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Saadh MJ, Allela OQB, Kareem RA, Baldaniya L, Ballal S, Vashishth R, Parmar M, Sameer HN, Hamad AK, Athab ZH, Adil M. Prognostic gene expression profile of colorectal cancer. Gene 2025; 955:149433. [PMID: 40122415 DOI: 10.1016/j.gene.2025.149433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2024] [Revised: 02/26/2025] [Accepted: 03/18/2025] [Indexed: 03/25/2025]
Abstract
Colorectal cancer is a major global health burden, with significant heterogeneity in clinical outcomes among patients. Identifying robust prognostic gene expression signatures can help stratify patients, guide treatment decisions, and improve clinical management. This review provides an overview of current prognostic gene expression profiles in colorectal cancer research. We have synthesized evidence from numerous published studies investigating the association between tumor gene expression patterns and patient survival outcomes. The reviewed literature reveals several promising gene signatures that have demonstrated the ability to predict disease-free survival and overall survival in CRC patients, independent of standard clinicopathological risk factors. These genes are crucial in fundamental biological processes, including cell cycle control, epithelial-mesenchymal transition, and immune regulation. The implementation of prognostic gene expression tests in clinical practice holds great potential for enabling more personalized management strategies for colorectal cancer.
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Affiliation(s)
- Mohamed J Saadh
- Faculty of Pharmacy, Middle East University, Amman 11831, Jordan.
| | | | | | - Lalji Baldaniya
- Marwadi University Research Center, Department of Pharmacy, Faculty of Health Sciences, Marwadi University, Rajkot 360003 Gujarat, India.
| | - Suhas Ballal
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
| | - Raghav Vashishth
- Department of Surgery, National Institute of Medical Sciences, NIMS University Rajasthan, Jaipur, India.
| | - Manisha Parmar
- Chandigarh Pharmacy College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, India.
| | - Hayder Naji Sameer
- Collage of Pharmacy, National University of Science and Technology, Dhi Qar 64001, Iraq.
| | | | - Zainab H Athab
- Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq.
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Zhang S, Liu Y, Liao W, Ron Zee Tan R, Bi R, Olivo M. Ex Vivo Tissue Classification Using Broadband Hyperspectral Imaging Endoscopy and Artificial Intelligence: A Pilot Study. IEEE SENSORS JOURNAL 2024; 24:24737-24749. [DOI: 10.1109/jsen.2024.3411165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency of Science (A*STAR), Fusionopolis, Singapore
| | - Yingxin Liu
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Jurong West, Singapore
| | - Wenjun Liao
- Department of Biomedical Engineering, National University of Singapore, Queenstown, Singapore
| | - Ryan Ron Zee Tan
- Department of Biomedical Engineering, National University of Singapore, Queenstown, Singapore
| | - Renzhe Bi
- A*STAR Skin Research Labs (A*SRL), Fusionopolis, Singapore
| | - Malini Olivo
- A*STAR Skin Research Labs (A*SRL), Fusionopolis, Singapore
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Chugh V, Basu A, Kaushik A, Manshu, Bhansali S, Basu AK. Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer. NANOSCALE 2024; 16:5458-5486. [PMID: 38391246 DOI: 10.1039/d3nr05648a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.
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Affiliation(s)
- Vibhas Chugh
- Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Mohali, Punjab 140306, India.
| | - Adreeja Basu
- Biological Science, St. John's University, New York, NY 10301, United States
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, Florida 33805, USA
| | - Manshu
- Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Mohali, Punjab 140306, India.
| | - Shekhar Bhansali
- Electrical and Computer Engineering, Florida International University, Miami, FL 33199, USA
| | - Aviru Kumar Basu
- Quantum Materials and Devices Unit, Institute of Nano Science and Technology, Mohali, Punjab 140306, India.
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Uchikov P, Khalid U, Kraev K, Hristov B, Kraeva M, Tenchev T, Chakarov D, Sandeva M, Dragusheva S, Taneva D, Batashki A. Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review. Diagnostics (Basel) 2024; 14:528. [PMID: 38472999 DOI: 10.3390/diagnostics14050528] [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: 02/04/2024] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. METHODS A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. RESULTS The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. CONCLUSIONS Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC's morbidity and mortality.
