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Jeong JS, Kang TH, Ju H, Cho CH. Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence. Heliyon 2024; 10:e33826. [PMID: 39027625 PMCID: PMC11255511 DOI: 10.1016/j.heliyon.2024.e33826] [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: 06/18/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024] Open
Abstract
Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.
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Affiliation(s)
- Jae-Seung Jeong
- Division of Artificial Intelligence Convergence Engineering, Sahmyook University, South Korea
| | - Tak Ho Kang
- Department of Laboratory Medicine, College of Medicine, Korea University Anam Hospital, South Korea
| | - Hyunsu Ju
- Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, South Korea
| | - Chi-Hyun Cho
- Department of Laboratory Medicine, College of Medicine, Korea University Ansan Hospital, South Korea
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2
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Szymaszek P, Tyszka-Czochara M, Ortyl J. Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence. Molecules 2024; 29:3164. [PMID: 38999115 PMCID: PMC11243723 DOI: 10.3390/molecules29133164] [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: 05/23/2024] [Revised: 06/14/2024] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.
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Affiliation(s)
- Patryk Szymaszek
- Department of Biotechnology and Physical Chemistry, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland
| | | | - Joanna Ortyl
- Department of Biotechnology and Physical Chemistry, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland
- Photo HiTech Ltd., Bobrzyńskiego 14, 30-348 Kraków, Poland
- Photo4Chem Ltd., Juliusza Lea 114/416A-B, 31-133 Cracow, Poland
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3
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Pregowska A, Roszkiewicz A, Osial M, Giersig M. How scanning probe microscopy can be supported by artificial intelligence and quantum computing? Microsc Res Tech 2024. [PMID: 38864463 DOI: 10.1002/jemt.24629] [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: 03/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024]
Abstract
The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.
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Affiliation(s)
- Agnieszka Pregowska
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Agata Roszkiewicz
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Magdalena Osial
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Michael Giersig
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
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4
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Viswanathan VS, Parmar V, Madabhushi A. Towards equitable AI in oncology. Nat Rev Clin Oncol 2024:10.1038/s41571-024-00909-8. [PMID: 38849530 DOI: 10.1038/s41571-024-00909-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 06/09/2024]
Abstract
Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.
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Affiliation(s)
| | - Vani Parmar
- Department of Breast Surgical Oncology, Punyashlok Ahilyadevi Holkar Head & Neck Cancer Institute of India, Mumbai, India
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
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5
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Frille A, Arends J, Abenavoli EM, Duke SA, Ferrara D, Gruenert S, Hacker M, Hesse S, Hofmann L, Holm SH, Lund TB, Rullmann M, Sandøe P, Sciagrà R, Shiyam Sundar LK, Tönjes A, Wirtz H, Yu J, Sabri O, Beyer T. "Metabolic fingerprints" of cachexia in lung cancer patients. Eur J Nucl Med Mol Imaging 2024; 51:2067-2069. [PMID: 38504039 PMCID: PMC11139725 DOI: 10.1007/s00259-024-06689-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 03/10/2024] [Indexed: 03/21/2024]
Affiliation(s)
- Armin Frille
- Department of Respiratory Medicine, Leipzig University, Liebigstrasse 20, 04103, Leipzig, Germany.
| | - Jann Arends
- Department of Medicine I, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Hugstetter Strasse 55, 79106, Freiburg, Germany
| | - Elisabetta M Abenavoli
- Nuclear Medicine, Azienda Ospedaliero Universitaria Careggi, Largo Brambilla, 3, 50134, Florence, Italy
| | - Shaul A Duke
- Department of Food and Resource Economics (IFRO), University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Copenhagen, Denmark
| | - Daria Ferrara
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Stefan Gruenert
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Marcus Hacker
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Swen Hesse
- Department of Nuclear Medicine, Leipzig University, Liebigstrasse 18, 04103, Leipzig, Germany
| | - Lukas Hofmann
- Department of Respiratory Medicine, Leipzig University, Liebigstrasse 20, 04103, Leipzig, Germany
- Department of Nuclear Medicine, Leipzig University, Liebigstrasse 18, 04103, Leipzig, Germany
| | - Sune H Holm
- Department of Food and Resource Economics (IFRO), University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Copenhagen, Denmark
| | - Thomas B Lund
- Department of Food and Resource Economics (IFRO), University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Copenhagen, Denmark
| | - Michael Rullmann
- Department of Nuclear Medicine, Leipzig University, Liebigstrasse 18, 04103, Leipzig, Germany
| | - Peter Sandøe
- Department of Food and Resource Economics (IFRO), University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Copenhagen, Denmark
- Department of Veterinary and Animal Sciences, Grønnegårdsvej 8, 1870, Frederiksberg C, Copenhagen, Denmark
| | - Roberto Sciagrà
- Nuclear Medicine, Azienda Ospedaliero Universitaria Careggi, Largo Brambilla, 3, 50134, Florence, Italy
| | - Lalith Kumar Shiyam Sundar
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Anke Tönjes
- Department of Endocrine Medicine, Leipzig University, Liebigstrasse 20, 04103, Leipzig, Germany
| | - Hubert Wirtz
- Department of Respiratory Medicine, Leipzig University, Liebigstrasse 20, 04103, Leipzig, Germany
| | - Josef Yu
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Osama Sabri
- Department of Nuclear Medicine, Leipzig University, Liebigstrasse 18, 04103, Leipzig, Germany
| | - Thomas Beyer
- Quantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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Assis de Souza A, Stubbs AP, Hesselink DA, Baan CC, Boer K. Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation. Transplantation 2024:00007890-990000000-00768. [PMID: 38773859 DOI: 10.1097/tp.0000000000005063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2024]
Abstract
Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many years. Nevertheless, despite the question of whether AI models add value to traditional modeling approaches, such as regression models, their "black box" nature is one of the factors that have hindered the translation from research to clinical practice. Several techniques that make such models understandable to humans were developed with the promise of increasing transparency in the support of medical decision-making. These techniques should help AI to close the gap between theory and practice by yielding trust in the model by doctors and patients, allowing model auditing, and facilitating compliance with emergent AI regulations. But is this also happening in the field of kidney transplantation? This review reports the use and explanation of "black box" models to diagnose and predict kidney allograft rejection, delayed graft function, graft failure, and other related outcomes after kidney transplantation. In particular, we emphasize the discussion on the need (or not) to explain ML models for biological discovery and clinical implementation in kidney transplantation. We also discuss promising future research paths for these computational tools.
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Affiliation(s)
- Alvaro Assis de Souza
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Andrew P Stubbs
- Department of Pathology and Clinical Bioinformatics, Erasmus MC Stubbs Group, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dennis A Hesselink
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Carla C Baan
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Karin Boer
- Department of Internal Medicine, Erasmus MC Transplant Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Khorsandi D, Rezayat D, Sezen S, Ferrao R, Khosravi A, Zarepour A, Khorsandi M, Hashemian M, Iravani S, Zarrabi A. Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like? J Mater Chem B 2024; 12:4584-4612. [PMID: 38686396 DOI: 10.1039/d4tb00310a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.
