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1
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Felici A, Peduzzi G, Pellungrini R, Campa D. Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types. Eur J Cancer 2025; 222:115440. [PMID: 40273730 DOI: 10.1016/j.ejca.2025.115440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2025] [Accepted: 03/25/2025] [Indexed: 04/26/2025]
Abstract
Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk prediction models have played a crucial role in epidemiology of several cancer types, they have limitations especially in the ability to process complex and multidimensional data. In contrast, artificial intelligence (AI) approaches represent a promising solution to overcome this limitation. AI techniques have the potential to identify complex patterns and relationships in data that traditional methods might overlook, making them especially useful for handling large and heterogeneous datasets analysed in cancer research. This review first examines the current state of the art of AI techniques, highlighting their differences and suitability for various data types. Then, offers a comprehensive analysis of the literature, focusing on the application of AI approaches in nineteen cancer types (bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, gynaecological cancers, head and neck cancer, haematological cancers, kidney cancer, liver cancer, lung cancer, melanoma, ovarian cancer, pancreatic cancer, prostate cancer, thyroid cancer and overall cancer), evaluating the models, metrics, and exposure variables used. Finally, the review discusses the application of AI in the clinical practice, along with an assessment of its potential limitations and future directions.
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Affiliation(s)
- Alessio Felici
- Department of Biology, University of Pisa, Via Luca Ghini, 13, Pisa 56126, Italy
| | - Giulia Peduzzi
- Department of Biology, University of Pisa, Via Luca Ghini, 13, Pisa 56126, Italy
| | - Roberto Pellungrini
- Classe di scienze, Scuola Normale Superiore, Piazza dei Cavalieri, 7, Pisa 56126, Italy
| | - Daniele Campa
- Department of Biology, University of Pisa, Via Luca Ghini, 13, Pisa 56126, Italy.
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Naskar S, Sharma S, Kuotsu K, Halder S, Pal G, Saha S, Mondal S, Biswas UK, Jana M, Bhattacharjee S. The biomedical applications of artificial intelligence: an overview of decades of research. J Drug Target 2025; 33:717-748. [PMID: 39744873 DOI: 10.1080/1061186x.2024.2448711] [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: 10/31/2024] [Revised: 12/13/2024] [Accepted: 12/26/2024] [Indexed: 01/11/2025]
Abstract
A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.
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Affiliation(s)
- Sweet Naskar
- Department of Pharmaceutics, Institute of Pharmacy, Kalyani, West Bengal, India
| | - Suraj Sharma
- Department of Pharmaceutics, Sikkim Professional College of Pharmaceutical Sciences, Sikkim, India
| | - Ketousetuo Kuotsu
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Suman Halder
- Medical Department, Department of Indian Railway, Kharagpur Division, Kharagpur, West Bengal, India
| | - Goutam Pal
- Service Dispensary, ESI Hospital, Hoogly, West Bengal, India
| | - Subhankar Saha
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Shubhadeep Mondal
- Department of Pharmacology, Momtaz Begum Pharmacy College, Rajarhat, West Bengal, India
| | - Ujjwal Kumar Biswas
- School of Pharmaceutical Science (SPS), Siksha O Anusandhan (SOA) University, Bhubaneswar, Odisha, India
| | - Mayukh Jana
- School of Pharmacy, Centurion University of Technology and Management, Centurion University, Bhubaneswar, Odisha, India
| | - Sunirmal Bhattacharjee
- Department of Pharmaceutics, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India
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Rao A, Haydel J, Ma S, Thrift AP, Nguyen-Wenker T, El-Serag HB. A Simple, Interpretable Machine Learning Model Based on Clinical Factors Accurately Predicts Incident Dysplasia or Malignancy in Barrett's Esophagus. Dig Dis Sci 2025:10.1007/s10620-025-09069-w. [PMID: 40293634 DOI: 10.1007/s10620-025-09069-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2024] [Accepted: 04/14/2025] [Indexed: 04/30/2025]
Abstract
PURPOSE Identifying patients likely to develop dysplasia or malignancy is critical for effective surveillance in patients with Barrett's Esophagus (BE). However, current predictive models are limited. We evaluated the performance of machine learning (ML) models in predicting incident dysplasia or malignancy in a cohort of veteran patients with BE. METHODS We analyzed data from 598 patients newly diagnosed with non-dysplastic BE (NDBE), BE indefinite for dysplasia (BE-IND), and BE with non-persistent low-grade dysplasia (LGD) at the Michael DeBakey Veterans Affairs Medical Center from November 1990 to January 2019 with follow-up through January 2024. Progressors were patients who developed persistent LGD, HGD, or EAC within 5 years of index endoscopy. Six models were evaluated, encompassing regression and ensemble-based ML methods. RESULTS Of 598 qualifying patients, 61 (10.2%) progressed. Longer segments and indefinite/non-persistent LGD pathology were associated with higher risk of progression in unadjusted analyses. BE segment length remained significant on multivariate analysis (OR 1.26; 95% CI 1.17-1.36 per 1 cm increase). A decision tree (DT) model, using only segment length, achieved the highest discrimination (AUROC = 0.79) and excellent sensitivity (93.3%). The DT model also identified segment length thresholds for risk stratification: < 0.95 cm (minimal risk), 0.95-2.44 cm (low), 2.44-9.45 cm (moderate), > 9.45 cm (high). CONCLUSIONS A simple, interpretable DT model with segment length as the sole predictor outperformed regression and complex ML-based models in predicting BE progressors. Findings align with European Society of Gastrointestinal Endoscopy (ESGE) guidelines suggesting tailored surveillance based on segment length and provide actionable thresholds. These results offer a practical ML tool for BE surveillance.
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Affiliation(s)
- Ashwin Rao
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA
| | - Jasmine Haydel
- Department of Internal Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Samuel Ma
- Department of Internal Medicine, Baylor College of Medicine, Houston, TX, USA
- School of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Aaron P Thrift
- Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Theresa Nguyen-Wenker
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA.
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Ahmed KS, Marcinak CT, Issaka SM, Ali MM, Zafar SN. Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy. J Surg Res 2025; 310:186-193. [PMID: 40288090 DOI: 10.1016/j.jss.2025.03.047] [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: 11/11/2024] [Revised: 03/17/2025] [Accepted: 03/22/2025] [Indexed: 04/29/2025]
Abstract
INTRODUCTION About 25% of patients undergoing pancreaticoduodenectomy (PD) for right-sided pancreatic ductal adenocarcinoma (PDAC) die within 1 y of diagnosis. These patients carry all the risks of significant morbidity with no survival advantage when compared to nonsurgical options. We aimed to determine if machine learning models have superior accuracy to traditional regression models at predicting futile surgery in patients with PDAC. METHODS We analyzed data from patients in the National Cancer Database undergoing PD for PDAC between 2004 and 2020. PD was defined as futile if the patient died within 12 mo of cancer diagnosis. We trained predictive models using 80% of the dataset and 16 preoperative input variables. Models included logistic regression, multilayer perceptron, decision tree, random forest, and gradient boosting classifiers. Models were tested on a 20% test set using area under the receiver operating characteristic curve and Brier scores. RESULTS Of the 66,331 patients identified, 34,260 (51.7%) were men, with a median age of 67 y (interquartile range, 59 to 74 y). A total of 16,772 (25.3%) patients met the criteria for futile surgery. The gradient boosting model outperformed other models with an area under the receiver operating characteristic curve of 0.689, followed by logistic regression (0.679), random forest (0.675), and decision tree (0.664). Key predictors of futile PD included advanced age (> 79 y), tumor size ≥ 4 cm, and poor differentiation. Neoadjuvant therapy was associated with lower futility risk. CONCLUSIONS We demonstrated the ability of machine learning models to predict the odds of futile PD with moderate accuracy. Although similar analyses are needed on more granular datasets, our study has important implications for shared decision-making and optimized care for patients with PDAC.
