1
|
Gambella A, Salvi M, Molinari F. Reply to: "Application of digital pathology in liver transplantation". J Hepatol 2024:S0168-8278(24)00350-7. [PMID: 38759888 DOI: 10.1016/j.jhep.2024.05.015] [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: 05/01/2024] [Accepted: 05/06/2024] [Indexed: 05/19/2024]
Affiliation(s)
- Alessandro Gambella
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy; Division of Liver and Transplant Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Massimo Salvi
- Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Biolab, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Filippo Molinari
- Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Biolab, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| |
Collapse
|
2
|
Gambella A, Salvi M, Molinaro L, Patrono D, Cassoni P, Papotti M, Romagnoli R, Molinari F. Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms. J Hepatol 2024; 80:495-504. [PMID: 38036009 DOI: 10.1016/j.jhep.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/23/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND & AIMS The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. METHODS We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). RESULTS Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively. CONCLUSIONS Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. IMPACT AND IMPLICATIONS We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.
Collapse
Affiliation(s)
- Alessandro Gambella
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy; Division of Liver and Transplant Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Massimo Salvi
- Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Biolab, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Luca Molinaro
- Division of Pathology, AOU Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Damiano Patrono
- General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Mauro Papotti
- Division of Pathology, Department of Oncology, University of Turin, Turin, Italy
| | - Renato Romagnoli
- General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy
| | - Filippo Molinari
- Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Biolab, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| |
Collapse
|
3
|
Al-Bahou R, Bruner J, Moore H, Zarrinpar A. Quantitative methods for optimizing patient outcomes in liver transplantation. Liver Transpl 2024; 30:311-320. [PMID: 38153309 DOI: 10.1097/lvt.0000000000000325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/11/2023] [Indexed: 12/29/2023]
Abstract
Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.
Collapse
Affiliation(s)
- Raja Al-Bahou
- Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Julia Bruner
- Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Helen Moore
- Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Ali Zarrinpar
- Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA
| |
Collapse
|
4
|
Zheng TL, Sha JC, Deng Q, Geng S, Xiao SY, Yang WJ, Byrne CD, Targher G, Li YY, Wang XX, Wu D, Zheng MH. Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning. Liver Int 2024; 44:330-343. [PMID: 38014574 DOI: 10.1111/liv.15799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/29/2023]
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.
Collapse
Affiliation(s)
- Tian-Lei Zheng
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jun-Cheng Sha
- Department of Interventional Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Qian Deng
- Department of Histopathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shu-Yuan Xiao
- Department of Pathology, University of Chicago Medicine, Chicago, Illinois, USA
| | - Wen-Jun Yang
- Department of Pathology, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Christopher D Byrne
- Southampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, and University of Southampton, Southampton, UK
| | - Giovanni Targher
- Department of Medicine, University of Verona, Verona, Italy
- IRCSS Sacro Cuore - Don Calabria Hospital, Negrar di Valpolicella, Italy
| | - Yang-Yang Li
- Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiang-Xue Wang
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Di Wu
- Department of Pathology, Xuzhou Central Hospital, Xuzhou, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| |
Collapse
|
5
|
Rahman MA, Yilmaz I, Albadri ST, Salem FE, Dangott BJ, Taner CB, Nassar A, Akkus Z. Artificial Intelligence Advances in Transplant Pathology. Bioengineering (Basel) 2023; 10:1041. [PMID: 37760142 PMCID: PMC10525684 DOI: 10.3390/bioengineering10091041] [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: 07/28/2023] [Revised: 08/15/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
Collapse
Affiliation(s)
- Md Arafatur Rahman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA
| | - Ibrahim Yilmaz
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Sam T. Albadri
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Fadi E. Salem
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Bryan J. Dangott
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - C. Burcin Taner
- Department of Transplantation Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aziza Nassar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Zeynettin Akkus
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| |
Collapse
|
6
|
Gorman BG, Lifson MA, Vidal NY. Artificial intelligence and frozen section histopathology: A systematic review. J Cutan Pathol 2023; 50:852-859. [PMID: 37394789 DOI: 10.1111/cup.14481] [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: 08/26/2022] [Revised: 05/14/2023] [Accepted: 05/29/2023] [Indexed: 07/04/2023]
Abstract
Frozen sections are a useful pathologic tool, but variable image quality may impede the use of artificial intelligence and machine learning in their interpretation. We aimed to identify the current research on machine learning models trained or tested on frozen section images. We searched PubMed and Web of Science for articles presenting new machine learning models published in any year. Eighteen papers met all inclusion criteria. All papers presented at least one novel model trained or tested on frozen section images. Overall, convolutional neural networks tended to have the best performance. When physicians were able to view the output of the model, they tended to perform better than either the model or physicians alone at the tested task. Models trained on frozen sections performed well when tested on other slide preparations, but models trained on only formalin-fixed tissue performed significantly worse across other modalities. This suggests not only that machine learning can be applied to frozen section image processing, but also use of frozen section images may increase model generalizability. Additionally, expert physicians working in concert with artificial intelligence may be the future of frozen section histopathology.
