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Sadeghi P, Karimi H, Lavafian A, Rashedi R, Samieefar N, Shafiekhani S, Rezaei N. Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective. Expert Rev Clin Immunol 2024:1-18. [PMID: 38771915 DOI: 10.1080/1744666x.2024.2359019] [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: 11/19/2023] [Accepted: 05/20/2024] [Indexed: 05/23/2024]
Abstract
INTRODUCTION Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients. AREAS COVERED This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases. EXPERT OPINION Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.
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Affiliation(s)
- Parniyan Sadeghi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hanie Karimi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Atiye Lavafian
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Semnan University of Medical Science, Semnan, Iran
| | - Ronak Rashedi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Noosha Samieefar
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad Shafiekhani
- Department of Biomedical Engineering, Buein Zahra Technical University, Qazvin, Iran
| | - Nima Rezaei
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Guo Z, Zhao M, Liu Z, Zheng J, Gong Y, Huang L, Xue J, Zhou X, Li S. Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection. PLoS Negl Trop Dis 2024; 18:e0012235. [PMID: 38870200 DOI: 10.1371/journal.pntd.0012235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Schistosomiasis japonica represents a significant public health concern in South Asia. There is an urgent need to optimize existing schistosomiasis diagnostic techniques. This study aims to develop models for the different stages of liver fibrosis caused by Schistosoma infection utilizing ultrasound radiomics and machine learning techniques. METHODS From 2018 to 2022, we retrospectively collected data on 1,531 patients and 5,671 B-mode ultrasound images from the Second People's Hospital of Duchang City, Jiangxi Province, China. The datasets were screened based on inclusion and exclusion criteria suitable for radiomics models. Liver fibrosis due to Schistosoma infection (LFSI) was categorized into four stages: grade 0, grade 1, grade 2, and grade 3. The data were divided into six binary classification problems, such as group 1 (grade 0 vs. grade 1) and group 2 (grade 0 vs. grade 2). Key radiomic features were extracted using Pyradiomics, the Mann-Whitney U test, and the Least Absolute Shrinkage and Selection Operator (LASSO). Machine learning models were constructed using Support Vector Machine (SVM), and the contribution of different features in the model was described by applying Shapley Additive Explanations (SHAP). RESULTS This study ultimately included 1,388 patients and their corresponding images. A total of 851 radiomics features were extracted for each binary classification problems. Following feature selection, 18 to 76 features were retained from each groups. The area under the receiver operating characteristic curve (AUC) for the validation cohorts was 0.834 (95% CI: 0.779-0.885) for the LFSI grade 0 vs. LFSI grade 1, 0.771 (95% CI: 0.713-0.835) for LFSI grade 1 vs. LFSI grade 2, and 0.830 (95% CI: 0.762-0.885) for LFSI grade 2 vs. LFSI grade 3. CONCLUSION Machine learning models based on ultrasound radiomics are feasible for classifying different stages of liver fibrosis caused by Schistosoma infection.
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Affiliation(s)
- Zhaoyu Guo
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
| | - Miaomiao Zhao
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Zhenhua Liu
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Jinxin Zheng
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfeng Gong
- School of Public Health, Fudan University, Shanghai, China
| | - Lulu Huang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
| | - Jingbo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaonong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shizhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Marti-Aguado D, Pazó J, Diaz-Gonzalez A, de Las Heras Páez de la Cadena B, Conthe A, Gallego Duran R, Rodríguez-Gandía MA, Turnes J, Romero-Gomez M. LiverAI: New tool in the landscape for liver health. GASTROENTEROLOGIA Y HEPATOLOGIA 2024; 47:646-648. [PMID: 38582150 DOI: 10.1016/j.gastrohep.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 04/08/2024]
Affiliation(s)
- David Marti-Aguado
- Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
| | - Javier Pazó
- AI and IT Solutions Manager, Spanish Association for the Study of the Liver (AEEH), Spain
| | - Alvaro Diaz-Gonzalez
- Gastroenterology and Hepatology Department, Clinical and Translational Research in Digestive Diseases Group, Valdecilla Research Institute (IDIVAL), Marqués de Valdecilla University Hospital, Santander, Spain
| | | | - Andres Conthe
- Department of Gastroenterology and Hepatology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Rocio Gallego Duran
- Digestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain
| | - Miguel A Rodríguez-Gandía
- Department of Gastroenterology and Hepatology, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain
| | - Juan Turnes
- Department of Gastroenterology and Hepatology, Complejo Hospitalario Universitario Pontevedra & IIS Galicia Sur, Spain
| | - Manuel Romero-Gomez
- Digestive Diseases Unit and CIBERehd, Virgen del Rocío University Hospital, Institute of Biomedicine of Seville (HUVR/CSIC/US), University of Seville, Seville, Spain
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Abinaya RJ, Rajakumar G. Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1008-1022. [PMID: 38351226 PMCID: PMC11169190 DOI: 10.1007/s10278-024-00995-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/23/2023] [Accepted: 12/20/2023] [Indexed: 06/13/2024]
Abstract
The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis.
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Affiliation(s)
- R Janani Abinaya
- Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India.
| | - G Rajakumar
- Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India
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Sridhar GR, Siva Prasad AV, Lakshmi G. Scope and caveats: Artificial intelligence in gastroenterology. Artif Intell Gastroenterol 2024; 5:91607. [DOI: 10.35712/aig.v5.i1.91607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/18/2024] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.
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Affiliation(s)
| | - Atmakuri V Siva Prasad
- Department of Gastroenterology, Institute of Gastroenterology, Visakhapatnam 530003, India
| | - Gumpeny Lakshmi
- Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare & Medical Technology, Visakhapatnam 530048, India
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Lusnig L, Sagingalieva A, Surmach M, Protasevich T, Michiu O, McLoughlin J, Mansell C, De' Petris G, Bonazza D, Zanconati F, Melnikov A, Cavalli F. Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis. Diagnostics (Basel) 2024; 14:558. [PMID: 38473030 DOI: 10.3390/diagnostics14050558] [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: 01/10/2024] [Revised: 02/17/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial. Recognizing the essential need for improved diagnostic precision, particularly for optimizing diagnosis time by swiftly handling easy-to-solve cases and allowing the expert time to focus on more complex cases, this study aims to develop cutting-edge algorithms that enhance the classification of liver biopsy images. Additionally, the challenge of maintaining data privacy arises when creating automated algorithmic solutions, as sharing patient data between hospitals is restricted, further complicating the development and validation process. This research tackles diagnostic accuracy by leveraging novel techniques from the rapidly evolving field of quantum machine learning, known for their superior generalization abilities. Concurrently, it addresses privacy concerns through the implementation of privacy-conscious collaborative machine learning with federated learning. We introduce a hybrid quantum neural network model that leverages real-world clinical data to assess non-alcoholic liver steatosis accurately. This model achieves an image classification accuracy of 97%, surpassing traditional methods by 1.8%. Moreover, by employing a federated learning approach that allows data from different clients to be shared while ensuring privacy, we maintain an accuracy rate exceeding 90%. This initiative marks a significant step towards a scalable, collaborative, efficient, and dependable computational framework that aids clinical pathologists in their daily diagnostic tasks.
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Affiliation(s)
- Luca Lusnig
- Terra Quantum AG, 9000 St. Gallen, Switzerland
- Research Unit of Paleoradiology and Allied Sciences, Laboratorio di Telematica Sanitaria-Struttura Complessa Informatica e Telecomunicazioni, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy
| | | | | | | | | | | | | | - Graziano De' Petris
- Laboratorio di Telematica Sanitaria-Struttura Complessa Informatica e Telecomunicazioni, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy
| | - Deborah Bonazza
- Department of Medical, Surgical and Health Sciences, University of Trieste, Cattinara Academic Hospital, 34149 Trieste, Italy
| | - Fabrizio Zanconati
- Department of Medical, Surgical and Health Sciences, University of Trieste, Cattinara Academic Hospital, 34149 Trieste, Italy
| | | | - Fabio Cavalli
- Research Unit of Paleoradiology and Allied Sciences, Laboratorio di Telematica Sanitaria-Struttura Complessa Informatica e Telecomunicazioni, Azienda Sanitaria Universitaria Giuliana Isontina, 34149 Trieste, Italy
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Shen Y, Huang J, Jia L, Zhang C, Xu J. Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma. Biochem Biophys Rep 2024; 37:101587. [PMID: 38107663 PMCID: PMC10724547 DOI: 10.1016/j.bbrep.2023.101587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023] Open
Abstract
Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.
