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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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Jain E, Patel A, Parwani AV, Shafi S, Brar Z, Sharma S, Mohanty SK. Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Int J Surg Pathol 2024; 32:433-448. [PMID: 37437093 DOI: 10.1177/10668969231185089] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.
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Affiliation(s)
- Ekta Jain
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Ankush Patel
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Zoya Brar
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Shivani Sharma
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
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Katayama A, Aoki Y, Watanabe Y, Horiguchi J, Rakha EA, Oyama T. Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers. Int J Clin Oncol 2024:10.1007/s10147-024-02513-3. [PMID: 38619651 DOI: 10.1007/s10147-024-02513-3] [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: 01/16/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.
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Affiliation(s)
- Ayaka Katayama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan.
| | - Yuki Aoki
- Center for Mathematics and Data Science, Gunma University, Maebashi, Japan
| | - Yukako Watanabe
- Clinical Training Center, Gunma University Hospital, Maebashi, Japan
| | - Jun Horiguchi
- Department of Breast Surgery, International University of Health and Welfare, Narita, Japan
| | - Emad A Rakha
- Department of Histopathology School of Medicine, University of Nottingham, University Park, Nottingham, UK
- Department of Pathology, Hamad Medical Corporation, Doha, Qatar
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, 3-39-22 Showamachi, Maebashi, Gunma, 371-8511, Japan
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Haj-Hosseini N, Lindblad J, Hasséus B, Kumar VV, Subramaniam N, Hirsch JM. Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges. J Maxillofac Oral Surg 2024; 23:23-32. [PMID: 38312957 PMCID: PMC10831018 DOI: 10.1007/s12663-022-01710-9] [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] [Received: 12/01/2021] [Accepted: 03/10/2022] [Indexed: 11/28/2022] Open
Abstract
Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists.
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Affiliation(s)
- Neda Haj-Hosseini
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Joakim Lindblad
- Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Bengt Hasséus
- Department of Oral Medicine and Pathology, Institute of Odontology, University of Gothenburg, The Sahlgrenska Academy, Gothenburg, Sweden
- Clinic of Oral Medicine, Public Dental Service, Gothenburg, Region Västra Götaland Sweden
| | - Vinay Vijaya Kumar
- Department of Head and Neck Oncology, Sri Shankara Cancer Hospital and Research Centre, Bangalore, India
- Department of Surgical Sciences, Odontology and Maxillofacial Surgery, Medical Faculty, Uppsala University, Uppsala, Sweden
| | - Narayana Subramaniam
- Department of Head and Neck Oncology, Sri Shankara Cancer Hospital and Research Centre, Bangalore, India
| | - Jan-Michaél Hirsch
- Department of Surgical Sciences, Odontology and Maxillofacial Surgery, Medical Faculty, Uppsala University, Uppsala, Sweden
- Department of Research & Development, Public Dental Services Region Stockholm, Stockholm, Sweden
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Gallo M, Krajňanský V, Nenutil R, Holub P, Brázdil T. Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability. N Biotechnol 2023; 78:52-67. [PMID: 37793603 DOI: 10.1016/j.nbt.2023.09.008] [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: 03/16/2023] [Revised: 08/29/2023] [Accepted: 09/30/2023] [Indexed: 10/06/2023]
Abstract
Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions.
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Affiliation(s)
- Matej Gallo
- Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic.
| | - Vojtěch Krajňanský
- Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic
| | - Rudolf Nenutil
- Department of Pathology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Petr Holub
- Institute of Computer Science, Masaryk University, Šumavská 416/15, 602 00 Brno, Czech Republic
| | - Tomáš Brázdil
- Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic
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Jang HJ, Go JH, Kim Y, Lee SH. Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer. Cancers (Basel) 2023; 15:5389. [PMID: 38001649 PMCID: PMC10670046 DOI: 10.3390/cancers15225389] [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] [Received: 10/04/2023] [Revised: 11/01/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.
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Affiliation(s)
- Hyun-Jong Jang
- Department of Physiology, CMC Institute for Basic Medical Science, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
| | - Jai-Hyang Go
- Department of Pathology, Dankook University College of Medicine, Cheonan 31116, Republic of Korea;
| | - Younghoon Kim
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
| | - Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea;
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7
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Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagn Pathol 2023; 18:109. [PMID: 37784122 PMCID: PMC10546747 DOI: 10.1186/s13000-023-01375-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/21/2023] [Indexed: 10/04/2023] Open
Abstract
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.
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Affiliation(s)
- Saba Shafi
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, E409 Doan Hall, 410 West 10th Ave, Columbus, OH, 43210, USA.
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Raciti P, Sue J, Retamero JA, Ceballos R, Godrich R, Kunz JD, Casson A, Thiagarajan D, Ebrahimzadeh Z, Viret J, Lee D, Schüffler PJ, DeMuth G, Gulturk E, Kanan C, Rothrock B, Reis-Filho J, Klimstra DS, Reuter V, Fuchs TJ. Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection. Arch Pathol Lab Med 2023; 147:1178-1185. [PMID: 36538386 DOI: 10.5858/arpa.2022-0066-oa] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2022] [Indexed: 09/29/2023]
Abstract
CONTEXT.— Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.— To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.— Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.— Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.— This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.