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Affiliation(s)
- Kaleem S Ahmed
- Division of Surgical Oncology, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Clayton T Marcinak
- Division of Surgical Oncology, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Sheriff M Issaka
- Division of Surgical Oncology, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Muhammad Maisam Ali
- Division of Surgical Oncology, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Syed Nabeel Zafar
- Division of Surgical Oncology, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
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Dafni MF, Shih M, Manoel AZ, Yousif MYE, Spathi S, Harshal C, Bhatt G, Chodnekar SY, Chune NS, Rasool W, Umar TP, Moustakas DC, Achkar R, Kumar H, Naz S, Acuña-Chavez LM, Evgenikos K, Gulraiz S, Ali ESM, Elaagib A, Uggh IHP. Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention. Cancer Causes Control 2025; 36:353-367. [PMID: 39672997 DOI: 10.1007/s10552-024-01942-9] [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: 07/07/2024] [Accepted: 11/18/2024] [Indexed: 12/15/2024]
Abstract
Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.
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Affiliation(s)
- Marianna-Foteini Dafni
- School of Medicine, Laboratory of Forensic Medicine and Toxicology, Aristotle Univerisity of Thessaloniki, Thessaloniki, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Mohamed Shih
- School of Medicine, Newgiza University, Giza, Egypt.
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece.
| | - Agnes Zanotto Manoel
- Faculty of Medicine, Federal University of Rio Grande, Rio Grande do Sul, Brazil
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Mohamed Yousif Elamin Yousif
- Faculty of Medicine, University of Khartoum, Khartoum, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Stavroula Spathi
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Chorya Harshal
- Faculty of Medicine, Medical College Baroda, Vadodara, India
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Gaurang Bhatt
- All India Institute of Medical Sciences, Rishikesh, India
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Swarali Yatin Chodnekar
- Faculty of Medicine, Teaching University Geomedi LLC, Tbilisi, Georgia
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Nicholas Stam Chune
- Faculty of Medicine, University of Nairobi, Nairobi, Kenya
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Warda Rasool
- Faculty of Medicine, King Edward Medical University, Lahore, Pakistan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Tungki Pratama Umar
- Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London, UK
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Dimitrios C Moustakas
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Robert Achkar
- Faculty of Medicine, Poznan University of Medical Sciences, Poznan, Poland
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Harendra Kumar
- Dow University of Health Sciences, Karachi, Pakistan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Suhaila Naz
- Tbilisi State Medical University, Tbilisi, Georgia
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Luis M Acuña-Chavez
- Facultad de Medicina de la Universidad Nacional de Trujillo, Trujillo, Peru
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Konstantinos Evgenikos
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Shaina Gulraiz
- Royal Bournemouth Hospital (University Hospitals Dorset), Bournemouth, UK
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Eslam Salih Musa Ali
- University of Dongola Faculty of Medicine and Health Science, Dongola, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Amna Elaagib
- Faculty of Medicine AlMughtaribeen University, Khartoum, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Innocent H Peter Uggh
- Kilimanjaro Clinical Research Institute, Kilimanjaro, Tanzania
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
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Huang X, Qin M, Fang M, Wang Z, Hu C, Zhao T, Qin Z, Zhu H, Wu L, Yu G, De Cobelli F, Xie X, Palumbo D, Tian J, Dong D. The application of artificial intelligence in upper gastrointestinal cancers. JOURNAL OF THE NATIONAL CANCER CENTER 2025; 5:113-131. [PMID: 40265096 PMCID: PMC12010392 DOI: 10.1016/j.jncc.2024.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 09/17/2024] [Accepted: 12/20/2024] [Indexed: 04/24/2025] Open
Abstract
Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.
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Affiliation(s)
- Xiaoying Huang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Minghao Qin
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- University of Science and Technology Beijing, Beijing, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Zipei Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Chaoen Hu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tongyu Zhao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- University of Science and Technology of China, Hefei, China
| | - Zhuyuan Qin
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- Beijing University of Chinese Medicine, Beijing, China
| | | | - Ling Wu
- KiangWu Hospital, Macau, China
| | | | | | | | - Diego Palumbo
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Zeng Z, Lin K, Li X, Li T, Li X, Li J, Ning Z, Liu Q, Xie S, Cao S, Du J. Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer. Front Oncol 2025; 14:1369765. [PMID: 39906667 PMCID: PMC11790440 DOI: 10.3389/fonc.2024.1369765] [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: 01/24/2024] [Accepted: 12/20/2024] [Indexed: 02/06/2025] Open
Abstract
Background and objective Nasopharyngeal carcinoma (NPC) is a rare disease in most parts of the world, but it is highly prevalent in South China. Epstein-Barr virus (EBV) is one of the major risk factors for NPC. Hence, understanding the factors associated with the reactivation of EBV from the latent stage is crucial for preventing NPC. This study aimed to investigate the risk factors for EBV reactivation associated with NPC in high-prevalence areas in China using a Bayesian network (BN) model combined with structural equation modeling tools. Methods The baseline information for this study was derived from NPC screening data from a population-based prospective cohort in Sihui City, Guangdong Province, China. We divided the data into a training dataset and a test dataset. We then constructed an interaction networktionba BN prediction model to explore the risk factors for EBV reactivation, which was compared with a conventional logistic regression model. Results A total of 12,579 participants were included in the analyses, with 1596 participant pairs finally included after the use of a nested case-control study. The results of multivariable logistic regression showed that only being older than 60 years (OR = 1.718, 95% CI = 1.273,2.322) and being a current smoker (OR = 1.477, 95% CI = 1.167 - 1.872) were the risk factors for EBV reactivation. The results of the model constructed using BN showed that age and smoking were directly associated with EBV reactivation. In contrast, sex, education level, tea drinking, cooking, and family history of cancer were indirectly associated with EBV reactivation. Further, we predicted the risk of EBV reactivation using Bayesian inference and visualized the BN inference. Model prediction performance was evaluated using the test dataset. The results showed that the BN model slightly outperformed the traditional logistic regression model in all metrics. Conclusions BN not only reflects the complex interaction between factors but also visualizes the prediction results. It has a promising application potential in the risk prediction of EBV reactivation associated with NPC.
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Affiliation(s)
- Zhiwen Zeng
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Kena Lin
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Xueqi Li
- Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, China
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Tong Li
- Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaoman Li
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Jiayi Li
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Zule Ning
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Qinxian Liu
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
| | - Shanghang Xie
- Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sumei Cao
- Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jinlin Du
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China
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Choi JHS, Jung DH. The role of psychological factors in predicting self-rated health: implications from machine learning models. PSYCHOL HEALTH MED 2025:1-13. [PMID: 39778189 DOI: 10.1080/13548506.2025.2450546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 12/31/2024] [Indexed: 01/11/2025]
Abstract
Self-rated health (SRH) is a significant predictor of future health outcomes. Despite the contribution of psychological factors in individuals' subjective health assessments, prior studies of machine learning-based prediction models primarily focused on health-related factors of SRH. Using the Midlife in the United States (MIDUS 2), the current study employed machine learning techniques to predict SRH based on a broad array of biological, psychological, and sociodemographic factors. Our analysis, involving logistic regression, LASSO regression, random forest, and XGBoost models, revealed robust predictive performance (AUPRC > 0.90) across all models. Emotion-related variables consistently emerged as vital predictors alongside health-related factors. The models highlighted the significance of psychological well-being, personality traits, and emotional states in determining individuals' subjective health ratings. Incorporating psychological factors into SRH prediction models offers a multifaceted perspective, enhancing our understanding of the complexities behind self-assessed health. This study underscores the necessity of considering emotional well-being alongside physical conditions in assessing and improving individuals' subjective health perceptions. Such insights hold promise for targeted interventions aimed at enhancing both physical health and emotional well-being to ameliorate subjective health assessments and potentially long-term health outcomes.