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Affiliation(s)
- Ye Shen
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
| | - Juanjie Huang
- Department of General Surgery, Dongguan Qingxi Hospital, Dongguan, 523660, China
| | - Lei Jia
- International Health Medicine Innovation Center, Shenzhen University, ShenZhen, 518060, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China
| | - Jianxing Xu
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
- Department of Radiology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, 213002, China
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Xia T, Zhao B, Li B, Lei Y, Song Y, Wang Y, Tang T, Ju S. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges. J Magn Reson Imaging 2024; 59:767-783. [PMID: 37647155 DOI: 10.1002/jmri.28982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ben Zhao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ying Lei
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Yuancheng Wang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Martínez-Blanco P, Suárez M, Gil-Rojas S, Torres AM, Martínez-García N, Blasco P, Torralba M, Mateo J. Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence. Diagnostics (Basel) 2024; 14:406. [PMID: 38396445 PMCID: PMC10888215 DOI: 10.3390/diagnostics14040406] [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: 12/31/2023] [Revised: 01/24/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. METHODS In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. RESULTS Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). CONCLUSIONS The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.
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Affiliation(s)
| | - Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Sergio Gil-Rojas
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | | | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Miguel Torralba
- Internal Medicine Unit, Guadalajara University Hospital, 19002 Guadalajara, Spain (M.T.)
- Faculty of Medicine, Universidad de Alcalá de Henares, 28801 Alcalá de Henares, Spain
- Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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Gao L, Wang W, Meng X, Zhang S, Xu J, Ju S, Wang YC. TPA: Two-stage progressive attention segmentation framework for hepatocellular carcinoma on multi-modality MRI. Med Phys 2024. [PMID: 38306473 DOI: 10.1002/mp.16968] [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: 10/25/2023] [Revised: 01/04/2024] [Accepted: 01/21/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and measurement of hepatocellular carcinoma (HCC). The multi-modality information contained in the multi-phase images of DCE-MRI is important for improving segmentation. However, this remains a challenging task due to the heterogeneity of HCC, which may cause one HCC lesion to have varied imaging appearance in each phase of DCE-MRI. In particular, some phases exhibit inconsistent sizes and boundaries will result in a lack of correlation between modalities, and it may pose inaccurate segmentation results. PURPOSE We aim to design a multi-modality segmentation model that can learn meaningful inter-phase correlation for achieving HCC segmentation. METHODS In this study, we propose a two-stage progressive attention segmentation framework (TPA) for HCC based on the transformer and the decision-making process of radiologists. Specifically, the first stage aims to fuse features from multi-phase images to identify HCC and provide localization region. In the second stage, a multi-modality attention transformer module (MAT) is designed to focus on the features that can represent the actual size. RESULTS We conduct training, validation, and test in a single-center dataset (386 cases), followed by external test on a batch of multi-center datasets (83 cases). Furthermore, we analyze a subgroup of data with weak inter-phase correlation in the test set. The proposed model achieves Dice coefficient of 0.822 and 0.772 in the internal and external test sets, respectively, and 0.829, 0.791 in the subgroup. The experimental results demonstrate that our model outperforms state-of-the-art models, particularly within subgroup. CONCLUSIONS The proposed TPA provides best segmentation results, and utilizing clinical prior knowledge for network design is practical and feasible.
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Affiliation(s)
- Lei Gao
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Weilang Wang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, Nanjing, China
| | - Xiangpan Meng
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, Nanjing, China
| | - Shuhang Zhang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, Nanjing, China
| | - Jun Xu
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Shenghong Ju
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, Nanjing, China
| | - Yuan-Cheng Wang
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, Nanjing, China
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11
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Dominguez DA, Wong P, Melstrom LG. Existing and emerging biomarkers in hepatocellular carcinoma: relevance in staging, determination of minimal residual disease, and monitoring treatment response: a narrative review. Hepatobiliary Surg Nutr 2024; 13:39-55. [PMID: 38322200 PMCID: PMC10839735 DOI: 10.21037/hbsn-22-526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/15/2023] [Indexed: 02/08/2024]
Abstract
Background and Objective With the development of novel active systemic therapies, the landscape of hepatocellular carcinoma (HCC) management is rapidly changing. However, HCC lacks sensitive and specific biomarkers to predict prognosis, monitor for minimal residual disease after locoregional therapy, and predict treatment response. In this review, we aim to summarize the best supporting evidence for refining existing, and development of novel biomarkers for staging, prognosis, determination of minimal residual disease and monitoring treatment response in HCC, focusing on those with evidence in clinical trials. Methods PubMed and Embase databases were searched using the keywords; hepatocellular carcinoma, biomarker, minimal residual disease, surveillance, prognosis, staging, alpha-fetoprotein (AFP), liquid biopsy, treatment response, adjuvant, immunotherapy. Relevant clinical studies were included. Key Content and Findings AFP remains the major workhorse as the most widely used biomarker in HCC, however, its lack of wide applicability due to the high proportion of patients with HCC who are AFP negative, limits its value throughout all stages of HCC management. Significant work has been done to combine AFP with other clinical and serologic factors to increase its accuracy and utility as a biomarkers. However, it is likely that other more novel biomarkers such as those obtained through liquid biopsy will provide the prognostic power necessary for applications such as detecting recurrence and predicting treatment response. Liquid biopsy provides not only a wealth of potential biomarkers including circulating tumor cells and cell-free RNA/DNA, but also the ability to examine the mutational characteristics of the tumor with next generation sequencing. While early evidence supports the potential impact of many new biomarkers, validation in large clinical trials is lacking. Conclusions This review highlights the paucity of sensitive and specific, widely applicable biomarkers, throughout all phases of management of HCC and summarizes evidence on biomarkers currently in use, as well as those in development and validation. Inclusion of biomarker analysis through clinical trials in HCC is critical to development of optimal therapeutic regimens, and improve patient outcomes.
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Affiliation(s)
- Dana A. Dominguez
- Department of Surgical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Paul Wong
- University of California, San Francisco, San Francisco, CA, USA
| | - Laleh G. Melstrom
- Department of Surgical Oncology, City of Hope National Medical Center, Duarte, CA, USA
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12
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Zhou S, Wang S, Xiang J, Han Z, Wang W, Zhang S, Opara NC, Ju S, Cui Y, Wang YC. Diagnostic performance of MRI for residual or recurrent hepatocellular carcinoma after locoregional treatment according to contrast agent type: a systematic review and meta‑analysis. Abdom Radiol (NY) 2024; 49:471-483. [PMID: 38200213 DOI: 10.1007/s00261-023-04143-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024]
Abstract
PURPOSE The ideal contrast agent for imaging patients with hepatocellular carcinoma (HCC) following locoregional therapies (LRT) remains uncertain. We conducted a meta-analysis to assess the diagnostic performance of magnetic resonance imaging with extracellular contrast agent (ECA-MRI) and hepatobiliary agent (EOB-MRI) in detecting residual or recurrence HCC following LRT. METHODS Original studies comparing the diagnostic performance of ECA-MRI and EOB-MRI were systematically identified through comprehensive searches in PubMed, EMBASE, Cochrane Library and Web of Science databases. The pooled sensitivity and specificity of ECA-MRI and EOB-MRI were calculated using a bivariate-random-effects model. Subgroup-analyses were conducted to compare the diagnostic performance of ECA-MRI and EOB-MRI according to different variables. Meta-regression analysis was employed to explore potential sources of study heterogeneity. RESULTS A total of 15 eligible studies encompassing 803 patients and 1018 lesions were included. Comparative analysis revealed no significant difference between ECA-MRI and EOB-MRI in the overall pooled sensitivity (87% vs. 79%) and specificity (92% vs. 96%) for the detection of residual or recurrent HCC after LRT (P = 0.41), with comparable areas under the HSROC of 0.95 and 0.92. Subgroup analyses indicated no significant diagnostic performance differences between ECA-MRI and EOB-MRI according to study design, type of LRT, most common etiology of liver disease, baseline lesion size, time of post-treated examination and MRI field strength (All P > 0.05). CONCLUSION ECA-MRI exhibited overall comparable diagnostic performance to EOB-MRI in assessing residual or recurrent HCC after LRT.
