Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
| | - Jeanette Baehr Georgsen
- Department of Pathology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, DK-8200 Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
| | - Mads Sloth Vinding
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
- Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Lasse Riis Østergaard
- Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7E, DK-9220 Aalborg, Denmark
| | - Torben Steiniche
- Department of Pathology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, DK-8200 Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
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106
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Ershadi MM, Rise ZR, Niaki STA. A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans. Comput Biol Med 2022; 150:106159. [PMID: 36257277 DOI: 10.1016/j.compbiomed.2022.106159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/28/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
AIM OF STUDY Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. METHODOLOGY/APPROACH The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for future treatment plans for GBM patients using clinical data, biomedical data, and different image data. A case study is presented based on the Cancer Genome Atlas Glioblastoma Multiforme dataset to prove the effectiveness of the proposed model. This dataset is analyzed using data preprocessing, experts' knowledge, and a feature reduction method based on the Principal Component Analysis. Then, the FCM clustering method is utilized to reinforce classifier learning. OUTCOMES OF STUDY The proposed model finds the best combination of Wrapper feature selection and classifier for each cluster based on different measures, including accuracy, sensitivity, specificity, precision, F-score, and G-mean according to a hierarchical structure. It has the best performance among other reinforced classifiers. Besides, this model is compatible with real-world medical processes for GBM patients based on clinical, biomedical, and image data.
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Affiliation(s)
- Mohammad Mahdi Ershadi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
| | - Zeinab Rahimi Rise
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
| | - Seyed Taghi Akhavan Niaki
- Department of Industrial Engineering, Sharif University of Technology, PO Box 11155-9414, Tehran, 1458889694, Iran.
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107
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Bettauer V, Costa ACBP, Omran RP, Massahi S, Kirbizakis E, Simpson S, Dumeaux V, Law C, Whiteway M, Hallett MT. A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies. Microbiol Spectr 2022; 10:e0147222. [PMID: 35972285 PMCID: PMC9604015 DOI: 10.1128/spectrum.01472-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/25/2022] [Indexed: 12/31/2022] Open
Abstract
We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies. This ranges from yeast white and opaque forms to hyphal and pseudohyphal filamentous morphologies. The software is based upon a fully convolutional one-stage (FCOS) object detector, a deep learning technique that uses an extensive set of images that we manually annotated with the location and morphology of each cell. We developed a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple yeast forms to complex filamentous architectures. Candescence achieves very good performance (~85% recall; 81% precision) on this difficult learning set, where some images contain hundreds of cells with substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology and how they intermix, we used a second technique from deep learning entitled generative adversarial networks. The resultant models allow us to identify and explore technical variables, developmental trajectories, and morphological switches. Importantly, the model allows us to quantitatively capture morphological plasticity observed with genetically modified strains or strains grown in different media and environments. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology. IMPORTANCE The fungus Candida albicans can "shape shift" between 12 morphologies in response to environmental variables. The cytoprotective capacity provided by this polymorphism makes C. albicans a formidable pathogen to treat clinically. Microscopy images of C. albicans colonies can contain hundreds of cells in different morphological states. Manual annotation of images can be difficult, especially as a result of densely packed and filamentous colonies and of technical artifacts from the microscopy itself. Manual annotation is inherently subjective, depending on the experience and opinion of annotators. Here, we built a deep learning approach entitled Candescence to parse images in an automated, quantitative, and objective fashion: each cell in an image is located and labeled with its morphology. Candescence effectively replaces simple rules based on visual phenotypes (size, shape, and shading) with neural circuitry capable of capturing subtle but salient features in images that may be too complex for human annotators.
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Affiliation(s)
- Van Bettauer
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada
| | | | | | - Samira Massahi
- Department of Biology, Concordia University, Montreal, Quebec, Canada
| | | | - Shawn Simpson
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada
| | - Vanessa Dumeaux
- Department of Anatomy and Cell Biology, Western University, London, Ontario, Canada
| | - Chris Law
- Centre for Microscopy and Cellular Imaging, Concordia University, Montreal, Quebec, Canada
| | - Malcolm Whiteway
- Department of Biology, Concordia University, Montreal, Quebec, Canada
| | - Michael T. Hallett
- Department of Biology, Concordia University, Montreal, Quebec, Canada
- Department of Biochemistry, Western University, London, Ontario, Canada
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108
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Brancati N, Anniciello AM, Pati P, Riccio D, Scognamiglio G, Jaume G, De Pietro G, Di Bonito M, Foncubierta A, Botti G, Gabrani M, Feroce F, Frucci M. BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images. Database (Oxford) 2022; 2022:6762252. [PMID: 36251776 PMCID: PMC9575967 DOI: 10.1093/database/baac093] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/16/2022] [Accepted: 10/01/2022] [Indexed: 11/11/2022]
Abstract
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it/
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109
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Yang P, Yin X, Lu H, Hu Z, Zhang X, Jiang R, Lv H. CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images. Med Image Anal 2022; 81:102539. [DOI: 10.1016/j.media.2022.102539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/11/2022] [Accepted: 07/11/2022] [Indexed: 12/01/2022]
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Liang Y, Guo GL, Zhang L. Current and Emerging Molecular Markers of Liver Diseases: A Pathogenic Perspective. Gene Expr 2022; 21:9-19. [PMID: 38911667 PMCID: PMC11192043 DOI: 10.14218/gejlr.2022.00010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In the past decade, with the rapid development of molecular medicine and the application of more sophisticated methods for disease diagnosis and treatment, a number of molecular markers have become available for liver diseases. Pathogenesis-related markers are likely to be effectively discovered and rigorously validated, due to the unique biological links to diseases. The present study reviews the predominant clinical and research articles in the previous decade to provide a pathogenic perspective of current and emerging biomarkers for liver diseases, including hepatocellular neoplasms (e.g. hepatocellular carcinoma), non-neoplastic hepatocellular diseases, intrahepatic biliary diseases, and other liver diseases. Although it remains challenging to cover all markers for the diagnosis and prognosis of liver diseases, current and emerging molecular markers in clinical practice and under investigation are reviewed in a wide spectrum of liver diseases, in order to help clinicians and researchers identify liver disease markers for reference.
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Affiliation(s)
- Yuanxin Liang
- Department of Pathology, Yale University, New Haven, Connecticut, USA
| | - Grace L Guo
- Department of Pharmacology and Toxicology, Ernst Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, USA
- Research and Development Service, Veterans Health Administration, New Jersey Health Care System, East Orange, New Jersey, USA
| | - Lanjing Zhang
- Department of Pathology, Princeton Medical Center, Plainsboro, New Jersey, USA
- Department of Chemical Biology, Ernst Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, USA
- Department of Biological Sciences, Rutgers University, Newark, New Jersey, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA
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111
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Folmsbee J, Zhang L, Lu X, Rahman J, Gentry J, Conn B, Vered M, Roy P, Gupta R, Lin D, Samankan S, Dhorajiva P, Peter A, Wang M, Israel A, Brandwein-Weber M, Doyle S. Histology segmentation using active learning on regions of interest in oral cavity squamous cell carcinoma. J Pathol Inform 2022; 13:100146. [PMID: 36268093 PMCID: PMC9577135 DOI: 10.1016/j.jpi.2022.100146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/15/2022] [Accepted: 09/22/2022] [Indexed: 11/28/2022] Open
Abstract
In digital pathology, deep learning has been shown to have a wide range of applications, from cancer grading to segmenting structures like glomeruli. One of the main hurdles for digital pathology to be truly effective is the size of the dataset needed for generalization to address the spectrum of possible morphologies. Small datasets limit classifiers' ability to generalize. Yet, when we move to larger datasets of whole slide images (WSIs) of tissue, these datasets may cause network bottlenecks as each WSI at its original magnification can be upwards of 100 000 by 100 000 pixels, and over a gigabyte in file size. Compounding this problem, high quality pathologist annotations are difficult to obtain, as the volume of necessary annotations to create a classifier that can generalize would be extremely costly in terms of pathologist-hours. In this work, we use Active Learning (AL), a process for iterative interactive training, to create a modified U-net classifier on the region of interest (ROI) scale. We then compare this to Random Learning (RL), where images for addition to the dataset for retraining are randomly selected. Our hypothesis is that AL shows benefits for generating segmentation results versus randomly selecting images to annotate. We show that after 3 iterations, that AL, with an average Dice coefficient of 0.461, outperforms RL, with an average Dice Coefficient of 0.375, by 0.086.