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Affiliation(s)
| | - Daniel Hong Jung
- Department of Public Policy and Management, University of Georgia, Athens, GA, USA
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Mustafa A, Wei C, Grovu R, Basman C, Kodra A, Maniatis G, Rutkin B, Weinberg M, Kliger C. Using novel machine learning tools to predict optimal discharge following transcatheter aortic valve replacement. Arch Cardiovasc Dis 2025; 118:26-34. [PMID: 39424448 DOI: 10.1016/j.acvd.2024.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/22/2024] [Accepted: 08/26/2024] [Indexed: 10/21/2024]
Abstract
BACKGROUND Although transcatheter aortic valve replacement has emerged as an alternative to surgical aortic valve replacement, it requires extensive healthcare resources, and optimal length of hospital stay has become increasingly important. This study was conducted to assess the potential of novel machine learning models (artificial neural network and eXtreme Gradient Boost) in predicting optimal hospital discharge following transcatheter aortic valve replacement. AIM To determine whether artificial neural network and eXtreme Gradient Boost models can be used to accurately predict optimal discharge following transcatheter aortic valve replacement. METHODS Data were collected from the 2016-2018 National Inpatient Sample database using International Classification of Diseases, Tenth Revision codes. Patients were divided into two cohorts based on length of hospital stay: optimal discharge (length of hospital stay 0-3 days); and late discharge (length of hospital stay 4-9 days). χ2 and t tests were performed to compare patient characteristics with optimal discharge and prolonged discharge. Logistic regression, artificial neural network and eXtreme Gradient Boost models were used to predict optimal discharge. Model performance was determined using area under the curve and F1 score. An area under the curve≥0.80 and an F1 score≥0.70 were considered strong predictive accuracy. RESULTS Twenty-five thousand and eight hundred and seventy-four patients who underwent transcatheter aortic valve replacement were analysed. Predictability of optimal discharge was similar amongst the models (area under the curve 0.80 in all models). In all models, patient disposition and elective procedure were the most important predictive factors. Coagulation disorder was the strongest co-morbidity predictor of whether a patient had an optimal discharge. CONCLUSIONS Artificial neural network and eXtreme Gradient Boost models had satisfactory performances, demonstrating similar accuracy to binary logistic regression in predicting optimal discharge following transcatheter aortic valve replacement. Further validation and refinement of these models may lead to broader clinical adoption.
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Affiliation(s)
- Ahmad Mustafa
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA.
| | - Chapman Wei
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Radu Grovu
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Craig Basman
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Arber Kodra
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Gregory Maniatis
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Bruce Rutkin
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Mitchell Weinberg
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
| | - Chad Kliger
- Department of Cardiology, Northwell Health, 2000 Marcus Avenue, Suite 300, New Hyde Park, NY, 11042-1069, USA
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10
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Chun L, Wang D, He L, Li D, Fu Z, Xue S, Su X, Zhou J. Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer. Sci Rep 2024; 14:22361. [PMID: 39333646 PMCID: PMC11436978 DOI: 10.1038/s41598-024-73837-3] [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: 07/02/2024] [Accepted: 09/20/2024] [Indexed: 09/29/2024] Open
Abstract
Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomograms and machine learning (ML) models, developing and validating an interpretable predictive model for paratracheal lymph node metastasis (PLNM) in cN0 PTC patients. We retrospectively selected 3213 PTC patients treated at the First Affiliated Hospital of Chongqing Medical University from 2016 to 2020. They were randomly divided into the training and test datasets with a 7:3 ratio. The 533 PTC patients treated at the Guangyuan Central Hospital from 2019 to 2022 were used as an external test sets. We developed and validated nine ML models using 10-fold cross-validation and grid search for hyperparameter tuning. The predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. The best model was compared to a traditional logistic regression-based nomogram. The XGBoost model achieved AUC values of 0.935, 0.857, and 0.775 in the training, validation, and test sets, respectively, significantly outperforming the traditional nomogram model with AUCs of 0.85, 0.844, and 0.769, respectively. SHapley Additive exPlanations (SHAP)-based visualization identified the top 10 predictive features of the XGBoost model, and a web-based calculator was created based on these features. ML is a reliable tool for predicting PLNM in cN0 PTC patients. The SHAP method provides valuable insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for paratracheal lymph node dissection.
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Affiliation(s)
- Lin Chun
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 404100, China
| | - Denghuan Wang
- Department of Thyroid and Breast Surgery, Guangyuan Central Hospital, Sichuan, 628400, China
| | - Liqiong He
- Department of Thyroid and Breast Surgery, Guangyuan Central Hospital, Sichuan, 628400, China
| | - Donglun Li
- Department of Nephrology, University Hospital Essen, University of Duisburg-Essen, 45147, Essen, Germany
| | - Zhiping Fu
- Department of Thyroid and Breast Surgery, Guangyuan Central Hospital, Sichuan, 628400, China
| | - Song Xue
- Intelligent Integrated Circuits and Systems Laboratory (SICS Lab), University of Electronic Science and Technology of China, Chengdu, 611730, China
| | - Xinliang Su
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 404100, China.
| | - Jing Zhou
- Department of Thyroid and Breast Surgery, Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401120, China.
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11
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Lee WR, Yoo KB, Noh JW, Lee M. Health expenditure trajectory and gastric cancer incidence in the National Health Insurance Senior Cohort: a nested case-control study. BMC Health Serv Res 2024; 24:1076. [PMID: 39285469 PMCID: PMC11406828 DOI: 10.1186/s12913-024-11494-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 08/27/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND Gastric cancer is the fourth most common cancer and highly prevalent in South Korea. As one of the predictors of gastric cancer, we focused on health utilization patterns and expenditures, as the surrogate variables of health conditions. This nested case-control study aimed to identify the association between health expenditure trajectory and incidence of gastric cancer. METHODS Data from the National Health Insurance Service Senior Cohort of South Korea were used. Individuals diagnosed with gastric cancer (N = 14,873) were matched to a non-diagnosed group (N = 44,619) in a 1:3 ratio using a nested case-control design. A latent class trajectory analysis was performed to identify the patterns of health expenditure among the matched participants. Furthermore, conditional logistic regression analysis was conducted to examine the relationship between healthcare expenditure trajectories and gastric cancer incidence. RESULTS Seven distinct health expenditure trajectories for five years were identified; consistently lowest (13.8%), rapidly increasing (5.9%), gradually increasing (13.8%), consistently second-highest (21.4%), middle-low (18.8%), gradually decreasing (13.1%), and consistently highest (13.2%). Compared to the middle-low group, individuals in the rapidly increasing [odds ratio (OR) = 2.11, 95% confidence interval (CI); 1.94-2.30], consistently lowest (OR = 1.40, 95% CI; 1.30-1.51), and gradually increasing (OR = 1.26, 95% CI; 1.17-1.35) groups exhibited a higher risk of developing gastric cancer. CONCLUSIONS Our findings suggest that health expenditure trajectories are predictors of gastric cancer. Potential risk groups can be identified by monitoring health expenditures.
