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Mela E, Tsapralis D, Papaconstantinou D, Sakarellos P, Vergadis C, Klontzas ME, Rouvelas I, Tzortzakakis A, Schizas D. Current Role of Artificial Intelligence in the Management of Esophageal Cancer. J Clin Med 2025; 14:1845. [PMID: 40142652 PMCID: PMC11943403 DOI: 10.3390/jcm14061845] [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: 01/21/2025] [Revised: 03/03/2025] [Accepted: 03/07/2025] [Indexed: 03/28/2025] Open
Abstract
Background/Objectives: Esophageal cancer (EC) represents a major global contributor to cancer-related mortality. The advent of artificial intelligence (AI), including machine learning, deep learning, and radiomics, holds promise for enhancing treatment decisions and predicting outcomes. The aim of this review is to present an overview of the current landscape and future perspectives of AI in the management of EC. Methods: A literature search was performed on MEDLINE using the following keywords: "Artificial Intelligence", "Esophageal cancer", "Barrett's esophagus", "Esophageal Adenocarcinoma", and "Esophageal Squamous cell carcinoma". All titles and abstracts were screened; the results included 41 studies. Results: Over the past five years, the number of studies focusing on the application of AI to the treatment and prognosis of EC has surged, leveraging increasingly larger datasets with external validation. The simultaneous incorporation in AI models of clinical factors and features from several imaging modalities displays improved predictive performance, which may enhance patient outcomes, based on direct personalized therapeutic options. However, clinicians and researchers must address existing limitations, conduct randomized controlled trials, and consider the ethical and legal aspects that arise to establish AI as a standard decision-support tool. Conclusions: AI applications may result in substantial advances in EC management, heralding a new era. Considering the complexity of EC as a clinical entity, the evolving potential of AI is anticipated to ameliorate patients' quality of life and survival rates.
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Affiliation(s)
- Evgenia Mela
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, 11527 Athens, Greece;
| | - Dimitrios Tsapralis
- Department of Surgery, General Hospital of Ierapetra, 72200 Ierapetra, Greece;
| | - Dimitrios Papaconstantinou
- Third Department of Surgery, National and Kapodistrian University of Athens, Attikon University Hospital, 12462 Athens, Greece;
| | - Panagiotis Sakarellos
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, 11527 Athens, Greece;
| | | | - Michail E. Klontzas
- Department for Clinical Science, Intervention and Technology (CLINTEC), Division of Radiology, Karolinska Institutet, 14152 Stockholm, Sweden; (M.E.K.); (A.T.)
- Department of Medical Imaging, University Hospital of Heraklion, 71500 Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), 71500 Heraklion, Greece
- Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 70013 Heraklion, Greece
| | - Ioannis Rouvelas
- Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Surgery and Oncology, Karolinska Institutet, 14152 Stockholm, Sweden;
- Department of Upper Abdominal Diseases, Karolinska University Hospital, Huddinge, 14152 Stockholm, Sweden
| | - Antonios Tzortzakakis
- Department for Clinical Science, Intervention and Technology (CLINTEC), Division of Radiology, Karolinska Institutet, 14152 Stockholm, Sweden; (M.E.K.); (A.T.)
- Medical Radiation Physics and Nuclear Medicine, Section for Nuclear Medicine, Karolinska University Hospital, Huddinge, 14152 Stockholm, Sweden
| | - Dimitrios Schizas
- First Department of Surgery, National and Kapodistrian University of Athens, Laikon General Hospital, 11527 Athens, Greece;
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Janoudi G, Uzun (Rada) M, Fell DB, Ray JG, Foster AM, Giffen R, Clifford T, Walker MC. Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review. PLOS DIGITAL HEALTH 2024; 3:e0000515. [PMID: 38776276 PMCID: PMC11111092 DOI: 10.1371/journal.pdig.0000515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/19/2024] [Indexed: 05/24/2024]
Abstract
Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.
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Affiliation(s)
- Ghayath Janoudi
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | | | - Deshayne B. Fell
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Joel G. Ray
- Departments of Medicine, Health Policy Management and Evaluation, and Obstetrics and Gynecology, St Michael’s Hospital, University of Toronto, Toronto, Canada
| | - Angel M. Foster
- Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
| | | | - Tammy Clifford
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Canadian Institute of Health Research, Government of Canada, Ottawa, Canada
| | - Mark C. Walker
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- International and Global Health Office, University of Ottawa, Ottawa, Canada
- Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada
- Department of Obstetrics, Gynecology & Newborn Care, The Ottawa Hospital, Ottawa, Canada
- BORN Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
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Lukomski L, Pisula J, Wirsik N, Damanakis A, Jung JO, Knipper K, Datta R, Schröder W, Gebauer F, Schmidt T, Quaas A, Bozek K, Bruns C, Popp F. Analyzing the Impact of Oncological Data at Different Time Points and Tumor Biomarkers on Artificial Intelligence Predictions for Five-Year Survival in Esophageal Cancer. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2024; 6:679-698. [DOI: 10.3390/make6010032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]
Abstract
AIM: In this study, we use Artificial Intelligence (AI), including Machine (ML) and Deep Learning (DL), to predict the long-term survival of resectable esophageal cancer (EC) patients in a high-volume surgical center. Our objective is to evaluate the predictive efficacy of AI methods for survival prognosis across different time points of oncological treatment. This involves comparing models trained with clinical data, integrating either Tumor, Node, Metastasis (TNM) classification or tumor biomarker analysis, for long-term survival predictions. METHODS: In this retrospective study, 1002 patients diagnosed with EC between 1996 and 2021 were analyzed. The original dataset comprised 55 pre- and postoperative patient characteristics and 55 immunohistochemically evaluated biomarkers following surgical intervention. To predict the five-year survival status, four AI methods (Random Forest RF, XG Boost XG, Artificial Neural Network ANN, TabNet TN) and Logistic Regression (LR) were employed. The models were trained using three predefined subsets of the training dataset as follows: (I) the baseline dataset (BL) consisting of pre-, intra-, and postoperative data, including the TNM but excluding tumor biomarkers, (II) clinical data accessible at the time of the initial diagnostic workup (primary staging dataset, PS), and (III) the PS dataset including tumor biomarkers from tissue microarrays (PS + biomarkers), excluding TNM status. We used permutation feature importance for feature selection to identify only important variables for AI-driven reduced datasets and subsequent model retraining. RESULTS: Model training on the BL dataset demonstrated similar predictive performances for all models (Accuracy, ACC: 0.73/0.74/0.76/0.75/0.73; AUC: 0.78/0.82/0.83/0.80/0.79 RF/XG/ANN/TN/LR, respectively). The predictive performance and generalizability declined when the models were trained with the PS dataset. Surprisingly, the inclusion of biomarkers in the PS dataset for model training led to improved predictions (PS dataset vs. PS dataset + biomarkers; ACC: 0.70 vs. 0.77/0.73 vs. 0.79/0.71 vs. 0.75/0.69 vs. 0.72/0.63 vs. 0.66; AUC: 0.77 vs. 0.83/0.80 vs. 0.85/0.76 vs. 0.86/0.70 vs. 0.76/0.70 vs. 0.69 RF/XG/ANN/TN/LR, respectively). The AI models outperformed LR when trained with the PS datasets. The important features shared after AI-driven feature selection in all models trained with the BL dataset included histopathological lymph node status (pN), histopathological tumor size (pT), clinical tumor size (cT), age at the time of surgery, and postoperative tracheostomy. Following training with the PS dataset with biomarkers, the important predictive features included patient age at the time of surgery, TP-53 gene mutation, Mesothelin expression, thymidine phosphorylase (TYMP) expression, NANOG homebox protein expression, and indoleamine 2,3-dioxygenase (IDO) expressed on tumor-infiltrating lymphocytes, as well as tumor-infiltrating Mast- and Natural killer cells. CONCLUSION: Different AI methods similarly predict the long-term survival status of patients with EC and outperform LR, the state-of-the-art classification model. Survival status can be predicted with similar predictive performance with patient data at an early stage of treatment when utilizing additional biomarker analysis. This suggests that individual survival predictions can be made early in cancer treatment by utilizing biomarkers, reducing the necessity for the pathological TNM status post-surgery. This study identifies important features for survival predictions that vary depending on the timing of oncological treatment.