Collapse
Affiliation(s)
- Benjamin G Gorman
- Mayo Clinic Alix School of Medicine, Rochester, Minnesota, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Rochester, Minnesota, USA
| | - Mark A Lifson
- Center for Digital Health, Mayo Clinic, Rochester, Minnesota, USA
| | - Nahid Y Vidal
- Department of Dermatology, Mayo Clinic, Rochester, Minnesota, USA
- Division of Dermatologic Surgery, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
7
|
He YF, Cheng K, Zhong ZT, Hou XL, An CZ, Zhang J, Chen W, Liu B, Yuan J, Zhao YD. Carbon quantum dot fluorescent probe for labeling and imaging of stellate cell on liver frozen section below freezing point. Anal Chim Acta 2023; 1260:341210. [PMID: 37121658 DOI: 10.1016/j.aca.2023.341210] [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: 01/15/2023] [Accepted: 04/11/2023] [Indexed: 05/02/2023]
Abstract
The targeted labeling imaging of stellate cells on liver frozen section by immunofluorescence is a very promising visualization technique to study the distribution of stellate cells in the liver. In this study, water soluble carbon quantum dots that can emit blue, green and yellow fluorescence are synthesized by the hydrothermal method, and their sizes are 3.2, 3.7, and 4.3 nm, respectively. The three carbon quantum dots have good fluorescence stability, and the quantum yields are 36.1%, 26.3% and 21%, respectively. When the mass fraction of KCl in the blue carbon quantum dot dispersion system is 13%, it still maintains the liquid state at -30 °C. The final fluorescent probe is obtained after the carbon quantum dots are coupled with the secondary antibody, spectral characterizations confirm that the conjugate probe still maintains protein immunoactivity and has good stability. Cell experiments prove that the probe has good biocompatibility, the rabbit anti-mouse Desmin antibody is used as the primary antibody, the results of cellular immunofluorescence imaging and flow cytometry show that the probe can specifically label hepatic stellate cell at -20 °C. The results of liver frozen section experiments show that hepatic stellate cell can be specifically targeted and labeled by the fluorescent probe. This labeling technology provides an important technical means for elucidating the structure and function of the liver at the cellular level, exploring the liver pathological change, and designing and developing drug.
Collapse
Affiliation(s)
- Yan-Fei He
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Kai Cheng
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Zi-Tao Zhong
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Xiao-Lin Hou
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Chang-Zhi An
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Jing Zhang
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Wei Chen
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Bo Liu
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China
| | - Yuan-Di Zhao
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China; Key Laboratory of Biomedical Photonics (HUST), Ministry of Education, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, PR China.
