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Burnett B, Zhou SM, Brophy S, Davies P, Ellis P, Kennedy J, Bandyopadhyay A, Parker M, Lyons RA. Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review. Diagnostics (Basel) 2023; 13:301. [PMID: 36673111 PMCID: PMC9858109 DOI: 10.3390/diagnostics13020301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/05/2023] [Accepted: 01/07/2023] [Indexed: 01/15/2023] Open
Abstract
The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.
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Affiliation(s)
- Bruce Burnett
- Swansea University Medical School, Swansea SA2 8PP, UK
| | - Shang-Ming Zhou
- Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK
| | - Sinead Brophy
- Swansea University Medical School, Swansea SA2 8PP, UK
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Ghani S, Bahrami S, Rafiee B, Eyvazi S, Yarian F, Ahangarzadeh S, Khalili S, Shahzamani K, Jafarisani M, Bandehpour M, Kazemi B. Recent developments in antibody derivatives against colorectal cancer; A review. Life Sci 2020; 265:118791. [PMID: 33220288 DOI: 10.1016/j.lfs.2020.118791] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 10/10/2020] [Accepted: 11/14/2020] [Indexed: 12/16/2022]
Abstract
Colorectal cancer (CRC) is the fourth most common cause of cancer and mortality worldwide and is the third most common cancer in men and women. Surgery, radiotherapy, and chemotherapy are conventionally used for the treatment of colorectal cancer. However, these methods are associated with various side effects on normal cells. Thus, new studies are moving towards more effective and non-invasive methods for treatment of colorectal cancer. Targeted therapy of CRC is a promising new approach to enhance the efficiency and decrease the toxicity of the treatment. In targeted therapy of CRC, antibody fragments can directly inhibit tumor cell growth and proliferation. They also can act as an ideal carrier for targeted delivery of anticancer drugs. In the present study, the structure and function of different formats of antibody fragments, immune-targeted therapy of CRC using antibody fragments will be discussed.
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Affiliation(s)
- Sepideh Ghani
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technology in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular & Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Samira Bahrami
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Behnam Rafiee
- Department of Pathobiology, Faculty of Veterinary Medicine, Shahrekord University, Shahrekord, Iran
| | - Shirin Eyvazi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fatemeh Yarian
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular & Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Shahrzad Ahangarzadeh
- Infectious Diseases and Tropical Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Kiana Shahzamani
- Isfahan Gastroenterology and Hepatology Research Center (IGHRC), Isfahan University of Medical Sciences, Isfahan, Iran
| | - Moslem Jafarisani
- Clinical Biochemistry, Cancer Prevention Research Center, Shahroud university of Medical Sciences, Shahroud, Iran
| | - Mojgan Bandehpour
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular & Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Bahram Kazemi
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Cellular & Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Sun W, Xie L, Chen L, Xiao M, Zhao Q, Zeng J, Peng Y, Shu L, Mao J. Development and validation of two aspiration prediction models in patients receiving nasogastric feeding. J Nurs Manag 2020; 28:1372-1380. [PMID: 32656865 DOI: 10.1111/jonm.13093] [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: 05/13/2020] [Revised: 06/27/2020] [Accepted: 07/06/2020] [Indexed: 11/26/2022]
Abstract
AIM To develop and validate two aspiration prediction models in patients receiving nasogastric feeding. BACKGROUND Aspiration is one of the most serious complications of nasogastric feeding. However, there is a lack of aspiration prediction models for nasogastric feeding. METHODS A total of 515 patients receiving nasogastric feeding were randomly selected for this unmatched case-control study, with 103 patients in the case group and 412 patients in the control group. Logistic regression was used to develop nomogram and Classification And Regression Tree (CART) models. The performances of the models were internally validated using 1,000 bootstrapped samples. RESULTS The predictive accuracy of the CART model (94.5%) was higher than that of the nomogram model (89.1%). The area under the receiver operating characteristic curve of the CART model (0.96) was slightly higher than that of the nomogram model (0.93). CONCLUSIONS The intubation depth, number of comorbidities, aspiration history, indwelling days, food type and the use of sedative-hypnotics may be used to identify aspiration risk. IMPLICATIONS FOR NURSING MANAGEMENT Two aspiration prediction models are provided for nurses to evaluate aspiration risk and increase the quality of nursing management.
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Affiliation(s)
- Wenjing Sun
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liling Xie
- Department of Nursing, First Branch of the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Limei Chen
- Department of Nursing, First Branch of the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qinghua Zhao
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jingjie Zeng
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Yihang Peng
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lingzhi Shu
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiayi Mao
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Imperiale TF, Monahan PO. Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence. Gastrointest Endosc Clin N Am 2020; 30:423-440. [PMID: 32439080 DOI: 10.1016/j.giec.2020.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Risk stratification is a system by which clinically meaningful separation of risk is achieved in a group of otherwise similar persons. Although parametric logistic regression dominates risk prediction, use of nonparametric and semiparametric methods, including artificial neural networks, is increasing. These statistical-learning and machine-learning methods, along with simple rules, are collectively referred to as "artificial intelligence" (AI). AI requires knowledge of study validity, understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment.
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Affiliation(s)
- Thomas F Imperiale
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Health Services Research and Development, Richard L. Roudebush VA Medical Center, Indianapolis, IN, USA; Regenstrief Institute, Inc., 1101 West 10th Street, Indianapolis, IN 46202, USA; Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Patrick O Monahan
- Department of Biostatistics, Indiana University School of Medicine and Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA; Health Information and Translational Sciences, 410 West 10th Street Suite 3000, Indianapolis, IN 46202, USA
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