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Nsairat H, Jaber AM, Faddah H, Ahmad S. Oleuropein impact on colorectal cancer. Future Sci OA 2024; 10:FSO. [PMID: 38817366 PMCID: PMC11137855 DOI: 10.2144/fsoa-2023-0131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/20/2023] [Indexed: 06/01/2024] Open
Abstract
Colorectal cancer (CRC) is considered the third most common cancer in the world. In Mediterranean region, olives and olive oil play a substantial role in diet and medical traditional behaviors. They totally believe that high consumption of olive products can treat a wide range of diseases and decrease risk of illness. Oleuropein is the main active antioxidant molecule found in pre-mature olive fruit and leaves. Recently, it has been demonstrated that oleuropein is used in cancer therapy as an anti-proliferative and apoptotic agent for some cancer cells. In this review, we would like to explore the conclusive effects of oleuropein on CRC with respect to in vitro and in vivo studies.
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Affiliation(s)
- Hamdi Nsairat
- Pharmacological & Diagnostic Research Center, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, 19328, Jordan
| | - Areej M Jaber
- Pharmacological & Diagnostic Research Center, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, 19328, Jordan
| | - Haya Faddah
- Pharmacological & Diagnostic Research Center, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, 19328, Jordan
| | - Somaya Ahmad
- Pharmacological & Diagnostic Research Center, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, 19328, Jordan
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2
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Tandon R, Agrawal S, Rathore NPS, Mishra AK, Jain SK. A systematic review on deep learning-based automated cancer diagnosis models. J Cell Mol Med 2024; 28:e18144. [PMID: 38426930 PMCID: PMC10906380 DOI: 10.1111/jcmm.18144] [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: 06/28/2023] [Revised: 12/08/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024] Open
Abstract
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
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Affiliation(s)
| | | | | | - Abhinava K. Mishra
- Molecular, Cellular and Developmental Biology DepartmentUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
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Zhang J, Xiang Y, Chen J, Liu L, Jin J, Zhu S. Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients. Front Med (Lausanne) 2024; 11:1354439. [PMID: 38390567 PMCID: PMC10881824 DOI: 10.3389/fmed.2024.1354439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Background Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model. Methods This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors' effect on overall survival and establish a novel CS-based nomogram. Results A total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41-50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance. Conclusion CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.
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Affiliation(s)
- Jin Zhang
- The First Affiliated Hospital of the Naval Medical University, Shanghai, China
- Shanghai Children's Medical Center, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yang Xiang
- The First Affiliated Hospital of the Naval Medical University, Shanghai, China
| | - Jiqiu Chen
- The First Affiliated Hospital of the Naval Medical University, Shanghai, China
| | - Lei Liu
- The First Affiliated Hospital of the Naval Medical University, Shanghai, China
- Shanghai Children's Medical Center, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jian Jin
- The First Affiliated Hospital of the Naval Medical University, Shanghai, China
| | - Shihui Zhu
- The First Affiliated Hospital of the Naval Medical University, Shanghai, China
- Shanghai Children's Medical Center, School of Medicine, Shanghai Jiaotong University, Shanghai, China
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Mohammadi G, Azizmohammad Looha M, Pourhoseingholi MA, Rezaei Tavirani M, Sohrabi S, Zareie Shab Khaneh A, Piri H, Alaei M, Parvani N, Vakilzadeh I, javadi S, Moradian Haft Cheshmeh Z, Razzaghi Z, Mahmoud Robati R, Zamanian Azodi M, Zarean Shahraki S, Talebi R, Charati Yazdani J, Motlagh ME, Khodakarim S, Hadavi M. Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study. Asian Pac J Cancer Prev 2024; 25:333-342. [PMID: 38285801 PMCID: PMC10911721 DOI: 10.31557/apjcp.2024.25.1.333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/19/2024] [Indexed: 01/31/2024] Open
Abstract
INTRODUCTION Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. MATERIAL AND METHODS A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). RESULTS Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65-0.75) and LGBM (AUC = 0.70, 95% CI 0.65-0.75) models achieved the highest predictive AUC values for CRC patient survival. CONCLUSIONS This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes.