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Affiliation(s)
- Shuwei Zhou
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Siyu Wang
- Department of Gastroenterology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007, Hunan, China
| | - Jian Xiang
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007, Hunan, China
| | - Zhongyu Han
- School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, 610000, China
| | - Weilang Wang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Shuhang Zhang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Noble Chibuike Opara
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Shenghong Ju
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China
| | - Ying Cui
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
| | - Yuan-Cheng Wang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009, China.
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13
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [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: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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14
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Sanyal AJ, Jha P, Kleiner DE. Digital pathology for nonalcoholic steatohepatitis assessment. Nat Rev Gastroenterol Hepatol 2024; 21:57-69. [PMID: 37789057 DOI: 10.1038/s41575-023-00843-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/23/2023] [Indexed: 10/05/2023]
Abstract
Histological assessment of nonalcoholic fatty liver disease (NAFLD) has anchored knowledge development about the phenotypes of the condition, their natural history and their clinical course. This fact has led to the use of histological assessment as a reference standard for the evaluation of efficacy of drug interventions for nonalcoholic steatohepatitis (NASH) - the more histologically active form of NAFLD. However, certain limitations of conventional histological assessment systems pose challenges in drug development. These limitations have spurred intense scientific and commercial development of machine learning and digital approaches towards the assessment of liver histology in patients with NAFLD. This research field remains an area in rapid evolution. In this Perspective article, we summarize the current conventional assessment of NASH and its limitations, the use of specific digital approaches for histological assessment, and their application to the study of NASH and its response to therapy. Although this is not a comprehensive review, the leading tools currently used to assess therapeutic efficacy in drug development are specifically discussed. The potential translation of these approaches to support routine clinical assessment of NAFLD and an agenda for future research are also discussed.
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Affiliation(s)
- Arun J Sanyal
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.
| | - Prakash Jha
- Food and Drug Administration, Silver Spring, MD, USA
| | - David E Kleiner
- Post-Mortem Section Laboratory of Pathology Center for Cancer Research National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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15
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Wiest IC, Gilbert S, Kather JN. From research to reality: The role of artificial intelligence applications in HCC care. Clin Liver Dis (Hoboken) 2024; 23:e0136. [PMID: 38567094 PMCID: PMC10986906 DOI: 10.1097/cld.0000000000000136] [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: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Affiliation(s)
- Isabella C. Wiest
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephen Gilbert
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jakob N. Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
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16
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McGenity C, Randell R, Bellamy C, Burt A, Cratchley A, Goldin R, Hubscher SG, Neil DAH, Quaglia A, Tiniakos D, Wyatt J, Treanor D. Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence. J Clin Pathol 2023; 77:27-33. [PMID: 36599660 PMCID: PMC10804041 DOI: 10.1136/jcp-2022-208614] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/24/2022] [Indexed: 01/05/2023]
Abstract
AIMS A survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National Liver Pathology External Quality Assurance scheme. The aim of this study was to understand attitudes and priorities of liver pathologists towards digital pathology and artificial intelligence (AI). METHODS The survey was distributed to all full consultant members of the UKLPG via email. This comprised 50 questions, with 48 multiple choice questions and 2 free-text questions at the end, covering a range of topics and concepts pertaining to the use of digital pathology and AI in liver disease. RESULTS Forty-two consultant histopathologists completed the survey, representing 36% of fully registered members of the UKLPG (42/116). Questions examining digital pathology showed respondents agreed with the utility of digital pathology for primary diagnosis 83% (34/41), second opinions 90% (37/41), research 85% (35/41) and training and education 95% (39/41). Fatty liver diseases were an area of demand for AI tools with 80% in agreement (33/41), followed by neoplastic liver diseases with 59% in agreement (24/41). Participants were concerned about AI development without pathologist involvement 73% (30/41), however, 63% (26/41) disagreed when asked whether AI would replace pathologists. CONCLUSIONS This study outlines current interest, priorities for research and concerns around digital pathology and AI for liver pathologists. The majority of UK liver pathologists are in favour of the application of digital pathology and AI in clinical practice, research and education.
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Affiliation(s)
- Clare McGenity
- Pathology and Data Analytics, University of Leeds, Leeds, UK
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Rebecca Randell
- Faculty of Health Sciences, University of Bradford, Bradford, UK
- Wolfson Centre for Applied Health Research, Bradford, UK
| | | | - Alastair Burt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alyn Cratchley
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Robert Goldin
- Division of Digestive Diseases, Imperial College London, London, UK
| | - Stefan G Hubscher
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Desley A H Neil
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
- Department of Cellular Pathology, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - Alberto Quaglia
- Department of Cellular Pathology, Royal Free Hospital, London, UK
| | - Dina Tiniakos
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
- Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece
| | - Judy Wyatt
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Darren Treanor
- Pathology and Data Analytics, University of Leeds, Leeds, UK
- Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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17
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Huang G, Jin Q, Mao Y. Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study. J Med Internet Res 2023; 25:e46891. [PMID: 37698911 PMCID: PMC10523217 DOI: 10.2196/46891] [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: 03/01/2023] [Revised: 08/02/2023] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. OBJECTIVE This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. METHODS The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)-recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. RESULTS The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. CONCLUSIONS Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.
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Affiliation(s)
- Guoqing Huang
- Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, China
- Health Science Center, Ningbo University, Ningbo, China
| | - Qiankai Jin
- Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, China
- Health Science Center, Ningbo University, Ningbo, China
| | - Yushan Mao
- Department of Endocrinology, The First Affiliated Hospital of Ningbo University, Ningbo, China
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18
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Boursier J, Roux M, Costentin C, Chaigneau J, Fournier-Poizat C, Trylesinski A, Canivet CM, Michalak S, Le Bail B, Paradis V, Bedossa P, Sturm N, de Ledinghen V, Newsome PN. Practical diagnosis of cirrhosis in non-alcoholic fatty liver disease using currently available non-invasive fibrosis tests. Nat Commun 2023; 14:5219. [PMID: 37633932 PMCID: PMC10460420 DOI: 10.1038/s41467-023-40328-4] [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: 01/12/2023] [Accepted: 07/24/2023] [Indexed: 08/28/2023] Open
Abstract
Unlike for advanced liver fibrosis, the practical rules for the early non-invasive diagnosis of cirrhosis in NAFLD remain not well defined. Here, we report the derivation and validation of a stepwise diagnostic algorithm in 1568 patients with NAFLD and liver biopsy coming from four independent cohorts. The study algorithm, using first the elastography-based tests Agile3+ and Agile4 and then the specialized blood tests FibroMeterV3G and CirrhoMeterV3G, provides stratification in four groups, the last of which is enriched in cirrhosis (71% prevalence in the validation set). A risk prediction chart is also derived to allow estimation of the individual probability of cirrhosis. The predicted risk shows excellent calibration in the validation set, and mean difference with perfect prediction is only -2.9%. These tools improve the personalized non-invasive diagnosis of cirrhosis in NAFLD.
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Affiliation(s)
- Jérôme Boursier
- Hepato-Gastroenterology and Digestive Oncology Department, Angers University Hospital, Angers, France.