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Affiliation(s)
- Woo-Ri Lee
- Department of Research and Analysis, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea
| | - Ki-Bong Yoo
- Division of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Republic of Korea
| | - Jin-Won Noh
- Division of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Republic of Korea
| | - Minjee Lee
- Department of Population Science and Policy, Southern Illinois University School of Medicine, 201 E. Madison Street, Springfield, IL, USA.
- Simmons Cancer Institute, Southern Illinois University School of Medicine, Springfield, IL, USA.
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12
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Xu L, Lyu J, Zheng X, Wang A. Risk Prediction Models for Gastric Cancer: A Scoping Review. J Multidiscip Healthc 2024; 17:4337-4352. [PMID: 39257385 PMCID: PMC11385365 DOI: 10.2147/jmdh.s479699] [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: 05/24/2024] [Accepted: 08/27/2024] [Indexed: 09/12/2024] Open
Abstract
Background Gastric cancer is a significant contributor to the global cancer burden. Risk prediction models aim to estimate future risk based on current and past information, and can be utilized for risk stratification in population screening programs for gastric cancer. This review aims to explore the research design of existing models, as well as the methods, variables, and performance of model construction. Methods Six databases were searched through to November 4, 2023 to identify appropriate studies. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Data sources included PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, focusing on gastric cancer risk prediction model studies. Results A total of 29 articles met the inclusion criteria, from which 28 original risk prediction models were identified that met the analysis criteria. The risk prediction model is screened, and the data extracted includes research characteristics, prediction variables selection, model construction methods and evaluation indicators. The area under the curve (AUC) of the models ranged from 0.560 to 0.989, while the C-statistics varied between 0.684 and 0.940. The number of predictor variables is mainly concentrated between 5 to 11. The top 5 most frequently included variables were age, helicobacter pylori (Hp), precancerous lesion, pepsinogen (PG), sex, and smoking. Age and Hp were the most consistently included variables. Conclusion This review enhances understanding of current gastric cancer risk prediction research and its future directions. The findings provide a strong scientific basis and technical support for developing more accurate gastric cancer risk models. We expect that these conclusions will point the way for future research and clinical practice in this area to assist in the early prevention and treatment of gastric cancer.
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Affiliation(s)
- Linyu Xu
- Department of Public Service, The First Affiliated Hospital of China Medical University, Shenyang, 110001, People’s Republic of China
| | - Jianxia Lyu
- Department of Public Service, The First Affiliated Hospital of China Medical University, Shenyang, 110001, People’s Republic of China
| | - Xutong Zheng
- Department of Public Service, The First Affiliated Hospital of China Medical University, Shenyang, 110001, People’s Republic of China
| | - Aiping Wang
- Department of Public Service, The First Affiliated Hospital of China Medical University, Shenyang, 110001, People’s Republic of China
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13
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Zhang X, Zhang M, Wei G, Wang J. Research on Predictive Auxiliary Diagnosis Method for Gastric Cancer Based on Non-Invasive Indicator Detection. APPLIED SCIENCES 2024; 14:6858. [DOI: 10.3390/app14166858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
Abstract
Chronic atrophic gastritis is a serious health issue beyond the stomach health problems that affect normal life. This study aimed to explore the influencing factors related to chronic atrophic gastritis (CAG) using non-invasive indicators and establish an optimal prediction model to aid in the clinical diagnosis of CAG. Electronic medical record data from 20,615 patients with CAG were analyzed, including routine blood tests, liver function tests, and coagulation tests. The logistic regression algorithm revealed that age, hematocrit, and platelet distribution width were significant influences suggesting chronic atrophic gastritis in the Chongqing population (p < 0.05), with an area under the curve (AUC) of 0.879. The predictive model constructed based on the Random Forest algorithm exhibited an accuracy of 83.15%, precision of 97.38%, recall of 77.36%, and an F1-score of 70.86%, outperforming the models constructed using XGBoost, KNN, and SVC algorithms in a comprehensive comparison. The prediction model derived from this study serves as a valuable tool for future studies and can aid in the prediction and screening of chronic atrophic gastritis.
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Affiliation(s)
- Xia Zhang
- School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
| | - Mao Zhang
- School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
| | - Gang Wei
- School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
| | - Jia Wang
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
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14
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Kim MK, Rouphael C, Wehbe S, Yoon JY, Wisnivesky J, McMichael J, Welch N, Dasarathy S, Zabor EC. Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model. GASTRO HEP ADVANCES 2024; 3:910-916. [PMID: 39286619 PMCID: PMC11402285 DOI: 10.1016/j.gastha.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 07/03/2024] [Indexed: 09/19/2024]
Abstract
Background and Aims Gastric cancer (GC) is a leading cause of cancer incidence and mortality globally. Population screening is limited by the low incidence and prevalence of GC in the United States. A risk prediction algorithm to identify high-risk patients allows for targeted GC screening. We aimed to determine the feasibility and performance of a logistic regression model based on electronic health records to identify individuals at high risk for noncardia gastric cancer (NCGC). Methods We included 614 patients who had a diagnosis of NCGC between ages 40 and 80 years and who were seen at our large tertiary medical center in multiple states between 2010 and 2021. Controls without a diagnosis of NCGC were randomly selected in a 1:10 ratio of cases to controls. Multiple imputation by chained equations for missing data followed by logistic regression on imputed datasets was used to estimate the probability of NCGC. Area under the curve and the 0.632 estimator was used as the estimate for discrimination. Results The 0.632 estimator value was 0.731, indicating robust model performance. Probability of NCGC was higher with increasing age (odds ratio [OR] = 1.16, 95% confidence interval [CI]: 1.04-1.3), male sex (OR = 1.97; 95% CI: 1.64-2.36), Black (OR = 3.07; 95% CI: 2.46-3.83) or Asian race (OR = 4.39; 95% CI: 2.60-7.42), tobacco use (OR = 1.61; 95% CI: 1.34-1.94), anemia (OR = 1.35; 95% CI: 1.09-1.68), and pernicious anemia (OR = 6.12, 95% CI: 3.42-10.95). Conclusion We demonstrate the feasibility and good performance of an electronic health record-based logistic regression model for estimating the probability of NCGC. Future studies will refine and validate this model, ultimately identifying a high-risk cohort who could be eligible for NCGC screening.
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Affiliation(s)
- Michelle Kang Kim
- Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio
| | - Carol Rouphael
- Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio
| | - Sarah Wehbe
- Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio
| | - Ji Yoon Yoon
- Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Juan Wisnivesky
- Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Pulmonary and Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - John McMichael
- Department of Surgery, Digestive Disease and Surgery Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | - Nicole Welch
- Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | - Srinivasan Dasarathy
- Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | - Emily C. Zabor
- Department of Quantitative Health Sciences, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio
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15
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Oh H, Cho S, Lee JA, Ryu S, Chang Y. Risk prediction model for gastric cancer within 5 years in healthy Korean adults. Gastric Cancer 2024; 27:675-683. [PMID: 38561527 DOI: 10.1007/s10120-024-01488-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 03/08/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Although endoscopy is commonly used for gastric cancer screening in South Korea, predictive models that integrate endoscopy results are scarce. We aimed to develop a 5-year gastric cancer risk prediction model using endoscopy results as a predictor. METHODS We developed a predictive model using the cohort data of the Kangbuk Samsung Health Study from 2011 to 2019. Among the 260,407 participants aged ≥20 years who did not have any previous history of cancer, 435 cases of gastric cancer were observed. A Cox proportional hazard regression model was used to evaluate the predictors and calculate the 5-year risk of gastric cancer. Harrell's C-statistics and Nam-D'Agostino χ2 test were used to measure the quality of discrimination and calibration ability, respectively. RESULTS We included age, sex, smoking status, alcohol consumption, family history of cancer, and previous results for endoscopy in the risk prediction model. This model showed sufficient discrimination ability [development cohort: C-Statistics: 0.800, 95% confidence interval (CI) 0.770-0.829; validation cohort: C-Statistics: 0.799, 95% CI 0.743-0.856]. It also performed well with effective calibration (development cohort: χ2 = 13.65, P = 0.135; validation cohort: χ2 = 15.57, P = 0.056). CONCLUSION Our prediction model, including young adults, showed good discrimination and calibration. Furthermore, this model considered a fixed time interval of 5 years to predict the risk of developing gastric cancer, considering endoscopic results. Thus, it could be clinically useful, especially for adults with endoscopic results.