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Affiliation(s)
- Jonathan Folmsbee
- Department of Pathology & Anatomical Sciences, University at Buffalo SUNY, Buffalo, NY, USA
- Department of Biomedical Engineering, University at Buffalo SUNY, Buffalo, NY, USA
| | - Lei Zhang
- Department of Pathology & Anatomical Sciences, University at Buffalo SUNY, Buffalo, NY, USA
| | - Xulei Lu
- Icahn School of Medicine, The Mount Sinai Hospital, New York, NY, USA
| | - Jawaria Rahman
- Department of Pathology, Case Western University, Cleveland, OH, USA
| | - John Gentry
- Department of Pathology, Nebraska Medical Health System, Omaha, NE, USA
| | - Brendan Conn
- Department of Pathology, University of Edinburgh, Edinburgh, UK
| | - Marilena Vered
- Department of Oral Pathology, Oral Medicine and Maxillofacial Imaging, School of Dental Medicine, Tel Aviv University, Tel Aviv, IL, USA
- Institute of Pathology, Sheba Medical Center, Tel Hashomer, Ramat Gan, IL, USA
| | - Paromita Roy
- Department of Pathology, Tata Memorial Cancer Center, Mumbai, IN, USA
| | - Ruta Gupta
- Department of Tissue Pathology and Diagnostic Oncology, NSW Health Pathology, Royal Prince Alfred Hospital and University of Sydney, Sydney, AU, USA
| | - Diana Lin
- Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shabnam Samankan
- Department of Pathology, George Washington University Hospital, Washington, DC, USA
| | - Pooja Dhorajiva
- Department of Oncologic Surgical Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anu Peter
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA
| | - Minhua Wang
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Anna Israel
- Department of Anatomic Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Scott Doyle
- Department of Pathology & Anatomical Sciences, University at Buffalo SUNY, Buffalo, NY, USA
- Department of Biomedical Engineering, University at Buffalo SUNY, Buffalo, NY, USA
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Singh R, Akram SV, Gehlot A, Buddhi D, Priyadarshi N, Twala B. Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability. SENSORS (BASEL, SWITZERLAND) 2022; 22:6619. [PMID: 36081087 PMCID: PMC9460902 DOI: 10.3390/s22176619] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 08/27/2022] [Accepted: 08/30/2022] [Indexed: 06/15/2023]
Abstract
The United Nations' sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.
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Affiliation(s)
- Rajesh Singh
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
- Department of Project Management, Universidad InternacionalIberoamericana, Campeche C.P. 24560, Mexico
| | - Shaik Vaseem Akram
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
- Law College of Dehradun, Uttaranchal University, Dehradun 248007, India
| | - Anita Gehlot
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
- Department of Project Management, Universidad InternacionalIberoamericana, Campeche C.P. 24560, Mexico
| | - Dharam Buddhi
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, India
| | - Bhekisipho Twala
- Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd., Pretoria West, Pretoria 0183, South Africa
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Zhang T, Chen J, Lu Y, Yang X, Ouyang Z. Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network. PLoS One 2022; 17:e0273355. [PMID: 35994484 PMCID: PMC9394838 DOI: 10.1371/journal.pone.0273355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/05/2022] [Indexed: 12/04/2022] Open
Abstract
OBJECTIVES This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. METHODS Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. RESULTS 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992-2000), the development period (2001-2015), and the rapid growth period (2016-2021). There were two technology frontiers in the budding period (1992-2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001-2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016-2021), namely digital pathology (DP), deep learning (DL) algorithms-convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. CONCLUSIONS Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking.
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Affiliation(s)
- Ting Zhang
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Juan Chen
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Yan Lu
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Xiaoyi Yang
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Zhaolian Ouyang
- Institute of Medical Information & Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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Baldelli E, Mandarano M, Bellezza G, Petricoin EF, Pierobon M. Analysis of neuroendocrine clones in NSCLCs using an immuno-guided laser-capture microdissection-based approach. CELL REPORTS METHODS 2022; 2:100271. [PMID: 36046628 PMCID: PMC9421534 DOI: 10.1016/j.crmeth.2022.100271] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/03/2022] [Accepted: 07/21/2022] [Indexed: 11/30/2022]
Abstract
Clonal evolution and lineage plasticity are key contributors to tumor heterogeneity and response to treatment in cancer. However, capturing signal transduction events in coexisting clones remains challenging from a technical perspective. In this study, we developed and tested a signal-transduction-based workflow to isolate and profile coexisting clones within a complex cellular system like non-small cell lung cancers (NSCLCs). Cooccurring clones were isolated under immunohistochemical guidance using laser-capture microdissection, and cell signaling activation portraits were measured using the reverse-phase protein microarray. To increase the translational potential of this work and capture druggable vulnerabilities within different clones, we measured expression/activation of a panel of key drug targets and downstream substrates of FDA-approved or investigational agents. We isolated intermixed clones, including poorly represented ones (<5% of cells), within the tumor microecology and identified molecular characteristics uniquely attributable to cancer cells that undergo lineage plasticity and neuroendocrine transdifferentiation in NSCLCs.
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Affiliation(s)
- Elisa Baldelli
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
| | - Martina Mandarano
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, University of Perugia, Perugia, Italy
| | - Guido Bellezza
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, University of Perugia, Perugia, Italy
| | - Emanuel F. Petricoin
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
- School of Systems Biology, George Mason University, Manassas, VA, USA
| | - Mariaelena Pierobon
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
- School of Systems Biology, George Mason University, Manassas, VA, USA
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Fiehn AMK, Reiss B, Gögenur M, Bzorek M, Gögenur I. Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer. Appl Immunohistochem Mol Morphol 2022; 30:493-500. [PMID: 35703148 DOI: 10.1097/pai.0000000000001041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/27/2022] [Indexed: 11/26/2022]
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide. Although clinical outcome varies among patients diagnosed within the same TNM stage it is the cornerstone in treatment decisions as well as follow-up programmes. Tumor-infiltrating lymphocytes have added value when evaluating survival outcomes. The aim of this study was to develop a fully automated method for quantification of subsets of T lymphocytes in the invasive margin and central tumor in patients with CRC based on Deep Learning powered artificial intelligence. The study cohort consisted of 163 consecutive patients with a primary diagnosis of CRC followed by a surgical resection. Double-labeling immunohistochemical staining with cytokeratin in combination with CD3 or CD8, respectively, was performed on 1 representative slide from each patient. Visiopharm Quantitative Digital Pathology software was used to develop Application Protocol Packages for visualization of architectural details (background, normal epithelium, cancer epithelium, surrounding tissue), identification of central tumor and invasive margin as well as subsequent quantitative analysis of immune cells. Fully automated counts for CD3 and CD8 positive T cells were obtained in 93% and 92% of the cases, respectively. In the remaining cases, manual editing was required. In conclusion, the development of a fully automated method for counting CD3 + and CD8 + lymphocytes in a cohort of patients with CRC provided excellent results eliminating not only observer variability in lymphocyte counts but also in identifying the regions of interest for the quantitative analysis. Validation of the performance of the Application Protocol Packages including clinical correlation is needed.