- HIFIH Laboratory, SFR ICAT 4208, Angers University, Angers, France.
| | - Marine Roux
- HIFIH Laboratory, SFR ICAT 4208, Angers University, Angers, France
| | - Charlotte Costentin
- Univ. Grenoble Alpes, Clinique Universitaire d'Hépato-gastroentérologie, CHU Grenoble Alpes, Grenoble, France
- Univ. Grenoble Alpes; Institute for Advanced Biosciences, CNRS UMR 5309-INSERM U1209, Grenoble, France
| | - Julien Chaigneau
- HIFIH Laboratory, SFR ICAT 4208, Angers University, Angers, France
| | | | | | - Clémence M Canivet
- Hepato-Gastroenterology and Digestive Oncology Department, Angers University Hospital, Angers, France
- HIFIH Laboratory, SFR ICAT 4208, Angers University, Angers, France
| | - Sophie Michalak
- HIFIH Laboratory, SFR ICAT 4208, Angers University, Angers, France
- Pathology Department, Angers University Hospital, Angers, France
| | - Brigitte Le Bail
- Pathology Department, Bordeaux University Hospital, Bordeaux, France
- Bordeaux Institute of Oncology, BRIC UMR U1312, INSERM, Université de Bordeaux, Bordeaux, France
| | - Valérie Paradis
- Department of Pathology, Physiology and Imaging, Beaujon Hospital Paris Diderot University, Paris, France
| | - Pierre Bedossa
- Department of Pathology, Physiology and Imaging, Beaujon Hospital Paris Diderot University, Paris, France
- Liverpat, Paris, France
| | - Nathalie Sturm
- Pathology Department, CHU Grenoble Alpes, Grenoble, France
| | - Victor de Ledinghen
- Bordeaux Institute of Oncology, BRIC UMR U1312, INSERM, Université de Bordeaux, Bordeaux, France
- Hepatology Unit, Haut Leveque hospital, Bordeaux University Hospital, Bordeaux, France
| | - Philip N Newsome
- National Institute for Health Research, Birmingham Biomedical Research Centre at University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Centre for Liver & Gastrointestinal Research, Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
- Liver Unit, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
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19
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Taru MG, Lupsor-Platon M. Exploring Opportunities to Enhance the Screening and Surveillance of Hepatocellular Carcinoma in Non-Alcoholic Fatty Liver Disease (NAFLD) through Risk Stratification Algorithms Incorporating Ultrasound Elastography. Cancers (Basel) 2023; 15:4097. [PMID: 37627125 PMCID: PMC10452922 DOI: 10.3390/cancers15164097] [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: 07/11/2023] [Revised: 08/08/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD), with its progressive form, non-alcoholic steatohepatitis (NASH), has emerged as a significant public health concern, affecting over 30% of the global population. Hepatocellular carcinoma (HCC), a complication associated with both cirrhotic and non-cirrhotic NAFLD, has shown a significant increase in incidence. A substantial proportion of NAFLD-related HCC occurs in non-cirrhotic livers, highlighting the need for improved risk stratification and surveillance strategies. This comprehensive review explores the potential role of liver ultrasound elastography as a risk assessment tool for HCC development in NAFLD and highlights the importance of effective screening tools for early, cost-effective detection and improved management of NAFLD-related HCC. The integration of non-invasive tools and algorithms into risk stratification strategies could have the capacity to enhance NAFLD-related HCC screening and surveillance effectiveness. Alongside exploring the potential advancement of non-invasive tools and algorithms for effectively stratifying HCC risk in NAFLD, we offer essential perspectives that could enable readers to improve the personalized assessment of NAFLD-related HCC risk through a more methodical screening approach.
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Affiliation(s)
- Madalina-Gabriela Taru
- Hepatology Department, Regional Institute of Gastroenterology and Hepatology “Octavian Fodor”, 400162 Cluj-Napoca, Romania;
- “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Monica Lupsor-Platon
- “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Medical Imaging Department, Regional Institute of Gastroenterology and Hepatology “Octavian Fodor”, 400162 Cluj-Napoca, Romania
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20
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Goyes D, Trivedi HD, Curry MP. Prognostic Models in Acute-on-Chronic Liver Failure. Clin Liver Dis 2023; 27:681-690. [PMID: 37380291 DOI: 10.1016/j.cld.2023.03.011] [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: 06/30/2023]
Abstract
Acute-on-chronic liver failure (ACLF) is a clinical syndrome characterized by severe hepatic dysfunction leading to multiorgan failure in patients with end-stage liver disease. ACLF is a challenging clinical syndrome with a rapid clinical course and high short-term mortality. There is no single uniform definition of ACLF or consensus in predicting ACLF-related outcomes, which makes comparing studies difficult and standardizing management protocols challenging. This review aims to provide insights into the common prognostic models that define and grade ACLF.
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Affiliation(s)
- Daniela Goyes
- Department of Medicine, Loyola Medicine - MacNeal Hospital, Berwyn, IL, USA
| | - Hirsh D Trivedi
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael P Curry
- Department of Medicine and Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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21
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Vetter M, Waldner MJ, Zundler S, Klett D, Bocklitz T, Neurath MF, Adler W, Jesper D. Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2023; 44:395-407. [PMID: 37001563 DOI: 10.1055/a-2066-9372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.
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Affiliation(s)
- Marcel Vetter
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Maximilian J Waldner
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Sebastian Zundler
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Daniel Klett
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Thomas Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-Universitat Jena, Jena, Germany
- Leibniz-Institute of Photonic Technology, Friedrich Schiller University Jena, Jena, Germany
| | - Markus F Neurath
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Werner Adler
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Daniel Jesper
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
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22
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Saber R, Henault D, Messaoudi N, Rebolledo R, Montagnon E, Soucy G, Stagg J, Tang A, Turcotte S, Kadoury S. Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases. J Transl Med 2023; 21:507. [PMID: 37501197 PMCID: PMC10375693 DOI: 10.1186/s12967-023-04175-7] [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: 03/09/2023] [Accepted: 04/30/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance. METHODS We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73High vs. CD73Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set. RESULTS TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73High vs rad-CD73Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020). CONCLUSIONS Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.
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Affiliation(s)
- Ralph Saber
- MedICAL Laboratory, Polytechnique Montréal, Montréal, H3T 1J4, Canada
- Imaging and Engineering Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis R10.430, Montréal, QC, H2X 0A9, Canada
| | - David Henault
- Cancer Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis, Room R10.430, Montréal, QC, H2X 0A9, Canada
- Hepato-Pancreato-Biliary Surgery and Liver Transplantation Service, Centre hospitalier de l'Université de Montréal, 1000, rue Saint-Denis, Montréal, QC, H2X 0C1, Canada
| | - Nouredin Messaoudi
- Cancer Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis, Room R10.430, Montréal, QC, H2X 0A9, Canada
- Hepato-Pancreato-Biliary Surgery and Liver Transplantation Service, Centre hospitalier de l'Université de Montréal, 1000, rue Saint-Denis, Montréal, QC, H2X 0C1, Canada
- Department of Surgery, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel) and Europe Hospitals, Brussels, Belgium
| | - Rolando Rebolledo
- Cancer Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis, Room R10.430, Montréal, QC, H2X 0A9, Canada
- Hepato-Pancreato-Biliary Surgery and Liver Transplantation Service, Centre hospitalier de l'Université de Montréal, 1000, rue Saint-Denis, Montréal, QC, H2X 0C1, Canada
| | - Emmanuel Montagnon
- Imaging and Engineering Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis R10.430, Montréal, QC, H2X 0A9, Canada
| | - Geneviève Soucy
- Pahology Department, Centre hospitalier de l'Université de Montréal, 1000, rue Saint-Denis, Montréal, QC, H2X 0C1, Canada
| | - John Stagg
- Cancer Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis, Room R10.430, Montréal, QC, H2X 0A9, Canada
| | - An Tang
- Imaging and Engineering Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis R10.430, Montréal, QC, H2X 0A9, Canada
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, H3T 1J4, Canada
| | - Simon Turcotte
- Cancer Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis, Room R10.430, Montréal, QC, H2X 0A9, Canada.
- Hepato-Pancreato-Biliary Surgery and Liver Transplantation Service, Centre hospitalier de l'Université de Montréal, 1000, rue Saint-Denis, Montréal, QC, H2X 0C1, Canada.
| | - Samuel Kadoury
- MedICAL Laboratory, Polytechnique Montréal, Montréal, H3T 1J4, Canada.
- Imaging and Engineering Axis, Centre de recherche du Centre Hospitalier de l'Université de Montréal/Institut du cancer de Montréal, 900 rue Saint-Denis R10.430, Montréal, QC, H2X 0A9, Canada.