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Affiliation(s)
- Hyungseok Oh
- Workplace Health Institute, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sunwoo Cho
- Workplace Health Institute, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Jung Ah Lee
- Department of Family Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
| | - Seungho Ryu
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
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16
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Ding Y. Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality. Diseases 2024; 12:139. [PMID: 39057110 PMCID: PMC11275333 DOI: 10.3390/diseases12070139] [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: 06/01/2024] [Revised: 06/24/2024] [Accepted: 06/29/2024] [Indexed: 07/28/2024] Open
Abstract
In recent years, the escalating environmental challenges have contributed to a rising incidence of cancer. The precise anticipation of cancer incidence and mortality rates has emerged as a pivotal focus in scientific inquiry, exerting a profound impact on the formulation of public health policies. This investigation adopts a pioneering machine learning framework to address this critical issue, utilizing a dataset encompassing 72,591 comprehensive records that include essential variables such as age, case count, population size, race, gender, site, and year of diagnosis. Diverse machine learning algorithms, including decision trees, random forests, logistic regression, support vector machines, and neural networks, were employed in this study. The ensuing analysis revealed testing accuracies of 62.17%, 61.92%, 54.53%, 55.72%, and 62.30% for the respective models. This state-of-the-art model not only enhances our understanding of cancer dynamics but also equips researchers and policymakers with the capability of making meticulous projections concerning forthcoming cancer incidence and mortality rates. Considering sustainability, the application of this advanced machine learning framework emphasizes the importance of judiciously utilizing extensive and intricate databases. By doing so, it facilitates a more sustainable approach to healthcare planning, allowing for informed decision-making that takes into account the long-term ecological and societal impacts of cancer-related policies. This integrative perspective underscores the broader commitment to sustainable practices in both health research and public policy formulation.
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Affiliation(s)
- Yuanzhao Ding
- School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
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17
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Pattilachan TM, Christodoulou M, Ross S. Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer. J Robot Surg 2024; 18:259. [PMID: 38900376 DOI: 10.1007/s11701-024-02005-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024]
Abstract
Gastric cancer remains a formidable health challenge worldwide; early detection and effective surgical intervention are critical for improving patient outcomes. This comprehensive review explores the evolving landscape of gastric cancer management, emphasizing the significant contributions of artificial intelligence (AI) in revolutionizing both diagnostic and therapeutic approaches. Despite advancements in the medical field, the subtle nature of early gastric cancer symptoms often leads to late-stage diagnoses, where survival rates are notably decreased. Historically, the treatment of gastric cancer has transitioned from palliative care to surgical resection, evolving further with the introduction of minimally invasive surgical (MIS) techniques. In the current era, AI has emerged as a transformative force, enhancing the precision of early gastric cancer detection through sophisticated image analysis, and supporting surgical decision-making with predictive modeling and real-time preop-, intraop-, and postoperative guidance. However, the deployment of AI in healthcare raises significant ethical, legal, and practical challenges, including the necessity for ongoing professional education and the development of standardized protocols to ensure patient safety and the effective use of AI technologies. Future directions point toward a synergistic integration of AI with clinical best practices, promising a new era of personalized, efficient, and safer gastric cancer management.
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Affiliation(s)
- Tara Menon Pattilachan
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA
| | - Maria Christodoulou
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA
| | - Sharona Ross
- AdventHealth Tampa, Surgery College of Medicine, Digestive Health Institute, University of Central Florida (UCF), 3000 Medical Park Drive, Suite #500, Tampa, FL, 33613, USA.
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18
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Yazici H, Ugurlu O, Aygul Y, Ugur MA, Sen YK, Yildirim M. Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians. BMC Emerg Med 2024; 24:101. [PMID: 38886641 PMCID: PMC11184860 DOI: 10.1186/s12873-024-01023-9] [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: 12/07/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUNDS Acute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA. METHODS Data regarding operated AA patients between 2012 and 2022 were analyzed retrospectively. Based on operative findings, patients were evaluated under two groups: perforated AA and none-perforated AA. The features that showed statistical significance (p < 0.05) in both univariate and multivariate analysis were included in the prediction models as input features. Five different error metrics and the area under the receiver operating characteristic curve (AUC) were used for model comparison. RESULTS A total number of 1132 patients were included in the study. Patients were divided into training (932 samples), testing (100 samples), and validation (100 samples) sets. Age, gender, neutrophil count, lymphocyte count, Neutrophil to Lymphocyte ratio, total bilirubin, C-Reactive Protein (CRP), Appendix Diameter, and PeriAppendicular Liquid Collection (PALC) were significantly different between the two groups. In the multivariate analysis, age, CRP, and PALC continued to show a significant difference in the perforated AA group. According to univariate and multivariate analysis, two data sets were used in the prediction model. K-Nearest Neighbors and Logistic Regression algorithms achieved the best prediction performance in the validation group with an accuracy of 96%. CONCLUSION The results showed that using only three input features (age, CRP, and PALC), the severity of AA can be predicted with high accuracy. The developed prediction model can be useful in clinical practice.
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Affiliation(s)
- Hilmi Yazici
- General Surgery Department, Marmara University Pendik Research and Training Hospital, Istanbul, Turkey.
| | - Onur Ugurlu
- Faculty of Engineering and Architecture, Izmir Bakircay University, Izmir, Turkey
| | - Yesim Aygul
- Department of Mathematics, Ege University, Izmir, Turkey
| | - Mehmet Alperen Ugur
- General Surgery Department, University of Health Sciences Izmir Bozyaka Research and Training Hospital, Izmir, Turkey
| | - Yigit Kaan Sen
- General Surgery Department, University of Health Sciences Izmir Bozyaka Research and Training Hospital, Izmir, Turkey
| | - Mehmet Yildirim
- General Surgery Department, University of Health Sciences Izmir Bozyaka Research and Training Hospital, Izmir, Turkey
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19
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Lin J, Zhu F, Dong X, Li R, Liu J, Xia J. Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback. Comput Biol Med 2024; 175:108535. [PMID: 38714049 DOI: 10.1016/j.compbiomed.2024.108535] [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: 01/30/2024] [Revised: 04/05/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
Gastric cancer (GC), an acknowledged malignant neoplasm, threatens life and digestive system functionality if not detected and addressed promptly in its nascent stages. The indispensability of early detection for GC to augment treatment efficacy and survival prospects forms the crux of this investigation. Our study introduces an innovative wrapper-based feature selection methodology, referred to as bCIFMVO-FKNN-FS, which integrates a crossover-information feedback multi-verse optimizer (CIFMVO) with the fuzzy k-nearest neighbors (FKNN) classifier. The primary goal of this initiative is to develop an advanced screening model designed to accelerate the identification of patients with early-stage GC. Initially, the capability of CIFMVO is validated through its application to the IEEE CEC benchmark functions, during which its optimization efficiency is measured against eleven cutting-edge algorithms across various dimensionalities-10, 30, 50, and 100. Subsequent application of the bCIFMVO-FKNN-FS model to the clinical data of 1632 individuals from Wenzhou Central Hospital-diagnosed with either early-stage GC or chronic gastritis-demonstrates the model's formidable predictive accuracy (83.395%) and sensitivity (87.538%). Concurrently, this investigation delineates age, gender, serum gastrin-17, serum pepsinogen I, and the serum pepsinogen I to serum pepsinogen II ratio as parameters significantly associated with early-stage GC. These insights not only validate the efficacy of our proposed model in the early screening of GC but also contribute substantively to the corpus of knowledge facilitating early diagnosis.