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Affiliation(s)
- Anne-Marie K Fiehn
- Department of Pathology
- Department of Surgery, Center for Surgical Science, Zealand University Hospital, Roskilde
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Mikail Gögenur
- Department of Surgery, Center for Surgical Science, Zealand University Hospital, Roskilde
| | | | - Ismail Gögenur
- Department of Surgery, Center for Surgical Science, Zealand University Hospital, Roskilde
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Orsulic S, John J, Walts AE, Gertych A. Computational pathology in ovarian cancer. Front Oncol 2022; 12:924945. [PMID: 35965569 PMCID: PMC9372445 DOI: 10.3389/fonc.2022.924945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/27/2022] [Indexed: 11/30/2022] Open
Abstract
Histopathologic evaluations of tissue sections are key to diagnosing and managing ovarian cancer. Pathologists empirically assess and integrate visual information, such as cellular density, nuclear atypia, mitotic figures, architectural growth patterns, and higher-order patterns, to determine the tumor type and grade, which guides oncologists in selecting appropriate treatment options. Latent data embedded in pathology slides can be extracted using computational imaging. Computers can analyze digital slide images to simultaneously quantify thousands of features, some of which are visible with a manual microscope, such as nuclear size and shape, while others, such as entropy, eccentricity, and fractal dimensions, are quantitatively beyond the grasp of the human mind. Applications of artificial intelligence and machine learning tools to interpret digital image data provide new opportunities to explore and quantify the spatial organization of tissues, cells, and subcellular structures. In comparison to genomic, epigenomic, transcriptomic, and proteomic patterns, morphologic and spatial patterns are expected to be more informative as quantitative biomarkers of complex and dynamic tumor biology. As computational pathology is not limited to visual data, nuanced subvisual alterations that occur in the seemingly “normal” pre-cancer microenvironment could facilitate research in early cancer detection and prevention. Currently, efforts to maximize the utility of computational pathology are focused on integrating image data with other -omics platforms that lack spatial information, thereby providing a new way to relate the molecular, spatial, and microenvironmental characteristics of cancer. Despite a dire need for improvements in ovarian cancer prevention, early detection, and treatment, the ovarian cancer field has lagged behind other cancers in the application of computational pathology. The intent of this review is to encourage ovarian cancer research teams to apply existing and/or develop additional tools in computational pathology for ovarian cancer and actively contribute to advancing this important field.
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Affiliation(s)
- Sandra Orsulic
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, United States
- *Correspondence: Sandra Orsulic,
| | - Joshi John
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States
- Department of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Ann E. Walts
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Arkadiusz Gertych
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
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The Significance of External Quality Assessment Schemes for Molecular Testing in Clinical Laboratories. Cancers (Basel) 2022; 14:cancers14153686. [PMID: 35954349 PMCID: PMC9367251 DOI: 10.3390/cancers14153686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/20/2022] [Accepted: 07/26/2022] [Indexed: 11/18/2022] Open
Abstract
Simple Summary Patients and clinicians often rely on the outcome of laboratory tests, but can we really trust these test results? Good quality management is key for laboratories to guarantee reliable test results. This review focusses on external quality assessment (EQA) schemes which are a tool for laboratories to examine and improve the quality of their testing routines. In this review, an overview of the role and importance of EQA schemes for clinical laboratories is given, and different types of EQA schemes and EQA providers available on the market are discussed, as well as recent developments in the EQA landscape. Abstract External quality assessment (EQA) schemes are a tool for clinical laboratories to evaluate and manage the quality of laboratory practice with the support of an independent party (i.e., an EQA provider). Depending on the context, there are different types of EQA schemes available, as well as various EQA providers, each with its own field of expertise. In this review, an overview of the general requirements for EQA schemes and EQA providers based on international guidelines is provided. The clinical and scientific value of these kinds of schemes for clinical laboratories, clinicians and patients are highlighted, in addition to the support EQA can provide to other types of laboratories, e.g., laboratories affiliated to biotech companies. Finally, recent developments and challenges in laboratory medicine and quality management, for example, the introduction of artificial intelligence in the laboratory and the shift to a more individual-approach instead of a laboratory-focused approach, are discussed. EQA schemes should represent current laboratory practice as much as possible, which poses the need for EQA providers to introduce latest laboratory innovations in their schemes and to apply up-to-date guidelines. By incorporating these state-of-the-art techniques, EQA aims to contribute to continuous learning.
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Automated histological classification for digital pathology images of colonoscopy specimen via deep learning. Sci Rep 2022; 12:12804. [PMID: 35896791 PMCID: PMC9329279 DOI: 10.1038/s41598-022-16885-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/18/2022] [Indexed: 11/09/2022] Open
Abstract
Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtained from colonoscopy-related specimen. Histopathological slides of colonoscopic biopsy or resection specimens were collected and grouped into six classes by disease category: adenocarcinoma, tubular adenoma (TA), traditional serrated adenoma (TSA), sessile serrated adenoma (SSA), hyperplastic polyp (HP), and non-specific lesions. Digital photographs were taken of each pathological slide to fine-tune two pre-trained convolutional neural networks, and the model performances were evaluated. A total of 1865 images were included from 703 patients, of which 10% were used as a test dataset. For six-class classification, the mean diagnostic accuracy was 97.3% (95% confidence interval [CI], 96.0–98.6%) by DenseNet-161 and 95.9% (95% CI 94.1–97.7%) by EfficientNet-B7. The per-class area under the receiver operating characteristic curve (AUC) was highest for adenocarcinoma (1.000; 95% CI 0.999–1.000) by DenseNet-161 and TSA (1.000; 95% CI 1.000–1.000) by EfficientNet-B7. The lowest per-class AUCs were still excellent: 0.991 (95% CI 0.983–0.999) for HP by DenseNet-161 and 0.995 for SSA (95% CI 0.992–0.998) by EfficientNet-B7. Deep learning models achieved excellent performances for discriminating adenocarcinoma from non-adenocarcinoma lesions with an AUC of 0.995 or 0.998. The pathognomonic area for each class was appropriately highlighted in digital images by saliency map, particularly focusing epithelial lesions. Deep learning models might be a useful tool to help the diagnosis for pathologic slides of colonoscopy-related specimens.
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Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study. Pathol Res Pract 2022; 237:154014. [PMID: 35870238 DOI: 10.1016/j.prp.2022.154014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Cutaneous malignant melanoma (CMM) accounts for the highest mortality rate among all skin cancers. Traditional histopathologic diagnosis may be limited by the pathologists' subjectivity. Second-opinion strategies and multidisciplinary consultations are usually performed to overcome this issue. An available solution in the future could be the use of automated solutions based on a computational algorithm that could help the pathologist in everyday practice. The aim of this pilot study was to investigate the potential diagnostic aid of a machine-based algorithm in the histopathologic diagnosis of CMM. METHODS We retrospectively examined excisional biopsies of 50 CMM and 20 benign congenital compound nevi. Hematoxylin and eosin (H&E) stained WSI were reviewed independently by two expert dermatopathologists. A fully automated pipeline for WSI processing to support the estimation and prioritization of the melanoma areas was developed. RESULTS The spatial distribution of the nuclei in the sample provided a multi-scale overview of the tumor. A global overview of the lesion's silhouette was achieved and, by increasing the magnification, the topological distribution of the nuclei and the most informative areas of interest for the CMM diagnosis were identified and highlighted. These silhouettes allow the histopathologist to discriminate between nevus and CMM with an accuracy of 96% without any extra information. CONCLUSION In this study we proposed an easy-to-use model that produces segmentations of CMM silhouettes at fine detail level.
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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Augustine TN. Weakly-supervised deep learning models in computational pathology. EBioMedicine 2022; 81:104117. [PMID: 35738047 PMCID: PMC9234201 DOI: 10.1016/j.ebiom.2022.104117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 11/12/2022] Open
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Elfer K, Dudgeon S, Garcia V, Blenman K, Hytopoulos E, Wen S, Li X, Ly A, Werness B, Sheth MS, Amgad M, Gupta R, Saltz J, Hanna MG, Ehinger A, Peeters D, Salgado R, Gallas BD. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms. J Med Imaging (Bellingham) 2022; 9:047501. [PMID: 35911208 PMCID: PMC9326105 DOI: 10.1117/1.jmi.9.4.047501] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.