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Affiliation(s)
- Patricia Raciti
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jillian Sue
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Juan A Retamero
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Rodrigo Ceballos
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Ran Godrich
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jeremy D Kunz
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Adam Casson
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Dilip Thiagarajan
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Zahra Ebrahimzadeh
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Julian Viret
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Donghun Lee
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Peter J Schüffler
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | | | - Emre Gulturk
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Christopher Kanan
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Brandon Rothrock
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jorge Reis-Filho
- The Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Reis-Filho, Reuter)
| | - David S Klimstra
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Victor Reuter
- The Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Reis-Filho, Reuter)
| | - Thomas J Fuchs
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
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Chen Y, Shao Z, Bian H, Fang Z, Wang Y, Cai Y, Wang H, Liu G, Li X, Zhang Y. dMIL-Transformer: Multiple Instance Learning Via Integrating Morphological and Spatial Information for Lymph Node Metastasis Classification. IEEE J Biomed Health Inform 2023; 27:4433-4443. [PMID: 37310831 DOI: 10.1109/jbhi.2023.3285275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Automated classification of lymph node metastasis (LNM) plays an important role in the diagnosis and prognosis. However, it is very challenging to achieve satisfactory performance in LNM classification, because both the morphology and spatial distribution of tumor regions should be taken into account. To address this problem, this article proposes a two-stage dMIL-Transformer framework, which integrates both the morphological and spatial information of the tumor regions based on the theory of multiple instance learning (MIL). In the first stage, a double Max-Min MIL (dMIL) strategy is devised to select the suspected top-K positive instances from each input histopathology image, which contains tens of thousands of patches (primarily negative). The dMIL strategy enables a better decision boundary for selecting the critical instances compared with other methods. In the second stage, a Transformer-based MIL aggregator is designed to integrate all the morphological and spatial information of the selected instances from the first stage. The self-attention mechanism is further employed to characterize the correlation between different instances and learn the bag-level representation for predicting the LNM category. The proposed dMIL-Transformer can effectively deal with the thorny classification in LNM with great visualization and interpretability. We conduct various experiments over three LNM datasets, and achieve 1.79%-7.50% performance improvement compared with other state-of-the-art methods.
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10
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Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med 2023; 13:1214. [PMID: 37623465 PMCID: PMC10455458 DOI: 10.3390/jpm13081214] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.
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Affiliation(s)
- Diana Gina Poalelungi
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
| | - Carmina Liana Musat
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Ana Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Marius Neagu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
- ‘Saint John’ Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Alin Ionut Piraianu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Iuliu Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
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11
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Scalco R, Hamsafar Y, White CL, Schneider JA, Reichard RR, Prokop S, Perrin RJ, Nelson PT, Mooney S, Lieberman AP, Kukull WA, Kofler J, Keene CD, Kapasi A, Irwin DJ, Gutman DA, Flanagan ME, Crary JF, Chan KC, Murray ME, Dugger BN. The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers. J Neuropathol Exp Neurol 2023; 82:202-211. [PMID: 36692179 PMCID: PMC9941826 DOI: 10.1093/jnen/nlac127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.
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Affiliation(s)
- Rebeca Scalco
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Yamah Hamsafar
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Stefan Prokop
- Department of Pathology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
| | | | - Sean Mooney
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Andrew P Lieberman
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Julia Kofler
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Christopher Dirk Keene
- Department Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | | | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Gutman
- Departments of Neurology, Psychiatry, and Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Margaret E Flanagan
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - John F Crary
- Department of Pathology, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Artificial Intelligence & Human Health, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kwun C Chan
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
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12
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Bidzińska J, Szurowska E. See Lung Cancer with an AI. Cancers (Basel) 2023; 15:cancers15041321. [PMID: 36831662 PMCID: PMC9954317 DOI: 10.3390/cancers15041321] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
A lot has happened in the field of lung cancer screening in recent months. The ongoing discussion and documentation published by the scientific community and policymakers are of great importance to the entire European community and perhaps beyond. Lung cancer is the main worldwide killer. Low-dose computed tomography-based screening, together with smoking cessation, is the only tool to fight lung cancer, as it has already been proven in the United States of America but also European randomized controlled trials. Screening requires a lot of well-organized specialized work, but it can be supported by artificial intelligence (AI). Here we discuss whether and how to use AI for patients, radiologists, pulmonologists, thoracic surgeons, and all hospital staff supporting screening process benefits.
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13
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Locke D, Hoyt CC. Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging. Front Mol Biosci 2023; 10:1051491. [PMID: 36845550 PMCID: PMC9948403 DOI: 10.3389/fmolb.2023.1051491] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient's likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.
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Affiliation(s)
- Darren Locke
- Clinical Assay Development, Akoya Biosciences, Marlborough, MA, United States,*Correspondence: Darren Locke,
| | - Clifford C. Hoyt
- Translational and Scientific Affairs, Akoya Biosciences, Marlborough, MA, United States
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14
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Wang X, Du Y, Yang S, Zhang J, Wang M, Zhang J, Yang W, Huang J, Han X. RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval. Med Image Anal 2023; 83:102645. [PMID: 36270093 DOI: 10.1016/j.media.2022.102645] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 07/21/2022] [Accepted: 09/27/2022] [Indexed: 02/07/2023]
Abstract
Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-aided diagnosis has been well developed to assist pathologists in decision-making. Content-based WSI retrieval can be a new approach to find highly correlated WSIs in a historically diagnosed WSI archive, which has the potential usages for assisted clinical diagnosis, medical research, and trainee education. During WSI retrieval, it is particularly challenging to encode the semantic content of histopathological images and to measure the similarity between images for interpretable results due to the gigapixel size of WSIs. In this work, we propose a Retrieval with Clustering-guided Contrastive Learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance. The proposed feature learning method makes use of existing large-scale unlabeled histopathological image data, which helps learn universal features that could be used directly for subsequent WSI retrieval tasks without extra fine-tuning. The proposed WSI retrieval method not only returns a set of WSIs similar to a query WSI, but also highlights patches or sub-regions of each WSI that share high similarity with patches of the query WSI, which helps pathologists interpret the searching results. Our WSI retrieval framework has been evaluated on the tasks of anatomical site retrieval and cancer subtype retrieval using over 22,000 slides, and the performance exceeds other state-of-the-art methods significantly (around 10% for the anatomic site retrieval in terms of average mMV@10). Besides, the patch retrieval using our learned feature representation offers a performance improvement of 24% on the TissueNet dataset in terms of mMV@5 compared with using ImageNet pre-trained features, which further demonstrates the effectiveness of the proposed CCL feature learning method.