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Affiliation(s)
- Danial Khorsandi
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
| | - Dorsa Rezayat
- Center for Global Design and Manufacturing, College of Engineering and Applied Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA
| | - Serap Sezen
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956 Istanbul, Türkiye
- Nanotechnology Research and Application Center, Sabanci University, Tuzla 34956 Istanbul, Türkiye
| | - Rafaela Ferrao
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
- University of Coimbra, Institute for Interdisciplinary Research, Doctoral Programme in Experimental Biology and Biomedicine (PDBEB), Portugal
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Türkiye
| | - Atefeh Zarepour
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai - 600 077, India
| | - Melika Khorsandi
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohammad Hashemian
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Siavash Iravani
- Independent Researcher, W Nazar ST, Boostan Ave, Isfahan, Iran.
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye.
- Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan
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8
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N. Kachouie N, Deebani W, Shutaywi M, Christiani DC. Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient. PLoS One 2024; 19:e0301131. [PMID: 38739669 PMCID: PMC11090345 DOI: 10.1371/journal.pone.0301131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/11/2024] [Indexed: 05/16/2024] Open
Abstract
Lung cancer is the second most diagnosed cancer and the first cause of cancer related death for men and women in the United States. Early detection is essential as patient survival is not optimal and recurrence rate is high. Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with the cancer. In this research, the similarities between cancer and paired peripheral blood samples are identified using maximal information coefficient (MIC) and the spatial locations with substantially high MIC scores in each chromosome are used for clustering analysis. The results showed that a sizable reduction of feature set can be obtained using only a subset of locations with high MIC values. The clustering performance was evaluated using both true rate and normalized mutual information (NMI). Clustering results using the reduced feature set outperformed the performance of clustering using entire feature set in several chromosomes that are highly associated with lung cancer with several identified oncogenes.
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Affiliation(s)
- Nezamoddin N. Kachouie
- Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, United States of America
| | - Wejdan Deebani
- Mathematics Department, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Meshal Shutaywi
- Mathematics Department, College of Science and Arts, King Abdulaziz University, Jeddah, Saudi Arabia
| | - David C. Christiani
- Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States of America
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States of America
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9
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Bhutiani N, Yousef MMG, Yousef A, Zeineddine M, Knafl M, Ratliff O, Fernando UP, Turin A, Zeineddine FA, Jin J, Alfaro-Munoz K, Goldstein D, Chang GJ, Kopetz S, Shen JP, Uppal A. Automated, High-Throughput Platform to Generate a High-Reliability, Comprehensive Rectal Cancer Database. JCO Clin Cancer Inform 2024; 8:e2300219. [PMID: 38759125 DOI: 10.1200/cci.23.00219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/07/2024] [Accepted: 03/13/2024] [Indexed: 05/19/2024] Open
Abstract
PURPOSE Dynamic operations platforms allow for cross-platform data extraction, integration, and analysis, although application of these platforms to large-scale oncology enterprises has not been described. This study presents a pipeline for automated, high-fidelity extraction, integration, and validation of cross-platform oncology data in patients undergoing treatment for rectal cancer at a single, high-volume institution. METHODS A dynamic operations platform was used to identify patients with rectal cancer treated at MD Anderson Cancer Center between 2016 and 2022 who had magnetic resonance imaging (MRI) imaging and preoperative treatment details available in the electronic health record (EHR). Demographic, clinicopathologic, tumor mutation, radiographic, and treatment data were extracted from the EHR using a methodology adaptable to any disease site. Data accuracy was assessed by manual review. Accuracy before and after implementation of synoptic reporting was determined for MRI data. RESULTS A total of 516 patients with localized rectal cancer were included. In the era after institutional adoption of synoptic reports, the dynamic operations platform extracted T (tumor) category data from the EHR with 95% accuracy compared with 87% before the use of synoptic reports, and N (lymph node) category with 88% compared with 58%. Correct extraction of pelvic sidewall adenopathy was 94% compared with 78%, and extramural vascular invasion accuracy was 99% compared with 89%. Neoadjuvant chemotherapy and radiation data were 99% accurate for patients who had synoptic data sources. CONCLUSION Using dynamic operations platforms enables automated cross-platform integration of multiparameter oncology data with high fidelity in patients undergoing multimodality treatment for rectal cancer. These pipelines can be adapted to other solid tumors and, together with standardized reporting, can increase efficiency in clinical research and the translation of actionable findings toward optimizing patient outcomes.
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Affiliation(s)
- Neal Bhutiani
- Department of Colon and Rectal Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mahmoud M G Yousef
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Abdelrahman Yousef
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mohammad Zeineddine
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mark Knafl
- Department Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Olivia Ratliff
- Information Services, Enterprise Development & Integration, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Uditha P Fernando
- Information Services, Enterprise Development & Integration, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Anastasia Turin
- Information Services, Enterprise Development & Integration, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Fadl A Zeineddine
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jeff Jin
- Information Services, Enterprise Development & Integration, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Kristin Alfaro-Munoz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - George J Chang
- Department of Colon and Rectal Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - John Paul Shen
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Abhineet Uppal
- Department of Colon and Rectal Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX
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Vashishat A, Patel P, Das Gupta G, Das Kurmi B. Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches. Stem Cell Rev Rep 2024; 20:881-899. [PMID: 38429620 DOI: 10.1007/s12015-024-10701-x] [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] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.
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Affiliation(s)
- Abhinav Vashishat
- Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India
| | - Preeti Patel
- Department of Pharmaceutical Chemistry, ISF College Pharmacy, GT Road, Moga, 142001, Punjab, India.
| | - Ghanshyam Das Gupta
- Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India
| | - Balak Das Kurmi
- Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India.
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11
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Wang Y, Zhang W, Chen L, Xie J, Zheng X, Jin Y, Zheng Q, Xue Q, Li B, He C, Chen H, Li Y. Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy. Biol Proced Online 2024; 26:10. [PMID: 38632527 PMCID: PMC11022344 DOI: 10.1186/s12575-024-00234-5] [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: 01/05/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. METHODS This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. RESULTS The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. CONCLUSION This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.
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Affiliation(s)
- Yichen Wang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China, 200032
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
| | - Wenhua Zhang
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
- Department of Future Technology, Shanghai University, Shanghai, China
| | - Lijun Chen
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China, 200032
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
| | - Jun Xie
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Xuebin Zheng
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Yan Jin
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China, 200032
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
| | - Qiang Zheng
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China, 200032
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
| | - Qianqian Xue
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China, 200032
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
| | - Bin Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Chuan He
- Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Haiquan Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yuan Li
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China, 200032.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, 200032.