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Affiliation(s)
- Katherine Elfer
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
- National Institutes of Health, National Cancer Institute, Division of Cancer Prevention, Cancer Prevention Fellowship Program, Bethesda, Maryland, United States
| | - Sarah Dudgeon
- Yale University Computational Biology and Bioinformatics, New Haven, Connecticut, United States
- Yale New Haven Hospital, Center for Outcomes Research and Evaluation, New Haven, Connecticut, United States
| | - Victor Garcia
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
| | - Kim Blenman
- School of Medicine, Yale Cancer Center, Department of Internal Medicine, Section of Medical Oncology, New Haven, Connecticut, United States
- Yale University, School of Engineering and Applied Science, Department of Computer Science, New Haven, Connecticut, United States
| | | | - Si Wen
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
| | - Xiaoxian Li
- Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, Georgia, United States
| | - Amy Ly
- Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Bruce Werness
- Inova Health System Department of Pathology, Falls Church, Virginia, United States
- Arrive Bio LLC, San Francisco, California, United States
| | - Manasi S. Sheth
- United States Food and Drug Administration (FDA), Center for Devices and Radiologic Health, Office of Product Evaluation and Quality, Office of Clinical Evidence and Analysis, Division of Biostatistics, White Oak, Maryland, United States
| | - Mohamed Amgad
- Northwestern University Feinberg School of Medicine, Department of Pathology, Chicago, Illinois, United States
| | - Rajarsi Gupta
- SUNY Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, New York, United States
| | - Joel Saltz
- SUNY Stony Brook Medicine, Department of Biomedical Informatics, Stony Brook, New York, United States
- SUNY Stony Brook Medicine, Department of Pathology, Stony Brook, New York, United States
| | - Matthew G. Hanna
- Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Anna Ehinger
- Lund University, Laboratory Medicine, Region Skåne, Department of Genetics and Pathology, Lund, Sweden
| | - Dieter Peeters
- Sint-Maarten Hospital, Department of Pathology, Mechelen, Belgium
- University of Antwerp, Department of Biomedical Sciences, Antwerp, Belgium
| | - Roberto Salgado
- Peter Mac Callum Cancer Centre, Division of Research, Melbourne, Australia
- GZA-ZNA Hospitals, Department of Pathology, Antwerp, Belgium
| | - Brandon D. Gallas
- United States Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging Diagnostics & Software Reliability, Silver Spring, Maryland, United States
- Address all correspondence to Brandon D. Gallas,
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Ray I, Raipuria G, Singhal N. Rethinking ImageNet Pre-training for Computational Histopathology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3059-3062. [PMID: 36086630 DOI: 10.1109/embc48229.2022.9871687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on a Histopathology dataset may provide better initialization. To prove this hypothesis, we train two commonly used Deep Learning model architectures - ResNet and DenseNet on a complex Histopathology classification dataset, and compare transfer learning performance with ImageNet pretrained weights. Based on the fine-tuning on three histopathology datasets including two different stains (H&E and IHC), we show that the domain specific pretrained weights are better suited for transfer learning. This is reflected by higher performance, lower training time as well as better feature reuse. Clinical Relevance - The paper establishes merit of using Histopathology domain specific pretrained weights rather than ImageNet pretrained weights.
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125
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Li H, Tsokos MG, Tsokos GC. Lymphocytes in the neighborhood: good or bad for the kidney? J Clin Invest 2022; 132:160657. [PMID: 35775489 PMCID: PMC9246371 DOI: 10.1172/jci160657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Lupus nephritis (LN) is common in people with systemic lupus erythematosus (SLE) and advances, almost invariably, to end-stage renal disease (ESRD). In this issue of the JCI, Abraham, Durkee, et al. presented a large-scale immune cell landscape of kidney biopsies from patients with LN by combining multiplexed confocal microscopy imaging with customized computer vision and quantification. The presence of diverse CD4– T cells in small neighborhoods, but not of B cells or CD4+ T cells in large neighborhoods, is linked to the development of ESRD. Unexpectedly, B cells in the kidney heralded a good prognosis. The precise location of different types of immune cells allows inference on possible interactions between different immune cells and also between immune and kidney-resident cells. The data have important implications on the development of prognostic tools and effective targeted therapies in patients with LN.
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Li X, Cen M, Xu J, Zhang H, Xu XS. Improving feature extraction from histopathological images through a fine-tuning ImageNet model. J Pathol Inform 2022; 13:100115. [PMID: 36268072 PMCID: PMC9577036 DOI: 10.1016/j.jpi.2022.100115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/05/2022] [Accepted: 06/24/2022] [Indexed: 11/04/2022] Open
Abstract
Background Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology. Pre-trained neural networks based on ImageNet database are often used to extract "off-the-shelf" features, achieving great success in predicting tissue types, molecular features, and clinical outcomes, etc. We hypothesize that fine-tuning the pre-trained models using histopathological images could further improve feature extraction, and downstream prediction performance. Methods We used 100 000 annotated H&E image patches for colorectal cancer (CRC) to fine-tune a pre-trained Xception model via a 2-step approach. The features extracted from fine-tuned Xception (FTX-2048) model and Image-pretrained (IMGNET-2048) model were compared through: (1) tissue classification for H&E images from CRC, same image type that was used for fine-tuning; (2) prediction of immune-related gene expression, and (3) gene mutations for lung adenocarcinoma (LUAD). Five-fold cross validation was used for model performance evaluation. Each experiment was repeated 50 times. Findings The extracted features from the fine-tuned FTX-2048 exhibited significantly higher accuracy (98.4%) for predicting tissue types of CRC compared to the "off-the-shelf" features directly from Xception based on ImageNet database (96.4%) (P value = 2.2 × 10-6). Particularly, FTX-2048 markedly improved the accuracy for stroma from 87% to 94%. Similarly, features from FTX-2048 boosted the prediction of transcriptomic expression of immune-related genes in LUAD. For the genes that had significant relationships with image features (P < 0.05, n = 171), the features from the fine-tuned model improved the prediction for the majority of the genes (139; 81%). In addition, features from FTX-2048 improved prediction of mutation for 5 out of 9 most frequently mutated genes (STK11, TP53, LRP1B, NF1, and FAT1) in LUAD. Conclusions We proved the concept that fine-tuning the pretrained ImageNet neural networks with histopathology images can produce higher quality features and better prediction performance for not only the same-cancer tissue classification where similar images from the same cancer are used for fine-tuning, but also cross-cancer prediction for gene expression and mutation at patient level.
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Affiliation(s)
- Xingyu Li
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Min Cen
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jinfeng Xu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Hong Zhang
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xu Steven Xu
- Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, New Jersey, USA
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Baek EB, Hwang JH, Park H, Lee BS, Son HY, Kim YB, Jun SY, Her J, Lee J, Cho JW. Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats. Diagnostics (Basel) 2022; 12:diagnostics12061478. [PMID: 35741291 PMCID: PMC9222125 DOI: 10.3390/diagnostics12061478] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022] Open
Abstract
Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.