| |
Collapse
|
8
|
Mairinoja L, Heikelä H, Blom S, Kumar D, Knuuttila A, Boyd S, Sjöblom N, Birkman EM, Rinne P, Ruusuvuori P, Strauss L, Poutanen M. Deep learning based image analysis of liver steatosis in mouse models. THE AMERICAN JOURNAL OF PATHOLOGY 2023:S0002-9440(23)00171-2. [PMID: 37236505 DOI: 10.1016/j.ajpath.2023.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 02/28/2023] [Accepted: 04/13/2023] [Indexed: 05/28/2023]
Abstract
The incidence of non-alcoholic fatty liver disease (NAFLD) is a continuously growing health problem worldwide, along with obesity. Therefore, both novel methods to efficiently study the manifestation of NAFLD and to analyze drug efficacy in pre-clinical models are needed. In the present study, we developed a deep neural network -based model to quantify micro- and macrovesicular steatosis in the liver on hematoxylin-eosin stained whole slide images (WSIs), using the cloud-based platform, Aiforia Create (Aiforia Technologies, Helsinki, Finland). The training data included a total of 101 WSIs from dietary interventions of wild-type mice and from two genetically modified (GM) mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of micro- and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists, and correlated well with the liver fat content measured by EcoMRI ex vivo, and the correlation with total liver triglycerides were notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections, and thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts.
Collapse
Affiliation(s)
- Laura Mairinoja
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland.
| | - Hanna Heikelä
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Sami Blom
- Aiforia Technologies Oyj, Pursimiehenkatu 29-31 D, 00150 Helsinki, Finland
| | - Darshan Kumar
- Aiforia Technologies Oyj, Pursimiehenkatu 29-31 D, 00150 Helsinki, Finland
| | - Anna Knuuttila
- Aiforia Technologies Oyj, Pursimiehenkatu 29-31 D, 00150 Helsinki, Finland
| | - Sonja Boyd
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290 Helsinki, Finland
| | - Nelli Sjöblom
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00290 Helsinki, Finland
| | - Eva-Maria Birkman
- Department of Pathology, Turku University Hospital and University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Petteri Rinne
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Pekka Ruusuvuori
- Cancer Research Unit, Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Leena Strauss
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Matti Poutanen
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine and Turku Center for Disease Modeling, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland; Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Medicinaregatan 3, 413 90 Gothenburg, Sweden
| |
Collapse
|
9
|
Esparza J, Shrestha U, Kleiner DE, Crawford JM, Vanatta J, Satapathy S, Tipirneni-Sajja A. Automated Segmentation and Morphological Characterization of Hepatic Steatosis and Correlation with Histopathology. J Clin Exp Hepatol 2023; 13:468-478. [PMID: 37250872 PMCID: PMC10213977 DOI: 10.1016/j.jceh.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/02/2022] [Indexed: 05/31/2023] Open
Abstract
Background/objectives Prevalence of nonalcoholic fatty liver disease (NAFLD) has increased to 25% of the world population. Hepatic steatosis is a hallmark feature of NAFLD and is assessed histologically using visual and ordinal fat grading criteria (0-3) from the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network (CRN) scoring system. The purpose of this study is to automatically segment and extract morphological characteristics and distributions of fat droplets (FDs) on liver histology images and find associations with severity of steatosis. Methods A previously published human cohort of 68 NASH candidates was graded for steatosis by an experienced pathologist using the Fat CRN grading system. The automated segmentation algorithm quantified fat fraction (FF) and fat-affected hepatocyte ratio (FHR), extracted fat morphology by calculating radius and circularity of FDs, and examined FDs distribution and heterogeneity using nearest neighbor distance and regional isotropy. Results Regression analysis and Spearman correlation (ρ) yielded high correlations for radius (R2 = 0.86, ρ = 0.72), nearest neighbor distance (R2 = 0.82, ρ = -0.82), regional isotropy (R2 = 0.84, ρ = 0.74), and FHR (R2 = 0.90, ρ = 0.85), and low correlation for circularity (R2 = 0.48, ρ = -0.32) with FF and pathologist grades, respectively. FHR showed a better distinction between pathologist Fat CRN grades compared to conventional FF measurements, making it a potential surrogate measure for Fat CRN scores. Our results showed variation in distribution of morphological features and steatosis heterogeneity within the same patient's biopsy sample as well as between patients of similar FF. Conclusions The fat percentage measurements, specific morphological characteristics, and patterns of distribution quantified with the automated segmentation algorithm showed associations with steatosis severity; however, future studies are warranted to evaluate the clinical significance of these steatosis features in progression of NAFLD and NASH.