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Affiliation(s)
- Petar Uchikov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Krasimir Kraev
- Department of Propaedeutics of Internal Diseases "Prof. Dr. Anton Mitov", Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Bozhidar Hristov
- Section "Gastroenterology", Second Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Maria Kraeva
- Department of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Tihomir Tenchev
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Dzhevdet Chakarov
- Department of Propaedeutics of Surgical Diseases, Section of General Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Milena Sandeva
- Department of Midwifery, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Snezhanka Dragusheva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Daniela Taneva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Atanas Batashki
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
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Nduma BN, Nkeonye S, Uwawah TD, Kaur D, Ekhator C, Ambe S. Use of Artificial Intelligence in the Diagnosis of Colorectal Cancer. Cureus 2024; 16:e53024. [PMID: 38410294 PMCID: PMC10895204 DOI: 10.7759/cureus.53024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 02/28/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.
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Affiliation(s)
| | - Stephen Nkeonye
- Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Davinder Kaur
- Internal Medicine, Medical City, North Richland Hills, USA
| | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, USA
| | - Solomon Ambe
- Neurology, Baylor Scott & White Health, McKinney, USA
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Yadav A, Kumar A. Artificial intelligence in rectal cancer: What is the future? Artif Intell Cancer 2023; 4:11-22. [DOI: 10.35713/aic.v4.i2.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 12/07/2023] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.
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Affiliation(s)
- Alka Yadav
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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Singh Y, Gogtay M, Yekula A, Soni A, Mishra AK, Tripathi K, Abraham GM. Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C. World J Hepatol 2023; 15:107-115. [PMID: 36744168 PMCID: PMC9896503 DOI: 10.4254/wjh.v15.i1.107] [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: 09/21/2022] [Revised: 10/21/2022] [Accepted: 11/14/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Hepatitis C virus is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. Several studies have shown that chronic hepatitis C (CHC) has an increased risk of the development of colorectal cancer (CRC). AIM To analyze this positive relationship and develop an artificial intelligence (AI)-based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection. METHODS To develop the AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data sets were split into 70:30 ratios for training and internal validation. The Scikit-learn standard scaler was used to scale values of continuous variables. Colonoscopy findings were used as the gold standard and deep learning architecture was used to train six ML models for prediction. A Flask (customizable Python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com. RESULTS Of 415 patients, 206 had colonoscopy results. On internal validation, the Bernoulli naive Bayes model predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support vector regressor predicted the number of adenomas with the least mean absolute error of 0.905. CONCLUSION Our AI-based tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with providing a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect, prompting a higher adenoma detection rate.
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Affiliation(s)
- Yuvaraj Singh
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States.
| | - Maya Gogtay
- Hospice and Palliative Medicine, University of Texas Health-San Antonio, San Antonio, TX 78201, United States
| | - Anuroop Yekula
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Aakriti Soni
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Ajay Kumar Mishra
- Division of Cardiology, Saint Vincent Hospital, Worcester, MA 01608, United States
| | - Kartikeya Tripathi
- Division of Gastroenterology and Hepatology, UMass Chan School-Baystate Medical Center, Springfield, MA 01199, United States
| | - G M Abraham
- Division of Infectious Disease, Chief of Medicine, Saint Vincent Hospital, Worcester, MA 01608, United States
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Liu G, Zhao J, Tian G, Li S, Lu Y. Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis. Front Oncol 2022; 12:925924. [PMID: 36518311 PMCID: PMC9742812 DOI: 10.3389/fonc.2022.925924] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 11/11/2022] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND In recent years, the rapid development of artificial intelligence (AI) technology has created a new diagnostic and therapeutic opportunity for colorectal cancer (CRC). Numerous academic and clinical studies have demonstrated that high-level auxiliary diagnosis and treatment systems based on AI technology can significantly improve the readability of medical data, objectively provide a reliable and comprehensive reference for physicians, reduce the experience gap between physicians, and aid physicians in making more accurate diagnosis decisions. In this study, we used bibliometric techniques to visually analyze the literature about AI in the CRC field and summarize the current situation and research hotspots in this field. METHODS The relevant literature on AI in the field of CRC research was obtained from the Web of Science Core Collection (WoSCC) database. The software CiteSpace was utilized to analyze the number of papers, countries, institutions, authors, journals, cited literature, and keywords of the included literature and generate a visual knowledge map. The present study aims to evaluate the origin, current hotspots, and research trends of AI in CRC using bibliometric analysis. RESULTS As of March 2022, 64 nations/regions, 230 institutions, 245 journals, and 300 authors had published 562 AI-related articles in the field of CRC. Since 2016, each year has seen an exponential increase. China and the United States were the largest contributors, with the largest number of beneficial research institutions and the closest collaboration relationship. The World Journal of Gastroenterology is this field's most widely published journal. Diagnosis and treatment research, gene and immunology research, intestinal polyp research, tumor grading research, gastrointestinal endoscopy research, and prognosis research comprised the six topics derived from high-frequency keyword cluster analysis. CONCLUSION In recent years, field research has been a popular topic of discussion. The results of our bibliometric analysis allow us to comprehend better the current situation and trend of this research field, and the quantitative data indicators can serve as a guide for the research and application of global scholars.