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Affiliation(s)
- Eun Bok Baek
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea; (E.B.B.); (H.-Y.S.)
| | - Ji-Hee Hwang
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
| | - Heejin Park
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
| | - Byoung-Seok Lee
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
| | - Hwa-Young Son
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea; (E.B.B.); (H.-Y.S.)
| | - Yong-Bum Kim
- Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea;
| | - Sang-Yeop Jun
- Research & Development Team, LAC Inc., Seoul 07807, Korea; (S.-Y.J.); (J.H.); (J.L.)
| | - Jun Her
- Research & Development Team, LAC Inc., Seoul 07807, Korea; (S.-Y.J.); (J.H.); (J.L.)
| | - Jaeku Lee
- Research & Development Team, LAC Inc., Seoul 07807, Korea; (S.-Y.J.); (J.H.); (J.L.)
| | - Jae-Woo Cho
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
- Correspondence: ; Tel.: +82-42-610-8023
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Chang S, Sadimin E, Yao K, Hamilton S, Aoun P, Pillai R, Muirhead D, Schmolze D. Establishment of a whole slide imaging-based frozen section service at a cancer center. J Pathol Inform 2022; 13:100106. [PMID: 36268067 PMCID: PMC9577038 DOI: 10.1016/j.jpi.2022.100106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/20/2022] [Accepted: 05/21/2022] [Indexed: 12/03/2022] Open
Abstract
Background In recent years, there has been a surge of interest in clinical digital pathology (DP). Hardware and software platforms have matured and become more affordable, and advances in artificial intelligence promise to transform the practice of pathology. At our institution, we are launching a stepwise process of DP adoption which will eventually encompass our entire workflow. Out of necessity, we began by establishing a whole slide imaging (WSI)-based frozen section service. Methods We proceeded in a systematic manner by first assembling a team of key stakeholders. We carefully evaluated the various options for digitizing frozen sections before deciding that a WSI-based solution made the most sense for us. We used a formalized evaluation system to quantify performance metrics that were relevant to us. After deciding on a WSI-based system, we likewise carefully considered the various whole slide scanners and digital slide management systems available before making decisions. Results During formal evaluation by pathologists, the WSI-based system outperformed competing platforms. Although implementation was relatively complex, we have been happy with the results and have noticed significant improvements in our frozen section turnaround time. Our users have been happy with the slide management system, which we plan on utilizing in future DP efforts. Conclusions There are various options for digitizing frozen section slides. Although WSI-based systems are more complex and expensive than some alternatives, they perform well and may make sense for institutions with a pre-existing or planned larger DP infrastructure.
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George RS, Htoo A, Cheng M, Masterson TM, Huang K, Adra N, Kaimakliotis HZ, Akgul M, Cheng L. Artificial intelligence in prostate cancer: Definitions, current research, and future directions. Urol Oncol 2022; 40:262-270. [PMID: 35430139 DOI: 10.1016/j.urolonc.2022.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/19/2022] [Accepted: 03/10/2022] [Indexed: 12/24/2022]
Abstract
Multiple novel modalities tasking artificial intelligence based computational pathology applications and integrating other variables, such as risk factors, tumor microenvironment, genomic testing data, laboratory findings, clinical history, and radiology findings, will improve diagnostic consistency and generate a synergistic diagnostic workflow. In this article, we present the concise and contemporary review on the utilization of artificial intelligence in prostate cancer and identify areas for possible future applications.
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Affiliation(s)
- Rose S George
- Department of Pathology and Laboratory Medicine, Albany Medical Center, Albany, NY
| | - Arkar Htoo
- Department of Pathology and Laboratory Medicine, Albany Medical Center, Albany, NY
| | - Michael Cheng
- Department of Medicine, Indianapolis, Indianapolis, IN
| | | | - Kun Huang
- Department of Medicine, Indianapolis, Indianapolis, IN; Department of Biostatistics and Health Data Science, Indianapolis, IN; Regenstrief Institute, Indianapolis, IN
| | - Nabil Adra
- Department of Medicine, Indianapolis, Indianapolis, IN; Department of Urology, Indianapolis, IN
| | | | - Mahmut Akgul
- Department of Pathology and Laboratory Medicine, Albany Medical Center, Albany, NY.
| | - Liang Cheng
- Department of Urology, Indianapolis, IN; Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN.
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Zhang L. The Challenges and Opportunities of Translational Pathology. JOURNAL OF CLINICAL AND TRANSLATIONAL PATHOLOGY 2022; 2:63-66. [PMID: 35874625 PMCID: PMC9302533 DOI: 10.14218/jctp.2022.00001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Translational pathology has not caught up with the quality and quantity of translational medicine. Thus, challenges and opportunities related to translational pathology are discussed here. Pathologists should actively participate in reverse translational research that seeks mechanistic insights to explain clinical findings and/or solve clinical problems. Challenges in translational pathology include ambiguity in defining translational pathology, pathologists' mindsets about translational research, lack of sufficient workforce and immature publication outlets. However, with collective wisdom and support of various stakeholders, we can expand the pool of pathologist scientists, build a translational pathology community and drive innovations in medicine through computational, molecular genetic/genomic and digital pathology approaches.
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Affiliation(s)
- Lanjing Zhang
- Department of Pathology, Princeton Medical Center, Plainsboro, NJ, USA
- Department of Biological Sciences, Rutgers University Newark, NJ, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Chemical Biology, Rutgers Ernest Mario School of Pharmacy, Piscataway, NJ, USA
- Correspondence to: Lanjing Zhang, Department of Pathology, Princeton Medical Center, 1 Plainsboro Rd., Plainsboro, NJ 08563, USA. Tel: +1-609-853-6833, Fax: +1-609-853-6841,
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Ghezloo F, Wang PC, Kerr KF, Brunyé TT, Drew T, Chang OH, Reisch LM, Shapiro LG, Elmore JG. An analysis of pathologists' viewing processes as they diagnose whole slide digital images. J Pathol Inform 2022; 13:100104. [PMID: 36268085 PMCID: PMC9576972 DOI: 10.1016/j.jpi.2022.100104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/07/2022] [Accepted: 04/09/2022] [Indexed: 10/27/2022] Open
Abstract
Although pathologists have their own viewing habits while diagnosing, viewing behaviors leading to the most accurate diagnoses are under-investigated. Digital whole slide imaging has enabled investigators to analyze pathologists' visual interpretation of histopathological features using mouse and viewport tracking techniques. In this study, we provide definitions for basic viewing behavior variables and investigate the association of pathologists' characteristics and viewing behaviors, and how they relate to diagnostic accuracy when interpreting whole slide images. We use recordings of 32 pathologists' actions while interpreting a set of 36 digital whole slide skin biopsy images (5 sets of 36 cases; 180 cases total). These viewport tracking data include the coordinates of a viewport scene on pathologists' screens, the magnification level at which that viewport was viewed, as well as a timestamp. We define a set of variables to quantify pathologists' viewing behaviors such as zooming, panning, and interacting with a consensus reference panel's selected region of interest (ROI). We examine the association of these viewing behaviors with pathologists' demographics, clinical characteristics, and diagnostic accuracy using cross-classified multilevel models. Viewing behaviors differ based on clinical experience of the pathologists. Pathologists with a higher caseload of melanocytic skin biopsy cases and pathologists with board certification and/or fellowship training in dermatopathology have lower average zoom and lower variance of zoom levels. Viewing behaviors associated with higher diagnostic accuracy include higher average and variance of zoom levels, a lower magnification percentage (a measure of consecutive zooming behavior), higher total interpretation time, and higher amount of time spent viewing ROIs. Scanning behavior, which refers to panning with a fixed zoom level, has marginally significant positive association with accuracy. Pathologists' training, clinical experience, and their exposure to a range of cases are associated with their viewing behaviors, which may contribute to their diagnostic accuracy. Research in computational pathology integrating digital imaging and clinical informatics opens up new avenues for leveraging viewing behaviors in medical education and training, potentially improving patient care and the effectiveness of clinical workflow.
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Affiliation(s)
- Fatemeh Ghezloo
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Pin-Chieh Wang
- Department of Medicine, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Kathleen F. Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Tad T. Brunyé
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA, USA
| | - Trafton Drew
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Oliver H. Chang
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Lisa M. Reisch
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Linda G. Shapiro
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Joann G. Elmore
- Department of Medicine, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA
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Isberg OG, Giunchiglia V, McKenzie JS, Takats Z, Jonasson JG, Bodvarsdottir SK, Thorsteinsdottir M, Xiang Y. Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning. Metabolites 2022; 12:455. [PMID: 35629959 PMCID: PMC9143055 DOI: 10.3390/metabo12050455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/10/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.