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Affiliation(s)
- Leandra Lukomski
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Juan Pisula
- Data science of Bioimages Lab, Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, Robert-Koch-Straße 21, 50937 Cologne, Germany
| | - Naita Wirsik
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Alexander Damanakis
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Jin-On Jung
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Karl Knipper
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Rabi Datta
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Wolfgang Schröder
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Florian Gebauer
- Department of General, Visceral and Cancer Surgery, Helios University Hospital Wuppertal, University Witten/Herdecke, Heusnerstraße 40, 42283 Wuppertal, Germany
| | - Thomas Schmidt
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Alexander Quaas
- Institute of Pathology, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Katarzyna Bozek
- Data science of Bioimages Lab, Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, Robert-Koch-Straße 21, 50937 Cologne, Germany
| | - Christiane Bruns
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Felix Popp
- Department of General, Visceral and Cancer Surgery, Faculty of Medicine and University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
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Christou CD, Tsoulfas G. Challenges involved in the application of artificial intelligence in gastroenterology: The race is on! World J Gastroenterol 2023; 29:6168-6178. [PMID: 38186861 PMCID: PMC10768398 DOI: 10.3748/wjg.v29.i48.6168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 12/27/2023] Open
Abstract
Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
- Center for Research and Innovation in Solid Organ Transplantation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
- Center for Research and Innovation in Solid Organ Transplantation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Mohan A, Asghar Z, Abid R, Subedi R, Kumari K, Kumar S, Majumder K, Bhurgri AI, Tejwaney U, Kumar S. Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review. Ann Med Surg (Lond) 2023; 85:4920-4927. [PMID: 37811030 PMCID: PMC10553069 DOI: 10.1097/ms9.0000000000001175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/05/2023] [Indexed: 10/10/2023] Open
Abstract
Esophageal cancer is a major cause of cancer-related mortality worldwide, with significant regional disparities. Early detection of precursor lesions is essential to improve patient outcomes. Artificial intelligence (AI) techniques, including deep learning and machine learning, have proved to be of assistance to both gastroenterologists and pathologists in the diagnosis and characterization of upper gastrointestinal malignancies by correlating with the histopathology. The primary diagnostic method in gastroenterology is white light endoscopic evaluation, but conventional endoscopy is partially inefficient in detecting esophageal cancer. However, other endoscopic modalities, such as narrow-band imaging, endocytoscopy, and endomicroscopy, have shown improved visualization of mucosal structures and vasculature, which provides a set of baseline data to develop efficient AI-assisted predictive models for quick interpretation. The main challenges in managing esophageal cancer are identifying high-risk patients and the disease's poor prognosis. Thus, AI techniques can play a vital role in improving the early detection and diagnosis of precursor lesions, assisting gastroenterologists in performing targeted biopsies and real-time decisions of endoscopic mucosal resection or endoscopic submucosal dissection. Combining AI techniques and endoscopic modalities can enhance the diagnosis and management of esophageal cancer, improving patient outcomes and reducing cancer-related mortality rates. The aim of this review is to grasp a better understanding of the application of AI in the diagnosis, treatment, and prognosis of esophageal cancer and how computer-aided diagnosis and computer-aided detection can act as vital tools for clinicians in the long run.
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Affiliation(s)
| | | | - Rabia Abid
- Liaquat College of Medicine and Dentistry
| | - Rasish Subedi
- Universal College of Medical Sciences, Siddharthanagar, Nepal
| | | | | | | | - Aqsa I. Bhurgri
- Shaheed Muhtarma Benazir Bhutto Medical University, Larkana, Pakistan
| | | | - Sarwan Kumar
- Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh
- Wayne State University, Michigan, USA
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Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
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Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
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Mozooni Z, Golestani N, Bahadorizadeh L, Yarmohammadi R, Jabalameli M, Amiri BS. The role of interferon-gamma and its receptors in gastrointestinal cancers. Pathol Res Pract 2023; 248:154636. [PMID: 37390758 DOI: 10.1016/j.prp.2023.154636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/02/2023]
Abstract
Gastrointestinal malignancies are the most prevalent type of cancer around the world. Even though numerous studies have evaluated gastrointestinal malignancies, the actual underlying mechanism is still unknown. These tumors have a poor prognosis and are frequently discovered at an advanced stage. Globally, there is an increase in the incidence and mortality of gastrointestinal malignancies, including those of the stomach, esophagus, colon, liver, and pancreas. Growth factors and cytokines are signaling molecules that are part of the tumor microenvironment and play a significant role in the development and spread of malignancies. IFN-γ induce its effects by activation of intracellular molecular networks. The main pathway involved in IFN-γ signaling is the JAK/STAT pathway, which regulates the transcription of hundreds of genes and mediates various biological responses. IFN-γ receptor is composed of two IFN-γR1 chains and two IFN-γR2 chains. Binding to IFN-γ, causes the intracellular domains of IFN-γR2 to oligomerize and transphosphorylate with IFN-γR1 which activates downstream signaling components: JAK1 and JAK2. These activated JAKs phosphorylate the receptor, creating binding sites for STAT1. STAT1 is then phosphorylated by JAK, resulting in the formation of STAT1 homodimers (gamma activated factors or GAFs) that translocate to the nucleus and regulate gene expression. The balance between positive and negative regulation of this pathway is crucial for immune responses and tumorigenesis. In this paper, we evaluate the dynamic roles of IFN- γ and its receptors in gastrointestinal cancers and present evidence that inhibiting IFN- γ signaling may be an effective treatment strategy.
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Affiliation(s)
- Zahra Mozooni
- Institute of Immunology and Infectious Diseases, Antimicrobial Resistance Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Nafiseh Golestani
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Leyla Bahadorizadeh
- Institute of Immunology and Infectious Diseases, Antimicrobial Resistance Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Internal Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Reyhaneh Yarmohammadi
- Doctoral Student Carolina University Winston, Salem, NC, USA; Skin and Stem Cell Research Center Tehran University of Medical Sciences, Tehran, Iran
| | | | - Bahareh Shateri Amiri
- Department of Internal Medicine, School of Medicine Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran
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Sahu VK, Ranjan A, Paul MK, Nagar S, Devarajan S, Aich J, Basu S. AI Techniques and IoT Applications Transforming the Future of Healthcare. ADVANCES IN HEALTHCARE INFORMATION SYSTEMS AND ADMINISTRATION 2023:210-233. [DOI: 10.4018/978-1-6684-5422-0.ch014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
Abstract
The role of artificial intelligence (AI) has advanced from an analysis and prediction tool to extending human capabilities. Currently, AI is more of a reliable assistant fueled by human experience and need of the hour in the healthcare along with simplifying daily life. AI and Internet of Things (IoT) have opened new avenues in intelligent diagnostics, drug discovery, clinical decision support, enhancing physician-patient communication, transcribing medical documents, and remote treatment. With the advent of enhanced computational power, AI has revolutionized discovery of optimal and efficient healthcare solutions and has accelerated the development of smart solutions involving IoT-based technologies. Starting from telemedicine to predict possible health disorders, AI is gaining focus to facilitate and advance healthcare solutions in developed and underdeveloped countries. This chapter deals with the scope of AI in the present scenario to future developments as AI will soon surpass human and poses threat pertaining to misuse of cognitive sciences development.