Collapse
Affiliation(s)
- Juan Esparza
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Utsav Shrestha
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - David E. Kleiner
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institute to Health, Bethesda, MD, USA
| | - James M. Crawford
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Jason Vanatta
- Department of Surgery, University of Tennessee Health and Science Center, Memphis, TN, USA
| | - Sanjaya Satapathy
- Liver Transplantation, North Shore University Hospital/Northwell Health, Manhasset, NY, USA
| | - Aaryani Tipirneni-Sajja
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, USA
| |
Collapse
|
10
|
He YF, Cheng K, Zhong ZT, Hou XL, An CZ, Chen W, Liu B, Zhao YD. Simultaneous labeling and multicolor fluorescence imaging of multiple immune cells on liver frozen section by polychromatic quantum dots below freezing points. J Colloid Interface Sci 2023; 636:42-54. [PMID: 36621128 DOI: 10.1016/j.jcis.2022.12.165] [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: 10/18/2022] [Revised: 12/24/2022] [Accepted: 12/30/2022] [Indexed: 01/02/2023]
Abstract
A method for simultaneous labeling and multicolor fluorescence imaging of different hepatic immune cells below freezing point is established based on quantum dots. In the experiment, carbon quantum dots with emission wavelength of 435 nm, CdTe@CdS quantum dots at 542 nm and CdSe@ZnS quantum dots at 604 nm are synthesized respectively, it is found that when the mass fractions of KCl (as antifreeze) are 12 %, 14 %, and 12 %, respectively, the three quantum dot dispersion systems remain liquid state at -20 °C. After they are conjugated with the corresponding secondary antibodies, agarose gel electrophoresis, circular dichroism and capillary electrophoresis confirm the effectiveness of conjugation. By indirect immunofluorescence method, the above three quantum dot fluorescent probes are used to simultaneously and specifically target a variety of liver immune cells, and the multi-color simultaneous imaging of different liver immune cells is realized under the same excitation wavelength, it is found that hepatic macrophages are arranged radially in the liver, hepatic stellate cells present punctate distribution, and hepatic sinusoidal endothelial cells present circular distribution, which is consistent with the results of H&E staining and ultrathin section TEM. This study provides an important technical means for elucidating the structure and function of the liver.
Collapse
Affiliation(s)
- Yan-Fei He
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Kai Cheng
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Zi-Tao Zhong
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Xiao-Lin Hou
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Chang-Zhi An
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Wei Chen
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Bo Liu
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Yuan-Di Zhao
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China; Key Laboratory of Biomedical Photonics (HUST), Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.
| |
Collapse
|
11
|
Lee SH, Jang HJ. Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology. Clin Mol Hepatol 2022; 28:754-772. [PMID: 35443570 PMCID: PMC9597228 DOI: 10.3350/cmh.2021.0394] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023] Open
Abstract
Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.
Collapse
Affiliation(s)
- Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul, Korea,Corresponding author : Hyun-Jong Jang Department of Physiology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea Tel: +82-2-2258-7274, Fax: +82-2-532-9575, E-mail:
| |
Collapse
|
12
|
A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images. Transplant Direct 2022; 8:e1361. [PMID: 35935028 PMCID: PMC9355111 DOI: 10.1097/txd.0000000000001361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/17/2022] [Accepted: 06/27/2022] [Indexed: 11/26/2022] Open
Abstract
Access to lifesaving liver transplantation is limited by a severe organ shortage. One factor contributing to the shortage is the high rate of discard in livers with histologic steatosis. Livers with <30% macrosteatosis are generally considered safe for transplant. However, histologic assessment of steatosis by a pathologist remains subjective and is often limited by image quality. Here, we address this bottleneck by creating an automated digital algorithm for calculating histologic steatosis using only images of liver biopsy histology obtained with a smartphone.