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Affiliation(s)
- Guangwei Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
| | - Jun Zhao
- Department of Pharmacy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangye Tian
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Shuai Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Yun Lu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
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Li Y, Chen L, Lv J, Chen X, Zeng B, Chen M, Guo W, Lin Y, Yu L, Hou J, Li J, Zhou P, Zhang W, Li S, Jin X, Cai W, Zhang K, Huang Y, Wang C, Fu F. Clinical application of artificial neural network (ANN) modeling to predict BRCA1/2 germline deleterious variants in Chinese bilateral primary breast cancer patients. BMC Cancer 2022; 22:1125. [PMID: 36324133 PMCID: PMC9628090 DOI: 10.1186/s12885-022-10160-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/19/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Bilateral breast cancer (BBC), as well as ovarian cancer, are significantly associated with germline deleterious variants in BRCA1/2, while BRCA1/2 germline deleterious variants carriers can exquisitely benefit from poly (ADP-ribose) polymerase (PARP) inhibitors. However, formal genetic testing could not be carried out for all patients due to extensive use of healthcare resources, which in turn results in high medical costs. To date, existing BRCA1/2 deleterious variants prediction models have been developed in women of European or other descent who are quite genetically different from Asian population. Therefore, there is an urgent clinical need for tools to predict the frequency of BRCA1/2 deleterious variants in Asian BBC patients balancing the increased demand for and cost of cancer genetics services. METHODS The entire coding region of BRCA1/2 was screened for the presence of germline deleterious variants by the next generation sequencing in 123 Chinese BBC patients. Chi-square test, univariate and multivariate logistic regression were used to assess the relationship between BRCA1/2 germline deleterious variants and clinicopathological characteristics. The R software was utilized to develop artificial neural network (ANN) and nomogram modeling for BRCA1/2 germline deleterious variants prediction. RESULTS Among 123 BBC patients, we identified a total of 20 deleterious variants in BRCA1 (8; 6.5%) and BRCA2 (12; 9.8%). c.5485del in BRCA1 is novel frameshift deleterious variant. Deleterious variants carriers were younger at first diagnosis (P = 0.0003), with longer interval between two tumors (P = 0.015), at least one medullary carcinoma (P = 0.001), and more likely to be hormone receptor negative (P = 0.006) and HER2 negative (P = 0.001). Area under the receiver operating characteristic curve was 0.903 in ANN and 0.828 in nomogram modeling individually (P = 0.02). CONCLUSION This study shows the spectrum of the BRCA1/2 germline deleterious variants in Chinese BBC patients and indicates that the ANN can accurately predict BRCA deleterious variants than conventional statistical linear approach, which confirms the BRCA1/2 deleterious variants carriers at the lowest costs without adding any additional examinations.