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Affiliation(s)
- Xiyue Wang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yuexi Du
- College of Engineering, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Sen Yang
- Tencent AI Lab, Shenzhen 518057, China
| | - Jun Zhang
- Tencent AI Lab, Shenzhen 518057, China
| | - Minghui Wang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China; College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
| | - Wei Yang
- Tencent AI Lab, Shenzhen 518057, China
| | | | - Xiao Han
- Tencent AI Lab, Shenzhen 518057, China.
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15
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Alchami FS, Iqbal Z, Björkhammer CN, Saeed MO, Ramakrishnan R, Clelland C, Ahmad F, Charles A. Whole Slide Imaging Integration with Lab Information Systems, a Study of the Requirements, Processes and Procedures Enabling a Reporting-Based Workflow. PATHOLOGY AND LABORATORY MEDICINE INTERNATIONAL 2023. [DOI: 10.2147/plmi.s388981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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16
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Zhao J, Han Z, Ma Y, Liu H, Yang T. Research progress in digital pathology: A bibliometric and visual analysis based on Web of Science. Pathol Res Pract 2022; 240:154171. [DOI: 10.1016/j.prp.2022.154171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022]
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17
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Song J, Im S, Lee SH, Jang HJ. Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images. Diagnostics (Basel) 2022; 12:2623. [PMID: 36359467 PMCID: PMC9689570 DOI: 10.3390/diagnostics12112623] [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: 09/04/2022] [Revised: 10/26/2022] [Accepted: 10/26/2022] [Indexed: 08/11/2023] Open
Abstract
Uterine cervical and endometrial cancers have different subtypes with different clinical outcomes. Therefore, cancer subtyping is essential for proper treatment decisions. Furthermore, an endometrial and endocervical origin for an adenocarcinoma should also be distinguished. Although the discrimination can be helped with various immunohistochemical markers, there is no definitive marker. Therefore, we tested the feasibility of deep learning (DL)-based classification for the subtypes of cervical and endometrial cancers and the site of origin of adenocarcinomas from whole slide images (WSIs) of tissue slides. WSIs were split into 360 × 360-pixel image patches at 20× magnification for classification. Then, the average of patch classification results was used for the final classification. The area under the receiver operating characteristic curves (AUROCs) for the cervical and endometrial cancer classifiers were 0.977 and 0.944, respectively. The classifier for the origin of an adenocarcinoma yielded an AUROC of 0.939. These results clearly demonstrated the feasibility of DL-based classifiers for the discrimination of cancers from the cervix and uterus. We expect that the performance of the classifiers will be much enhanced with an accumulation of WSI data. Then, the information from the classifiers can be integrated with other data for more precise discrimination of cervical and endometrial cancers.
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Affiliation(s)
- JaeYen Song
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Soyoung Im
- Department of Hospital Pathology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 16247, Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
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18
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Lee SH, Lee Y, Jang H. Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer. Int J Cancer 2022; 152:298-307. [DOI: 10.1002/ijc.34251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Sung Hak Lee
- Department of Hospital Pathology Seoul St. Mary's Hospital
| | - Yujin Lee
- Department of Hospital Pathology St. Vincent's Hospital
| | - Hyun‐Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine The Catholic University of Korea Seoul South Korea
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19
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The evolving landscape of Anatomic Pathology. Crit Rev Oncol Hematol 2022; 178:103776. [DOI: 10.1016/j.critrevonc.2022.103776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 12/11/2022] Open
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20
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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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21
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van der Kamp A, Waterlander TJ, de Bel T, van der Laak J, van den Heuvel-Eibrink MM, Mavinkurve-Groothuis AMC, de Krijger RR. Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future? Pediatr Dev Pathol 2022; 25:380-387. [PMID: 35238696 DOI: 10.1177/10935266211059809] [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] [Indexed: 11/16/2022]
Abstract
Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.
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Affiliation(s)
- Ananda van der Kamp
- 541199Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Tomas J Waterlander
- 541199Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Thomas de Bel
- Department of Pathology, 234134Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen van der Laak
- Department of Pathology, 234134Radboud University Medical Center, Nijmegen, the Netherlands.,Center for Medical Image Science and Visualization, 4566Linköping University, Linköping, Sweden
| | | | | | - Ronald R de Krijger
- 541199Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands.,Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
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22
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Merve A, Schneider U, Kara E, Papadopoulou C, Stenzel W. Muscle biopsy in myositis: What the rheumatologist needs to know. Best Pract Res Clin Rheumatol 2022; 36:101763. [PMID: 35773136 DOI: 10.1016/j.berh.2022.101763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The appropriate analysis of skeletal muscle tissues is a key element in many diagnostic procedures and can deliver valuable information about the organ that is affected. Although arguably the frequency of muscle biopsy may be declining in certain domains where genetic analysis is now the first line of diagnostic evaluation, it still has an important role in assessment of patients with neuromuscular disorders such as congenital myopathies, muscular dystrophies, metabolic and inflammatory diseases. Here, we have comprehensively discussed the aspects of a modern and fruitful approach to muscle biopsy histopathological studies in rheumatological disorders. We have focussed on the neuromuscular involvement in myositis and its differential diagnoses in both adult and paediatric settings. We have also covered the clinical indications for the biopsy, technical aspects and practical points relevant for the rheumatologists. Finally, we have critically discussed the current and future opportunities that a muscle biopsy may offer and its limitations.
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Affiliation(s)
- Ashirwad Merve
- Department of Neuropathology, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK; Department of Histopathology, Great Ormond Street Hospital for Children, London, UK
| | - Udo Schneider
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Rheumatology, Charitéplatz 1, 10117 Berlin, Germany
| | - Eleanna Kara
- Department of Neuropathology, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
| | | | - Werner Stenzel
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany.
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23
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Forest T, Aeffner F, Bangari DS, Bawa B, Carter J, Fikes J, High W, Hayashi SM, Jacobsen M, McKinney L, Rudmann D, Steinbach T, Schumacher V, Turner O, Ward JM, Willson CJ. Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies. Toxicol Pathol 2022; 50:531-543. [PMID: 35657014 DOI: 10.1177/01926233221099273] [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] [Indexed: 11/15/2022]
Abstract
The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.