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Affiliation(s)
- Vishal Kumar Sahu
- Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pune, India
| | - Amit Ranjan
- Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pune, India
| | - Manash K. Paul
- Division of Pulmonary and Critical Care Medicine, University of California, Los Angeles, USA
| | - Shuchi Nagar
- Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pune, India
| | - Shine Devarajan
- School of Biotechnology and Bioinformatics, D.Y. Patil University (Deemed), Navi Mumbai, India
| | - Jyotirmoi Aich
- School of Biotechnology and Bioinformatics, D.Y. Patil University (Deemed), Navi Mumbai, India
| | - Soumya Basu
- Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Dr. D.Y. Patil Vidyapeeth, Pune, India
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9
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Ke W, Crist RM, Clogston JD, Stern ST, Dobrovolskaia MA, Grodzinski P, Jensen MA. Trends and patterns in cancer nanotechnology research: A survey of NCI's caNanoLab and nanotechnology characterization laboratory. Adv Drug Deliv Rev 2022; 191:114591. [PMID: 36332724 PMCID: PMC9712232 DOI: 10.1016/j.addr.2022.114591] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022]
Abstract
Cancer nanotechnologies possess immense potential as therapeutic and diagnostic treatment modalities and have undergone significant and rapid advancement in recent years. With this emergence, the complexities of data standards in the field are on the rise. Data sharing and reanalysis is essential to more fully utilize this complex, interdisciplinary information to answer research questions, promote the technologies, optimize use of funding, and maximize the return on scientific investments. In order to support this, various data-sharing portals and repositories have been developed which not only provide searchable nanomaterial characterization data, but also provide access to standardized protocols for synthesis and characterization of nanomaterials as well as cutting-edge publications. The National Cancer Institute's (NCI) caNanoLab is a dedicated repository for all aspects pertaining to cancer-related nanotechnology data. The searchable database provides a unique opportunity for data mining and the use of artificial intelligence and machine learning, which aims to be an essential arm of future research studies, potentially speeding the design and optimization of next-generation therapies. It also provides an opportunity to track the latest trends and patterns in nanomedicine research. This manuscript provides the first look at such trends extracted from caNanoLab and compares these to similar metrics from the NCI's Nanotechnology Characterization Laboratory, a laboratory providing preclinical characterization of cancer nanotechnologies to researchers around the globe. Together, these analyses provide insight into the emerging interests of the research community and rise of promising nanoparticle technologies.
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Affiliation(s)
- Weina Ke
- Bioinformatics and Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Rachael M Crist
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Jeffrey D Clogston
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Stephan T Stern
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Marina A Dobrovolskaia
- Nanotechnology Characterization Laboratory, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States
| | - Piotr Grodzinski
- Nanodelivery Systems and Devices Branch, Cancer Imaging Program, National Cancer Institute, Rockville, MD, United States
| | - Mark A Jensen
- Bioinformatics and Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, United States.
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Gan PL, Huang S, Pan X, Xia HF, Lü MH, Zhou X, Tang XW. The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis. Medicine (Baltimore) 2022; 101:e31931. [PMID: 36451438 PMCID: PMC9704924 DOI: 10.1097/md.0000000000031931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022] Open
Abstract
Artificial intelligence (AI) has been used for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention and shown promising and stimulating results. This study aimed to determine the development trends and research hotspots of AI in digestive endoscopy by visualizing articles. Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection on April 25, 2022. VOSviewer and CiteSpace were used to assess and plot the research outputs. This analytical research was based on original articles and reviews. A total of 524 records of AI research in digestive endoscopy, published between 2005 and 2022, were retrieved. The number of articles has increased 27-fold from 2017 to 2021. Fifty-one countries and 994 institutions contributed to all publications. Asian countries had the highest number of publications. China, the USA, and Japan were consistently the leading driving forces and mainly contributed (26%, 21%, and 14.31%, respectively). With a solid academic reputation in this area, Japan has the highest number of citations per article. Tada Tomohiro published the most articles and received the most citations.. Gastrointestinal endoscopy published the largest number of publications, and 4 of the top 10 cited papers were published in this journal. "The Classification," "ulcerative colitis," "capsule endoscopy," "polyp detection," and "early gastric cancer" were the leading research hotspots. Our study provides systematic elaboration for researchers to better understand the development of AI in gastrointestinal endoscopy.
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Affiliation(s)
- Pei-Ling Gan
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Shu Huang
- Department of Gastroenterology, the People’s Hospital of Lianshui, Huaian, China
| | - Xiao Pan
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hui-Fang Xia
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Mu-Han Lü
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiao-Wei Tang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
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Al Yousef MZ, Yasky AF, Al Shammari R, Ferwana MS. Early prediction of diabetes by applying data mining techniques: A retrospective cohort study. Medicine (Baltimore) 2022; 101:e29588. [PMID: 35866773 PMCID: PMC9302319 DOI: 10.1097/md.0000000000029588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Saudi Arabia ranks 7th globally in terms of diabetes prevalence, and its prevalence is expected to reach 45.36% by 2030. The cost of diabetes is expected to increase to 27 billion Saudi riyals in cases where undiagnosed individuals are also documented. Prevention and early detection can effectively address these challenges. OBJECTIVE To improve healthcare services and assist in building predictive models to estimate the probability of diabetes in patients. METHODS A chart review, which was a retrospective cohort study, was conducted at the National Guard Health Affairs in Riyadh, Saudi Arabia. Data were collected from 5 hospitals using National Guard Health Affairs databases. We used 38 attributes of 21431 patients between 2015 and 2019. The following phases were performed: (1) data collection, (2) data preparation, (3) data mining and model building, and (4) model evaluation and validation. Subsequently, 6 algorithms were compared with and without the synthetic minority oversampling technique. RESULTS The highest performance was found in the Bayesian network, which had an area under the curve of 0.75 and 0.71. CONCLUSION Although the results were acceptable, they could be improved. In this context, missing data owing to technical issues played a major role in affecting the performance of our model. Nevertheless, the model could be used in prevention, health monitoring programs, and as an automated mass population screening tool without the need for extra costs compared to traditional methods.
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Affiliation(s)
- Mohammed Zeyad Al Yousef
- Family Medicine, King Abdulaziz Medical City, King Abdullah International Medical Research Center, Riyadh, Kingdom of Saudi Arabia
- *Correspondence: Mohammed Zeyad Al Yousef, Family Medicine, King Abdulaziz Medical City/King Abdullah International Medical Research Center, Ar Rimayah, Riyadh 14812, Kingdom of Saudi Arabia (e-mail: )
| | - Adel Fouad Yasky
- Family Medicine, King Abdulaziz Medical City, King Abdullah International Medical Research Center, Riyadh, Kingdom of Saudi Arabia
| | - Riyad Al Shammari
- Department of Health Informatics, College of Public Health and Health Informatics, King Saud bin Abdul-Aziz University for Health Sciences, Riyadh, Saudi Arabia
- Centre of Excellence in Health Informatics, Riyadh, Saudi Arabia
| | - Mazen S. Ferwana
- Family Medicine and Primary Healthcare Department, King Abdulaziz Medical City, Riyadh, Kingdom of Saudi Arabia
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Penrice DD, Rattan P, Simonetto DA. Artificial Intelligence and the Future of Gastroenterology and Hepatology. GASTRO HEP ADVANCES 2022; 1:581-595. [PMID: 39132066 PMCID: PMC11307848 DOI: 10.1016/j.gastha.2022.02.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/22/2022] [Indexed: 08/13/2024]
Abstract
The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.