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Affiliation(s)
- Yan Li
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Lili Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Jinxing Lv
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
- Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, 610000, Chengdu, China
| | - Xiaobin Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Bangwei Zeng
- Nosocomial Infection Control Branch, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Minyan Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Wenhui Guo
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Liuwen Yu
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Jialin Hou
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Jing Li
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Peng Zhou
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Wenzhe Zhang
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Shengmei Li
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Xuan Jin
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Weifeng Cai
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Kun Zhang
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Yeyuan Huang
- Fujian Medical University, 350001, Fuzhou, Fujian Province, China
| | - Chuan Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China.
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China.
| | - Fangmeng Fu
- Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
- Department of General Surgery, Fujian Medical University Union Hospital, 350001, Fuzhou, Fujian Province, China.
- Breast Cancer Institute, Fujian Medical University, 350001, Fuzhou, Fujian Province, China.
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11
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Volovat SR, Augustin I, Zob D, Boboc D, Amurariti F, Volovat C, Stefanescu C, Stolniceanu CR, Ciocoiu M, Dumitras EA, Danciu M, Apostol DGC, Drug V, Shurbaji SA, Coca LG, Leon F, Iftene A, Herghelegiu PC. Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI. Cancers (Basel) 2022; 14:4834. [PMID: 36230757 PMCID: PMC9562853 DOI: 10.3390/cancers14194834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/18/2022] [Accepted: 09/29/2022] [Indexed: 12/09/2022] Open
Abstract
Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota's composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.
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Affiliation(s)
- Simona-Ruxandra Volovat
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Iolanda Augustin
- Department of Medical Oncology, AI.Trestioreanu Institute of Oncology, 022328 Bucharest, Romania
| | - Daniela Zob
- Department of Medical Oncology, AI.Trestioreanu Institute of Oncology, 022328 Bucharest, Romania
| | - Diana Boboc
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Florin Amurariti
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Constantin Volovat
- Department of Medical Oncology, “Euroclinic” Center of Oncology, 2 Vasile Conta Str., 700106 Iasi, Romania
| | - Cipriana Stefanescu
- Department of Biophysics and Medical Physics-Nuclear Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Cati Raluca Stolniceanu
- Department of Biophysics and Medical Physics-Nuclear Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Manuela Ciocoiu
- Department of Pathophysiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Eduard Alexandru Dumitras
- Department of Pathophysiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Anesthesiology and Intensive Care, Regional Institute of Oncology, 700115 Iasi, Romania
| | - Mihai Danciu
- Pathology Department, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | | | - Vasile Drug
- Department of Gastroenterology, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
- Gastroenterology Clinic, Institute of Gastroenterology and Hepatology, ‘St. Spiridon’ Clinical Hospital, 700115 Iasi, Romania
| | - Sinziana Al Shurbaji
- Gastroenterology Clinic, Institute of Gastroenterology and Hepatology, ‘St. Spiridon’ Clinical Hospital, 700115 Iasi, Romania
| | - Lucia-Georgiana Coca
- Faculty of Computer Science, Alexandru Ioan Cuza University, 700115 Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 700115 Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, Alexandru Ioan Cuza University, 700115 Iasi, Romania
| | - Paul-Corneliu Herghelegiu
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 700115 Iasi, Romania
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12
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Ginghina O, Hudita A, Zamfir M, Spanu A, Mardare M, Bondoc I, Buburuzan L, Georgescu SE, Costache M, Negrei C, Nitipir C, Galateanu B. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. Front Oncol 2022; 12:856575. [PMID: 35356214 PMCID: PMC8959149 DOI: 10.3389/fonc.2022.856575] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 01/19/2023] Open
Abstract
Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.
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Affiliation(s)
- Octav Ginghina
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Ariana Hudita
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marius Zamfir
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Andrada Spanu
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Mara Mardare
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Irina Bondoc
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | | | - Sergiu Emil Georgescu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marieta Costache
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Carolina Negrei
- Department of Toxicology, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
| | - Cornelia Nitipir
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Oncology, Elias University Emergency Hospital, Bucharest, Romania
| | - Bianca Galateanu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
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13
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Vobugari N, Raja V, Sethi U, Gandhi K, Raja K, Surani SR. Advancements in Oncology with Artificial Intelligence-A Review Article. Cancers (Basel) 2022; 14:1349. [PMID: 35267657 PMCID: PMC8909088 DOI: 10.3390/cancers14051349] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 02/05/2023] Open
Abstract
Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.