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12
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Pei J, Yan Y, Jayaraman S, Rajagopal P, Natarajan PM, Umapathy VR, Gopathy S, Roy JR, Sadagopan JC, Thalamati D, Palanisamy CP, Mironescu M. A review on advancements in the application of starch-based nanomaterials in biomedicine: Precision drug delivery and cancer therapy. Int J Biol Macromol 2024; 265:130746. [PMID: 38467219 DOI: 10.1016/j.ijbiomac.2024.130746] [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: 10/07/2023] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 03/13/2024]
Abstract
The burgeoning field of starch-based nanomaterials in biomedical applications has perceived notable progressions, with a particular emphasis on their pivotal role in precision drug delivery and the inhibition of tumor growth. The complicated challenges in current biomedical research require innovative approaches for improved therapeutic outcomes, prompting an exploration into the possible of starch-based nanomaterials. The conceptualization of this review emerged from recognizing the need for a comprehensive examination of the structural attributes, versatile properties, and mechanisms underlying the efficiency of starch-based nanomaterials in inhibiting tumor growth and enabling targeted drug delivery. This review delineates the substantial growth in utilizing starch-based nanomaterials, elucidating their small size, high surface-volume ratio, and biocompatibility, predominantly emphasizing their possible to actively recognize cancer cells, deliver anticancer drugs, and combat tumors efficiently. The investigation of these nanomaterials encompasses to improving biocompatibility and targeting specific tissues, thereby contributing to the evolving landscape of precision medicine. The review accomplishes by highlighting the auspicious strategies and modern developments in the field, envisioning a future where starch-based nanomaterials play a transformative role in molecular nanomaterials, evolving biomedical sciences. The translation of these advancements into clinical applications holds the potential to revolutionize targeted drug delivery and expand therapeutic outcomes in the realm of precision medicine.
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Affiliation(s)
- JinJin Pei
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - Yuqiang Yan
- Department of anaesthesia, Xi'an Central Hospital, No. 161, West 5th Road, Xincheng District, Xi'an 710003, China
| | - Selvaraj Jayaraman
- Centre of Molecular Medicine and Diagnostics (COMManD), Department of Biochemistry, Saveetha Dental College & Hospital, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai 600077, India
| | - Ponnulakshmi Rajagopal
- Central Research Laboratory, Meenakshi Ammal Dental College and Hospital, Meenakshi Academy of Higher Education and Research (Deemed to be University), Chennai-600 095, India
| | - Prabhu Manickam Natarajan
- Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman University, Ajman, United Arab Emirates
| | - Vidhya Rekha Umapathy
- Department of Public Health Dentistry, Thai Moogambigai Dental College and Hospital, Chennai-600107, India
| | - Sridevi Gopathy
- Department of Physiology, SRM Dental College, Ramapuram campus, Chennai 600089, India
| | - Jeane Rebecca Roy
- Department of Anatomy, Bhaarath Medical College and hospital, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu 600 073, India
| | - Janaki Coimbatore Sadagopan
- Department of Anatomy, Bhaarath Medical College and hospital, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu 600 073, India
| | | | - Chella Perumal Palanisamy
- Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
| | - Monica Mironescu
- Faculty of Agricultural Sciences Food Industry and Environmental Protection, Lucian Blaga University of Sibiu, Sibiu 550024, Romania.
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Xie W, Gan Y, Wang L, Si Y, Li Q, Song T, Wei P, Wu Z, Zhang G. Tumor Microenvironment-Activated Nanostructure to Enhance MRI Capability and Nanozyme Activity for Highly Tumor-Specific Multimodal Theranostics. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2306446. [PMID: 38105592 DOI: 10.1002/smll.202306446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/30/2023] [Indexed: 12/19/2023]
Abstract
Copper-based nanozymes exhibit excellent antitumor activity but are easily inactivated due to the disturbance of proteins or other macromolecules with sulfhydryl. A tumor microenvironment-responsive CuMnO@Fe3O4 (CMF) core-shell nanozyme for highly efficient tumor theranostics is developed. A platelet-derived growth factor receptor-β-recognizing cyclic peptide (PDGFB) target is conjugated to the surface of CMF to fabricate a tumor-specific nanozyme (PCMF). The core-shell nanostructure significantly avoids the oxidation and inactivation of copper-based nanozyme, promoting the antitumor activity of PCMF. The weak acid- and GSH-activated T1 and T2 relaxation rate of PCMF contributes to T1 and T2 dual contrast imaging at the tumor site. In addition, the PCMF disintegrates and produces some metal ions that possess Fenton catalytic activity (i.e., Cu+, Mn2+, and Fe2+) under TME. This process significantly depletes GSH, accelerates Fenton and Fenton-like reactions, enhances cellular reactive oxygen species (ROS) levels, and induces cancer cell apoptosis and ferroptosis. PCMF also exhibits photothermal functions, so it can be used in combined photothermal therapy, ferroptosis therapy, and chemodynamic therapy, improving anticancer activity. This work provides insights into the design of an exquisite nanostructure for high-sensitive and tumor-specific theranostics.
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Affiliation(s)
- Wenteng Xie
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P. R. China
- University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Yuehao Gan
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P. R. China
- University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Lulu Wang
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China
| | - Yuanchun Si
- Stomatologic Hospital and College, Key Laboratory of Oral Diseases Research of Anhui Province, Anhui Medical University, Hefei, 230032, P.R. China
| | - Qingdong Li
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P. R. China
- University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Tianwei Song
- School of Pharmacy, Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, 264003, P. R. China
| | - Pengfei Wei
- School of Pharmacy, Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, 264003, P. R. China
| | - Zhengyan Wu
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P. R. China
- University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Guilong Zhang
- School of Pharmacy, Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, 264003, P. R. China
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14
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Jiang X, Li T, Wang J, Zhang Z, Chen X, Zhang J, Zhao X. Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study. Cancer Biother Radiopharm 2024; 39:169-177. [PMID: 38193811 DOI: 10.1089/cbr.2023.0162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC). Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance. Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data. Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.
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Affiliation(s)
- Xiaojing Jiang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Tianyue Li
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
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15
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Wu H, Jiang Y, Tian H, Ye X, Cui C, Shi S, Chen M, Ding Z, Li S, Huang Z, Luo Y, Peng Q, Xu J, Dong F. Sonography-based multimodal information platform for identifying the surgical pathology of ductal carcinoma in situ. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108039. [PMID: 38266556 DOI: 10.1016/j.cmpb.2024.108039] [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: 04/23/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making. PURPOSE To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS. MATERIALS AND METHODS Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC). RESULTS In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net. CONCLUSION By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow.
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Affiliation(s)
- Huaiyu Wu
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Yitao Jiang
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China; Research and Development Department, Microport Prophecy, Shanghai 201203, China
| | - Hongtian Tian
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Xiuqin Ye
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Chen Cui
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Siyuan Shi
- Research and Development Department, Illuminate, LLC, Shenzhen, Guangdong 518000, China
| | - Ming Chen
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Zhimin Ding
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Shiyu Li
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Zhibin Huang
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Yuwei Luo
- Department of Breast Surgery, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China; Department of General Surgery, Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China
| | - Quanzhou Peng
- Department of Pathology, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, Guangdong, China.
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16
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Xianyu Z, Correia C, Ung CY, Zhu S, Billadeau DD, Li H. The Rise of Hypothesis-Driven Artificial Intelligence in Oncology. Cancers (Basel) 2024; 16:822. [PMID: 38398213 PMCID: PMC10886811 DOI: 10.3390/cancers16040822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.