- Department of Computer and Software Engineering, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, H3T 1J4, Canada.
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, H3T 1J4, Canada.
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23
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Naik SN, Forlano R, Manousou P, Goldin R, Angelini ED. Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning. BIOLOGICAL IMAGING 2023; 3:e17. [PMID: 38510166 PMCID: PMC10951930 DOI: 10.1017/s2633903x23000144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/08/2023] [Accepted: 06/23/2023] [Indexed: 03/22/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of , an F1 score of and an AUC of . These results set new state-of-the-art benchmarks for this application.
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Affiliation(s)
- Sneha N. Naik
- ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, United Kingdom
- Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Roberta Forlano
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
| | - Pinelopi Manousou
- Department of Hepatology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Robert Goldin
- Section for Pathology, Imperial College, London, United Kingdom
| | - Elsa D. Angelini
- ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, United Kingdom
- Heffner Biomedical Imaging Lab, Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Faculty of Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
- Telecom Paris, Institut Polytechnique de Paris, LTCI, Palaiseau, France
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24
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Mansur A, Vrionis A, Charles JP, Hancel K, Panagides JC, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers (Basel) 2023; 15:cancers15112928. [PMID: 37296890 DOI: 10.3390/cancers15112928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.
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Affiliation(s)
| | - Andrea Vrionis
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA
| | - Jonathan P Charles
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA
| | - Kayesha Hancel
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Farzad Moloudi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Shams Iqbal
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
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25
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Wang ZY, Guo ZH. Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases. Chin J Integr Med 2023:10.1007/s11655-023-3639-7. [PMID: 37222830 DOI: 10.1007/s11655-023-3639-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2023] [Indexed: 05/25/2023]
Abstract
High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.
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Affiliation(s)
- Zi-Yan Wang
- The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Zhi-Hua Guo
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China.
- Hunan Key Laboratory of Colleges and Universities of Intelligent Traditional Chinese Medicine Diagnosis and Preventive Treatment of Chronic Diseases of Hunan Universities of Chinese Medicine, Changsha, 410208, China.
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26
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Moroney J, Trivella J, George B, White SB. A Paradigm Shift in Primary Liver Cancer Therapy Utilizing Genomics, Molecular Biomarkers, and Artificial Intelligence. Cancers (Basel) 2023; 15:2791. [PMID: 37345129 DOI: 10.3390/cancers15102791] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/02/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Primary liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related death. Conventional therapies offer limited survival benefit despite improvements in locoregional liver-directed therapies, which highlights the underlying complexity of liver cancers. This review explores the latest research in primary liver cancer therapies, focusing on developments in genomics, molecular biomarkers, and artificial intelligence. Attention is also given to ongoing research and future directions of immunotherapy and locoregional therapies of primary liver cancers.
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Affiliation(s)
- James Moroney
- Division of Vascular and Interventional Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Juan Trivella
- Division of Gastroenterology and Hepatology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ben George
- Division of Hematology and Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Sarah B White
- Division of Vascular and Interventional Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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27
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Pomohaci MD, Grasu MC, Dumitru RL, Toma M, Lupescu IG. Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13091663. [PMID: 37175054 PMCID: PMC10178485 DOI: 10.3390/diagnostics13091663] [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: 03/08/2023] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. However, due to organ shortages, this treatment can only be applied to carefully selected patients according to clinical guidelines. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging, like radiology. With the help of these technologies, laborious tasks can be automated, and new lesion imaging criteria can be developed based on pixel-level analysis. Our objectives are to review the developing AI applications that could be implemented to better stratify liver transplant candidates. The papers analysed applied AI for liver segmentation, evaluation of steatosis, sarcopenia assessment, lesion detection, segmentation, and characterization. A liver transplant is an optimal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease. Furthermore, AI could provide solutions for improving the management of liver transplant candidates to improve survival.
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Affiliation(s)
- Mihai Dan Pomohaci
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mugur Cristian Grasu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Radu Lucian Dumitru
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mihai Toma
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Ioana Gabriela Lupescu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
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28
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Su PY, Chen YY, Lin CY, Su WW, Huang SP, Yen HH. Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver. Diagnostics (Basel) 2023; 13:diagnostics13081407. [PMID: 37189508 DOI: 10.3390/diagnostics13081407] [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: 02/27/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
The reported prevalence of non-alcoholic fatty liver disease in studies of lean individuals ranges from 7.6% to 19.3%. The aim of the study was to develop machine-learning models for the prediction of fatty liver disease in lean individuals. The present retrospective study included 12,191 lean subjects with a body mass index < 23 kg/m2 who had undergone a health checkup from January 2009 to January 2019. Participants were divided into a training (70%, 8533 subjects) and a testing group (30%, 3568 subjects). A total of 27 clinical features were analyzed, except for medical history and history of alcohol or tobacco consumption. Among the 12,191 lean individuals included in the present study, 741 (6.1%) had fatty liver. The machine learning model comprising a two-class neural network using 10 features had the highest area under the receiver operating characteristic curve (AUROC) value (0.885) among all other algorithms. When applied to the testing group, we found the two-class neural network exhibited a slightly higher AUROC value for predicting fatty liver (0.868, 0.841-0.894) compared to the fatty liver index (FLI; 0.852, 0.824-0.81). In conclusion, the two-class neural network had greater predictive value for fatty liver than the FLI in lean individuals.
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Affiliation(s)
- Pei-Yuan Su
- Department of Internal Medicine, Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
- College of Medicine, National Chung Hsing University, Taichung 400, Taiwan
| | - Yang-Yuan Chen
- Department of Internal Medicine, Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
- Department of Hospitality Management, MingDao University, Changhua 500, Taiwan
| | - Chun-Yu Lin
- Department of Family Medicine, Yumin Hospital, Nantou 540, Taiwan
| | - Wei-Wen Su
- Department of Internal Medicine, Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
| | - Siou-Ping Huang
- Department of Internal Medicine, Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
| | - Hsu-Heng Yen
- Department of Internal Medicine, Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
- College of Medicine, National Chung Hsing University, Taichung 400, Taiwan
- General Education Center, Chienkuo Technology University, Changhua 500, Taiwan
- Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 500, Taiwan
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29
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Gary PJ, Lal A, Simonetto DA, Gajic O, Gallo de Moraes A. Acute on chronic liver failure: prognostic models and artificial intelligence applications. Hepatol Commun 2023; 7:02009842-202304010-00015. [PMID: 36972378 PMCID: PMC10043584 DOI: 10.1097/hc9.0000000000000095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023] Open
Abstract
Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.
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Affiliation(s)
- Phillip J Gary
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Alice Gallo de Moraes
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
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30
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Narendran G, Walunj A, Kumar AM, Jeyachandran P, Awwad NS, Ibrahium HA, Gorji MR, Perumal DA. Experimental Demonstration of Compact Polymer Mass Transfer Device Manufactured by Additive Manufacturing with Hydrogel Integration to Bio-Mimic the Liver Functions. Bioengineering (Basel) 2023; 10:bioengineering10040416. [PMID: 37106603 PMCID: PMC10135587 DOI: 10.3390/bioengineering10040416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
In this paper, we designed and demonstrated a stimuli-responsive hydrogel that mimics the mass diffusion function of the liver. We have controlled the release mechanism using temperature and pH variations. Additive manufacturing technology was used to fabricate the device with nylon (PA-12), using selective laser sintering (SLS). The device has two compartment sections: the lower section handles the thermal management, and feeds temperature-regulated water into the mass transfer section of the upper compartment. The upper chamber has a two-layered serpentine concentric tube; the inner tube carries the temperature-regulated water to the hydrogel using the given pores. Here, the hydrogel is present in order to facilitate the release of the loaded methylene blue (MB) into the fluid. By adjusting the fluid’s pH, flow rate, and temperature, the deswelling properties of the hydrogel were examined. The weight of the hydrogel was maximum at 10 mL/min and decreased by 25.29% to 10.12 g for the flow rate of 50 mL/min. The cumulative MB release at 30 °C increased to 47% for the lower flow rate of 10 mL/min, and the cumulative release at 40 °C climbed to 55%, which is 44.7% more than at 30 °C. The MB release rates considerably increased when the pH dropped from 12 to 8, showing that the lower pH had a major impact on the release of MB from the hydrogel. Only 19% of the MB was released at pH 12 after 50 min, and after that, the release rate remained nearly constant. At higher fluid temperatures, the hydrogels lost approximately 80% of their water in just 20 min, compared to a loss of 50% of their water at room temperature. The outcomes of this study may contribute to further developments in artificial organ design.