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Affiliation(s)
- Olof Gerdur Isberg
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland;
| | - Valentina Giunchiglia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - James S. McKenzie
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
| | - Jon Gunnlaugur Jonasson
- Department of Pathology, Landspitali the National University Hospital, Hringbraut, 101 Reykjavik, Iceland;
- Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, 101 Reykjavik, Iceland
| | | | - Margret Thorsteinsdottir
- Faculty of Pharmaceutical Sciences, University of Iceland, Hofsvallagata 53, 107 Reykjavik, Iceland
- Biomedical Center, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland;
| | - Yuchen Xiang
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (O.G.I.); (V.G.); (J.S.M.); (Z.T.)
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Usual interstitial pneumonia: a clinically significant pattern, but not the final word. Mod Pathol 2022; 35:589-593. [PMID: 35210554 DOI: 10.1038/s41379-022-01054-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/08/2022] [Accepted: 01/14/2022] [Indexed: 12/13/2022]
Abstract
Usual interstitial pneumonia (UIP) is a concept that is deeply entrenched in clinical practice and the prognostic significance of UIP is well established, but the field continues to suffer from the lack of a true gold standard for diagnosing fibrotic interstitial lung disease (ILD). The meaning and usage of UIP have shifted over time and this term is prone to misinterpretation and poor diagnostic agreement. For pathologists, it is worth reflecting on the limitations of UIP and our true role in the care of patients with ILD, a controversial topic explored in two point-counterpoint editorials published simultaneously in this journal. Current diagnostic guidelines are ambiguous and difficult to apply in clinical practice. Further complicating matters for the pathologist is the paradigm shift that occurred with the advent of anti-fibrotic agents, necessitating increased focus on the most likely etiology of fibrosis rather than simply the pattern of fibrosis when pulmonologists select appropriate therapy. Despite the wealth of information locked in tissue samples that could provide novel insights into fibrotic ILDs, pulmonologists increasingly shy away from obtaining biopsies, likely because pathologists no longer provide sufficient value to offset the risks of a biopsy procedure, and pathologic assessment is insufficiently reliable to meaningfully inform therapeutic decisionmaking. To increase the value of biopsies, pathologists must first recognize the problems with UIP as a diagnostic term. Second, pathologists must realize that the primary goal of a biopsy is to determine the most likely etiology to target with therapy, requiring a shift in diagnostic focus. Third, pathologists must devise and validate new classifications and criteria that are evidence-based, biologically relevant, easy to use, and predictive of outcome and treatment response. Only after the limitations of UIP are understood will pathologists provide maximum diagnostic value from biopsies to clinicians today and advance the field forward.
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Chantziantoniou N. BestCyte® Cell Sorter Imaging System: Primary and Adjudicative Whole Slide Image Rescreening Review Times of 500 ThinPrep Pap Test Thin-layers - An Intra-observer, Time-Surrogate Analysis of Diagnostic Confidence Potentialities. J Pathol Inform 2022; 13:100095. [PMID: 36268084 PMCID: PMC9576977 DOI: 10.1016/j.jpi.2022.100095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/10/2022] [Indexed: 11/17/2022] Open
Abstract
Background The novel Artificial Intelligence-driven BestCyte® Cell Sorter Imaging System (BestCyte) enables hybrid digital screening through classification and sorting of tiles depicting cells in 8 galleries or whole slide image (WSI) reviews. Objectives (1) Analyze expenditures of time (minutes) for primary BestCyte cell sorter screening and adjudicative WSI rescreening of 500 blinded, randomized ThinPrep thin-layers to determine review times per Bethesda nomenclature; (2) Analyze review times for NILM qualifier diagnoses reflecting increasing interpretive complexity (i.e., Inflammation, Reactive/Repair, Bacterial cytolysis, Bacterial vaginosis, Atrophy, and Atrophic vaginitis); (3) Challenge accuracy of primary diagnoses (Downgraded, Upheld, and Upgraded) following adjudicative WSI rescreening to assess correlated review times as surrogate indicators of diagnostic confidence in BestCyte functionality (i.e., learning curve); and (4) Correlate primary and adjudicative diagnoses to calculate intra-observer reproducibility Kappa coefficients per Bethesda nomenclature. Results Of 500 thin-layers, the mean [primary/adjudicative rescreening review times (minutes)] were: Overall study [1.38/3.94], NILM [1.23/3.02], ASCUS [1.18/2.53], ASC-H [1.73/4.86], AGUS [1.84/6.34], LSIL [1.49/4.16], HSIL [1.52/4.10], CA [0.65/2.57]. Of 500 primary Bethesda diagnoses: 2 (0.40%) downgraded; 483 (96.6%) upheld; 15 (3.00%) upgraded after adjudicative WSI rescreening. Of 354 NILM diagnoses: 0 downgraded; 344 (97.2%) upheld; 10 (2.82%) upgraded. Of 34 ASCUS diagnoses: 2 (5.88%) downgraded; 28 (82.4%) upheld; 4 (11.8%) upgraded. Of 17 ASC-H diagnoses: 0 downgraded; 16 (94.1%) upheld; 1 (5.88%) upgraded. Of AGUS (n=1), LSIL (n=24), HSIL (n=52), CA (n=1), UNSAT (n=17): 100% upheld. Kappa coefficients with 95% (Confidence Intervals): Overall study 0.9305 (0.8983–0.9627), NILM 0.9429 (0.9110–0.9748), ASCUS 0.8378 (0.7393–0.9363), ASC-H 0.9112 (0.8113–0.9999), AGUS 1.0 (1.0–1.0), LSIL 0.9189 (0.8400–0.9978), HSIL 0.9894 (0.9685–0.9999), CA 1.0 (1.0–1.0), UNSAT 1.0 (1.0–1.0). Primary BestCyte cell image review time trends for NILM, ASCUS, LSIL, and HSIL, revealed plateaus relative to decreasing respective adjudicative WSI rescreening times. Conclusions Given innovative robustness, BestCyte accommodates interpretive fundamentals, enabling shorter ThinPrep thin-layer review times with optimal intra-observer concordance per Bethesda nomenclature through classifying, ranking, sorting, and displaying clinically relevant cells efficiently in galleries. BestCyte fosters continuously optimizing diagnostic confidence learning curves; may supplant manual microscopy for primary screening.
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Rabbani N, Kim GYE, Suarez CJ, Chen JH. Applications of machine learning in routine laboratory medicine: Current state and future directions. Clin Biochem 2022; 103:1-7. [PMID: 35227670 PMCID: PMC9007900 DOI: 10.1016/j.clinbiochem.2022.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/04/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023]
Abstract
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.
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Affiliation(s)
- Naveed Rabbani
- Department of Clinical Informatics, Lucile Packard Children's Hospital, Palo Alto, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Grace Y E Kim
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jonathan H Chen
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA; Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA
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Alrafiah AR. Application and performance of artificial intelligence technology in cytopathology. Acta Histochem 2022; 124:151890. [PMID: 35366580 DOI: 10.1016/j.acthis.2022.151890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/17/2022] [Accepted: 03/24/2022] [Indexed: 11/27/2022]
Abstract
Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent advancements in computer vision and machine learning. The first use of AI in a pathology lab was in cytopathology, when a computer-assisted Pap test screening was created. Initially designed to diagnose rather than screen, there was a lot of disagreement concerning their wide use to clinical specimens. However, whole-slide imaging of both gynaecological and non-gynaecological histopathology have been the subject of recent AI work. An overview of the literature on AI in cytopathology is provided in this brief review. To be more precise, it intends to emphasize the relevance of applications of AI algorithms to gynaecological and non-gynaecologic cytology. Between January 2000 and December 2021, a search on artificial intelligence in cytopathology was conducted in several well-known databases, including PubMed, Web of Science, Scopus, Embase, and Google Scholar. Only full-text papers that could be accessed online were evaluated.