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Affiliation(s)
- Jiejun Lin
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Fangchao Zhu
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Xiaoyu Dong
- Department of Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Rizeng Li
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jisheng Liu
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jianfu Xia
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
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20
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Wang DQ, Xu WH, Cheng XW, Hua L, Ge XS, Liu L, Gao X. Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer. Front Immunol 2024; 15:1407632. [PMID: 38840913 PMCID: PMC11150638 DOI: 10.3389/fimmu.2024.1407632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
Background Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions. Materials and methods A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions. Results The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS. Conclusion A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.
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Affiliation(s)
- Dan-qi Wang
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Wen-huan Xu
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-wei Cheng
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Lei Hua
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-song Ge
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Li Liu
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiang Gao
- Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
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21
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Li M, Gao N, Wang SL, Guo YF, Liu Z. Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis. World J Gastrointest Oncol 2024; 16:2200-2218. [PMID: 38764808 PMCID: PMC11099465 DOI: 10.4251/wjgo.v16.i5.2200] [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: 01/19/2024] [Revised: 02/08/2024] [Accepted: 03/11/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND The lack of specific symptoms of gastric cancer (GC) causes great challenges in its early diagnosis. Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the survival rates. AIM To assist physicians in identifying changes in the output of publications and research hotspots related to risk factors for GC, constructing a list of key risk factors, and providing a reference for early identification of patients at high risk for GC. METHODS Research articles on risk factors for GC were searched in the Web of Science core collection, and relevant information was extracted after screening. The literature was analyzed using Microsoft Excel 2019, CiteSpace V, and VOSviewer 1.6.18. RESULTS A total of 2514 papers from 72 countries and 2507 research institutions were retrieved. China (n = 1061), National Cancer Center (n = 138), and Shoichiro Tsugane (n = 36) were the most productive country, institution, or author, respectively. The research hotspots in the study of risk factors for GC are summarized in four areas, namely: Helicobacter pylori (H. pylori) infection, single nucleotide polymorphism, bio-diagnostic markers, and GC risk prediction models. CONCLUSION In this study, we found that H. pylori infection is the most significant risk factor for GC; single-nucleotide polymorphism (SNP) is the most dominant genetic factor for GC; bio-diagnostic markers are the most promising diagnostic modality for GC. GC risk prediction models are the latest current research hotspot. We conclude that the most important risk factors for the development of GC are H. pylori infection, SNP, smoking, diet, and alcohol.
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Affiliation(s)
- Meng Li
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Ning Gao
- Department of Acupuncture and Moxibustion, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Shao-Li Wang
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Yu-Feng Guo
- Department of Acupuncture and Moxibustion, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Zhen Liu
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
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22
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Chen Z, Wang Y, Ying MTC, Su Z. Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease. J Nephrol 2024; 37:1027-1039. [PMID: 38315278 PMCID: PMC11239734 DOI: 10.1007/s40620-023-01878-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 12/26/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients. METHODS A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output. RESULTS The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94-0.99; average precision = 0.97, 95% CI 0.97-0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73-0.98; average precision = 0.90, 95% CI 0.86-0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features' impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension. CONCLUSION This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.
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Affiliation(s)
- Ziman Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
| | - Yingli Wang
- Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China
| | - Michael Tin Cheung Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
| | - Zhongzhen Su
- Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China
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23
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Islam W, Abdoli N, Alam TE, Jones M, Mutembei BM, Yan F, Tang Q. A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients. Diagnostics (Basel) 2024; 14:954. [PMID: 38732368 PMCID: PMC11083029 DOI: 10.3390/diagnostics14090954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. OBJECTIVE This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. METHODS A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. RESULTS Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. CONCLUSIONS This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.
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Affiliation(s)
- Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (W.I.); (N.A.)
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA; (W.I.); (N.A.)
| | - Tasfiq E. Alam
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Meredith Jones
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Bornface M. Mutembei
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Feng Yan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
| | - Qinggong Tang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.J.); (B.M.M.); (F.Y.)
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24
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Huang RJ, Huang ES, Mudiganti S, Chen T, Martinez MC, Ramrakhiani S, Han SS, Hwang JH, Palaniappan LP, Liang SY. Risk of Gastric Adenocarcinoma in a Multiethnic Population Undergoing Routine Care: An Electronic Health Records Cohort Study. Cancer Epidemiol Biomarkers Prev 2024; 33:547-556. [PMID: 38231023 PMCID: PMC10990787 DOI: 10.1158/1055-9965.epi-23-1200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/05/2023] [Accepted: 01/12/2024] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Gastric adenocarcinoma (GAC) is often diagnosed at advanced stages and portends a poor prognosis. We hypothesized that electronic health records (EHR) could be leveraged to identify individuals at highest risk for GAC from the population seeking routine care. METHODS This was a retrospective cohort study, with endpoint of GAC incidence as ascertained through linkage to an institutional tumor registry. We utilized 2010 to 2020 data from the Palo Alto Medical Foundation, a large multispecialty practice serving Northern California. The analytic cohort comprised individuals ages 40-75 receiving regular ambulatory care. Variables collected included demographic, medical, pharmaceutical, social, and familial data. Electronic phenotyping was based on rule-based methods. RESULTS The cohort comprised 316,044 individuals and approximately 2 million person-years (p-y) of observation. 157 incident GACs occurred (incidence 7.9 per 100,000 p-y), of which 102 were non-cardia GACs (incidence 5.1 per 100,000 p-y). In multivariable analysis, male sex [HR: 2.2, 95% confidence interval (CI): 1.6-3.1], older age, Asian race (HR: 2.5, 95% CI: 1.7-3.7), Hispanic ethnicity (HR: 1.9, 95% CI: 1.1-3.3), atrophic gastritis (HR: 4.6, 95% CI: 2.2-9.3), and anemia (HR: 1.9, 95% CI: 1.3-2.6) were associated with GAC risk; use of NSAID was inversely associated (HR: 0.3, 95% CI: 0.2-0.5). Older age, Asian race, Hispanic ethnicity, atrophic gastritis, and anemia were associated with non-cardia GAC. CONCLUSIONS Routine EHR data can stratify the general population for GAC risk. IMPACT Such methods may help triage populations for targeted screening efforts, such as upper endoscopy.