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31
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Yu JH, Lee HA, Kim SU. Noninvasive imaging biomarkers for liver fibrosis in nonalcoholic fatty liver disease: current and future. Clin Mol Hepatol 2023; 29:S136-S149. [PMID: 36503205 PMCID: PMC10029967 DOI: 10.3350/cmh.2022.0436] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is increasingly prevalent worldwide and becoming a major cause of liver disease-related morbidity and mortality. The presence of liver fibrosis in patients with NAFLD is closely related to prognosis, including the development of hepatocellular carcinoma and other complications of cirrhosis. Therefore, assessment of the presence of significant or advanced liver fibrosis is crucial. Although liver biopsy has been considered the "gold standard" method for evaluating the degree of liver fibrosis, it is not suitable for extensive use in all patients with NAFLD owing to its invasiveness and high cost. Therefore, noninvasive biochemical and imaging biomarkers have been developed to overcome the limitations of liver biopsy. Imaging biomarkers for the stratification of liver fibrosis have been evaluated in patients with NAFLD using different imaging techniques, such as transient elastography, shear wave elastography, and magnetic resonance elastography. Furthermore, artificial intelligence and deep learning methods are increasingly being applied to improve the diagnostic accuracy of imaging techniques and overcome the pitfalls of existing imaging biomarkers. In this review, we describe the usefulness and future prospects of noninvasive imaging biomarkers that have been studied and used to evaluate the degree of liver fibrosis in patients with NAFLD.
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Affiliation(s)
- Jung Hwan Yu
- Department of Internal Medicine, Inha University Hospital and School of Medicine, Incheon, Korea
| | - Han Ah Lee
- Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Seung Up Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Korea
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32
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Stulpinas R, Zilenaite-Petrulaitiene D, Rasmusson A, Gulla A, Grigonyte A, Strupas K, Laurinavicius A. Prognostic Value of CD8+ Lymphocytes in Hepatocellular Carcinoma and Perineoplastic Parenchyma Assessed by Interface Density Profiles in Liver Resection Samples. Cancers (Basel) 2023; 15:cancers15020366. [PMID: 36672317 PMCID: PMC9857181 DOI: 10.3390/cancers15020366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/28/2022] [Accepted: 01/04/2023] [Indexed: 01/08/2023] Open
Abstract
Hepatocellular carcinoma (HCC) often emerges in the setting of long-standing inflammatory liver disease. CD8 lymphocytes are involved in both the antitumoral response and hepatocyte damage in the remaining parenchyma. We investigated the dual role of CD8 lymphocytes by assessing density profiles at the interfaces of both HCC and perineoplastic liver parenchyma with surrounding stroma in whole-slide immunohistochemistry images of surgical resection samples. We applied a hexagonal grid-based digital image analysis method to sample the interface zones and compute the CD8 density profiles within them. The prognostic value of the indicators was explored in the context of clinicopathological, peripheral blood testing, and surgery data. Independent predictors of worse OS were a low standard deviation of CD8+ density along the tumor edge, high mean CD8+ density within the epithelial aspect of the perineoplastic liver-stroma interface, longer duration of surgery, a higher level of aspartate transaminase (AST), and a higher basophil count in the peripheral blood. A combined score, derived from these five independent predictors, enabled risk stratification of the patients into three prognostic categories with a 5-year OS probability of 76%, 40%, and 8%. Independent predictors of longer RFS were stage pT1, shorter duration of surgery, larger tumor size, wider tumor-free margin, and higher mean CD8+ density in the epithelial aspect of the tumor-stroma interface. We conclude that (1) our computational models reveal independent and opposite prognostic impacts of CD8+ cell densities at the interfaces of the malignant and non-malignant epithelium interfaces with the surrounding stroma; and (2) together with pathology, surgery, and laboratory data, comprehensive prognostic models can be constructed to predict patient outcomes after liver resection due to HCC.
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Affiliation(s)
- Rokas Stulpinas
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
- Correspondence:
| | - Dovile Zilenaite-Petrulaitiene
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
| | - Allan Rasmusson
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
| | - Aiste Gulla
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Agne Grigonyte
- Faculty of Medicine, Vilnius University, 03101 Vilnius, Lithuania
| | - Kestutis Strupas
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, 03101 Vilnius, Lithuania
| | - Arvydas Laurinavicius
- Faculty of Medicine, Institute of Biomedical Sciences, Department of Pathology, Forensic Medicine and Pharmacology, Vilnius University, 03101 Vilnius, Lithuania
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, 08406 Vilnius, Lithuania
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Top-Down Proteomics Detection of Potential Salivary Biomarkers for Autoimmune Liver Diseases Classification. Int J Mol Sci 2023; 24:ijms24020959. [PMID: 36674470 PMCID: PMC9866740 DOI: 10.3390/ijms24020959] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
(1) Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases characterized by chronic hepatic inflammation and progressive liver fibrosis. The possible use of saliva as a diagnostic tool has been explored in several oral and systemic diseases. The use of proteomics for personalized medicine is a rapidly emerging field. (2) Salivary proteomic data of 36 healthy controls (HCs), 36 AIH and 36 PBC patients, obtained by liquid chromatography/mass spectrometry top-down pipeline, were analyzed by multiple Mann-Whitney test, Kendall correlation, Random Forest (RF) analysis and Linear Discriminant Analysis (LDA); (3) Mann-Whitney tests provided indications on the panel of differentially expressed salivary proteins and peptides, namely cystatin A, statherin, histatin 3, histatin 5 and histatin 6, which were elevated in AIH patients with respect to both HCs and PBC patients, while S100A12, S100A9 short, cystatin S1, S2, SN and C showed varied levels in PBC with respect to HCs and/or AIH patients. RF analysis evidenced a panel of salivary proteins/peptides able to classify with good accuracy PBC vs. HCs (83.3%), AIH vs. HCs (79.9%) and PBC vs. AIH (80.2%); (4) RF appears to be an attractive machine-learning tool suited for classification of AIH and PBC based on their different salivary proteomic profiles.
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34
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Jaffe A, Taddei TH, Giannini EG, Ilagan-Ying YC, Colombo M, Strazzabosco M. Alternative HCC prognostic biomarkers and use of artificial intelligence in the management of HCC. Liver Int 2023; 43:258-259. [PMID: 36426717 DOI: 10.1111/liv.15482] [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: 11/15/2022] [Accepted: 11/19/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Ariel Jaffe
- Liver Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.,Smilow Cancer Hospital and Liver Cancer Program, New Haven, Connecticut, USA
| | - Tamar H Taddei
- Liver Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Edoardo G Giannini
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Ysabel C Ilagan-Ying
- Liver Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Mario Strazzabosco
- Liver Center, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.,Smilow Cancer Hospital and Liver Cancer Program, New Haven, Connecticut, USA
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35
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Gallage S, Avila JEB, Ramadori P, Focaccia E, Rahbari M, Ali A, Malek NP, Anstee QM, Heikenwalder M. A researcher's guide to preclinical mouse NASH models. Nat Metab 2022; 4:1632-1649. [PMID: 36539621 DOI: 10.1038/s42255-022-00700-y] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 10/26/2022] [Indexed: 12/24/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) and its inflammatory form, non-alcoholic steatohepatitis (NASH), have quickly risen to become the most prevalent chronic liver disease in the Western world and are risk factors for the development of hepatocellular carcinoma (HCC). HCC is not only one of the most common cancers but is also highly lethal. Nevertheless, there are currently no clinically approved drugs for NAFLD, and NASH-induced HCC poses a unique metabolic microenvironment that may influence responsiveness to certain treatments. Therefore, there is an urgent need to better understand the pathogenesis of this rampant disease to devise new therapies. In this line, preclinical mouse models are crucial tools to investigate mechanisms as well as novel treatment modalities during the pathogenesis of NASH and subsequent HCC in preparation for human clinical trials. Although, there are numerous genetically induced, diet-induced and toxin-induced models of NASH, not all of these models faithfully phenocopy and mirror the human pathology very well. In this Perspective, we shed some light onto the most widely used mouse models of NASH and highlight some of the key advantages and disadvantages of the various models with an emphasis on 'Western diets', which are increasingly recognized as some of the best models in recapitulating the human NASH pathology and comorbidities.