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Affiliation(s)
- Daniel D. Penrice
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Puru Rattan
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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Gong X, Zheng B, Xu G, Chen H, Chen C. Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer. J Thorac Dis 2022; 13:6240-6251. [PMID: 34992804 PMCID: PMC8662490 DOI: 10.21037/jtd-21-1107] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/24/2021] [Indexed: 01/15/2023]
Abstract
Background Accurate prognostic estimation for esophageal cancer (EC) patients plays an important role in the process of clinical decision-making. The objective of this study was to develop an effective model to predict the 5-year survival status of EC patients using machine learning (ML) algorithms. Methods We retrieved the information of patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) Program, including 24 features. A total of 8 ML models were applied to the selected dataset to classify the EC patients in terms of 5-year survival status, including 3 newly developed gradient boosting models (GBM), XGBoost, CatBoost, and LightGBM, 2 commonly used tree-based models, gradient boosting decision trees (GBDT) and random forest (RF), and 3 other ML models, artificial neural networks (ANN), naive Bayes (NB), and support vector machines (SVM). A 5-fold cross-validation was used in model performance measurement. Results After excluding records with missing data, the final study population comprised 10,588 patients. Feature selection was conducted based on the χ2 test, however, the experiment results showed that the complete dataset provided better prediction of outcomes than the dataset with removal of non-significant features. Among the 8 models, XGBoost had the best performance [area under the receiver operating characteristic (ROC) curve (AUC): 0.852 for XGBoost, 0.849 for CatBoost, 0.850 for LightGBM, 0.846 for GBDT, 0.838 for RF, 0.844 for ANN, 0.833 for NB, and 0.789 for SVM]. The accuracy and logistic loss of XGBoost were 0.875 and 0.301, respectively, which were also the best performances. In the XGBoost model, the SHapley Additive exPlanations (SHAP) value was calculated and the result indicated that the four features: reason no cancer-directed surgery, Surg Prim Site, age, and stage group had the greatest impact on predicting the outcomes. Conclusions The XGBoost model and the complete dataset can be used to construct an accurate prognostic model for patients diagnosed with EC which may be applicable in clinical practice in the future.
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Affiliation(s)
- Xian Gong
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Bin Zheng
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Guobing Xu
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Hao Chen
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
| | - Chun Chen
- Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China
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14
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Almomani A, Hitawala A, Abureesh M, Qapaja T, Alshaikh D, Zmaili M, Saleh MA, Alkhayyat M. Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up. Artif Intell Gastroenterol 2021; 2:85-93. [DOI: 10.35712/aig.v2.i3.85] [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: 04/25/2021] [Revised: 05/18/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.
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Affiliation(s)
- Ashraf Almomani
- Department of Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, United States
| | - Asif Hitawala
- Department of Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, United States
| | - Mohammad Abureesh
- Department of Internal Medicine, Staten Island University Hospital, New York City, NY 10305, United States
| | - Thabet Qapaja
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
| | - Dana Alshaikh
- School of Medicine, Mutah University, Alkarak 61710, Jordan
| | - Mohammad Zmaili
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
| | - Mohannad Abou Saleh
- Department of Gastroenterology and Hepatology, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
| | - Motasem Alkhayyat
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
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16
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PD_BiBIM: Biclustering-based biomarker identification in ESCC microarray data. J Biosci 2021. [DOI: 10.1007/s12038-021-00171-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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17
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Giulietti M, Cecati M, Sabanovic B, Scirè A, Cimadamore A, Santoni M, Montironi R, Piva F. The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors. Diagnostics (Basel) 2021; 11:206. [PMID: 33573278 PMCID: PMC7912267 DOI: 10.3390/diagnostics11020206] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 02/07/2023] Open
Abstract
The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.
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Affiliation(s)
- Matteo Giulietti
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Monia Cecati
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Berina Sabanovic
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Andrea Scirè
- Department of Life and Environmental Sciences, Polytechnic University of Marche, 60126 Ancona, Italy;
| | - Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of Marche, United Hospitals, 60126 Ancona, Italy; (A.C.); (R.M.)
| | - Matteo Santoni
- Oncology Unit, Macerata Hospital, 62012 Macerata, Italy;
| | - Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of Marche, United Hospitals, 60126 Ancona, Italy; (A.C.); (R.M.)
| | - Francesco Piva
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
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Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World J Gastroenterol 2020; 26:7287-7298. [PMID: 33362384 PMCID: PMC7739161 DOI: 10.3748/wjg.v26.i46.7287] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/02/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.
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Affiliation(s)
- Gulshan Parasher
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Morgan Wong
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Manmeet Rawat
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
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Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020; 92:807-812. [PMID: 32565184 DOI: 10.1016/j.gie.2020.06.040] [Citation(s) in RCA: 289] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/11/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now that AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which are reviewed in further detail by several other articles in this issue of Gastrointestinal Endoscopy.
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Affiliation(s)
- Vivek Kaul
- Division of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, New York, USA
| | - Sarah Enslin
- Division of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, New York, USA
| | - Seth A Gross
- Division of Gastroenterology & Hepatology, NYU Langone Health System, New York, New York, USA
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20
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Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020; 26:5256-5271. [PMID: 32994686 PMCID: PMC7504247 DOI: 10.3748/wjg.v26.i35.5256] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/29/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett's esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert's performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.
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Affiliation(s)
- Yu-Hang Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lin-Jie Guo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xiang-Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med (Lausanne) 2020; 7:27. [PMID: 32118012 PMCID: PMC7012990 DOI: 10.3389/fmed.2020.00027] [Citation(s) in RCA: 226] [Impact Index Per Article: 45.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/17/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.
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Affiliation(s)
- Giovanni Briganti
- Medical Informatics, School of Medicine, Université Libre de Bruxelles, Brussels, Belgium
- Unit of Epidemiology, Biostatistics and Clinical Research, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
| | - Olivier Le Moine
- Medical Informatics, School of Medicine, Université Libre de Bruxelles, Brussels, Belgium
- Hopital Erasme, Université Libre de Bruxelles, Brussels, Belgium
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22
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Anirvan P, Meher D, Singh SP. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check. Euroasian J Hepatogastroenterol 2020; 10:92-97. [PMID: 33511071 PMCID: PMC7801886 DOI: 10.5005/jp-journals-10018-1322] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is being increasingly explored in different domains of gastroenterology, particularly in endoscopic image analysis, cancer screening, and prognostication models. It is widely touted to become an integral part of routine endoscopies, considering the bulk of data handled by endoscopists and the complex nature of critical analyses performed. However, the application of AI in endoscopy in resource-constrained settings remains fraught with problems. We conducted an extensive literature review using the PubMed database on articles covering the application of AI in endoscopy and the difficulties encountered in resource-constrained settings. We have tried to summarize in the present review the potential problems that may hinder the application of AI in such settings. Hopefully, this review will enable endoscopists and health policymakers to ponder over these issues before trying to extrapolate the advancements of AI in technically advanced settings to those having constraints at multiple levels. How to cite this article: Anirvan P, Meher D, Singh SP. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check. Euroasian J Hepato-Gastroenterol 2020;10(2): 92–97.
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Affiliation(s)
- Prajna Anirvan
- Department of Gastroenterology, SCB Medical College, Cuttack, Odisha, India
| | - Dinesh Meher
- Department of Gastroenterology, SCB Medical College, Cuttack, Odisha, India
| | - Shivaram P Singh
- Department of Gastroenterology, SCB Medical College, Cuttack, Odisha, India
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Wang H, Li G. Extreme learning machine Cox model for high-dimensional survival analysis. Stat Med 2019; 38:2139-2156. [PMID: 30632193 PMCID: PMC6498851 DOI: 10.1002/sim.8090] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 10/11/2018] [Accepted: 12/12/2018] [Indexed: 11/07/2022]
Abstract
Some interesting recent studies have shown that neural network models are useful alternatives in modeling survival data when the assumptions of a classical parametric or semiparametric survival model such as the Cox (1972) model are seriously violated. However, to the best of our knowledge, the plausibility of adapting the emerging extreme learning machine (ELM) algorithm for single-hidden-layer feedforward neural networks to survival analysis has not been explored. In this paper, we present a kernel ELM Cox model regularized by an L0 -based broken adaptive ridge (BAR) penalization method. Then, we demonstrate that the resulting method, referred to as ELMCoxBAR, can outperform some other state-of-art survival prediction methods such as L1 - or L2 -regularized Cox regression, random survival forest with various splitting rules, and boosted Cox model, in terms of its predictive performance using both simulated and real world datasets. In addition to its good predictive performance, we illustrate that the proposed method has a key computational advantage over the above competing methods in terms of computation time efficiency using an a real-world ultra-high-dimensional survival data.