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Affiliation(s)
- Robert J Huang
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California
| | - Edward S Huang
- Department of Gastroenterology, Palo Alto Medical Foundation, San Jose, California
| | - Satish Mudiganti
- Palo Alto Medical Foundation Research Institute, Palo Alto Medical Foundation, Palo Alto, California
| | - Tony Chen
- Palo Alto Medical Foundation Research Institute, Palo Alto Medical Foundation, Palo Alto, California
| | - Meghan C Martinez
- Palo Alto Medical Foundation Research Institute, Palo Alto Medical Foundation, Palo Alto, California
| | - Sanjay Ramrakhiani
- Department of Gastroenterology, Palo Alto Medical Foundation, San Jose, California
| | - Summer S Han
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California
| | - Joo Ha Hwang
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, California
| | - Latha P Palaniappan
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California
| | - Su-Ying Liang
- Palo Alto Medical Foundation Research Institute, Palo Alto Medical Foundation, Palo Alto, California
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25
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Park H, Jung SY, Han MK, Jang Y, Moon YR, Kim T, Shin SY, Hwang H. Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management. J Pers Med 2024; 14:316. [PMID: 38541058 PMCID: PMC10971532 DOI: 10.3390/jpm14030316] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 12/04/2024] Open
Abstract
This study investigates the feasibility of accurately predicting adverse health events without relying on costly data acquisition methods, such as laboratory tests, in the era of shifting healthcare paradigms towards community-based health promotion and personalized preventive healthcare through individual health risk assessments (HRAs). We assessed the incremental predictive value of four categories of predictor variables-demographic, lifestyle and family history, personal health device, and laboratory data-organized by data acquisition costs in the prediction of the risks of mortality and five chronic diseases. Machine learning methodologies were employed to develop risk prediction models, assess their predictive performance, and determine feature importance. Using data from the National Sample Cohort of the Korean National Health Insurance Service (NHIS), which includes eligibility, medical check-up, healthcare utilization, and mortality data from 2002 to 2019, our study involved 425,148 NHIS members who underwent medical check-ups between 2009 and 2012. Models using demographic, lifestyle, family history, and personal health device data, with or without laboratory data, showed comparable performance. A feature importance analysis in models excluding laboratory data highlighted modifiable lifestyle factors, which are a superior set of variables for developing health guidelines. Our findings support the practicality of precise HRAs using demographic, lifestyle, family history, and personal health device data. This approach addresses HRA barriers, particularly for healthy individuals, by eliminating the need for costly and inconvenient laboratory data collection, advancing accessible preventive health management strategies.
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Affiliation(s)
- Hayoung Park
- KakaoHealthCare Corp., Seongnam-si 13529, Gyeonggi-do, Republic of Korea; (H.P.); (Y.R.M.); (T.K.); (S.-Y.S.); (H.H.)
| | - Se Young Jung
- Department of Digital Healthcare, Seoul National University Bundang Hospital, Seongnam-si 13620, Gyeonggi-do, Republic of Korea; (S.Y.J.); (Y.J.)
| | - Min Kyu Han
- KakaoHealthCare Corp., Seongnam-si 13529, Gyeonggi-do, Republic of Korea; (H.P.); (Y.R.M.); (T.K.); (S.-Y.S.); (H.H.)
| | - Yeonhoon Jang
- Department of Digital Healthcare, Seoul National University Bundang Hospital, Seongnam-si 13620, Gyeonggi-do, Republic of Korea; (S.Y.J.); (Y.J.)
| | - Yeo Rae Moon
- KakaoHealthCare Corp., Seongnam-si 13529, Gyeonggi-do, Republic of Korea; (H.P.); (Y.R.M.); (T.K.); (S.-Y.S.); (H.H.)
| | - Taewook Kim
- KakaoHealthCare Corp., Seongnam-si 13529, Gyeonggi-do, Republic of Korea; (H.P.); (Y.R.M.); (T.K.); (S.-Y.S.); (H.H.)
| | - Soo-Yong Shin
- KakaoHealthCare Corp., Seongnam-si 13529, Gyeonggi-do, Republic of Korea; (H.P.); (Y.R.M.); (T.K.); (S.-Y.S.); (H.H.)
| | - Hee Hwang
- KakaoHealthCare Corp., Seongnam-si 13529, Gyeonggi-do, Republic of Korea; (H.P.); (Y.R.M.); (T.K.); (S.-Y.S.); (H.H.)
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26
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Srivastava S, Jain P. Computational Approaches: A New Frontier in Cancer Research. Comb Chem High Throughput Screen 2024; 27:1861-1876. [PMID: 38031782 DOI: 10.2174/0113862073265604231106112203] [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: 06/30/2023] [Revised: 09/08/2023] [Accepted: 09/21/2023] [Indexed: 12/01/2023]
Abstract
Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.
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Affiliation(s)
- Shubham Srivastava
- Department of Pharmacy, IIMT College of Pharmacy, Uttar Pradesh, 201310, India
| | - Pushpendra Jain
- Department of Pharmacy, IIMT College of Pharmacy, Uttar Pradesh, 201310, India
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27
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Ke X, Cai X, Bian B, Shen Y, Zhou Y, Liu W, Wang X, Shen L, Yang J. Predicting early gastric cancer risk using machine learning: A population-based retrospective study. Digit Health 2024; 10:20552076241240905. [PMID: 38559579 PMCID: PMC10979538 DOI: 10.1177/20552076241240905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Background Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators. Methods Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results. Results The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses. Conclusions The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.
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Affiliation(s)
- Xing Ke
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyu Cai
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bingxian Bian
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanheng Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yunlan Zhou
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Liu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Xu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lisong Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
| | - Junyao Yang
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
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28
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Kumar V, Gaddam M, Moustafa A, Iqbal R, Gala D, Shah M, Gayam VR, Bandaru P, Reddy M, Gadaputi V. The Utility of Artificial Intelligence in the Diagnosis and Management of Pancreatic Cancer. Cureus 2023; 15:e49560. [PMID: 38156176 PMCID: PMC10754023 DOI: 10.7759/cureus.49560] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 12/30/2023] Open
Abstract
Artificial intelligence (AI) has made significant advancements in the medical domain in recent years. AI, an expansive field comprising Machine Learning (ML) and, within it, Deep Learning (DL), seeks to emulate the intricate operations of the human brain. It examines vast amounts of data and plays a crucial role in decision-making, overcoming limitations related to human evaluation. DL utilizes complex algorithms to analyze data. ML and DL are subsets of AI that utilize hard statistical techniques that help machines consistently improve at tasks with experience. Pancreatic cancer is more common in developed countries and is one of the leading causes of cancer-related mortality worldwide. Managing pancreatic cancer remains a challenge despite significant advancements in diagnosis and treatment. AI has secured an almost ubiquitous presence in the field of oncological workup and management, especially in gastroenterology malignancies. AI is particularly useful for various investigations of pancreatic carcinoma because it has specific radiological features that enable diagnostic procedures without the requirement of a histological study. However, interpreting and evaluating resulting images is not always simple since images vary as the disease progresses. Secondly, a number of factors may impact prognosis and response to the treatment process. Currently, AI models have been created for diagnosing, grading, staging, and predicting prognosis and treatment response. This review presents the most up-to-date knowledge on the use of AI in the diagnosis and treatment of pancreatic carcinoma.
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Affiliation(s)
- Vikash Kumar
- Internal Medicine, The Brooklyn Hospital Center, Brooklyn, USA
| | | | - Amr Moustafa
- Internal Medicine, The Brooklyn Hospital Center, Brooklyn, USA
| | - Rabia Iqbal
- Internal Medicine, The Brooklyn Hospital Center, Brooklyn, USA
| | - Dhir Gala
- Internal Medicine, American University of the Caribbean School of Medicine, Sint Maarten, SXM
| | - Mili Shah
- Internal Medicine, American University of the Caribbean School of Medicine, Sint Maarten, SXM
| | - Vijay Reddy Gayam
- Gastroenterology and Hepatology, The Brooklyn Hospital Center, Brooklyn, USA
| | - Praneeth Bandaru
- Gastroenterology and Hepatology, The Brooklyn Hospital Center, Brooklyn, USA
| | - Madhavi Reddy
- Gastroenterology and Hepatology, The Brooklyn Hospital Center, Brooklyn, USA
| | - Vinaya Gadaputi
- Gastroenterology and Hepatology, Blanchard Valley Health System, Findlay, USA
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Wong MCS, Leung EY, Yau STY, Chan SC, Xie S, Xu W, Huang J. Prediction algorithm for gastric cancer in a general population: A validation study. Cancer Med 2023; 12:20544-20553. [PMID: 37855240 PMCID: PMC10660462 DOI: 10.1002/cam4.6629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 09/04/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Worldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population. METHODS We included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve. RESULTS Age, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non-use of aspirin, non-steroidal anti-inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model. CONCLUSIONS This study has validated a simple, accurate, and easy-to-use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation.