Collapse
|
13
|
Jiang X, Yang Z, Wang S, Deng S. “Big Data” Approaches for Prevention of the Metabolic Syndrome. Front Genet 2022; 13:810152. [PMID: 35571045 PMCID: PMC9095427 DOI: 10.3389/fgene.2022.810152] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/28/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.
Collapse
Affiliation(s)
- Xinping Jiang
- Department of United Ultrasound, The First Hospital of Jilin University, Changchun, China
| | - Zhang Yang
- Department of Vascular Surgery, The First Hospital of Jilin University, Changchun, China
| | - Shuai Wang
- Department of Vascular Surgery, The First Hospital of Jilin University, Changchun, China
| | - Shuanglin Deng
- Department of Oncological Neurosurgery, The First Hospital of Jilin University, Changchun, China
- *Correspondence: Shuanglin Deng,
| |
Collapse
|
14
|
Cifci D, Foersch S, Kather JN. Artificial intelligence to identify genetic alterations in conventional histopathology. J Pathol 2022; 257:430-444. [PMID: 35342954 DOI: 10.1002/path.5898] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/09/2022] [Accepted: 03/23/2022] [Indexed: 11/10/2022]
Abstract
Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold-standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53 and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Didem Cifci
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.,Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| |
Collapse
|
15
|
Artificial Intelligence in Surgery: A Research Team Perspective. Curr Probl Surg 2022; 59:101125. [DOI: 10.1016/j.cpsurg.2022.101125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
16
|
A Clinical Tool to Guide Selection and Utilization of Marginal Donor Livers With Graft Steatosis in Liver Transplantation. Transplant Direct 2022; 8:e1280. [PMID: 35047662 PMCID: PMC8759620 DOI: 10.1097/txd.0000000000001280] [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: 06/16/2021] [Revised: 11/03/2021] [Accepted: 11/19/2021] [Indexed: 11/26/2022] Open
Abstract
Supplemental Digital Content is available in the text. Background. Donor liver biopsy (DLBx) in liver transplantation provides information on allograft quality; however, predicting outcomes from these allografts remains difficult. Methods. Between 2006 and 2015, 16 691 transplants with DLBx were identified from the Standard Transplant Analysis and Research database. Cox proportional hazard regression analyses identified donor and recipient characteristics associated with 30-d, 90-d, 1-y, and 3-y graft survival. A composite model, the Liver Transplant After Biopsy (LTAB) score, was created. The Mini-LTAB was then derived consisting of only donor age, macrosteatosis on DLBx, recipient model for end-stage liver disease score, and cold ischemic time. Risk groups were identified for each score and graft survival was evaluated. P values <0.05 were considered significant. Results. The LTAB model used 14 variables and 5 risk groups and identified low-, mild-, moderate-, high-, and severe-risk groups. Compared with moderate-risk recipients, severe-risk recipients had increased risk of graft loss at 30 d (hazard ratio, 3.270; 95% confidence interval, 2.568-4.120) and at 1 y (2.258; 1.928-2.544). The Mini-LTAB model identified low-, moderate-, and high-risk groups. Graft survival in Mini-LTAB high-risk transplants was significantly lower than moderate- or low-risk transplants at all time points. Conclusions. The LTAB and Mini-LTAB scores represent guiding principles and provide clinically useful tools for the successful selection and utilization of marginal allografts in liver transplantation.