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Affiliation(s)
- Hong Wang
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Gang Li
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, California
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Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25:1666-1683. [PMID: 31011253 PMCID: PMC6465941 DOI: 10.3748/wjg.v25.i14.1666] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 03/04/2019] [Accepted: 03/16/2019] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
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Affiliation(s)
- Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
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Shew M, New J, Wichova H, Koestler DC, Staecker H. Using Machine Learning to Predict Sensorineural Hearing Loss Based on Perilymph Micro RNA Expression Profile. Sci Rep 2019; 9:3393. [PMID: 30833669 PMCID: PMC6399453 DOI: 10.1038/s41598-019-40192-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 02/07/2019] [Indexed: 02/06/2023] Open
Abstract
Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacological or molecular treatments challenging. A key to improving the diagnosis of inner ear disorders is the development of reliable biomarkers for different inner ear diseases. Analysis of microRNAs (miRNA) in tissue and body fluid samples has gained significant momentum as a diagnostic tool for a wide variety of diseases. In previous work, we have shown that miRNA profiling in inner ear perilymph is feasible and may demonstrate distinctive miRNA expression profiles unique to different diseases. A first step in developing miRNAs as biomarkers for inner ear disease is linking patterns of miRNA expression in perilymph to clinically available metrics. Using machine learning (ML), we demonstrate we can build disease specific algorithms that predict the presence of sensorineural hearing loss using only miRNA expression profiles. This methodology not only affords the opportunity to understand what is occurring on a molecular level, but may offer an approach to diagnosing patients with active inner ear disease.
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Affiliation(s)
- Matthew Shew
- University of Kansas School of Medicine, Department of Otolaryngology-Head and Neck Surgery, Kansas City, KS, USA.
| | - Jacob New
- University of Kansas School of Medicine, Kansas City, KS, USA
| | - Helena Wichova
- University of Kansas School of Medicine, Department of Otolaryngology-Head and Neck Surgery, Kansas City, KS, USA
| | - Devin C Koestler
- University of Kansas School of Medicine, Department of Biostatistics, Kansas City, KS, USA
| | - Hinrich Staecker
- University of Kansas School of Medicine, Department of Otolaryngology-Head and Neck Surgery, Kansas City, KS, USA
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Seligman B, Tuljapurkar S, Rehkopf D. Machine learning approaches to the social determinants of health in the health and retirement study. SSM Popul Health 2018; 4:95-99. [PMID: 29349278 PMCID: PMC5769116 DOI: 10.1016/j.ssmph.2017.11.008] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Revised: 11/15/2017] [Accepted: 11/16/2017] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. METHODS A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. RESULTS All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data (R2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. DISCUSSION Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.
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Affiliation(s)
- Benjamin Seligman
- Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | | | - David Rehkopf
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA
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Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes. Int J Genomics 2018; 2018:8124950. [PMID: 29546047 PMCID: PMC5818887 DOI: 10.1155/2018/8124950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 11/06/2017] [Accepted: 11/14/2017] [Indexed: 11/17/2022] Open
Abstract
In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.
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Obrzut B, Kusy M, Semczuk A, Obrzut M, Kluska J. Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods. BMC Cancer 2017; 17:840. [PMID: 29233120 PMCID: PMC5727988 DOI: 10.1186/s12885-017-3806-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 11/21/2017] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5-year overall survival prediction in patients with cervical cancer treated by radical hysterectomy. METHODS The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model. RESULTS The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse. CONCLUSIONS The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy.
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Affiliation(s)
- Bogdan Obrzut
- Department of Gynaecology and Obstetrics, Faculty of Medicine, University of Rzeszow, Lwowska 60, Rzeszow, 35-301 Poland
| | - Maciej Kusy
- Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959 Poland
| | - Andrzej Semczuk
- IIND Department of Gynecology, Lublin Medical University, al. Raclawickie 1, Lublin, 20-059 Poland
| | - Marzanna Obrzut
- Faculty of Medicine, University of Rzeszow, al. Kopisto 2a, Rzeszow, 35-959 Poland
| | - Jacek Kluska
- Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, Rzeszow, 35-959 Poland
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Abstract
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.
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Affiliation(s)
- Joseph A. Cruz
- Departments of Biological Science and Computing Science, University of Alberta Edmonton, AB, Canada T6G 2E8
| | - David S. Wishart
- Departments of Biological Science and Computing Science, University of Alberta Edmonton, AB, Canada T6G 2E8
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Complementarity of Clinician Judgment and Evidence Based Models in Medical Decision Making: Antecedents, Prospects, and Challenges. BIOMED RESEARCH INTERNATIONAL 2016; 2016:1425693. [PMID: 27642588 PMCID: PMC5013221 DOI: 10.1155/2016/1425693] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 07/25/2016] [Indexed: 01/02/2023]
Abstract
Early accounts of the development of modern medicine suggest that the clinical skills, scientific competence, and doctors' judgment were the main impetus for treatment decision, diagnosis, prognosis, therapy assessment, and medical progress. Yet, clinician judgment has its own critics and is sometimes harshly described as notoriously fallacious and an irrational and unfathomable black box with little transparency. With the rise of contemporary medical research, the reputation of clinician judgment has undergone significant reformation in the last century as its fallacious aspects are increasingly emphasized relative to the evidence based options. Within the last decade, however, medical forecasting literature has seen tremendous change and new understanding is emerging on best ways of sharing medical information to complement the evidence based medicine practices. This review revisits and highlights the core debate on clinical judgments and its interrelations with evidence based medicine. It outlines the key empirical results of clinician judgments relative to evidence based models and identifies its key strengths and prospects, the key limitations and conditions for the effective use of clinician judgment, and the extent to which it can be optimized and professionalized for medical use.
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Zhang Y, Guo SL, Han LN, Li TL. Application and Exploration of Big Data Mining in Clinical Medicine. Chin Med J (Engl) 2016; 129:731-8. [PMID: 26960378 PMCID: PMC4804421 DOI: 10.4103/0366-6999.178019] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE To review theories and technologies of big data mining and their application in clinical medicine. DATA SOURCES Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. STUDY SELECTION Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. RESULTS This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. CONCLUSION Big data mining has the potential to play an important role in clinical medicine.