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Affiliation(s)
- Zilin Xianyu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Shizhen Zhu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Daniel D. Billadeau
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
- Department of Immunology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
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17
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Feng X, Shu W, Li M, Li J, Xu J, He M. Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview. J Transl Med 2024; 22:131. [PMID: 38310237 PMCID: PMC10837897 DOI: 10.1186/s12967-024-04915-3] [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: 10/31/2023] [Accepted: 01/20/2024] [Indexed: 02/05/2024] Open
Abstract
The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.
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Affiliation(s)
- Xiaobing Feng
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Wen Shu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Mingya Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyu Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyao Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Min He
- College of Electrical and Information Engineering, Hunan University, Changsha, China.
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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18
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Truhn D, Tayebi Arasteh S, Saldanha OL, Müller-Franzes G, Khader F, Quirke P, West NP, Gray R, Hutchins GGA, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Brobeil A, Yuan T, Chang-Claude J, Hoffmeister M, Foersch S, Han T, Keil S, Schulze-Hagen M, Isfort P, Bruners P, Kaissis G, Kuhl C, Nebelung S, Kather JN. Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Med Image Anal 2024; 92:103059. [PMID: 38104402 PMCID: PMC10804934 DOI: 10.1016/j.media.2023.103059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 04/28/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.
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Affiliation(s)
- Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Soroosh Tayebi Arasteh
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Oliver Lester Saldanha
- 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
| | - Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Firas Khader
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Nicholas P West
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Richard Gray
- Clinical Trial Service Unit, University of Oxford, Oxford, United Kingdom
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom; Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Jenny Chang-Claude
- Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Tianyu Han
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Sebastian Keil
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Maximilian Schulze-Hagen
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Peter Isfort
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philipp Bruners
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Georgios Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany; Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - 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; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
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19
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Walton NA, Nagarajan R, Wang C, Sincan M, Freimuth RR, Everman DB, Walton DC, McGrath SP, Lemas DJ, Benos PV, Alekseyenko AV, Song Q, Gamsiz Uzun E, Taylor CO, Uzun A, Person TN, Rappoport N, Zhao Z, Williams MS. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc 2024; 31:536-541. [PMID: 38037121 PMCID: PMC10797281 DOI: 10.1093/jamia/ocad211] [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: 02/11/2023] [Revised: 10/09/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
OBJECTIVE Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.
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Affiliation(s)
- Nephi A Walton
- Division of Medical Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112 ,United States
| | - Radha Nagarajan
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA 90025, United States
- Information Services Department, Children’s Hospital of Orange County, Orange, CA 92868, United States
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Murat Sincan
- Flatiron Health, New York, NY 10013, United States
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57107, United States
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - David B Everman
- EverMed Genetics and Genomics Consulting LLC, Greenville, SC 29607, United States
| | | | - Scott P McGrath
- CITRIS Health, CITRIS and Banatao Institute, University of California Berkeley, Berkeley, CA 94720, United States
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, United States
| | - Alexander V Alekseyenko
- Department of Public Health Sciences, Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29403, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Ece Gamsiz Uzun
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI 02915, United States
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
| | - Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States
| | - Alper Uzun
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
- Legorreta Cancer Center, Brown University, Providence, RI 02915, United States
| | - Thomas Nate Person
- Department of Bioinformatics and Genomics, Huck Institutes of the Life Sciences, Penn State University, Bloomsburg, PA 16802, United States
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, United States
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20
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Raghav KPS, Loree JM, Kopetz S. PRESSING Need of Precision Care in HER2-Positive Colorectal Cancer: The ELEPHANT in the Room. Clin Cancer Res 2024; 30:260-262. [PMID: 37975903 PMCID: PMC10841812 DOI: 10.1158/1078-0432.ccr-23-2580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/25/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023]
Abstract
Although dual HER2 inhibition has shown promising clinical activity in patients with RAS wild-type HER2-positive metastatic colorectal cancer, predictive biomarkers of response/resistance are less well characterized. Activating HER2/RTK/MAPK genomic alterations appears to blunt the clinical benefit of dual anti-HER2 therapy and may hold a potential albeit partial role in patient selection. See related article by Randon et al., p. 436.
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Affiliation(s)
- Kanwal P. S. Raghav
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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21
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Musleh A. Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:839-855. [PMID: 38393882 DOI: 10.3233/xst-230284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient's condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon a clear anatomical orientation before endoscopic sinus surgery. In this study, a unique method is presented for detecting and diagnosing paranasal sinus disorders using machine learning. The researchers behind the current study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accurately evaluate the paranasal sinuses in CT scans. The proposed technology makes it feasible to automatically cut down on the number of CT scan images that require investigators to manually search through them all. In addition, the approach offers an automatic segmentation that may be used to locate the paranasal sinus region and crop it accordingly. As a result, the suggested method dramatically reduces the amount of data that is necessary during the training phase. As a result, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy. The suggested method not only successfully identifies sinus irregularities but also automatically executes the necessary segmentation without requiring any manual cropping. This eliminates the need for time-consuming and error-prone human labor. When tested with actual CT scans, the method in question was discovered to have an accuracy of 95.16 percent while retaining a sensitivity of 99.14 percent throughout.
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Affiliation(s)
- Abdullah Musleh
- Department of Surgery, King Khalid University, Abha, Saudi Arabia
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22
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Zargarzadeh A, Javanshir E, Ghaffari A, Mosharkesh E, Anari B. Artificial intelligence in cardiovascular medicine: An updated review of the literature. J Cardiovasc Thorac Res 2023; 15:204-209. [PMID: 38357567 PMCID: PMC10862032 DOI: 10.34172/jcvtr.2023.33031] [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: 11/27/2023] [Accepted: 12/10/2023] [Indexed: 02/16/2024] Open
Abstract
Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.
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Affiliation(s)
| | - Elnaz Javanshir
- Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Ghaffari
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Erfan Mosharkesh
- Faculty of Veterinary Medicine, University of Tabriz, Tabriz, Iran
| | - Babak Anari
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
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23
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Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [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: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
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Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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24
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Prasanth BK, Alkhowaiter S, Sawarkar G, Dharshini BD, R Baskaran A. Unlocking Early Cancer Detection: Exploring Biomarkers, Circulating DNA, and Innovative Technological Approaches. Cureus 2023; 15:e51090. [PMID: 38274938 PMCID: PMC10808885 DOI: 10.7759/cureus.51090] [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] [Accepted: 12/25/2023] [Indexed: 01/27/2024] Open
Abstract
Research and development improvements in early cancer diagnosis have had a significant positive impact on health. In the treatment and prevention of cancer, early detection is essential. In this context, biomarkers are essential because they offer important information on the state of cells at any particular time. Cells go through unique changes when they shift from a healthy condition to a malignant state, changes that appropriate biomarkers may pick up. Recent advancements have been made to identify and characterize circulating cancer-specific mutations in cell-free circulating DNA derived from tumors and tumor cells. A patient's delay between the time they first detect symptoms and the time they contact a doctor has been noted for many cancer forms. The tumor's location and features significantly impact the presentation of symptoms judged appropriate for early diagnosis. Lack of knowledge of the severity of the symptoms may be one cause for this delay. Our review is largely focused on the ongoing developments of early diagnosis in the study of biomarkers, circulating DNA for diagnosis, the biology of early challenges, early symptoms, liquid biopsies, detectable by imaging, established tumor markers, plasma DNA technologies, gender differences, and artificial intelligence (AI) in diagnosis. This review aims to determine and evaluate Indicators for detecting early cancer, assessing medical conditions, and evaluating potential risks. For Individuals with a heightened likelihood of developing cancer or who have already been diagnosed, early identification is crucial for enhancing prognosis and raising the likelihood of effective treatment.