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Affiliation(s)
| | | | | | | | | | | | - Wanda High
- High Preclinical Pathology Consulting, Rochester, New York, USA
| | - Shim-Mo Hayashi
- Laboratory of Veterinary Pathology, Tokyo University of Agriculture and Technology, Fuchu, Tokyo, Japan
- Division of Food Additives, National Institute of Health Sciences, Kawasaki, Kanagawa, Japan
| | - Matthew Jacobsen
- Regulatory Safety Centre of Excellence, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - LuAnn McKinney
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Daniel Rudmann
- Charles River Laboratories International, Inc., Wilmington, Massachusetts, USA
| | - Thomas Steinbach
- Experimental Pathology Laboratories, Inc., Research Triangle Park, North Carolina, USA
| | | | | | | | - Cynthia J Willson
- Integrated Laboratory Systems, Research Triangle Park, North Carolina, USA
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24
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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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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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Zhu J, Liu M, Li X. Progress on deep learning in digital pathology of breast cancer: a narrative review. Gland Surg 2022; 11:751-766. [PMID: 35531111 PMCID: PMC9068546 DOI: 10.21037/gs-22-11] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/04/2022] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology. METHODS A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. KEY CONTENT AND FINDINGS DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches. CONCLUSIONS Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.
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Affiliation(s)
- Jingjin Zhu
- School of Medicine, Nankai University, Tianjin, China
| | - Mei Liu
- Department of Pathology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Xiru Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
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Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2022; 14:cancers14061370. [PMID: 35326521 PMCID: PMC8946647 DOI: 10.3390/cancers14061370] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising. Abstract Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.
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Affiliation(s)
- Hwa-Yen Chiu
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Division of Internal Medicine, Hsinchu Branch, Taipei Veterans General Hospital, Hsinchu 310, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precis Oncol 2022; 6:14. [PMID: 35236916 PMCID: PMC8891271 DOI: 10.1038/s41698-022-00252-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 12/16/2021] [Indexed: 12/19/2022] Open
Abstract
Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet.
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Schapiro D, Sokolov A, Yapp C, Chen YA, Muhlich JL, Hess J, Creason AL, Nirmal AJ, Baker GJ, Nariya MK, Lin JR, Maliga Z, Jacobson CA, Hodgman MW, Ruokonen J, Farhi SL, Abbondanza D, McKinley ET, Persson D, Betts C, Sivagnanam S, Regev A, Goecks J, Coffey RJ, Coussens LM, Santagata S, Sorger PK. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat Methods 2022; 19:311-315. [PMID: 34824477 PMCID: PMC8916956 DOI: 10.1038/s41592-021-01308-y] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 09/22/2021] [Indexed: 01/02/2023]
Abstract
Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.
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Affiliation(s)
- Denis Schapiro
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Computational Biomedicine and Institute of Pathology, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
| | - Artem Sokolov
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Clarence Yapp
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA
| | - Yu-An Chen
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Jeremy L Muhlich
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Joshua Hess
- Vaccine and Immunotherapy Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Allison L Creason
- Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Ajit J Nirmal
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gregory J Baker
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Maulik K Nariya
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Zoltan Maliga
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Connor A Jacobson
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Matthew W Hodgman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Juha Ruokonen
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Domenic Abbondanza
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Daniel Persson
- Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Courtney Betts
- Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA
| | - Shamilene Sivagnanam
- Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Jeremy Goecks
- Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
| | - Robert J Coffey
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lisa M Coussens
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA
| | - Sandro Santagata
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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30
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Liu Y, Levenson RM, Jenkins MW. Slide Over: Advances in Slide-Free Optical Microscopy as Drivers of Diagnostic Pathology. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:180-194. [PMID: 34774514 PMCID: PMC8883436 DOI: 10.1016/j.ajpath.2021.10.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 02/03/2023]
Abstract
Conventional analysis using clinical histopathology is based on bright-field microscopy of thinly sliced tissue specimens. Although bright-field microscopy is a simple and robust method of examining microscope slides, the preparation of the slides needed is a lengthy and labor-intensive process. Slide-free histopathology, however, uses direct imaging of intact, minimally processed tissue samples using advanced optical-imaging systems, bypassing the extended workflow now required for the preparation of tissue sections. This article explains the technical basis of slide-free microscopy, reviews common slide-free optical microscopy techniques, and discusses the opportunities and challenges involved in clinical implementation.
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Affiliation(s)
- Yehe Liu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Richard M. Levenson
- Department of Pathology and Laboratory Medicine, University of California–Davis, Sacramento, California,Address correspondence to Richard M. Levenson, M.D., UC Davis Health, Path Building, 4400 V St., Sacramento, CA 95817; or Michael W. Jenkins, Ph.D., 2109 Adelbert Rd., Wood Bldg., WG28, Cleveland, OH 44106.
| | - Michael W. Jenkins
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio,Address correspondence to Richard M. Levenson, M.D., UC Davis Health, Path Building, 4400 V St., Sacramento, CA 95817; or Michael W. Jenkins, Ph.D., 2109 Adelbert Rd., Wood Bldg., WG28, Cleveland, OH 44106.
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Yao X, Pathak V, Xi H, Chaware A, Cooke C, Kim K, Xu S, Li Y, Dunn T, Chandra Konda P, Zhou KC, Horstmeyer R. Increasing a microscope's effective field of view via overlapped imaging and machine learning. OPTICS EXPRESS 2022; 30:1745-1761. [PMID: 35209329 PMCID: PMC8970696 DOI: 10.1364/oe.445001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/22/2021] [Accepted: 12/14/2021] [Indexed: 05/03/2023]
Abstract
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.