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Affiliation(s)
- Yue Zhang
- Department of Cardiovascular Internal Medicine, Nanlou Branch of Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Shu-Li Guo
- State Key Laboratory of Intelligent Control and Decision, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Li-Na Han
- Department of Cardiovascular Internal Medicine, Nanlou Branch of Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Tie-Ling Li
- Department of Cadre Physiotherapy, Chinese People's Liberation Army General Hospital, Beijing 100853, China
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Sapra R, Mehrotra S, Nundy S. Artificial Neural Networks: Prediction of mortality/survival in gastroenterology. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.cmrp.2015.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT. Bone Marrow Transplant 2013; 49:332-7. [DOI: 10.1038/bmt.2013.146] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2013] [Revised: 07/31/2013] [Accepted: 08/03/2013] [Indexed: 01/18/2023]
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Yang HX, Feng W, Wei JC, Zeng TS, Li ZD, Zhang LJ, Lin P, Luo RZ, He JH, Fu JH. Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma. Br J Cancer 2013; 109:1109-16. [PMID: 23942069 PMCID: PMC3778272 DOI: 10.1038/bjc.2013.379] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 04/25/2013] [Accepted: 06/24/2013] [Indexed: 12/18/2022] Open
Abstract
Background: We aim to develop effective models for predicting postoperative distant metastasis for oesophageal squamous cell carcinoma (OSCC) for the purpose of guiding tailored therapy. Methods: We used data from two centres to establish training (n=319) and validation (n=164) cohorts. All patients underwent curative surgical treatment. The clinicopathological features and 23 immunomarkers detected by immunohistochemistry were involved for variable selection. We constructed eight support vector machine (SVM)-based nomograms (SVM1–SVM4 and SVM1'–SVM4'). The nomogram constructed with the training cohort was tested further with the validation cohort. Results: The outcome of the SVM1 model in predicting postoperative distant metastasis was as follows: sensitivity, 44.7% specificity, 90.9% positive predictive value, 81.0% negative predictive value, 65.6% and overall accuracy, 69.5%. The corresponding outcome of the SVM2 model was as follows: 44.7%, 92.1%, 82.9%, 65.9%, and 70.1%, respectively. The corresponding outcome of the SVM3 model was as follows: 55.3%, 93.2%, 87.5%, 70.7%, and 75.6%, respectively. The SVM4 model was the most effective nomogram in prediction, and the corresponding outcome was as follows: 56.6%, 97.7%, 95.6%, 72.3%, and 78.7%, respectively.Similar results were observed in SVM1', SVM2', SVM3', and SVM4', respectively. Conclusion: The SVM-based models integrating clinicopathological features and molecular markers as variables are helpful in selecting the patients of OSCC with high risk of postoperative distant metastasis.
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Affiliation(s)
- H X Yang
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou City, Guangdong Province 510060, China
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Wang C, Li L, Wang L, Ping Z, Flory MT, Wang G, Xi Y, Li W. Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach. Diabetes Res Clin Pract 2013; 100:111-8. [PMID: 23453177 DOI: 10.1016/j.diabres.2013.01.023] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Revised: 10/15/2012] [Accepted: 01/23/2013] [Indexed: 01/22/2023]
Abstract
AIM To develop and evaluate an effective classification approach without biochemical parameters to identify those at high risk of T2DM in rural adults. METHODS A cross-sectional survey was conducted. Of 8640 subjects who met inclusion criteria, 75% (N1=6480) were randomly selected to provide training set for constructing artificial neural network (ANN) and multivariate logistic regression (MLR) models. The remaining 25% (N2=2160) were assigned to validation set for performance comparisons of the ANN and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the validation set. RESULTS The prevalence rates of T2DM were 8.66% (n=561) and 9.21% (n=199) in training and validation sets, respectively. For ANN model, the sensitivity, specificity, positive and negative predictive value for identifying T2DM were 86.93%, 79.14%, 31.86%, and 98.18%, respectively, while MLR model were only 60.80%, 75.48%, 21.78%, and 94.52%, respectively. Area under the ROC curve (AUC) value for identifying T2DM when using the ANN model was 0.891, showing more accurate predictive performance than the MLR model (AUC=0.744) (P=0.0001). CONCLUSION The ANN model is an effective classification approach for identifying those at high risk of T2DM based on demographic, lifestyle and anthropometric data.
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Affiliation(s)
- Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
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Ansari D, Nilsson J, Andersson R, Regnér S, Tingstedt B, Andersson B. Artificial neural networks predict survival from pancreatic cancer after radical surgery. Am J Surg 2013; 205:1-7. [PMID: 23245432 DOI: 10.1016/j.amjsurg.2012.05.032] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2011] [Revised: 03/19/2012] [Accepted: 05/10/2012] [Indexed: 12/11/2022]
Abstract
BACKGROUND Artificial neural networks (ANNs) are nonlinear pattern recognition techniques that can be used as a tool in medical decision making. The objective of this study was to develop an ANN model for predicting survival in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS A flexible nonlinear survival model based on ANNs was designed by using clinical and histopathological data from 84 patients who underwent resection for PDAC. RESULTS Seven of 33 potential risk variables were selected to construct the ANN, including lymph node metastasis, differentiation, body mass index, age, resection margin status, peritumoral inflammation, and American Society of Anesthesiologists grade. Three variables (ie, lymph node metastasis, leukocyte count, and tumor location) were significant according to Cox regression analysis. Harrell's concordance index for the ANN model was .79, and for Cox regression it was .67. CONCLUSIONS For the first time, ANNs have been used to successfully predict individual long-term survival for patients after radical surgery for PDAC.
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Affiliation(s)
- Daniel Ansari
- Department of Surgery, Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
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Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents. PLoS One 2012; 7:e43834. [PMID: 22952780 PMCID: PMC3429495 DOI: 10.1371/journal.pone.0043834] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2012] [Accepted: 07/30/2012] [Indexed: 11/29/2022] Open
Abstract
Background Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population. Methods Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35–78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve. Results Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001). Conclusion The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.
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Ng T, Chew L, Yap CW. A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy. J Palliat Med 2012; 15:863-9. [PMID: 22690950 DOI: 10.1089/jpm.2011.0417] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Palliative chemotherapy is often administered to terminally ill cancer patients to relieve symptoms. Yet, unnecessary use of chemotherapy can worsen patients' quality of life due to treatment-related toxicities. Thus, accurate prediction of survival in terminally ill patients can help clinicians decide on the most appropriate palliative care for these patients. However, studies have shown that clinicians often make imprecise predictions of survival in cancer patients. Hence, the purpose of this study was to create a clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy. MATERIALS AND METHODS Data were obtained from a retrospective study of 400 randomly selected terminally ill cancer patients in the National Cancer Centre Singapore (NCCS) from 2008 to 2009. After removing patients with missing data, there were 325 patients remaining for model development. Three classification algorithms, naive Bayes (NB), neural network (NN), and support vector machine (SVM) were used to create the models. A final model with the best prediction performance was then selected to develop the tool. RESULTS The NN model had the best prediction performance. The accuracy, specificity, sensitivity, and area under the curve (AUC) of this model were 78%, 82%, 74%, and 0.857, respectively. Five patient attributes (albumin level, alanine transaminase level (ATL), absolute neutrophil count, Eastern Cooperative Oncology Group (ECOG) status, and number of metastatic sites) were included in the model. CONCLUSIONS A decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy was created. With further validation, this tool coupled with the professional judgment of clinicians can help improve patient care.
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Affiliation(s)
- Terence Ng
- Department of Pharmacy, National University of Singapore, Singapore
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Akagami M, Kawada K, Kubo H, Kawada M, Takahashi M, Kaganoi J, Kato S, Itami A, Shimada Y, Watanabe G, Sakai Y. Transcriptional factor Prox1 plays an essential role in the antiproliferative action of interferon-γ in esophageal cancer cells. Ann Surg Oncol 2011; 18:3868-77. [PMID: 21452064 DOI: 10.1245/s10434-011-1683-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2010] [Indexed: 02/03/2023]
Abstract
BACKGROUND We previously reported interferon-γ (IFN-γ)-induced apoptosis in 10 (32%) of 31 esophageal squamous cell carcinoma (ESCC) cell lines. However, the molecular basis of antiproliferative action by IFN-γ remains elusive. Here we demonstrate that IFN-γ induces transcriptional factor Prox1, and we explore the link between Prox1 and the IFN-γ system in ESCC cells. METHODS By using ESCC cell lines, we investigated the relationship between p53 mutations and the responsibility to IFN-γ, and studied the role of Prox1 in the antiproliferative effect of IFN-γ by knockdown and overexpression methods. RESULTS p53 mutations were found in seven of nine ESCC cell lines responsible for IFN-γ. The frequency was not different from that of p53 mutations in total ESCC cell lines (21 of 28 cell lines). Treatment of ESCC cells with IFN-β but not IFN-γ resulted in increase of p53 messenger RNA (mRNA) expression, whereas IFN-γ but not IFN-β induced cell growth inhibition of ESCCs harboring p53 mutations. IFN-γ induced Prox1 expression in ESCC cells but not in those transfected with dominant-negative STAT1. Cell growth inhibition by IFN-γ was significantly suppressed in ESCC cells transfected with Prox1 short interfering RNA (siRNA). In addition, overexpression of Prox1 induced antiproliferative effect in ESCC cells. We also demonstrate that Prox1 is expressed in primary esophageal cancer tissues (five of nine samples treated with neoadjuvant chemotherapy before surgery). CONCLUSIONS Prox1 mediates the antiproliferative effect by IFN-γ in ESCC cells. Prox1 may be a candidate target for novel therapeutic strategies of ESCCs.