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Affiliation(s)
- B Krishna Prasanth
- Department of Community Medicine, Sree Balaji Medical College and Hospital, Bharath Institute of Higher Education and Research, Chennai, IND
| | - Saad Alkhowaiter
- Department of Gastroenterology, College of Medicine, King Khalid University Hospital, Riyadh, SAU
| | - Gaurav Sawarkar
- Rachana Sharir, Mahatma Gandhi Ayurveda College, Hospital and Research Centre, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - B Divya Dharshini
- Department of Biochemistry, Government Medical College, Khammam, Telangana, IND
| | - Ajay R Baskaran
- Department of Psychiatry, National Health Service, Shrewsbury, GBR
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25
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Pham TD, Sun X. Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients. Cancer Med 2023; 12:21502-21518. [PMID: 38014709 PMCID: PMC10726782 DOI: 10.1002/cam4.6672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/25/2023] [Accepted: 10/20/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without. METHODS The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression. RESULTS The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve. CONCLUSION These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry Queen MaryUniversity of London Turner StreetLondonUK
| | - Xiao‐Feng Sun
- Division of Oncology Department of Biomedical and Clinical SciencesLinkoping UniversityLinkopingSweden
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26
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Al-Rajab M, Lu J, Xu Q, Kentour M, Sawsa A, Shuweikeh E, Joy M, Arasaradnam R. A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset. PLoS One 2023; 18:e0286791. [PMID: 37917732 PMCID: PMC10621932 DOI: 10.1371/journal.pone.0286791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/20/2023] [Indexed: 11/04/2023] Open
Abstract
Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.
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Affiliation(s)
- Murad Al-Rajab
- College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Joan Lu
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Qiang Xu
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Mohamed Kentour
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Ahlam Sawsa
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
- Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - Emad Shuweikeh
- School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom
| | - Mike Joy
- University of Warwick, Coventry, United Kingdom
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27
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Ding M, Yan J, Chao G, Zhang S. Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review). Oncol Rep 2023; 50:199. [PMID: 37772392 DOI: 10.3892/or.2023.8636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/07/2023] [Indexed: 09/30/2023] Open
Abstract
Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.
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Affiliation(s)
- Menglu Ding
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Junbin Yan
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Guanqun Chao
- Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, P.R. China
| | - Shuo Zhang
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
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28
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Lei M, Wu B, Zhang Z, Qin Y, Cao X, Cao Y, Liu B, Su X, Liu Y. A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study. J Med Internet Res 2023; 25:e47590. [PMID: 37870889 PMCID: PMC10628690 DOI: 10.2196/47590] [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: 03/25/2023] [Revised: 07/05/2023] [Accepted: 08/24/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. OBJECTIVE This study aimed to develop a machine learning-based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. METHODS This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches-logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine-were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. RESULTS In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was -0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. CONCLUSIONS This study develops a machine learning-based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.
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Affiliation(s)
- Mingxing Lei
- Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China
- Department of Orthopedics, Hainan Hospital of Chinese PLA General Hospital, Hainan, China
- Chinese PLA Medical School, Beijing, China
| | - Bing Wu
- Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China
- Department of Orthopedics, The First Medical Center of PLA General Hospital, Beijing, China
| | - Zhicheng Zhang
- Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Yong Qin
- Department of Joint and Sports Medicine Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuyong Cao
- Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Yuncen Cao
- Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Baoge Liu
- Department of Orthopedics, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiuyun Su
- Intelligent Medical Innovation institute, Southern University of Science and Technology Hospital, Shenzhen, China
| | - Yaosheng Liu
- Senior Department of Orthopedics, The Fourth Medical Center of PLA General Hospital, Beijing, China
- Department of Orthopedics, The Fifth Medical Center of PLA General Hospital, Beijing, China
- National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, PLA General Hospital, Beijing, China
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29
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El Naqa I, Karolak A, Luo Y, Folio L, Tarhini AA, Rollison D, Parodi K. Translation of AI into oncology clinical practice. Oncogene 2023; 42:3089-3097. [PMID: 37684407 DOI: 10.1038/s41388-023-02826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.
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Affiliation(s)
- Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Les Folio
- Diagnostic Imaging & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Ahmad A Tarhini
- Cutaneous Oncology and Immunology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Dana Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
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30
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Kuo CY, Kuo LJ, Lin YK. Artificial intelligence based system for predicting permanent stoma after sphincter saving operations. Sci Rep 2023; 13:16039. [PMID: 37749194 PMCID: PMC10519982 DOI: 10.1038/s41598-023-43211-w] [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: 05/08/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023] Open
Abstract
Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels. Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.
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Affiliation(s)
- Chih-Yu Kuo
- Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan
| | - Li-Jen Kuo
- Division of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan
| | - Yen-Kuang Lin
- Graduate Institute of Athletics and Coaching Science, National Taiwan Sport University, Taoyuan, Taiwan.
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Han W, Wang N, Han M, Liu X, Sun T, Xu J. Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis. Cancer Med 2023; 12:19301-19319. [PMID: 37676050 PMCID: PMC10557844 DOI: 10.1002/cam4.6503] [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: 11/20/2022] [Revised: 07/22/2023] [Accepted: 08/25/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier. METHODS In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions. RESULTS The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances. CONCLUSIONS GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC.
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Affiliation(s)
- Wenjie Han
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
| | - Na Wang
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
| | - Mengzhen Han
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
| | - Xiaolin Liu
- Liaoning Kanghui Biotechnology Co., LtdShenyangChina
| | - Tao Sun
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Key Laboratory of Liaoning Breast Cancer ResearchShenyangChina
- Department of Breast MedicineCancer Hospital of Dalian University of Technology, Liaoning Cancer HospitalShenyangChina
| | - Junnan Xu
- Department of Breast Medicine 1Cancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of PharmacologyCancer Hospital of China Medical University, Liaoning Cancer HospitalShenyangChina
- Department of Breast MedicineCancer Hospital of Dalian University of Technology, Liaoning Cancer HospitalShenyangChina
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Sharma A, Kumar R, Garg P. Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images. Int J Med Inform 2023; 177:105142. [PMID: 37422969 DOI: 10.1016/j.ijmedinf.2023.105142] [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: 05/26/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy. METHODS Various CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states-polyps, ulcerative colitis, and esophagitis-as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness. RESULTS The CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all. CONCLUSION The findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.
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Affiliation(s)
- Anju Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab 160062, India
| | - Rajnish Kumar
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab 160062, India.