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Affiliation(s)
- Suchira Gallage
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- The M3 Research Institute, Eberhard Karls University Tübingen, Tuebingen, Germany.
| | - Jose Efren Barragan Avila
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pierluigi Ramadori
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Enrico Focaccia
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mohammad Rahbari
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Adnan Ali
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nisar P Malek
- The M3 Research Institute, Eberhard Karls University Tübingen, Tuebingen, Germany
- Department Internal Medicine I, Eberhard-Karls University, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany
| | - Quentin M Anstee
- Newcastle NIHR Biomedical Research Centre, Newcastle upon Tyne Hospitals, NHS Foundation Trust, Newcastle upon Tyne, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Mathias Heikenwalder
- Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- The M3 Research Institute, Eberhard Karls University Tübingen, Tuebingen, Germany.
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany.
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36
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Ülger Y, Delik A. Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2022; 42:398-406. [PMID: 36448439 DOI: 10.1080/15257770.2022.2152046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing protein 3 (PNPLA3) plays an important role in the development of a number of liver diseases ranging from steatosis, chronic hepatitis, cirrhosis and HCC. Due to the increase in the prevalence of NAFLD, new models are being developed with machine learning, deep learning, artificial neural network (ANN) algorithms in the field of artificial intelligence (AI) to determine low-cost, noninvasive diagnostic methods. Models developed with ANN from AI modules are important in order to examine biochemical and genomic information in detail in the diagnosis of NAFLD. The aim of this study is to develop a simple ANN model using biochemical and genotypic parameters in the diagnosis of NAFLD. A total of 300 patients followed up with the diagnosis of NAFLD and 100 controls were included in the study. The data set was divided into two as training and test set. Genotyping of PNPLA3 (CC, CG, GG) as genomic analysis was performed with real time PCR device. The algorithm used for the diagnosis of NAFLD was designed using age, body mass index (BMI), mean platelet volume (MPV), insulin resistance (IR), alanine aminotransferase (ALT), genotype PNPLA3 (CC, CG, GG) parameters. MLP Classifier algorithm from ANN was used in the development of the model. ANN algorithms are used in python programming language. Statistical analyzes were made in SPSS program. Percent accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall, and f1-score results were determined. The accuracy percentage was determined as 0.979 in the train set and 0.970 in the test set. The Log Loss value was set to 0.09. The developed neural network achieved an accuracy percentage of 97.0% during testing, with an area under the ROC curve value of 0.95. We think that the ANN model developed with genomic and biochemical parameters can be used as a cost-effective, noninvasive new predictive diagnostic model in clinical practice in the diagnosis of NAFLD.
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Affiliation(s)
- Yakup Ülger
- Faculty of Medicine, Department of Gastroenterology, Cukurova University, Adana, Turkey
| | - Anıl Delik
- Faculty of Medicine, Department of Gastroenterology, Cukurova University, Adana, Turkey
- Faculty of Science and Literature Department of Biology, Cukurova University, Adana, Turkey
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37
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Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides. Nutrients 2022; 14:nu14224810. [PMID: 36432497 PMCID: PMC9697201 DOI: 10.3390/nu14224810] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/31/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Nature may have the answer to many of our questions about human, animal, and environmental health. Natural bioactives, especially when harvested from sustainable plant and food sources, provide a plethora of molecular solutions to nutritionally actionable, chronic conditions. The spectrum of these conditions, such as metabolic, immune, and gastrointestinal disorders, has changed with prolonged human life span, which should be matched with an appropriately extended health span, which would in turn favour more sustainable health care: "adding years to life and adding life to years". To date, bioactive peptides have been undervalued and underexploited as food ingredients and drugs. The future of translational science on bioactive peptides-and natural bioactives in general-is being built on (a) systems-level rather than reductionist strategies for understanding their interdependent, and at times synergistic, functions; and (b) the leverage of artificial intelligence for prediction and discovery, thereby significantly reducing the time from idea and concept to finished solutions for consumers and patients. This new strategy follows the path from benefit definition via design to prediction and, eventually, validation and production.
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38
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Gerussi A, Scaravaglio M, Cristoferi L, Verda D, Milani C, De Bernardi E, Ippolito D, Asselta R, Invernizzi P, Kather JN, Carbone M. Artificial intelligence for precision medicine in autoimmune liver disease. Front Immunol 2022; 13:966329. [PMID: 36439097 PMCID: PMC9691668 DOI: 10.3389/fimmu.2022.966329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/13/2022] [Indexed: 09/10/2023] Open
Abstract
Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.
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Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Miki Scaravaglio
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Laura Cristoferi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | | | - Chiara Milani
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Elisabetta De Bernardi
- Department of Medicine and Surgery and Tecnomed Foundation, University of Milano - Bicocca, Monza, Italy
| | | | - Rosanna Asselta
- Humanitas Clinical and Research Center, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Marco Carbone
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
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39
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Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies. Sci Rep 2022; 12:19236. [PMID: 36357500 PMCID: PMC9649648 DOI: 10.1038/s41598-022-23905-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advanced form non-alcoholic steatohepatitis (NASH), and ultimately to cirrhosis or liver cancer. The current gold standard for diagnosis and assessment of NAFLD/NASH is liver biopsy followed by microscopic analysis by a pathologist. The Kleiner score is frequently used for a semi-quantitative assessment of disease progression. In this scoring system the features of active injury (steatosis, inflammation, and ballooning) and a separated fibrosis score are quantified. The procedure is time consuming for pathologists, scores have limited resolution and are subject to variation. We developed an automated deep learning method that provides full reproducibility and higher resolution. The system was established with 296 human liver biopsies and tested on 171 human liver biopsies with pathologist ground truth scores. The method is inspired by the way pathologist's analyze liver biopsies. First, the biopsies are analyzed microscopically for the relevant histopathological features. Subsequently, histopathological features are aggregated to a per-biopsy score. Scores are in the identical numeric range as the pathologist's ballooning, inflammation, steatosis, and fibrosis scores, but on a continuous scale. Resulting scores followed a pathologist's ground truth (quadratic weighted Cohen's κ on the test set: for steatosis 0.66, for inflammation 0.24, for ballooning 0.43, for fibrosis 0.62, and for the NAFLD activity score (NAS) 0.52. Mean absolute errors on a test set: for steatosis 0.29, for inflammation 0.53, for ballooning 0.61, for fibrosis 0.78, and for the NAS 0.77).
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40
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Tonon M, Moreau R. Using machine learning for predicting outcomes in ACLF. Liver Int 2022; 42:2354-2355. [PMID: 36162084 DOI: 10.1111/liv.15399] [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: 08/11/2022] [Accepted: 08/13/2022] [Indexed: 01/22/2023]
Affiliation(s)
- Marta Tonon
- Unit of Internal Medicine and Hepatology, University Hospital of Padova, Padova, Italy
| | - Richard Moreau
- European Foundation for the Study of Chronic Liver Failure (EF CLIF), Barcelona, Spain.,INSERM, Université de Paris Cité, Centre de Recherche sur l'Inflammation (CRI), Paris, France.,Assistance Publique-Hôpitaux de Paris (AP-HP), and Hôpital Beaujon, Service d'Hépatologie, Clichy, France
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41
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Liver Function-How to Screen and to Diagnose: Insights from Personal Experiences, Controlled Clinical Studies and Future Perspectives. J Pers Med 2022; 12:jpm12101657. [PMID: 36294796 PMCID: PMC9605048 DOI: 10.3390/jpm12101657] [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: 08/18/2022] [Revised: 09/21/2022] [Accepted: 09/29/2022] [Indexed: 01/24/2023] Open
Abstract
Acute and chronic liver disease is a relevant problem worldwide. Liver function plays a crucial role in the course of liver diseases not only in estimating prognosis but also with regard to therapeutic interventions. Within this review, we discuss and evaluate different tools from screening to diagnosis and give insights from personal experiences, controlled clinical studies and future perspectives. Finally, we offer our novel diagnostic algorithm to screen patients with presumptive acute or chronic liver disease in the daily clinical routine.