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Affiliation(s)
- Xing Yao
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Vinayak Pathak
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Haoran Xi
- Computer Science, Duke University, Durham, NC 27708, USA
| | - Amey Chaware
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Colin Cooke
- Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Kanghyun Kim
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Shiqi Xu
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Yuting Li
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Timothy Dunn
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Neurosurgery, Duke University, Durham, NC 27708, USA
| | | | - Kevin C. Zhou
- Biomedical Engineering, Duke University, Durham, NC 27708, USA
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32
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Foran DJ, Durbin EB, Chen W, Sadimin E, Sharma A, Banerjee I, Kurc T, Li N, Stroup AM, Harris G, Gu A, Schymura M, Gupta R, Bremer E, Balsamo J, DiPrima T, Wang F, Abousamra S, Samaras D, Hands I, Ward K, Saltz JH. An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities. J Pathol Inform 2022; 13:5. [PMID: 35136672 PMCID: PMC8794027 DOI: 10.4103/jpi.jpi_31_21] [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: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). MATERIALS AND METHODS As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. RESULTS Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. CONCLUSION To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.
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Affiliation(s)
- David J. Foran
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Eric B. Durbin
- Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Wenjin Chen
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Evita Sadimin
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Imon Banerjee
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Nan Li
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Antoinette M. Stroup
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Gerald Harris
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Annie Gu
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Maria Schymura
- New York State Cancer Registry, New York State Department of Health, Albany, NY, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Joseph Balsamo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Isaac Hands
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Kevin Ward
- Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
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Shakir MN, Dugger BN. Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future. J Neuropathol Exp Neurol 2022; 81:2-15. [PMID: 34981115 PMCID: PMC8825756 DOI: 10.1093/jnen/nlab122] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.
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Affiliation(s)
- Mustafa N Shakir
- From the Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA (MNS, BND)
| | - Brittany N Dugger
- From the Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA (MNS, BND)
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Mubarak M. Move from Traditional Histopathology to Digital and Computational Pathology: Are we Ready? Indian J Nephrol 2022; 32:414-415. [PMID: 36568597 PMCID: PMC9775606 DOI: 10.4103/ijn.ijn_508_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 09/07/2021] [Accepted: 12/25/2021] [Indexed: 12/27/2022] Open
Affiliation(s)
- Muhammed Mubarak
- Department of Histopathology, Sindh Institute of Urology and Transplantation (SIUT), Karachi, Pakistan,Address for correspondence: Dr. Muhammed Mubarak, Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi - 74200, Pakistan. E-mail:
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Perincheri S. Tumor Microenvironment of Lymphomas and Plasma Cell Neoplasms: Broad Overview and Impact on Evaluation for Immune Based Therapies. Front Oncol 2021; 11:719140. [PMID: 34956859 PMCID: PMC8692247 DOI: 10.3389/fonc.2021.719140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/16/2021] [Indexed: 12/20/2022] Open
Abstract
Lymphomas and plasma cell neoplasms are a heterogenous group of malignancies derived from lymphocytes. They are a significant cause of patient morbidity and mortality. Advances in morphologic, immunophenotypic and molecular techniques have led to better understanding of the pathogenesis and diagnosis of these neoplasms. Advances in treatment, particularly immune-based therapies, increasingly allow for targeted therapies of these diseases. Mechanistic studies using animal models and clinical trials have revealed the importance of the tumor microenvironment on disease pathogenesis, progression, and response to therapy in these malignancies. Simultaneous progress in diagnostic techniques has made it feasible to generate high-resolution, high-throughput data from the tumor microenvironment with spatial context. As the armamentarium of targeted therapies and diagnostic techniques grows, there is potential to harness these advances to better stratify patients for targeted therapies, including immune-based therapies, in hematologic malignancies.
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Fraggetta F, L’Imperio V, Ameisen D, Carvalho R, Leh S, Kiehl TR, Serbanescu M, Racoceanu D, Della Mea V, Polonia A, Zerbe N, Eloy C. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics (Basel) 2021; 11:2167. [PMID: 34829514 PMCID: PMC8623219 DOI: 10.3390/diagnostics11112167] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/15/2021] [Accepted: 11/19/2021] [Indexed: 12/12/2022] Open
Abstract
The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.
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Affiliation(s)
- Filippo Fraggetta
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Pathology Unit, “Gravina” Hospital, Caltagirone, ASP Catania, Via Portosalvo 1, 95041 Caltagirone, Italy
| | - Vincenzo L’Imperio
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Medicine and Surgery, Pathology, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, 20900 Monza, Italy
| | - David Ameisen
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Imginit SAS, 152 Boulevard du Montparnasse, 75014 Paris, France
| | - Rita Carvalho
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Sabine Leh
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Pathology, Haukeland University Hospital, Jonas Lies Vei 65, 5021 Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Jonas Lies Vei 87, 5021 Bergen, Norway
| | - Tim-Rasmus Kiehl
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Mircea Serbanescu
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Daniel Racoceanu
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, Inserm, CNRS, APHP, Inria Team “Aramis”, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
| | - Vincenzo Della Mea
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
| | - Antonio Polonia
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology of Porto University (Ipatimup), 4200-804 Porto, Portugal
- Medical Faculty, University of Porto, 4200-319 Porto, Portugal
| | - Norman Zerbe
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Catarina Eloy
- European Society of Digital and Integrative Pathology (ESDIP), Rua da Constituição n°668, 1° Esq/Traseiras, 4200-194 Porto, Portugal; (F.F.); (V.L.); (D.A.); (R.C.); (S.L.); (T.-R.K.); (M.S.); (D.R.); (V.D.M.); (A.P.); (N.Z.)
- Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology of Porto University (Ipatimup), 4200-804 Porto, Portugal
- Medical Faculty, University of Porto, 4200-319 Porto, Portugal
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Guo C, Jiang S, Yang L, Song P, Wang T, Shao X, Zhang Z, Murphy M, Zheng G. Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality. OPTICS EXPRESS 2021; 29:39669-39684. [PMID: 34809325 DOI: 10.1364/oe.441892] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/04/2021] [Indexed: 05/18/2023]
Abstract
Whole slide imaging (WSI) has moved the traditional manual slide inspection process to the era of digital pathology. A typical WSI system translates the sample to different positions and captures images using a high numerical aperture (NA) objective lens. Performing oil-immersion microscopy is a major obstacle for WSI as it requires careful liquid handling during the scanning process. Switching between dry objective and oil-immersion lens is often impossible as it disrupts the acquisition process. For a high-NA objective lens, the sub-micron depth of field also poses a challenge to acquiring in-focus images of samples with uneven topography. Additionally, it implies a small field of view for each tile, thus limiting the system throughput and resulting in a long acquisition time. Here we report a deep learning-enabled WSI platform, termed DeepWSI, to substantially improve the system performance and imaging throughput. With this platform, we show that images captured with a regular dry objective lens can be transformed into images comparable to that of a 1.4-NA oil immersion lens. Blurred images with defocus distance from -5 µm to +5 µm can be virtually refocused to the in-focus plane post measurement. We demonstrate an equivalent data throughput of >2 gigapixels per second, the highest among existing WSI systems. Using the same deep neural network, we also report a high-resolution virtual staining strategy and demonstrate it for Fourier ptychographic WSI. The DeepWSI platform may provide a turnkey solution for developing high-performance diagnostic tools for digital pathology.
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Li X, Davis RC, Xu Y, Wang Z, Souma N, Sotolongo G, Bell J, Ellis M, Howell D, Shen X, Lafata KJ, Barisoni L. Deep learning segmentation of glomeruli on kidney donor frozen sections. J Med Imaging (Bellingham) 2021; 8:067501. [PMID: 34950750 PMCID: PMC8685284 DOI: 10.1117/1.jmi.8.6.067501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/08/2021] [Indexed: 10/15/2023] Open
Abstract
Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution ( n = 123 ) and at external institutions ( n = 135 ) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (ratio: 0.8:0.2), and external WSIs were used as an independent testing dataset. Nonsclerotic ( n = 22767 ) and sclerotic ( n = 1366 ) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic and sclerotic glomeruli. DL-derived, manual segmentation, and reported glomerular count (standard of care) were compared. Results: The average Dice similarity coefficient testing was 0.90 and 0.83. And the F 1 , recall, and precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for nonsclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation-derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.
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Affiliation(s)
- Xiang Li
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
| | - Richard C. Davis
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Yuemei Xu
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
- Nanjing Drum Tower Hospital, Department of Pathology, Nanjing, China
| | - Zehan Wang
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Nao Souma
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
| | - Gina Sotolongo
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Jonathan Bell
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Matthew Ellis
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
- Duke University, Department of Surgery, Durham, North Carolina, United States
| | - David Howell
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Xiling Shen
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Kyle J. Lafata
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Laura Barisoni
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
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Brázdil T, Gallo M, Nenutil R, Kubanda A, Toufar M, Holub P. Automated annotations of epithelial cells and stroma in hematoxylin-eosin-stained whole-slide images using cytokeratin re-staining. JOURNAL OF PATHOLOGY CLINICAL RESEARCH 2021; 8:129-142. [PMID: 34716754 PMCID: PMC8822376 DOI: 10.1002/cjp2.249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 09/22/2021] [Accepted: 09/29/2021] [Indexed: 11/24/2022]
Abstract
The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large‐scale guidance of AI methods to identify the epithelial component. The workflow is based on re‐staining existing hematoxylin and eosin (H&E) formalin‐fixed paraffin‐embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin‐rich regions, using cell nuclei positions that are visible in both the original and the re‐stained slide. The registration method has been compared to state‐of‐the‐art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U‐Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real‐world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI‐assisted tumor assessment directly from scanned H&E sections. In addition, the re‐staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.
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Affiliation(s)
- Tomáš Brázdil
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Matej Gallo
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Rudolf Nenutil
- Department of Pathology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Andrej Kubanda
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Martin Toufar
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Holub
- Institute of Computer Science, Masaryk University, Brno, Czech Republic
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Drew T, Lavelle M, Kerr KF, Shucard H, Brunyé TT, Weaver DL, Elmore JG. More scanning, but not zooming, is associated with diagnostic accuracy in evaluating digital breast pathology slides. J Vis 2021; 21:7. [PMID: 34636845 PMCID: PMC8525842 DOI: 10.1167/jov.21.11.7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/15/2021] [Indexed: 12/02/2022] Open
Abstract
Diagnoses of medical images can invite strikingly diverse strategies for image navigation and visual search. In computed tomography screening for lung nodules, distinct strategies, termed scanning and drilling, relate to both radiologists' clinical experience and accuracy in lesion detection. Here, we examined associations between search patterns and accuracy for pathologists (N = 92) interpreting a diverse set of breast biopsy images. While changes in depth in volumetric images reveal new structures through movement in the z-plane, in digital pathology changes in depth are associated with increased magnification. Thus, "drilling" in radiology may be more appropriately termed "zooming" in pathology. We monitored eye-movements and navigation through digital pathology slides to derive metrics of how quickly the pathologists moved through XY (scanning) and Z (zooming) space. Prior research on eye-movements in depth has categorized clinicians as either "scanners" or "drillers." In contrast, we found that there was no reliable association between a clinician's tendency to scan or zoom while examining digital pathology slides. Thus, in the current work we treated scanning and zooming as continuous predictors rather than categorizing as either a "scanner" or "zoomer." In contrast to prior work in volumetric chest images, we found significant associations between accuracy and scanning rate but not zooming rate. These findings suggest fundamental differences in the relative value of information types and review behaviors across two image formats. Our data suggest that pathologists gather critical information by scanning on a given plane of depth, whereas radiologists drill through depth to interrogate critical features.