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Zheng L, Liao P, Wu X, Cao M, Cui W, Lu L, Xu H, Zhu L, Lyu B, Wang X, Teng P, Wang J, Vogrin S, Plummer C, Luan G, Gao JH. An artificial intelligence-based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. J Neural Eng 2023; 20:046036. [PMID: 37615416 DOI: 10.1088/1741-2552/acef92] [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: 04/28/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
Objective.Magnetoencephalography (MEG) is a powerful non-invasive diagnostic modality for presurgical epilepsy evaluation. However, the clinical utility of MEG mapping for localising epileptic foci is limited by its low efficiency, high labour requirements, and considerable interoperator variability. To address these obstacles, we proposed a novel artificial intelligence-based automated magnetic source imaging (AMSI) pipeline for automated detection and localisation of epileptic sources from MEG data.Approach.To expedite the analysis of clinical MEG data from patients with epilepsy and reduce human bias, we developed an autolabelling method, a deep-learning model based on convolutional neural networks and a hierarchical clustering method based on a perceptual hash algorithm, to enable the coregistration of MEG and magnetic resonance imaging, the detection and clustering of epileptic activity, and the localisation of epileptic sources in a highly automated manner. We tested the capability of the AMSI pipeline by assessing MEG data from 48 epilepsy patients.Main results.The AMSI pipeline was able to rapidly detect interictal epileptiform discharges with 93.31% ± 3.87% precision based on a 35-patient dataset (with sevenfold patientwise cross-validation) and robustly rendered accurate localisation of epileptic activity with a lobar concordance of 87.18% against interictal and ictal stereo-electroencephalography findings in a 13-patient dataset. We also showed that the AMSI pipeline accomplishes the necessary processes and delivers objective results within a much shorter time frame (∼12 min) than traditional manual processes (∼4 h).Significance.The AMSI pipeline promises to facilitate increased utilisation of MEG data in the clinical analysis of patients with epilepsy.
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Affiliation(s)
- Li Zheng
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Pan Liao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
| | - Xiuwen Wu
- Changping Laboratory, Beijing, People's Republic of China
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Miao Cao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
| | - Wei Cui
- Center for Biomedical Engineering, University of Science and Technology of China, Anhui, People's Republic of China
| | - Lingxi Lu
- Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, People's Republic of China
| | - Hui Xu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Linlin Zhu
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Bingjiang Lyu
- Changping Laboratory, Beijing, People's Republic of China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Pengfei Teng
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jing Wang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Simon Vogrin
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Chris Plummer
- Department of Neuroimaging, Swinburne University of Technology, Melbourne, Australia
| | - Guoming Luan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, People's Republic of China
- Beijing Key Laboratory of Epilepsy, Capital Medical University, Beijing, People's Republic of China
| | - Jia-Hong Gao
- Beijing City Key Laboratory of Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
- Changping Laboratory, Beijing, People's Republic of China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China
- McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China
- National Biomedical Imaging Center, Peking University, Beijing, People's Republic of China
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Liu X, Shi J, Li Z, Huang Y, Zhang Z, Zhang C. The Present and Future of Artificial Intelligence in Urological Cancer. J Clin Med 2023; 12:4995. [PMID: 37568397 PMCID: PMC10419644 DOI: 10.3390/jcm12154995] [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: 05/05/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.
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Affiliation(s)
| | | | | | | | - Zhihong Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
| | - Changwen Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
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Wang R, Xiong K, Wang Z, Wu D, Hu B, Ruan J, Sun C, Ma D, Li L, Liao S. Immunodiagnosis - the promise of personalized immunotherapy. Front Immunol 2023; 14:1216901. [PMID: 37520576 PMCID: PMC10372420 DOI: 10.3389/fimmu.2023.1216901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/21/2023] [Indexed: 08/01/2023] Open
Abstract
Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. Here, we coined the term "immunodiagnosis" to describe the blueprint of the immunodiagnostic model. We illustrated the features that should be included in immunodiagnostic model and the strategy of constructing the immunodiagnostic model. Lastly, we discussed the incorporation of this immunodiagnosis model in clinical practice in hopes of improving the prognosis of tumor immunotherapy.
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Affiliation(s)
- Renjie Wang
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kairong Xiong
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhimin Wang
- Division of Endocrinology and Metabolic Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Di Wu
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bai Hu
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinghan Ruan
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chaoyang Sun
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ding Ma
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Li
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shujie Liao
- Department of Obstetrics and Gynecology, Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Akashi T, Okumura T, Terabayashi K, Yoshino Y, Tanaka H, Yamazaki T, Numata Y, Fukuda T, Manabe T, Baba H, Miwa T, Watanabe T, Hirano K, Igarashi T, Sekine S, Hashimoto I, Shibuya K, Hojo S, Yoshioka I, Matsui K, Yamada A, Sasaki T, Fujii T. The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer. Oncol Lett 2023; 26:320. [PMID: 37332339 PMCID: PMC10272959 DOI: 10.3892/ol.2023.13906] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/25/2023] [Indexed: 06/20/2023] Open
Abstract
Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.
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Affiliation(s)
- Takahisa Akashi
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Tomoyuki Okumura
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Kenji Terabayashi
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Yuki Yoshino
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Haruyoshi Tanaka
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takeyoshi Yamazaki
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Yoshihisa Numata
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takuma Fukuda
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takahiro Manabe
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Hayato Baba
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takeshi Miwa
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Toru Watanabe
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Katsuhisa Hirano
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Takamichi Igarashi
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Shinichi Sekine
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Isaya Hashimoto
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Kazuto Shibuya
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Shozo Hojo
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Isaku Yoshioka
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Koshi Matsui
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
| | - Akane Yamada
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Tohru Sasaki
- Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Tsutomu Fujii
- Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan
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Li D, Xu W, Chang Y, Xiao Y, He Y, Ren S. Advances in landscape and related therapeutic targets of the prostate tumor microenvironment. Acta Biochim Biophys Sin (Shanghai) 2023. [PMID: 37294106 DOI: 10.3724/abbs.2023092] [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: 06/10/2023] Open
Abstract
The distinct tumor microenvironment (TME) of prostate cancer (PCa), which promotes tumor proliferation and progression, consists of various stromal cells, immune cells, and a dense extracellular matrix (ECM). The understanding of the prostate TME extends to tertiary lymphoid structures (TLSs) and metastasis niches to provide a more concise comprehension of tumor metastasis. These constituents collectively structure the hallmarks of the pro-tumor TME, including immunosuppressive, acidic, and hypoxic niches, neuronal innervation, and metabolic rewiring. In combination with the knowledge of the tumor microenvironment and the advancement of emerging therapeutic technologies, several therapeutic strategies have been developed, and some of them have been tested in clinical trials. This review elaborates on PCa TME components, summarizes various TME-targeted therapies, and provides insights into PCa carcinogenesis, progression, and therapeutic strategies.