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Affiliation(s)
- Masatoshi Akagami
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Turner SD, Dudek SM, Ritchie MD. ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData Min 2010; 3:5. [PMID: 20875103 PMCID: PMC2955681 DOI: 10.1186/1756-0381-3-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2010] [Accepted: 09/27/2010] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. METHODS Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. RESULTS We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. CONCLUSIONS We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.
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Affiliation(s)
- Stephen D Turner
- Center for Human Genetics Research, Departments of Molecular Physiology & Biophysics and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Scott M Dudek
- Center for Human Genetics Research, Departments of Molecular Physiology & Biophysics and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Marylyn D Ritchie
- Center for Human Genetics Research, Departments of Molecular Physiology & Biophysics and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
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Moyes LH, Anderson JE, Forshaw MJ. Proposed follow up programme after curative resection for lower third oesophageal cancer. World J Surg Oncol 2010; 8:75. [PMID: 20815912 PMCID: PMC2940774 DOI: 10.1186/1477-7819-8-75] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2010] [Accepted: 09/04/2010] [Indexed: 12/30/2022] Open
Abstract
The incidence of oesophageal adenocarcinoma has risen throughout the Western world over the last three decades. The prognosis remains poor as many patients are elderly and present with advanced disease. Those patients who are suitable for resection remain at high risk of disease recurrence. It is important that cancer patients take part in a follow up protocol to detect disease recurrence, offer psychological support, manage nutritional disorders and facilitate audit of surgical outcomes. Despite the recognition that regular postoperative follow up plays a key role in ongoing care of cancer patients, there is little consensus on the nature of the process. This paper reviews the published literature to determine the optimal timing and type of patient follow up for those after curative oesophageal resection.
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Affiliation(s)
- L H Moyes
- Oesophagogastric Unit University Department of Surgery Glasgow Royal Infirmary 84 Castle Street Glasgow G4 0SF, UK.
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42
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Huang S, Xu Y, Yue L, Wei S, Liu L, Gan X, Zhou S, Nie S. Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area. Hypertens Res 2010; 33:722-6. [PMID: 20505678 DOI: 10.1038/hr.2010.73] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Hypertension (HTN) has been proven to be associated with an increased risk of cardiovascular diseases. The purpose of the study was to examine risk factors for HTN and to develop a prediction model to estimate HTN risk for rural residents over the age of 35 years. This study was based on a cross-sectional survey of 3054 rural community residents (N=3054). Participants were divided into two groups: a training set (N1=2438) and a validation set (N2=616). The differences between the training set and validation set were not statistically significant. The predictors of HTN risk were identified from the training set using logistic regression analysis. Some risk factors were significantly associated with HTN, such as a high educational level (EL) (odds ratio (OR)=0.744), a predominantly sedentary job (OR=1.090), a positive family history of HTN (OR=1.614), being overweight (OR=1.525), dysarteriotony (OR=1.101), alcohol intake (OR=0.760), a salty diet (OR=1.146), more vegetable and fruit intake (OR=0.882), meat consumption (OR=0.787) and regular physical exercise (OR=0.866). We established the predictive models using logistic regression model (LRM) and artificial neural network (ANN). The accuracy of the models was compared by receiver operating characteristic (ROC) when the models were applied to the validation set. The ANN model (area under the curve (AUC)=0.900+/-0.014) proved better than the LRM (AUC=0.732+/-0.026) in terms of evaluating the HTN risk because it had a larger area under the ROC curve.
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Affiliation(s)
- Shuqiong Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
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Pandey B, Mishra R. Knowledge and intelligent computing system in medicine. Comput Biol Med 2009; 39:215-30. [PMID: 19201398 DOI: 10.1016/j.compbiomed.2008.12.008] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2008] [Revised: 11/24/2008] [Accepted: 12/17/2008] [Indexed: 01/04/2023]
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Cho YK. Reply to Comparison of 7 Staging Systems for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization. Cancer 2008. [DOI: 10.1002/cncr.23508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Amiri Z, Mohammad K, Mahmoudi M, Zeraati H, Fotouhi A. Assessment of gastric cancer survival: using an artificial hierarchical neural network. Pak J Biol Sci 2008; 11:1076-1084. [PMID: 18819544 DOI: 10.3923/pjbs.2008.1076.1084] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
This study is designed to assess the application of neural networks in comparison to the Kaplan-Meier and Cox proportional hazards model in the survival analysis. Three hundred thirty gastric cancer patients admitted to and surgically treated were assessed and their post-surgical survival was determined. The observed baseline survival was determined with the three methods of Kaplan-Meier product limit estimator, Cox and the neural network and results were compared. Then the binary independent variables were entered into the model. Data were randomly divided into two groups of 165 each to test the models and assess the reproducibility. The Chi-square test and the multiple logistic model were used to ensure the groups were similar and the data was divided randomly. To compare subgroups, we used the log-rank test. In the next step, the probability of survival in different periods was computed based on the training group data using the Cox proportional hazards and a neural network and estimating Cox coefficient values and neural network weights (with 3 nodes in hidden layer). Results were used for predictions in the test group data and these predictions were compared using the Kaplan-Meier product limit estimator as the gold standard. Friedman and Kruskal-Wallis tests were used for comparisons as well. All statistical analyses were performed using SPSS version 11.5, Matlab version 7.2, Statistica version 6.0 and S_PLUS 2000. The significance level was considered 5% (alpha = 0.05). The three methods used showed no significance difference in base survival probabilities. Overall, there was no significant difference among the survival probabilities or the trend of changes in survival probabilities calculated with the three methods, but the 4 year (48th month) and 4.5 year (54th month) survival rates were significantly different with Cox compared to standard and estimated probabilities in the neural network (p < 0.05). Kaplan-Meier and Cox showed almost similar results for the baseline survival probabilities, but results with the neural network were different: higher probabilities up to the 4th year, then comparable with the other two methods. Estimates from Cox proportional hazards and the neural network with three nodes in hidden layer were compared with the estimate from the Kaplan-Meier estimator as the gold standard. Neither comparison showed statistically significant differences. The standard error ratio of the two estimate groups by Cox and the neural network to Kaplan-Meier were no significant differences, it indicated that the neural network was more accurate. Although we do not suggest neural network methods to estimate the baseline survival probability, it seems these models is more accurately estimated as compared with the Cox proportional hazards, especially with today's advanced computer sciences that allow complex calculations. These methods are preferable because they lack the limitations of conventional models and obviate the need for unnecessary assumptions including those related to the proportionality of hazards and linearity.