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Affiliation(s)
- Gohar Mohammadi
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mehdi Azizmohammad Looha
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mohammad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | | | - Samaneh Sohrabi
- Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amirali Zareie Shab Khaneh
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Hassan Piri
- Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Maryam Alaei
- Cardiovascular Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Naser Parvani
- Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Iman Vakilzadeh
- Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Sara javadi
- Vice Chancellor for Research & Technology, Shiraz University of Medical Sciences, Shiraz, Iran.
| | | | - Zahra Razzaghi
- Laser Application in Medical Sciences Research Center. Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Mahmoud Robati
- Department of Dermatology, Director of Skin Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mona Zamanian Azodi
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Saba Zarean Shahraki
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Raheleh Talebi
- Department of Mathematics at Architecture and Computer Engineering, University of Applied Sciences (unit 10), Tehran, Iran.
| | | | - Mohammad Esmaeil Motlagh
- Department of Pediatrics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Soheila Khodakarim
- Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Melika Hadavi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
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Adiwinata R, Tandarto K, Arifputra J, Waleleng BJ, Gosal F, Rotty L, Winarta J, Waleleng A, Simadibrata P, Simadibrata M. The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis. Asian Pac J Cancer Prev 2023; 24:3655-3663. [PMID: 38019222 PMCID: PMC10772777 DOI: 10.31557/apjcp.2023.24.11.3655] [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: 06/25/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
INTRODUCTION Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR. METHODS The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed. RESULTS A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias. CONCLUSION AI may improve colonoscopy result quality through improving PDR and ADR.
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Affiliation(s)
- Randy Adiwinata
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Kevin Tandarto
- S.K Lerik Regional Public Hospital, Kupang, East Nusa Tenggara, Indonesia.
| | - Jonathan Arifputra
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Bradley Jimmy Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Fandy Gosal
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Luciana Rotty
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Jeanne Winarta
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Andrew Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Paulus Simadibrata
- Department of Internal Medicine, Abdi Waluyo Hospital, Jakarta, Indonesia.
| | - Marcellus Simadibrata
- Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia.
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International BR. Retracted: Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data. BIOMED RESEARCH INTERNATIONAL 2023; 2023:9867970. [PMID: 37564149 PMCID: PMC10412314 DOI: 10.1155/2023/9867970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 08/12/2023]
Abstract
[This retracts the article DOI: 10.1155/2022/1467070.].
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Alarood AA, Faheem M, Al‐Khasawneh MA, Alzahrani AIA, Alshdadi AA. Secure medical image transmission using deep neural network in e-health applications. Healthc Technol Lett 2023; 10:87-98. [PMID: 37529409 PMCID: PMC10388229 DOI: 10.1049/htl2.12049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/13/2023] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.
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Affiliation(s)
| | - Muhammad Faheem
- School of Technology and InnovationsUniversity of VaasaVaasaFinland
| | - Mahmoud Ahmad Al‐Khasawneh
- School of Information TechnologySkyline University CollegeUniversity City SharjahSharjahUnited Arab Emirates
| | - Abdullah I. A. Alzahrani
- Department of Computer Science, Collage of Science and Humanities in Al QuwaiiyahShaqra UniversityShaqraSaudi Arabia
| | - Abdulrahman A. Alshdadi
- Department of Information Systems and Technology, College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia
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Jain S, Naicker D, Raj R, Patel V, Hu YC, Srinivasan K, Jen CP. Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials. Diagnostics (Basel) 2023; 13:diagnostics13091563. [PMID: 37174954 PMCID: PMC10178016 DOI: 10.3390/diagnostics13091563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/16/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.
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Affiliation(s)
- Somit Jain
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Dharmik Naicker
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Ritu Raj
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Vedanshu Patel
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Chun-Ping Jen
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Mechanical Engineering and Advanced Institute of Manufacturing for High-Tech Innovations, National Chung Cheng University, Chia-Yi 62102, Taiwan
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