Collapse
|
17
|
He Y, An CZ, Hou XL, Zhong ZT, Li CQ, Chen W, Liu B, Zhao YD. CdTe@CdS quantum dots for labeling and imaging of macrophages in liver frozen section below freezing point. J Mater Chem B 2022; 10:2952-2962. [DOI: 10.1039/d1tb02781f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
CdTe@CdS core-shell quantum dots with different particle sizes are synthesized by aqueous method, and the coating of CdS shell layer improves the quantum yield (36%→59%) and fluorescence stability (37%→77%) of...
Collapse
|
18
|
Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis. Therap Adv Gastroenterol 2021; 14:17562848211062807. [PMID: 34987607 PMCID: PMC8721422 DOI: 10.1177/17562848211062807] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/02/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis. METHODS A systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated. RESULTS Twenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91-0.99), 0.98 (95% CI: 0.89-1.00), 0.98 (95% CI: 0.93-1.00), and 0.95 (95% CI: 0.88-0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66-0.82), 0.82 (95% CI: 0.74-0.88), 0.75 (95% CI: 0.60-0.86), and 0.82 (0.74-0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75-0.85), 0.69 (95%CI: 0.53-0.82) for identifying NASH, as well as 0.99-1.00 and 0.76-1.00 for diagnosing liver fibrosis stage F1-F4, respectively. CONCLUSION AI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation. PROTOCOL REGISTRATION PROSPERO (CRD42021230391).
Collapse
Affiliation(s)
| | | | | | - Sombat Treeprasertsuk
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| |
Collapse
|
19
|
He YF, Chen JW, An CZ, Hou XL, Zhong ZT, Li CQ, Chen W, Liu B, Zhao YD. Labeling of liver cells with CdSe/ZnS quantum dot-based fluorescence probe below freezing point. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 263:120203. [PMID: 34325172 DOI: 10.1016/j.saa.2021.120203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/22/2021] [Accepted: 07/15/2021] [Indexed: 06/13/2023]
Abstract
In this paper, CdSe/ZnS quantum dots (QDs) with particle size of 5.5 ~ 9.3 nm were synthesized, and the fluorescence emission ranged from 545 ~ 616 nm. When the volume fraction of ethanol was 30%, the water-soluble QD dispersion system remained liquid under -20 °C freezing conditions, the fluorescence intensity increased with a decrease in temperature, and the quantum yield reached 79% at -20 °C. The endothelial cell adhesion molecule CD31 antibody (anti-CD31) was used as the primary antibody, QDs were coupled with IgG as the secondary antibody (QD-Ab), and effective labeling of hepatic sinusoid endothelial cells was achieved at -20 °C. Fluorescence imaging and flow cytometry analysis showed that the labeling efficiency was as high as 97%, indicating that QDs have an important application prospect in microscopic section tomography of the liver.
Collapse
Affiliation(s)
- Yan-Fei He
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Jian-Wei Chen
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Chang-Zhi An
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Xiao-Lin Hou
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Zi-Tao Zhong
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Chao-Qing Li
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Wei Chen
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Bo Liu
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Yuan-Di Zhao
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China; Key Laboratory of Biomedical Photonics (HUST), Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
| |
Collapse
|
20
|
Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR, Bihorac A, Loftus TJ. Machine Learning Applications in Solid Organ Transplantation and Related Complications. Front Immunol 2021; 12:739728. [PMID: 34603324 PMCID: PMC8481939 DOI: 10.3389/fimmu.2021.739728] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
Collapse
Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Daniel Delitto
- Department of Surgery, Johns Hopkins University, Baltimore, MD, United States
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida Health, Gainesville, FL, United States.,Department of Orthopedics, University of Florida Health, Gainesville, FL, United States.,Department of Information Systems/Operations Management, University of Florida Health, Gainesville, FL, United States
| | - Ali Zarrinpar
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Philip A Efron
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.,Department of Computer and Information Science and Engineering University of Florida, Gainesville, FL, United States.,Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Azra Bihorac