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Affiliation(s)
- Nikitha Vobugari
- Department of Internal Medicine, Medstar Washington Hospital Center, Washington, DC 20010, USA; (N.V.); (K.G.)
| | - Vikranth Raja
- Department of Medicine, P.S.G Institute of Medical Sciences and Research, Coimbatore 641004, Tamil Nadu, India;
| | - Udhav Sethi
- School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Kejal Gandhi
- Department of Internal Medicine, Medstar Washington Hospital Center, Washington, DC 20010, USA; (N.V.); (K.G.)
| | - Kishore Raja
- Department of Pediatric Cardiology, University of Minnesota, Minneapolis, MN 55454, USA;
| | - Salim R. Surani
- Department of Pulmonary and Critical Care, Texas A&M University, College Station, TX 77843, USA
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Cianci P, Restini E. Artificial intelligence in colorectal cancer management. Artif Intell Cancer 2021; 2:79-89. [DOI: 10.35713/aic.v2.i6.79] [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: 12/09/2021] [Revised: 12/22/2021] [Accepted: 12/29/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications.
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Affiliation(s)
- Pasquale Cianci
- Department of Surgery and Traumatology, ASL BAT, Lorenzo Bonomo Hospital, Andria 76123, Puglia, Italy
| | - Enrico Restini
- Department of Surgery and Traumatology, ASL BAT, Lorenzo Bonomo Hospital, Andria 76123, Puglia, Italy
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15
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Song J, Ding C, Huang Q, Luo T, Xu X, Chen Z, Li S. Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma. Med Phys 2021; 48:7891-7899. [PMID: 34669994 DOI: 10.1002/mp.15307] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSE This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma. METHODS A total of 665 patients with lung adenocarcinoma (528/137) were recruited from two different institutions. In the training set, an 18-layer convolutional neural network (CNN) and fivefold cross-validation strategy were used to establish a CNN model. Subsequently, an independent external validation cohort from the other institution was used to evaluate the predictive efficacy of the CNN model. Grad-weighted class activation mapping (Grad-CAM) technology was used for the visual interpretation of the CNN model. In addition, this study also compared the prediction abilities of the radiomics and CNN models. Receiver operating characteristic (ROC) curves, accuracy and precision values, and recall and F1-score were used to evaluate the effectiveness of the CNN model and compare its performance with that of the radiomics model. RESULTS In the validation set, the micro- and macroaverage values of the area under the ROC curve of the CNN model to identify the three EGFR subtypes were 0.78 and 0.79, respectively. All evaluation indicators of the CNN model were better than those of the radiomics model. CONCLUSIONS Our study confirmed the potential of DL for predicting the EGFR mutation status in lung adenocarcinoma. The imaging phenotypes of the three mutation subtypes were found to be different, which can provide a basis for choosing more accurate and personalized treatment in patients with lung adenocarcinoma.
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Affiliation(s)
- Jiangdian Song
- School of Medical Informatics, China Medical University, Shenyang, China
| | - Changwei Ding
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qinlai Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ting Luo
- Department of Radiology, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Xiaoman Xu
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zongjian Chen
- School of Medical Informatics, China Medical University, Shenyang, China
| | - Shu Li
- School of Medical Informatics, China Medical University, Shenyang, China
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16
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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Liu XL, Shao CY, Sun L, Liu YY, Hu LW, Cong ZZ, Xu Y, Wang RC, Yi J, Wang W. An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients. J Thorac Dis 2020; 12:5580-5592. [PMID: 33209391 PMCID: PMC7656440 DOI: 10.21037/jtd-20-1956] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Background Current preoperative staging for lymph nodal status remains inaccurate. The purpose of this study was to build an artificial neural network (ANN) model to predict pathologic nodal involvement in clinical stage I–II esophageal squamous cell carcinoma (ESCC) patients and then validated the performance of the model. Methods A total of 523 patients (training set: 350; test set: 173) with clinical staging I–II ESCC who underwent esophagectomy and reconstruction were enrolled in this study. Their post-surgical pathological results were assessed and analysed. An ANN model was established for predicting pathologic nodal positive patients in the training set, which was validated in the test set. A receiver operating characteristic (ROC) curve was also created to illustrate the performance of the predictive model. Results Of the enrolled 523 patients with ESCC, 41.3% of the patients were confirmed pathologic nodal positive (216/523). The ANN staging system identified the tumour invasion depth, tumour length, dysphagia, tumour differentiation and lymphovascular invasion (LVI) as predictors for pathologic lymph node metastases. The C-index for the ANN model verified in the test set was 0.852, which demonstrated that the ANN model had a good predictive performance. Conclusions The ANN model presented good performance for predicting pathologic lymph node metastasis and added indicators not included in current staging criteria and might help improve the staging strategies.