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Affiliation(s)
- Trafton Drew
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Mark Lavelle
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Hannah Shucard
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Tad T Brunyé
- Department of Psychology, Tufts University, Medford, MA, USA
| | - Donald L Weaver
- Department of Pathology & Laboratory Medicine, University of Vermont, Burlington, VT, USA
| | - Joann G Elmore
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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Caselli E, Pelliccia C, Teti V, Bellezza G, Mandarano M, Ferri I, Hartmann K, Laible M, Sahin U, Varga Z, Lupi C, Stracci F, Sidoni A. Looking for more reliable biomarkers in breast cancer: Comparison between routine methods and RT-qPCR. PLoS One 2021; 16:e0255580. [PMID: 34555047 PMCID: PMC8460001 DOI: 10.1371/journal.pone.0255580] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 07/19/2021] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Decades of quality control efforts have raised the standards of immunohistochemistry (IHC), the principle method used for biomarker testing in breast cancer; however, computational pathology and reverse transcription quantitative PCR (RT-qPCR) may also hold promise for additional substantial improvements. METHODS Herein, we investigated discrepancies in the assessment of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and marker of proliferation Ki67 comparing routinely obtained IHC (and FISH) data (ORI) with the results of manual (REV) and semi-automated (DIA) re-evaluation of the original IHC slides and then with RNA expression data from the same tissue block using the MammaTyper® (MT) gene expression assay. RESULTS Correlation for ER and PR was high between ORI IHC and the other three study methods (REV, DIA and RT-qPCR). For HER2, 10 out of 96 discrepant cases can be detected between ORI and REV that involved at least one call in the equivocal category (except for one case). For Ki67, 22 (29.1%) cases were categorized differently by either REV alone (n = 17), DIA alone (n = 15) or both (n = 10) and 28 cases (29.2%) for RT-qPCR. Most of the discrepant Ki67 cases changed from low to high between the original and following assessment and belonged to the intermediate Ki67 expression range (between 9 and 30%). CONCLUSIONS Determination of the breast cancer biomarkers ER, PR, HER2 and Ki67 at the mRNA level shows high degree of correlation with IHC and compares well with correlations between original with subsequent independent manual or semi-automated IHC assessments. The use of methods with wider dynamic range and higher reproducibility such as RT-qPCR may offer more precise assessment of endocrine responsiveness, improve Ki67 standardization and help resolve HER2 cases that remain equivocal or ambiguous by IHC/FISH. In summary, our findings seem to configure RT-qPCR as a complementary method to be used in cases of either equivocal results or presenting, at the traditional determination assays, biomarkers expressions close to the cut-off values.
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Affiliation(s)
- Emanuele Caselli
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Cristina Pelliccia
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Valeria Teti
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Guido Bellezza
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Martina Mandarano
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | - Ivana Ferri
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
| | | | | | - Ugur Sahin
- BioNTech Diagnostics GmbH, Mainz, Germany
| | - Zsuzsanna Varga
- Institute for Pathology and Molecular Pathology, Universitätsspital Zürich, Zürich, Switzerland
| | | | - Fabrizio Stracci
- Umbria Cancer Registry, Perugia, Italy
- Department of Medicine and Surgery, Section of Public Health, University of Perugia, Perugia, Italy
| | - Angelo Sidoni
- Department of Medicine and Surgery, Section of Anatomic Pathology and Histology, Medical School, University of Perugia, Perugia, Italy
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Abstract
Whole slide imaging (WSI), ever since its first introduction about two decades ago, has been validated for a number of applications in the field of pathology. The recent approval of US FDA to a WSI system for use in primary surgical pathology diagnosis has opened avenues for wider acceptance and application of this technology in routine practice. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide opportunities of its newer applications. Its benefits are innumerable such as ease of access through internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Various barriers such as the high cost, technical glitches, and professional hesitation to adopt a new technology have hindered its use in pathology. This review article summarizes the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It highlights the benefits, limitations, and challenges delaying the use of this technology in routine practice. The review is targeted at students, residents, and budding pathologists to better acquaint them with the key aspects of state-of-the-art technology and enable them to implement WSI judiciously.
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Anisuzzaman D, Barzekar H, Tong L, Luo J, Yu Z. A deep learning study on osteosarcoma detection from histological images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102931] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Dery B, Zaixiang L. Scanning Electron Microscopy (SEM) as an Effective Tool for Determining the Morphology and Mechanism of Action of Functional Ingredients. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1939368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Bede Dery
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, PR China
| | - Lou Zaixiang
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, PR China
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Azam AS, Miligy IM, Kimani PKU, Maqbool H, Hewitt K, Rajpoot NM, Snead DRJ. Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis. J Clin Pathol 2021; 74:448-455. [PMID: 32934103 PMCID: PMC8223673 DOI: 10.1136/jclinpath-2020-206764] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature. OBJECTIVES We aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process. METHODS A comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies. RESULTS Twenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%). CONCLUSION The results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.
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Affiliation(s)
- Ayesha S Azam
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, West Midlands, UK
| | - Islam M Miligy
- Nottingham Breast Cancer Research Centre (NBCRC), School of Medicine, University of Nottingham, Nottingham, Nottinghamshire, UK
| | - Peter K-U Kimani
- Warwick Medical School, University of Warwick, Coventry, West Midlands, UK
| | - Heeba Maqbool
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
| | - Katherine Hewitt
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
| | - Nasir M Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, West Midlands, UK
| | - David R J Snead
- Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, Coventry, UK
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Samuelson MI, Chen SJ, Boukhar SA, Schnieders EM, Walhof ML, Bellizzi AM, Robinson RA, Rajan K D A. Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis. Am J Clin Pathol 2021; 155:638-648. [PMID: 33511392 PMCID: PMC7929400 DOI: 10.1093/ajcp/aqaa280] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The ongoing global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitates adaptations in the practice of surgical pathology at scale. Primary diagnosis by whole-slide imaging (WSI) is a key component that would aid departments in providing uninterrupted histopathology diagnosis and maintaining revenue streams from disruption. We sought to perform rapid validation of the use of WSI in primary diagnosis meeting recommendations of the College of American Pathologists guidelines. METHODS Glass slides from clinically reported cases from 5 participating pathologists with a preset washout period were digitally scanned and reviewed in settings identical to typical reporting. Cases were classified as concordant or with minor or major disagreement with the original diagnosis. Randomized subsampling was performed, and mean concordance rates were calculated. RESULTS In total, 171 cases were included and distributed equally among participants. For the group as a whole, the mean concordance rate in sampled cases (n = 90) was 83.6% counting all discrepancies and 94.6% counting only major disagreements. The mean pathologist concordance rate in sampled cases (n = 18) ranged from 90.49% to 97%. CONCLUSIONS We describe a novel double-blinded method for rapid validation of WSI for primary diagnosis. Our findings highlight the occurrence of a range of diagnostic reproducibility when deploying digital methods.