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42
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Ghaffari Laleh N, Truhn D, Veldhuizen GP, Han T, van Treeck M, Buelow RD, Langer R, Dislich B, Boor P, Schulz V, Kather JN. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun 2022; 13:5711. [PMID: 36175413 PMCID: PMC9522657 DOI: 10.1038/s41467-022-33266-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) are highly susceptible to white- and black-box adversarial attacks in clinically relevant weakly-supervised classification tasks. Adversarially robust training and dual batch normalization (DBN) are possible mitigation strategies but require precise knowledge of the type of attack used in the inference. We demonstrate that vision transformers (ViTs) perform equally well compared to CNNs at baseline, but are orders of magnitude more robust to white- and black-box attacks. At a mechanistic level, we show that this is associated with a more robust latent representation of clinically relevant categories in ViTs compared to CNNs. Our results are in line with previous theoretical studies and provide empirical evidence that ViTs are robust learners in computational pathology. This implies that large-scale rollout of AI models in computational pathology should rely on ViTs rather than CNN-based classifiers to provide inherent protection against perturbation of the input data, especially adversarial attacks. Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.
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Affiliation(s)
- Narmin Ghaffari Laleh
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Gregory Patrick Veldhuizen
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Tianyu Han
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Marko van Treeck
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany
| | - Roman D Buelow
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Rupert Langer
- Institute of Pathology, University of Bern, Bern, Switzerland.,Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
| | - Bastian Dislich
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Peter Boor
- Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Volkmar Schulz
- Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Physics Institute III B, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, RWTH Aachen university, Aachen, Germany. .,Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany. .,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. .,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK. .,Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
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43
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Shmatko A, Ghaffari Laleh N, Gerstung M, Kather JN. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. NATURE CANCER 2022; 3:1026-1038. [PMID: 36138135 DOI: 10.1038/s43018-022-00436-4] [Citation(s) in RCA: 111] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative information from digital histopathology images. AI is expected to reduce workload for human experts, improve the objectivity and consistency of pathology reports, and have a clinical impact by extracting hidden information from routinely available data. Here, we describe how AI can be used to predict cancer outcome, treatment response, genetic alterations and gene expression from digitized histopathology slides. We summarize the underlying technologies and emerging approaches, noting limitations, including the need for data sharing and standards. Finally, we discuss the broader implications of AI in cancer research and oncology.
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Affiliation(s)
- Artem Shmatko
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | | | - Moritz Gerstung
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
- Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
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44
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Ong W, Zhu L, Zhang W, Kuah T, Lim DSW, Low XZ, Thian YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers (Basel) 2022; 14:4025. [PMID: 36011018 PMCID: PMC9406500 DOI: 10.3390/cancers14164025] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/10/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd., Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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45
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Lin B, Ma Y, Wu S. Multi-Omics and Artificial Intelligence-Guided Data Integration in Chronic Liver Disease: Prospects and Challenges for Precision Medicine. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:415-421. [PMID: 35925812 DOI: 10.1089/omi.2022.0079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Chronic liver disease (CLD) is a significant planetary health burden. CLD includes a broad range of liver pathologies from different causes, for example, hepatitis B virus infection, fatty liver disease, hepatocellular carcinoma, and nonalcoholic fatty liver disease or the metabolic associated fatty liver disease. Biomarker and diagnostic discovery, and new molecular targets for precision treatments are timely and sorely needed in CLD. In this context, multi-omics data integration is increasingly being facilitated by artificial intelligence (AI) and attendant digital transformation of systems science. While the digital transformation of multi-omics integrative analyses is still in its infancy, there are noteworthy prospects, hope, and challenges for diagnostic and therapeutic innovation in CLD. This expert review aims at the emerging knowledge frontiers as well as gaps in multi-omics data integration at bulk tissue levels, and those including single cell-level data, gut microbiome data, and finally, those incorporating tissue-specific information. We refer to AI and related digital transformation of the CLD research and development field whenever possible. This review of the emerging frontiers at the intersection of systems science and digital transformation informs future roadmaps to bridge digital technology discovery and clinical omics applications to benefit planetary health and patients with CLD.
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Affiliation(s)
- Biaoyang Lin
- Zhejiang California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Department of Urology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Yingying Ma
- Zhejiang California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, China
- Hangzhou Proprium Biotech Co. Ltd., Hangzhou, China
| | - ShengJun Wu
- Department of Clinical Laboratories, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Baek EB, Hwang JH, Park H, Lee BS, Son HY, Kim YB, Jun SY, Her J, Lee J, Cho JW. Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats. Diagnostics (Basel) 2022; 12:diagnostics12061478. [PMID: 35741291 PMCID: PMC9222125 DOI: 10.3390/diagnostics12061478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022] Open
Abstract
Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.
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Affiliation(s)
- Eun Bok Baek
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea; (E.B.B.); (H.-Y.S.)
| | - Ji-Hee Hwang
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
| | - Heejin Park
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
| | - Byoung-Seok Lee
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
| | - Hwa-Young Son
- College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Korea; (E.B.B.); (H.-Y.S.)
| | - Yong-Bum Kim
- Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea;
| | - Sang-Yeop Jun
- Research & Development Team, LAC Inc., Seoul 07807, Korea; (S.-Y.J.); (J.H.); (J.L.)
| | - Jun Her
- Research & Development Team, LAC Inc., Seoul 07807, Korea; (S.-Y.J.); (J.H.); (J.L.)
| | - Jaeku Lee
- Research & Development Team, LAC Inc., Seoul 07807, Korea; (S.-Y.J.); (J.H.); (J.L.)
| | - Jae-Woo Cho
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon 34114, Korea; (J.-H.H.); (H.P.); (B.-S.L.)
- Correspondence: ; Tel.: +82-42-610-8023
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Khanna NN, Maindarkar M, Saxena A, Ahluwalia P, Paul S, Srivastava SK, Cuadrado-Godia E, Sharma A, Omerzu T, Saba L, Mavrogeni S, Turk M, Laird JR, Kitas GD, Fatemi M, Barqawi AB, Miner M, Singh IM, Johri A, Kalra MM, Agarwal V, Paraskevas KI, Teji JS, Fouda MM, Pareek G, Suri JS. Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics (Basel) 2022; 12:1249. [PMID: 35626404 PMCID: PMC9141739 DOI: 10.3390/diagnostics12051249] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/14/2022] [Accepted: 05/15/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Mahesh Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (M.M.); (S.P.)
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
| | - Ajit Saxena
- Department of Urology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India;
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (M.M.); (S.P.)
| | - Saurabh K. Srivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad 244001, India;
| | - Elisa Cuadrado-Godia
- Department of Neurology, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (T.O.); (M.T.)
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 176 74 Athens, Greece;
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (T.O.); (M.T.)
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, NY 55905, USA;
| | - Al Baha Barqawi
- Division of Urology, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
| | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | | | - Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA;
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
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48
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Heinz CN, Echle A, Foersch S, Bychkov A, Kather JN. The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups. Histopathology 2022; 80:1121-1127. [PMID: 35373378 DOI: 10.1111/his.14659] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/17/2022] [Accepted: 04/02/2022] [Indexed: 11/30/2022]
Abstract
AIMS Artificial intelligence (AI) provides a powerful tool to extract information from digitized histopathology whole slide images. In the last five years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into. METHODS To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing sub-fields of computational pathology with a focus on solid tumors. RESULTS The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in sub-groups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high across subgroups. CONCLUSIONS Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.
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Affiliation(s)
- Céline N Heinz
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Amelie Echle
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Sebastian Foersch
- Department of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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49
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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.
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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
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