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Affiliation(s)
- Duocai Li
- Department of Urology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Weidong Xu
- Department of Urology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Yifan Chang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Yutian Xiao
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Yundong He
- Shanghai Key Laboratory of Regulatory Biology, School of Life Sciences, East China Normal University, Shanghai 200062, China
| | - Shancheng Ren
- Department of Urology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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Jiang C, Wang K, Yan L, Yao H, Shi H, Lin R. Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database. Cancer Med 2023; 12:12413-12424. [PMID: 37165971 PMCID: PMC10278508 DOI: 10.1002/cam4.5949] [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: 01/20/2023] [Revised: 03/18/2023] [Accepted: 04/02/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND The study aims to evaluate the performance of three advanced machine learning algorithms and a traditional Cox proportional hazard (CoxPH) model in predicting the overall survival (OS) of patients with pancreatic neuroendocrine neoplasms (PNENs). METHOD The clinicopathological dataset obtained from the Surveillance, Epidemiology, and End Results database was randomly assigned to the training set and testing set at a ratio of 7:3. The concordance index (C-index) and integrated Brier score (IBS) were used to compare the predictive performance of the models. The accuracy of the model in predicting the 5-year and 10-year survival rates was compared using the receiver operating characteristic curve, decision curve analysis (DCA) and calibration curve. RESULTS This study included 3239 patients with PNENs in total. The DeepSurv model had the highest C-index of 0.7882 in the testing set and training set and the lowest IBS of 0.1278 in the testing set compared with the CoxPH, neural multitask logistic and random survival forest models (C-index = 0.7501, 0.7616, and 0.7612, respectively; IBS = 0.1397, 0.1418, and 0.1432, respectively). Moreover, the DeepSurv model had the highest accuracy in predicting 5- and 10-year OS rates (area under the curve: 0.87 and 0.90). DCA showed that the DeepSurv model had high potential for clinical decisions in 5- and 10-year OS models. Finally, we developed an online application based on the DeepSurv model for clinical use (https://whuh-ml-neuroendocrinetumor-app-predict-oyw5km.streamlit.app/). CONCLUSIONS All four models analyzed above can predict the prognosis of PNENs well, among which the DeepSurv model has the best prediction performance.
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Affiliation(s)
- Chen Jiang
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Kan Wang
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Hailing Yao
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Huiying Shi
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Rong Lin
- Department of Gastroenterology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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Zhang G, Bao C, Liu Y, Wang Z, Du L, Zhang Y, Wang F, Xu B, Zhou SK, Liu R. 18F-FDG-PET/CT-based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery. EJNMMI Res 2023; 13:49. [PMID: 37231321 DOI: 10.1186/s13550-023-00985-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND The determination of pathological grading has a guiding significance for the treatment of pancreatic ductal adenocarcinoma (PDAC) patients. However, there is a lack of an accurate and safe method to obtain pathological grading before surgery. The aim of this study is to develop a deep learning (DL) model based on 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) for a fully automatic prediction of preoperative pathological grading of pancreatic cancer. METHODS A total of 370 PDAC patients from January 2016 to September 2021 were collected retrospectively. All patients underwent 18F-FDG-PET/CT examination before surgery and obtained pathological results after surgery. A DL model for pancreatic cancer lesion segmentation was first developed using 100 of these cases and applied to the remaining cases to obtain lesion regions. After that, all patients were divided into training set, validation set, and test set according to the ratio of 5:1:1. A predictive model of pancreatic cancer pathological grade was developed using the features computed from the lesion regions obtained by the lesion segmentation model and key clinical characteristics of the patients. Finally, the stability of the model was verified by sevenfold cross-validation. RESULTS The Dice score of the developed PET/CT-based tumor segmentation model for PDAC was 0.89. The area under curve (AUC) of the PET/CT-based DL model developed on the basis of the segmentation model was 0.74, with an accuracy, sensitivity, and specificity of 0.72, 0.73, and 0.72, respectively. After integrating key clinical data, the AUC of the model improved to 0.77, with its accuracy, sensitivity, and specificity boosted to 0.75, 0.77, and 0.73, respectively. CONCLUSION To the best of our knowledge, this is the first deep learning model to end-to-end predict the pathological grading of PDAC in a fully automatic manner, which is expected to improve clinical decision-making.
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Affiliation(s)
- Gong Zhang
- Medical School of Chinese PLA, Beijing, China
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Chengkai Bao
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Yanzhe Liu
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Zizheng Wang
- Senior Department of Hepatology, The Fifth Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Lei Du
- Department of Nuclear Medicine, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Yue Zhang
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Fei Wang
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Baixuan Xu
- Department of Nuclear Medicine, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
| | - S Kevin Zhou
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China.
| | - Rong Liu
- Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China.
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Gao Q, Yang L, Lu M, Jin R, Ye H, Ma T. The artificial intelligence and machine learning in lung cancer immunotherapy. J Hematol Oncol 2023; 16:55. [PMID: 37226190 DOI: 10.1186/s13045-023-01456-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/17/2023] [Indexed: 05/26/2023] Open
Abstract
Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.
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Affiliation(s)
- Qing Gao
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Luyu Yang
- Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, 101149, China
| | - Mingjun Lu
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Renjing Jin
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Huan Ye
- Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, 101149, China
| | - Teng Ma
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
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Li H, Zhang J, Loh YH, Billadeau DD. Editorial: Decoding immunologic complex systems in cancer: the cutting-edge technologies revolutionizing cancer immunology and immunotherapy. Front Immunol 2023; 14:1208589. [PMID: 37215101 PMCID: PMC10196616 DOI: 10.3389/fimmu.2023.1208589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Affiliation(s)
- Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, United States
| | - Jin Zhang
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences and the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Institute of Hematology, Zhejiang University, Hangzhou, China
- Zhejiang Laboratory for Systems and Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Yuin Han Loh
- Cell Fate Engineering and Therapeutics Lab, Cell Biology and Therapies Division, ASTAR Institute of Molecular and Cell Biology, Singapore, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- National University of Singapore (NUS) Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | - Daniel D. Billadeau
- Division of Oncology Research, College of Medicine, Mayo Clinic, Rochester, MN, United States
- Department of Immunology, College of Medicine, Mayo Clinic, Rochester, MN, United States
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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Tayebi Arasteh S, Isfort P, Saehn M, Mueller-Franzes G, Khader F, Kather JN, Kuhl C, Nebelung S, Truhn D. Collaborative training of medical artificial intelligence models with non-uniform labels. Sci Rep 2023; 13:6046. [PMID: 37055456 PMCID: PMC10102221 DOI: 10.1038/s41598-023-33303-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/11/2023] [Indexed: 04/15/2023] Open
Abstract
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an institution might provide a dataset of chest radiographs containing labels denoting the presence of pneumonia, while another institution might have a focus on determining the presence of metastases in the lung. Training a single AI model utilizing all these data is not feasible with conventional federated learning (FL). This prompts us to propose an extension to the widespread FL process, namely flexible federated learning (FFL) for collaborative training on such data. Using 695,000 chest radiographs from five institutions from across the globe-each with differing labels-we demonstrate that having heterogeneously labeled datasets, FFL-based training leads to significant performance increase compared to conventional FL training, where only the uniformly annotated images are utilized. We believe that our proposed algorithm could accelerate the process of bringing collaborative training methods from research and simulation phase to the real-world applications in healthcare.