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Affiliation(s)
- Megan I Samuelson
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Stephanie J Chen
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Sarag A Boukhar
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Eric M Schnieders
- Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Mackenzie L Walhof
- Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Andrew M Bellizzi
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Robert A Robinson
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
| | - Anand Rajan K D
- Department of Pathology, University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA, USA
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Ramaswamy V, Tejaswini BN, Uthaiah SB. Remote Reporting During a Pandemic Using Digital Pathology Solution: Experience from a Tertiary Care Cancer Center. J Pathol Inform 2021; 12:20. [PMID: 34267985 PMCID: PMC8274304 DOI: 10.4103/jpi.jpi_109_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 12/28/2020] [Accepted: 03/01/2021] [Indexed: 12/23/2022] Open
Abstract
Background: Remote reporting in anatomic pathology is an important advantage of digital pathology that has not been much explored. The COVID-19 pandemic has provided an opportunity to explore this important application of digital pathology system in a tertiary care cancer center to ensure patient care and staff safety. Regulatory guidelines have been described for remote reporting following the pandemic. Herein, we describe our experience of validation of digital pathology workflow for remote reporting to encourage pathologists to utilize this facility which opens door for multiple, multidisciplinary collaborations. Objective: To demonstrate the validation and the operational feasibility of remote reporting using a digital pathology system. Materials and Methods: Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months. Three pathologists with different models of consumer-grade laptops reviewed the cases remotely to assess the diagnostic concordance and operational feasibility of the modified workflow. The slides were digitized on a USFDA-approved Philips UFS 300 scanner at ×40 resolution (0.25 μm/pixel) and viewed on the Image Management System through a web browser. All the essential parameters were reported for each case. After successful validation, 886 cases were reported remotely from March 29, 2020, to June 30, 2020, prospectively. Light microscopy formed the gold standard reference in remote reporting. Results: 100% major diagnostic concordance was observed in the validation of remote reporting in the retrospective and prospective studies using consumer-grade laptops. The deferral rate was 0.34%. 97.6% of histopathology and 100% of frozen sections were signed out within the turnaround time. Network speed and a lack of virtual private network did not significantly affect the study. Conclusion: This study of validation and reporting of complete pathology cases remotely, including their operational feasibility during a public health emergency, proves that remote sign-out using a digital pathology system is not inferior to WSIs on medical-grade monitors and light microscopy. Such studies on remote reporting open the door for the use of digital pathology for interinstitutional consultation and collaboration: Its main intended use.
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Affiliation(s)
- Veena Ramaswamy
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
| | - B N Tejaswini
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
| | - Sowmya B Uthaiah
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
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Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021; 12:17. [PMID: 34221633 PMCID: PMC8240548 DOI: 10.4103/jpi.jpi_67_20] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/20/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022] Open
Abstract
We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.
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Affiliation(s)
- Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | | | - Douglas Hartman
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Brian Roehmholdt
- Department of Pathology, Southern California Permanente Medical Group, La Canada Flintridge, CA, USA
| | | | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Marilyn Bui
- Department of Anatomic Pathology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Mark D. Zarella
- Johns Hopkins Medicine Pathology Informatics, Baltimore, MD 21287, USA
| | - Victoria LaRosa
- Education Services Department, Oracle Corp, Austin, Texas, USA
| | | | | | | | - James Madory
- Department of Pathology, Medical University of South Carolina, Charleston, SC, USA
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Trueblood JS, Eichbaum Q, Seegmiller AC, Stratton C, O'Daniels P, Holmes WR. Disentangling prevalence induced biases in medical image decision-making. Cognition 2021; 212:104713. [PMID: 33819847 DOI: 10.1016/j.cognition.2021.104713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/01/2021] [Accepted: 03/27/2021] [Indexed: 11/30/2022]
Abstract
Many important real-world decision tasks involve the detection of rarely occurring targets (e.g., weapons in luggage, potentially cancerous abnormalities in radiographs). Over the past decade, it has been repeatedly demonstrated that extreme prevalence (both high and low) leads to an increase in errors. While this "prevalence effect" is well established, the cognitive and/or perceptual mechanisms responsible for it are not. One reason for this is that the most common tool for analyzing prevalence effects, Signal Detection Theory, cannot distinguish between different biases that might be present. Through an application to pathology image-based decision-making, we illustrate that an evidence accumulation modeling framework can be used to disentangle different types of biases. Importantly, our results show that prevalence influences both response expectancy and stimulus evaluation biases, with novices (students, N = 96) showing a more pronounced response expectancy bias and experts (medical laboratory professionals, N = 19) showing a more pronounced stimulus evaluation bias.
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Affiliation(s)
| | - Quentin Eichbaum
- Vanderbilt Pathology Education Research Group (VPERG), Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center (VUMC), USA.
| | - Adam C Seegmiller
- Vanderbilt Pathology Education Research Group (VPERG), Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center (VUMC), USA.
| | - Charles Stratton
- Vanderbilt Pathology Education Research Group (VPERG), Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center (VUMC), USA.
| | | | - William R Holmes
- Department of Physics and Astronomy, Vanderbilt University, USA.
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50
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Cui M, Zhang DY. Artificial intelligence and computational pathology. J Transl Med 2021; 101:412-422. [PMID: 33454724 PMCID: PMC7811340 DOI: 10.1038/s41374-020-00514-0] [Citation(s) in RCA: 146] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/08/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023] Open
Abstract
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.
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Affiliation(s)
- Miao Cui
- St. Luke's Roosevelt Hospital Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA
| | - David Y Zhang
- Pathology and Laboratory Services, VA Medical Center, New York, NY, 10010, USA.
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