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Bankhead P. Developing image analysis methods for digital pathology. J Pathol 2022; 257:391-402. [PMID: 35481680 PMCID: PMC9324951 DOI: 10.1002/path.5921] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/04/2022]
Abstract
The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Peter Bankhead
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.,Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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138
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Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology. Sci Rep 2022; 12:6965. [PMID: 35484289 PMCID: PMC9051062 DOI: 10.1038/s41598-022-11009-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 04/18/2022] [Indexed: 12/04/2022] Open
Abstract
Deep myxoid soft tissue lesions have posed a diagnostic challenge for pathologists due to significant histological overlap and regional heterogeneity, especially when dealing with small biopsies which have profoundly low accuracy. However, accurate diagnosis is important owing to difference in biological behaviors and response to adjuvant therapy, that will guide the extent of surgery and the need for neo-adjuvant therapy. Herein, we trained two convolutional neural network models based on a total of 149,130 images representing diagnoses of extra skeletal myxoid chondrosarcoma, intramuscular myxoma, low-grade fibromyxoid sarcoma, myxofibrosarcoma and myxoid liposarcoma. Both AI models outperformed all the pathologists, with a significant improvement of accuracy up to 97% compared to average pathologists of 69.7% (p < 0.00001), corresponding to 90% reduction in error rate. The area under curve of the best AI model was on average 0.9976. It could assist pathologists in clinical practice for accurate diagnosis of deep soft tissue myxoid lesions, and guide clinicians for precise and optimal treatment for patients.
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139
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Shi YC, Li J, Li SJ, Li ZP, Zhang HJ, Wu ZY, Wu ZY. Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms. World J Clin Cases 2022; 10:3729-3738. [PMID: 35647170 PMCID: PMC9100718 DOI: 10.12998/wjcc.v10.i12.3729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Microvascular tissue reconstruction is a well-established, commonly used technique for a wide variety of the tissue defects. However, flap failure is associated with an additional hospital stay, medical cost burden, and mental stress. Therefore, understanding of the risk factors associated with this event is of utmost importance.
AIM To develop machine learning-based predictive models for flap failure to identify the potential factors and screen out high-risk patients.
METHODS Using the data set of 946 consecutive patients, who underwent microvascular tissue reconstruction of free flap reconstruction for head and neck, breast, back, and extremity, we established three machine learning models including random forest classifier, support vector machine, and gradient boosting. Model performances were evaluated by the indicators such as area under the curve of receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was performed for the most critical variables in the random forest model.
RESULTS Post-surgery, the flap failure event occurred in 34 patients (3.6%). The machine learning models based on various preoperative and intraoperative variables were successfully developed. Among them, the random forest classifier reached the best performance in receiver operating characteristic curve, with an area under the curve score of 0.770 in the test set. The top 10 variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity. Interestingly, only age, body mass index, and ischemic time were statistically associated with the outcomes.
CONCLUSION Machine learning-based algorithms, especially the random forest classifier, were very important in categorizing patients at high risk of flap failure. The occurrence of flap failure was a multifactor-driven event and was identified with numerous factors that warrant further investigation. Importantly, the successful application of machine learning models may help the clinician in decision-making, understanding the underlying pathologic mechanisms of the disease, and improving the long-term outcome of patients.
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Affiliation(s)
- Yu-Cang Shi
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Jie Li
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Shao-Jie Li
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Zhan-Peng Li
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Hui-Jun Zhang
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Ze-Yong Wu
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Zhi-Yuan Wu
- Department of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
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140
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Dey P. Artificial neural network in diagnostic cytology. Cytojournal 2022; 19:27. [PMID: 35510103 PMCID: PMC9063555 DOI: 10.25259/cytojournal_33_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 08/28/2021] [Indexed: 11/29/2022] Open
Abstract
The artificial neural network (ANN) is a computer software design or model that simulates the biological neural network of the human brain. Instead of biological neurons, ANN is composed of many layers of nodes that carry the signal and process it to make the final decision. ANN is a modern technology that is widely used in different fields of science. The ANN is reshaping the medical system and the various areas of pathology. In this paper, the basic concept and applications of ANN in cytology have been discussed. In this paper, the various articles published on ANN in the field of cytology have been systemically reviewed. The ANN is relatively less used in cytology. After introducing convolutional neural network and whole slide scanners in the commercial market, it is now essential to have thorough knowledge in this field to start diagnostic application of ANN.
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141
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El Agouri H, Azizi M, El Attar H, El Khannoussi M, Ibrahimi A, Kabbaj R, Kadiri H, BekarSabein S, EchCharif S, Mounjid C, El Khannoussi B. Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: first Moroccan prospective study on a private dataset. BMC Res Notes 2022; 15:66. [PMID: 35183227 PMCID: PMC8857730 DOI: 10.1186/s13104-022-05936-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 01/29/2022] [Indexed: 11/18/2022] Open
Abstract
Objective Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. For this reason, the diagnosis and classification of breast cancer using Deep learning algorithms have attracted a lot of attention. Therefore, our study aimed to design a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. We collected overall 328 digital slides, from 116 of surgical breast specimens diagnosed with invasive breast carcinoma of non-specific type, and referred to the histopathology department of the National Institute of Oncology in Rabat, Morocco. We used two models of deep neural network architectures in order to accurately classify the images into one of three categories: normal tissue-benign lesions, in situ carcinoma or invasive carcinoma. Results Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. We reported high degrees of overall correct classification accuracy (88%), and sensitivity (95%) for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision. The results of the present study showed that the designed classification model has a good generalization performance in predicting diagnosis of breast cancer, in spite of the limited size of the data. To our knowledge, this approach can be highly compared with other common methods in the automated analysis of breast cancer images reported in literature.
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Affiliation(s)
- H El Agouri
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco.
| | - M Azizi
- Datapathology, 20000, Casablanca, Morocco
| | - H El Attar
- Anatomic Pathology Laboratory Ennassr, 24000, El Jadida, Morocco
| | | | - A Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical & Pharmacy School, Mohammed Vth University in Rabat, 10100, Rabat, Morocco
| | - R Kabbaj
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - H Kadiri
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - S BekarSabein
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - S EchCharif
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
| | - C Mounjid
- Pathology Department, Oncology National Institute, Faculty of Sciences, Mohammed V University, 10100, Rabat, Morocco
| | - B El Khannoussi
- Pathology Department, Oncology National Institute, Faculty of Medicine and Pharmacy, Mohammed V University, 10100, Rabat, Morocco
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142
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A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Sci Rep 2022; 12:2222. [PMID: 35140318 PMCID: PMC8828883 DOI: 10.1038/s41598-022-06264-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/24/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive’s unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists’ annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into ‘low risk’ (benign, inflammation) and ‘high risk’ (dysplasia, malignancy) categories. We further trained the composite AI-model’s performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.
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143
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Komori T. Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System. J Transl Med 2022; 102:126-133. [PMID: 34504304 DOI: 10.1038/s41374-021-00667-6] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 08/13/2021] [Accepted: 08/13/2021] [Indexed: 12/15/2022] Open
Abstract
The grading of gliomas based on histological features has been a subject of debate for several decades. A consensus has not yet been reached because of technical limitations and inter-observer variations. While the traditional grading system has failed to stratify the risk of IDH-mutant astrocytoma, canonical histological and proliferative markers may be applicable to the risk stratification of IDH-wild-type astrocytoma. Numerous studies have examined molecular markers in order to obtain more clinically relevant information that will improve the risk stratification of gliomas. The CDKN2A/B homozygous deletion for IDH-mutant astrocytoma and the following three criteria for IDH-wild-type astrocytoma: the concurrent gain of whole chromosome 7 and loss of whole chromosome 10, TERT promoter mutations, and EGFR amplification, were identified as independent molecular markers of the worst clinical outcomes. Therefore, the 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System adopted these molecular markers into the revised grading criteria of IDH-mutant and -wild-type astrocytoma, respectively, as a grading system within tumor types. Of note, several recent studies have shown that some low-grade IDH-wild-type astrocytoma lacking both the molecular glioblastoma signature and genetic alterations typical of pediatric-type gliomas may demonstrate a relatively indolent clinical course, suggesting the existence of lower-grade adult IDH-wild-type astrocytoma. In terms of oligodendroglioma, IDH-mutant, and 1p/19q codeleted, consistent makers that predict poor outcomes have not yet been identified, and, thus, the current criteria have remained unchanged. Molecular testing to fulfill the revised WHO criteria is, however, not always available worldwide, and in that case, an integrated diagnosis combining all available complementary information is highly recommended. This review discusses controversial issues surrounding legacy grading systems and newly identified potential genetic markers of adult diffuse gliomas and provides perspectives on future grading systems.