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States.,Department of Medicine, University of Florida Health, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States.,Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States
| |
Collapse
|
21
|
Liver Transplantation With Grafts From Super Obese Donors. Transplant Direct 2021; 7:e770. [PMID: 34557587 PMCID: PMC8454911 DOI: 10.1097/txd.0000000000001225] [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: 06/30/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022] Open
Abstract
There are limited data on liver transplant (LT) outcomes with grafts from super obese donors. The present study aims to evaluate a unique cohort of recipients following LT using grafts from donors with body mass index (BMI) ≥50. Methods Patients receiving grafts from donors with BMI ≥50 and BMI <50 from 2010 to 2019 were identified. A 1:2 case-control match was conducted to compare outcomes between the groups. Survival was analyzed using the Kaplan-Meier curves. Results Six hundred sixty-five adult LTs were performed in the study period. Eighteen patients receiving a graft from a donor with BMI ≥50 were identified and matched to 36 patients receiving a graft from a donor with BMI <50. Grafts from male donors were significantly lower in the donor BMI ≥50 group when compared with the donor BMI <50 group (16.7% versus 66.7%, P = 0.001). Liver biopsy was performed in 77.8% of grafts in the donor BMI ≥50 group, whereas only in 38.8% of the grafts in the donor BMI <50 group (P = 0.007). Recipients in the donor BMI ≥50 group had a significantly higher diagnosis rate of hepatocellular carcinoma pretransplant versus the donor BMI <50 group (38.9% versus 8.3%, respectively; P = 0.006). Major complications within 30 d did not differ statistically between groups. Biliary complications within the first 30 d were equal among groups (16.7%). Subanalysis comparing the super obese donor group versus the nonobese donor group showed no differences in terms of postoperative complications, readmission rate, graft rejection, or major complications including the need for reoperation, retransplantation, or mortality. Graft and patient survival at 1-, 3-, and 5-y graft were similar between the donor BMI ≥50 group versus donor BMI <50 group (94%/89%/89% versus 88%/88%/88%, P = 0.89, and 94%/94%/94% versus 88%/88%/88%, P = 0.48, respectively). Conclusions LT with carefully selected grafts from super obese donors can be safely performed with outcomes comparable with non-super obese donor livers. Therefore, these types of grafts could represent a safe means to expand the donor pool.
Collapse
|
22
|
Schwantes IR, Axelrod DA. Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation. CURRENT TRANSPLANTATION REPORTS 2021; 8:235-240. [PMID: 34341714 PMCID: PMC8317681 DOI: 10.1007/s40472-021-00336-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 01/24/2023]
Abstract
Purpose of Review Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients. Recent Findings Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival. Summary AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.
Collapse
Affiliation(s)
- Issac R Schwantes
- Department of Surgery, Oregon Health & Science University, Portland, OR USA
| | - David A Axelrod
- Organ Transplant Center, University of Iowa, 200 Hawkins Dr, Iowa City, LA 52240 USA
| |
Collapse
|
23
|
Kobayashi S, Saltz JH, Yang VW. State of machine and deep learning in histopathological applications in digestive diseases. World J Gastroenterol 2021; 27:2545-2575. [PMID: 34092975 PMCID: PMC8160628 DOI: 10.3748/wjg.v27.i20.2545] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/27/2021] [Accepted: 04/29/2021] [Indexed: 02/06/2023] Open
Abstract
Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.
Collapse
Affiliation(s)
- Soma Kobayashi
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
| | - Joel H Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
| | - Vincent W Yang
- Department of Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, United States
- Department of Physiology and Biophysics, Renaissance School of Medicine, Stony Brook University, Stony Brook , NY 11794, United States
| |
Collapse
|
24
|
Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation. SENSORS 2021; 21:s21061993. [PMID: 33808978 PMCID: PMC8001362 DOI: 10.3390/s21061993] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 12/23/2022]
Abstract
Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.
Collapse
|
25
|
AI finally provides augmented intelligence to liver surgeons. EBioMedicine 2020; 61:103064. [PMID: 33096474 PMCID: PMC7578663 DOI: 10.1016/j.ebiom.2020.103064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 11/29/2022] Open
|