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Affiliation(s)
- Zohreh Amiri
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Sato F, Jin Z, Schulmann K, Wang J, Greenwald BD, Ito T, Kan T, Hamilton JP, Yang J, Paun B, David S, Olaru A, Cheng Y, Mori Y, Abraham JM, Yfantis HG, Wu TT, Fredericksen MB, Wang KK, Canto M, Romero Y, Feng Z, Meltzer SJ. Three-tiered risk stratification model to predict progression in Barrett's esophagus using epigenetic and clinical features. PLoS One 2008; 3:e1890. [PMID: 18382671 PMCID: PMC2270339 DOI: 10.1371/journal.pone.0001890] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2007] [Accepted: 02/20/2008] [Indexed: 11/29/2022] Open
Abstract
Background Barrett's esophagus predisposes to esophageal adenocarcinoma. However, the value of endoscopic surveillance in Barrett's esophagus has been debated because of the low incidence of esophageal adenocarcinoma in Barrett's esophagus. Moreover, high inter-observer and sampling-dependent variation in the histologic staging of dysplasia make clinical risk assessment problematic. In this study, we developed a 3-tiered risk stratification strategy, based on systematically selected epigenetic and clinical parameters, to improve Barrett's esophagus surveillance efficiency. Methods and Findings We defined high-grade dysplasia as endpoint of progression, and Barrett's esophagus progressor patients as Barrett's esophagus patients with either no dysplasia or low-grade dysplasia who later developed high-grade dysplasia or esophageal adenocarcinoma. We analyzed 4 epigenetic and 3 clinical parameters in 118 Barrett's esophagus tissues obtained from 35 progressor and 27 non-progressor Barrett's esophagus patients from Baltimore Veterans Affairs Maryland Health Care Systems and Mayo Clinic. Based on 2-year and 4-year prediction models using linear discriminant analysis (area under the receiver-operator characteristic (ROC) curve: 0.8386 and 0.7910, respectively), Barrett's esophagus specimens were stratified into high-risk (HR), intermediate-risk (IR), or low-risk (LR) groups. This 3-tiered stratification method retained both the high specificity of the 2-year model and the high sensitivity of the 4-year model. Progression-free survivals differed significantly among the 3 risk groups, with p = 0.0022 (HR vs. IR) and p<0.0001 (HR or IR vs. LR). Incremental value analyses demonstrated that the number of methylated genes contributed most influentially to prediction accuracy. Conclusions This 3-tiered risk stratification strategy has the potential to exert a profound impact on Barrett's esophagus surveillance accuracy and efficiency.
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Affiliation(s)
- Fumiaki Sato
- Division of Gastroenterology and Hepatology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
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Wang Y, Liu D, Chen P, Koeffler HP, Tong X, Xie D. Negative feedback regulation of IFN-gamma pathway by IFN regulatory factor 2 in esophageal cancers. Cancer Res 2008; 68:1136-43. [PMID: 18281489 DOI: 10.1158/0008-5472.can-07-5021] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
IFN-gamma is an antitumor cytokine that inhibits cell proliferation and induces apoptosis after engagement with the IFN-gamma receptors (IFNGR) expressed on target cells, whereas IFN regulatory factor 2 (IRF-2) is able to block the effects of IFN-gamma by repressing transcription of IFN-gamma-induced genes. Thus far, few studies have explored the influences of IFN-gamma on human esophageal cancer cells. In the present study, therefore, we investigated in detail the functions of IFN-gamma in esophageal cancer cells. The results in clinical samples of human esophageal cancers showed that the level of IFN-gamma was increased in tumor tissues and positively correlated with tumor progression and IRF-2 expression, whereas the level of IFNGR1 was decreased and negatively correlated with tumor progression and IRF-2 expression. Consistently, in vitro experiments showed that low concentration of IFN-gamma induced the expression of IRF-2 with potential promotion of cell growth, and moreover, IRF-2 was able to suppress IFNGR1 transcription in human esophageal cancer cells by binding a specific motif in IFNGR1 promoter, which lowered the sensitivity of esophageal cancer cells to IFN-gamma. Taken together, our results disclosed a new IRF-2-mediated inhibitory mechanism for IFN-gamma-induced pathway in esophageal cancer cells: IFN-gamma induced IRF-2 up-regulation, then up-regulated IRF-2 decreased endogenous IFNGR1 level, and finally, the loss of IFNGR1 turned to enhance the resistance of esophageal cancer cells to IFN-gamma. Accordingly, the results implied that IRF-2 might act as a mediator for the functions of IFN-gamma and IFNGR1 in human esophageal cancers.
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Affiliation(s)
- Yan Wang
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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Matharoo-Ball B, Ball G, Rees R. Clinical proteomics: discovery of cancer biomarkers using mass spectrometry and bioinformatics approaches--a prostate cancer perspective. Vaccine 2008; 25 Suppl 2:B110-21. [PMID: 17916461 DOI: 10.1016/j.vaccine.2007.06.040] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2007] [Revised: 06/01/2007] [Accepted: 06/15/2007] [Indexed: 10/24/2022]
Abstract
Prostate cancer (PCa) is an intractable disease, where diagnosis and clinical prediction of the disease course and response to treatment is compromised by the lack of objective and robust biomarker assays. In late stage metastatic disease, treatment options are limited, although it is recognized that some patients may benefit from immunotherapy and in particular vaccine therapy. However, research into biomarkers that correlate with the clinical outcome of immunotherapy has lagged behind vaccine development. Thus, proteomic tools are increasingly being utilized for the discovery of biomarkers which will allow us to make clinical decisions about patient treatment at an earlier stage and should aid in shortening the development time for vaccines. In this review we will summarize the various proteomic platforms used to investigate new biomarkers in PCa for better patient diagnosis, prognosis, patient stratification, treatment monitoring and clinical surrogate endpoints. We will discuss method limitations and highlight the key areas of research required for understanding the etiology of PCa.
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Affiliation(s)
- Balwir Matharoo-Ball
- Interdisciplinary Biomedical Research Centre, School of Biomedical and Natural Sciences, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK
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Wang Y, Liu DP, Chen PP, Koeffler HP, Tong XJ, Xie D. Involvement of IFN regulatory factor (IRF)-1 and IRF-2 in the formation and progression of human esophageal cancers. Cancer Res 2007; 67:2535-43. [PMID: 17363571 DOI: 10.1158/0008-5472.can-06-3530] [Citation(s) in RCA: 79] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
IFN regulatory factor (IRF)-1 and IRF-2 are generally regarded as a tumor suppressor and an oncoprotein, respectively. However, little is known about their expression and function in esophageal squamous cell carcinomas (ESCC). In our present work, IRF-1 expression was decreased and IRF-2 expression was increased in ESCCs compared with matched normal esophageal tissues. Moreover, statistical data indicated that IRF-2 expression was tightly correlated with progression of ESCCs. As expected, overexpression of either IRF-1 or IRF-2 in an ESCC cell line resulted in either suppression or enhancement of cell growth, respectively. Also, proliferation- and apoptosis-related molecules (p21(WAF1/CIP1), cyclin-D1, Bcl-2, and histone H4) were regulated by IRF-1 and IRF-2. Additionally, high levels of IRF-2 blocked the function of IRF-1 by preventing the latter from translocating into the nucleus; in contrast, knock down of IRF-2 by small interfering RNA permitted nuclear localization and activity of IRF-1. In vivo assay using nude mice indicated that the tumorigenicity of ESCC cells was enhanced with IRF-2 overexpression but dramatically attenuated after forced expression of IRF-1. In conclusion, IRF-1 and IRF-2 are able to regulate tumorigenicity of ESCC cells as antioncoprotein and oncoprotein, respectively. Relative amounts of IRF-1 to IRF-2 are functionally very important for the development and progression of ESCCs, and reduction of the ratio of IRF-1/IRF-2 may lead to the enhancement of tumorigenicity of ESCC cells. Therefore, levels of IRF-1 and IRF-2 are useful indicators in diagnosis and prognosis for ESCCs, and these molecules are potential drug targets for ESCC therapy.
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Affiliation(s)
- Yan Wang
- Laboratory of Molecular Oncology, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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