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Affiliation(s)
- Takashi Komori
- Department of Laboratory Medicine and Pathology (Neuropathology), Tokyo Metropolitan Neurological Hospital, 2-6-1 Musashidai, Fuchu, Tokyo, 183-0042, Japan.
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144
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Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 2022; 22:114-126. [PMID: 34663944 PMCID: PMC8810682 DOI: 10.1038/s41568-021-00408-3] [Citation(s) in RCA: 158] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/08/2021] [Indexed: 02/07/2023]
Abstract
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.
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Affiliation(s)
- Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pegah Khosravi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jianjiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Fisher NC, Loughrey MB, Coleman HG, Gelbard MD, Bankhead P, Dunne PD. Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods. Histopathology 2022; 80:485-500. [PMID: 34580909 DOI: 10.1111/his.14574] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/07/2021] [Accepted: 09/25/2021] [Indexed: 12/17/2022]
Abstract
AIMS Tumour budding (TB) is an established prognostic feature in multiple cancers but is not routinely assessed in pathology practice. Efforts to standardise and automate assessment have shifted from haematoxylin and eosin (H&E)-stained images towards cytokeratin immunohistochemistry. The aim of this study was to compare manual H&E and cytokeratin assessment methods with a semi-automated approach built within QuPath open-source software. METHODS AND RESULTS TB was assessed in cores from the advancing tumour edge in a cohort of stage II/III colon cancers (n = 186). The total numbers of buds detected with each method were as follows: manual H&E, n = 503; manual cytokeratin, n = 2290; and semi-automated, n = 5138. More than four times the number of buds were identified manually with cytokeratin assessment than with H&E assessment. One thousand seven hundred and thirty-four individual buds were identified with both manual and semi-automated assessments applied to cytokeratin images, representing 75.7% of the buds identified manually (n = 2290) and 33.7% of the buds detected with the semi-automated method (n = 5138). Higher semi-automated TB scores were due to any discrete area of cytokeratin immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to the inclusion of tumour cell clusters within glandular lumina ('luminal pseudobuds'). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores (ρ = 0.81, P < 0.0001). All methods of TB assessment demonstrated poorer survival associated with higher TB scores. CONCLUSIONS We present a new QuPath-based approach to TB assessment, which compares favourably with established methods and offers a freely available, rapid and transparent tool that is also applicable to whole slide images.
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Affiliation(s)
- Natalie C Fisher
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
- Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, UK
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Helen G Coleman
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | | | - Peter Bankhead
- Edinburgh Pathology, Edinburgh, UK
- Centre for Genomic & Experimental Medicine, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Philip D Dunne
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
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146
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Espinoza R, Wong B, Fu D. Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis. JOURNAL OF VISUALIZED EXPERIMENTS : JOVE 2022:10.3791/63484. [PMID: 35188120 PMCID: PMC8957265 DOI: 10.3791/63484] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Stimulated Raman scattering (SRS) microscopy has emerged as a powerful optical imaging technique for tissue diagnosis. In recent years, two-color SRS has been shown to be able to provide hematoxylin and eosin (H&E)-equivalent images that allow fast and reliable diagnosis of brain cancer. Such capability has enabled exciting intraoperative cancer diagnosis applications. Two-color SRS imaging of tissue can be done with either a picosecond or femtosecond laser source. Femtosecond lasers have the advantage of enabling flexible imaging modes, including fast hyperspectral imaging and real-time, two-color SRS imaging. A spectral-focusing approach with chirped laser pulses is typically used with femtosecond lasers to achieve high spectral resolution. Two-color SRS acquisition can be realized with orthogonal modulation and lock-in detection. The complexity of pulse chirping, modulation, and characterization is a bottleneck for the widespread adoption of this method. This article provides a detailed protocol to demonstrate the implementation and optimization of spectral-focusing SRS and real-time, two-color imaging of mouse brain tissue in the epi-mode. This protocol can be used for a broad range of SRS imaging applications that leverage the high speed and spectroscopic imaging capability of SRS.
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Affiliation(s)
| | - Brian Wong
- Department of Chemistry, University of Washington
| | - Dan Fu
- Department of Chemistry, University of Washington
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147
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Teranikar T, Lim J, Ijaseun T, Lee J. Development of Planar Illumination Strategies for Solving Mysteries in the Sub-Cellular Realm. Int J Mol Sci 2022; 23:1643. [PMID: 35163562 PMCID: PMC8835835 DOI: 10.3390/ijms23031643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/22/2021] [Accepted: 01/25/2022] [Indexed: 02/04/2023] Open
Abstract
Optical microscopy has vastly expanded the frontiers of structural and functional biology, due to the non-invasive probing of dynamic volumes in vivo. However, traditional widefield microscopy illuminating the entire field of view (FOV) is adversely affected by out-of-focus light scatter. Consequently, standard upright or inverted microscopes are inept in sampling diffraction-limited volumes smaller than the optical system's point spread function (PSF). Over the last few decades, several planar and structured (sinusoidal) illumination modalities have offered unprecedented access to sub-cellular organelles and 4D (3D + time) image acquisition. Furthermore, these optical sectioning systems remain unaffected by the size of biological samples, providing high signal-to-noise (SNR) ratios for objective lenses (OLs) with long working distances (WDs). This review aims to guide biologists regarding planar illumination strategies, capable of harnessing sub-micron spatial resolution with a millimeter depth of penetration.
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Affiliation(s)
| | | | | | - Juhyun Lee
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 75022, USA; (T.T.); (J.L.); (T.I.)
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148
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Deeper sections reveal residual tumor cells in rectal cancer specimens diagnosed with pathological complete response following neoadjuvant treatment. Virchows Arch 2022; 480:1041-1049. [DOI: 10.1007/s00428-022-03287-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/22/2021] [Accepted: 01/21/2022] [Indexed: 10/19/2022]
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149
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Gothankar J, Doke P, Murarkar S. Digital technology for improving health. Indian J Public Health 2022; 66:399-400. [PMID: 37039161 DOI: 10.4103/ijph.ijph_1557_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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150
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Drogt J, Milota M, Vos S, Bredenoord A, Jongsma K. Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations. Mod Pathol 2022; 35:1540-1550. [PMID: 35927490 PMCID: PMC9596368 DOI: 10.1038/s41379-022-01123-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/24/2022]
Abstract
Recent progress in the development of artificial intelligence (AI) has sparked enthusiasm for its potential use in pathology. As pathology labs are currently starting to shift their focus towards AI implementation, a better understanding how AI tools can be optimally aligned with the medical and social context of pathology daily practice is urgently needed. Strikingly, studies often fail to mention the ways in which AI tools should be integrated in the decision-making processes of pathologists, nor do they address how this can be achieved in an ethically sound way. Moreover, the perspectives of pathologists and other professionals within pathology concerning the integration of AI within pathology remains an underreported topic. This article aims to fill this gap in the literature and presents the first in-depth interview study in which professionals' perspectives on the possibilities, conditions and prerequisites of AI integration in pathology are explicated. The results of this study have led to the formulation of three concrete recommendations to support AI integration, namely: (1) foster a pragmatic attitude toward AI development, (2) provide task-sensitive information and training to health care professionals working in pathology departments and (3) take time to reflect upon users' changing roles and responsibilities.
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Affiliation(s)
- Jojanneke Drogt
- Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands.
| | - Megan Milota
- grid.7692.a0000000090126352Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands
| | - Shoko Vos
- grid.10417.330000 0004 0444 9382Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Annelien Bredenoord
- grid.7692.a0000000090126352Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands
| | - Karin Jongsma
- grid.7692.a0000000090126352Department of Medical Humanities, University Medical Center, Utrecht, The Netherlands
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