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Affiliation(s)
- Xiao-Long Liu
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Chen-Ye Shao
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Lei Sun
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yi-Yang Liu
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Li-Wen Hu
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhuang-Zhuang Cong
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yang Xu
- Department of Cardiothoracic Surgery, Jinling Hospital, Jinling School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Rong-Chun Wang
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jun Yi
- Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wei Wang
- Department of Thoracic Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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18
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Wang Y, He X, Nie H, Zhou J, Cao P, Ou C. Application of artificial intelligence to the diagnosis and therapy of colorectal cancer. Am J Cancer Res 2020; 10:3575-3598. [PMID: 33294256 PMCID: PMC7716173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/14/2020] [Indexed: 06/12/2023] Open
Abstract
Artificial intelligence (AI) is a relatively new branch of computer science involving many disciplines and technologies, including robotics, speech recognition, natural language and image recognition or processing, and machine learning. Recently, AI has been widely applied in the medical field. The effective combination of AI and big data can provide convenient and efficient medical services for patients. Colorectal cancer (CRC) is a common type of gastrointestinal cancer. The early diagnosis and treatment of CRC are key factors affecting its prognosis. This review summarizes the research progress and clinical application value of AI in the investigation, early diagnosis, treatment, and prognosis of CRC, to provide a comprehensive theoretical basis for AI as a promising diagnostic and treatment tool for CRC.
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Affiliation(s)
- Yutong Wang
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Xiaoyun He
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
- Department of Endocrinology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Hui Nie
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Jianhua Zhou
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Pengfei Cao
- Department of Hematology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, China
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19
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Wang Y, Nie H, He X, Liao Z, Zhou Y, Zhou J, Ou C. The emerging role of super enhancer-derived noncoding RNAs in human cancer. Theranostics 2020; 10:11049-11062. [PMID: 33042269 PMCID: PMC7532672 DOI: 10.7150/thno.49168] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 08/23/2020] [Indexed: 02/06/2023] Open
Abstract
Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.
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MESH Headings
- Antineoplastic Agents/pharmacology
- Antineoplastic Agents/therapeutic use
- Biomarkers, Tumor/analysis
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Carcinogenesis/drug effects
- Carcinogenesis/genetics
- Cell Proliferation/drug effects
- Cell Proliferation/genetics
- Drug Resistance, Neoplasm/genetics
- Enhancer Elements, Genetic/genetics
- Gene Expression Regulation, Neoplastic/drug effects
- Gene Expression Regulation, Neoplastic/genetics
- Humans
- Molecular Targeted Therapy/methods
- Neoplasms/diagnosis
- Neoplasms/drug therapy
- Neoplasms/genetics
- Neoplasms/pathology
- Precision Medicine/methods
- RNA, Untranslated/analysis
- RNA, Untranslated/genetics
- RNA, Untranslated/metabolism
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Affiliation(s)
- Yutong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Hui Nie
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Xiaoyun He
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhiming Liao
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yangying Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Jianhua Zhou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Chunlin Ou
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
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Advances in Near-Infrared Spectroscopy and Related Computational Methods. Molecules 2019; 24:molecules24234370. [PMID: 31795360 PMCID: PMC6930588 DOI: 10.3390/molecules24234370] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 11/26/2019] [Indexed: 02/07/2023] Open
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