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Affiliation(s)
- Soroosh Tayebi Arasteh
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Peter Isfort
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Marwin Saehn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Gustav Mueller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Firas Khader
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Medical Faculty Carl Gustav Carus, Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
- Division of 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 (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr. 30, 52074, Aachen, Germany.
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Lin-Rahardja K, Weaver DT, Scarborough JA, Scott JG. Evolution-Informed Strategies for Combating Drug Resistance in Cancer. Int J Mol Sci 2023; 24:6738. [PMID: 37047714 PMCID: PMC10095117 DOI: 10.3390/ijms24076738] [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: 02/27/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023] Open
Abstract
The ever-changing nature of cancer poses the most difficult challenge oncologists face today. Cancer's remarkable adaptability has inspired many to work toward understanding the evolutionary dynamics that underlie this disease in hopes of learning new ways to fight it. Eco-evolutionary dynamics of a tumor are not accounted for in most standard treatment regimens, but exploiting them would help us combat treatment-resistant effectively. Here, we outline several notable efforts to exploit these dynamics and circumvent drug resistance in cancer.
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Affiliation(s)
- Kristi Lin-Rahardja
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Davis T. Weaver
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jessica A. Scarborough
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob G. Scott
- Systems Biology & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Translational Hematology & Oncology, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44106, USA
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Zhu M, Guo M, Liu CQ, Songyang Z, Dou WX, Xiong Y. Deep-learning model AIBISI predicts bacterial infection across cancer types based on pathological images. Heliyon 2023; 9:e15400. [PMID: 37101624 PMCID: PMC10123254 DOI: 10.1016/j.heliyon.2023.e15400] [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: 01/10/2023] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 04/28/2023] Open
Abstract
Microorganisms play an important role in many physiological functions. Many studies have found that bacteria also regulate cancer susceptibility and tumor progression by affecting some metabolic or immune system signaling pathways. However, current bacterial detection methods are inaccurate or inefficient. Thus, we constructed a deep neural network (AIBISI) based on hematoxylin and eosin (H&E)-stained pathology slides to predict and visualize bacterial infection. Our model performance achieved as high as 0.81 of AUC (area under the ROC curve) within cancer type. We also built a pan-cancer model to predict bacterial infection across cancer types. To facilitate clinical usage, AIBISI visualized image areas affected by possible infection. Importantly, we successfully validated our model (AUC = 0.755) in pathological images from an independent patient cohort of stomach cancer (n = 32). To our best knowledge, this is the first artificial intelligence (AI)-based model to investigate bacterial infection in pathology images and has the potential to enable fast clinical decision related to pathogens in tumors.
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Affiliation(s)
- Miaosong Zhu
- School of Life Sciences, Sun Yat-sen University, 510006, Guangzhou, China
| | - Mengbiao Guo
- School of Life Sciences, Sun Yat-sen University, 510006, Guangzhou, China
| | - Chao-Qun Liu
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, China
| | - Zhou Songyang
- School of Life Sciences, Sun Yat-sen University, 510006, Guangzhou, China
| | - Wen-Xian Dou
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, China
| | - Yuanyan Xiong
- School of Life Sciences, Sun Yat-sen University, 510006, Guangzhou, China
- Corresponding author.
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
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Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. PLoS One 2023; 18:e0277572. [PMID: 36862751 PMCID: PMC9980829 DOI: 10.1371/journal.pone.0277572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/29/2022] [Indexed: 03/03/2023] Open
Abstract
In this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN could successfully regress major confounding factors in the vast clinical dataset. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in AD detection. By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019; 84.6% with MUCRAN vs. 72.5% without MUCRAN) and for data from other hospitals (90.3% from Brigham and Women's Hospital and 81.0% from other hospitals). MUCRAN offers a generalizable approach for deep-learning-based disease detection in heterogenous clinical data.
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Dai X, Chen Y. Computational Biomaterials: Computational Simulations for Biomedicine. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204798. [PMID: 35916024 DOI: 10.1002/adma.202204798] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/23/2022] [Indexed: 05/14/2023]
Abstract
With the flourishing development of material simulation methods (quantum chemistry methods, molecular dynamics, Monte Carlo, phase field, etc.), extensive adoption of computing technologies (high-throughput, artificial intelligence, machine learning, etc.), and the invention of high-performance computing equipment, computational simulation tools have sparked the fundamental mechanism-level explorations to predict the diverse physicochemical properties and biological effects of biomaterials and investigate their enormous application potential for disease prevention, diagnostics, and therapeutics. Herein, the term "computational biomaterials" is proposed and the computational methods currently used to explore the inherent properties of biomaterials, such as optical, magnetic, electronic, and acoustic properties, and the elucidation of corresponding biological behaviors/effects in the biomedical field are summarized/discussed. The theoretical calculation of the physiochemical properties/biological performance of biomaterials applied in disease diagnosis, drug delivery, disease therapeutics, and specific paradigms such as biomimetic biomaterials is discussed. Additionally, the biosafety evaluation applications of theoretical simulations of biomaterials are presented. Finally, the challenges and future prospects of such computational simulations for biomaterials development are clarified. It is anticipated that these simulations would offer various methodologies for facilitating the development and future clinical translations/utilization of versatile biomaterials.
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Affiliation(s)
- Xinyue Dai
- Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China
| | - Yu Chen
- Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, 200444, P. R. China
- School of Medicine, Shanghai University, Shanghai, 200444, P. R. China
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Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer. Heliyon 2023; 9:e13094. [PMID: 36785834 PMCID: PMC9918765 DOI: 10.1016/j.heliyon.2023.e13094] [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: 07/23/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
Neoadjuvant systemic treatment before surgery is a prevalent regimen in the patients with advanced-stage or high-risk tumor, which has shaped the treatment strategies and cancer survival in the past decades. However, some patients present with poor response to the neoadjuvant treatment. Therefore, it is of great significance to develop tools to help distinguish the patients that could achieve pathological complete response before surgery to avoid inappropriate treatment. Here, this study demonstrated a multi-task deep learning tool called DeepInteg. In the DeepInteg framework, the segmentation module was constructed based on the CE-Net with a context extractor to achieve end-to-end delineation of region of interest (ROI) from radiological images, then the features of segmented Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of each case were fused and input to the classification module based on a convolution neural network for treatment outcome prediction. The dataset with 1700 MRI and CT slices collected from the prospectively randomized clinical trial (NCT01211210) on systemic treatment for rectal cancer was used to develop and systematically optimize DeepInteg. As a result, DeepInteg achieved automatic segmentation of tumoral ROI with Dices of 0.766 and 0.719 and mIoUs of 0.788 and 0.756 in CT and MRI images, respectively. In addition, DeepInteg achieved AUC of 0.833, accuracy of 0.826 and specificity of 0.856 in the prediction for pathological complete response after treatment, which showed better performance compared with the model based on CT or MRI alone. This study provide a robust framework to develop disease-specific tools for automatic delineation of ROI and clinical outcome prediction. The well-trained DeepInteg could be readily applied in clinic to predict pathological complete response after neoadjuvant therapy in rectal cancer patients.
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