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Affiliation(s)
- Martin C. S. Wong
- The Jockey Club School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongSARChina
- Centre for Health Education and Health Promotion, Faculty of MedicineChinese University of Hong KongHong KongSARChina
- School of Public HealthThe Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- School of Public HealthThe Peking UniversityBeijingChina
- School of Public HealthFudan UniversityShanghaiChina
| | - Eman Yee‐man Leung
- The Jockey Club School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongSARChina
| | - Sarah T. Y. Yau
- The Jockey Club School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongSARChina
| | - Sze Chai Chan
- The Jockey Club School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongSARChina
| | - Shaohua Xie
- Department of Molecular medicine and SurgeryKarolinska InstitutetSweden
| | - Wanghong Xu
- School of Public HealthFudan UniversityShanghaiChina
| | - Junjie Huang
- The Jockey Club School of Public Health and Primary Care, Faculty of MedicineChinese University of Hong KongHong KongSARChina
- Centre for Health Education and Health Promotion, Faculty of MedicineChinese University of Hong KongHong KongSARChina
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Ma X, Pierce E, Anand H, Aviles N, Kunk P, Alemazkoor N. Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancer. BMC Cancer 2023; 23:910. [PMID: 37759332 PMCID: PMC10536729 DOI: 10.1186/s12885-023-11422-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/20/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The goal of therapy for many patients with advanced stage malignancies, including those with metastatic gastric and esophageal cancers, is to extend overall survival while also maintaining quality of life. After weighing the risks and benefits of treatment with palliative chemotherapy (PC) with non-curative intent, many patients decide to pursue treatment. It is known that a subset of patients who are treated with PC experience significant side effects without clinically significant survival benefits from PC. METHODS We use data from 150 patients with stage-IV gastric and esophageal cancers to train machine learning models that predict whether a patient with stage-IV gastric or esophageal cancers would benefit from PC, in terms of increased survival duration, at very early stages of the treatment. RESULTS Our findings show that machine learning can predict with high accuracy whether a patient will benefit from PC at the time of diagnosis. More accurate predictions can be obtained after only two cycles of PC (i.e., about 4 weeks after diagnosis). The results from this study are promising with regard to potential improvements in quality of life for patients near the end of life and a potential overall survival benefit by optimizing systemic therapy earlier in the treatment course of patients.
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Affiliation(s)
- Xiaoyuan Ma
- Department of Statistics, University of Virginia, Charlottesville, USA
| | - Eric Pierce
- School of Medicine, University of Virginia, Charlottesville, USA
| | - Harsh Anand
- System and Information Engineering, University of Virginia, Charlottesville, USA
| | - Natalie Aviles
- Department of Sociology, University of Virginia, Charlottesville, USA
| | - Paul Kunk
- School of Medicine, University of Virginia, Charlottesville, USA
| | - Negin Alemazkoor
- System and Information Engineering, University of Virginia, Charlottesville, USA.
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Xing L, Zhang X, Guo Y, Bai D, Xu H. XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population. Am J Orthod Dentofacial Orthop 2023; 164:357-367. [PMID: 36959014 DOI: 10.1016/j.ajodo.2023.01.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 01/01/2023] [Accepted: 01/01/2023] [Indexed: 03/25/2023]
Abstract
INTRODUCTION Prediction of lip prominence based on hard-tissue measurements could be helpful in orthodontic treatment planning and has been challenging and formidable thus far. METHODS A machine learning-based cross-sectional study was conducted on 1549 patients. Hard-tissue measurements and demographic information were used as the input features. Seven popular machine learning algorithms were applied to the datasets to predict upper and lower lip prominence. The algorithm that performed the best was selected for the construction of the prediction model. Evaluation of feature importance was conducted using 3 classical methods. RESULTS Among the 7 algorithms, the XGBoost model performed the best in the prediction of the distances between labrale superius or labrale inferius to the esthetics plane (UL-EP and LL-EP distances), with root mean square error values of 1.25, 1.49 and r2 values of 0.755 and 0.683, respectively. Among the 14 input features, the L1-NB distance contributed the most to the prominences of the upper and lower lips. A lip prominence predictor was developed to facilitate clinical application by deploying the prediction model into a downloadable tool kit. CONCLUSIONS The XGBoost model performed well with high accuracy and practicability in predicting upper and lower lip prominence. The artificial intelligence-aided predictor could serve as a reference for orthodontic treatment planning.
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Affiliation(s)
- Lu Xing
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China
| | - Xiaoqi Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China
| | - Yongwen Guo
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China; Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ding Bai
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China; Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Hui Xu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China; Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
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Nguyen NLT, Dang NDT, Vu QVAN, Dang AK, Ta TVAN. A Model for Gastric Cancer Risk Prediction Based on MUC1 Polymorphisms and Health-risk Behaviors in a Vietnamese Population. In Vivo 2023; 37:2347-2356. [PMID: 37652501 PMCID: PMC10500499 DOI: 10.21873/invivo.13339] [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: 06/03/2023] [Revised: 07/02/2023] [Accepted: 07/05/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND/AIM Although the expression of mucin 1(MUC1) and prostate stem cell antigen (PSCA) genes is correlated with gastric cancer development and progression, the utility of these two genes as biomarkers of gastric cancer prognosis still needs to be confirmed in clinical practice. This study aimed to develop a model predictive of gastric cancer that integrates several significant single nucleotide polymorphisms (SNPs) of MUC1 and PSCA genes, and some health-risk behavior factors in a Vietnamese population. PATIENTS AND METHODS A total of 302 patients with primary gastric carcinoma and 304 healthy persons were included in a case-control study. The generalized linear model was used with the profile of age, sex, history of smoking and using alcohol, personal and family medical history of stomach diseases, and the SNPs of MUC1 and PSCA. The prognostic value of the model was assessed by the area under a receiver operating characteristic curve (AUC) and Akaike Information Criterion (AIC) values. RESULTS In male participants, the final model, consisting of age, sex, history of smoking and using alcohol, personal and family medical history of stomach diseases and SNP MUC1 rs4072037, provided acceptable discrimination, with an AUC of 0.6374 and the lowest AIC value (539.53). In female participants, the predictive model including age, sex, history of smoking and using alcohol, personal and family medical history of stomach diseases, SNPs MUC1 rs4072037 and rs2070803 had an AUC of 0.6937 and AIC of 266.80. The calibration plots of the male model approximately fitted the ideal calibration line. CONCLUSION The predictive model based on age, sex, medical history, and genetic and health-risk behavior factors has a high potential in determining gastric cancer. Further studies that elucidate other genetic variants should be carried out to define high-risk gastric cancer groups and propose appropriate personalized prevention.
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Affiliation(s)
| | - Ngoc Dung Thi Dang
- Hanoi Medical University Hospital, Hanoi Medical University, Hanoi, Vietnam;
| | - Quy VAN Vu
- Hanoi Medical University Hospital, Hanoi Medical University, Hanoi, Vietnam
| | - Anh Kim Dang
- School of Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | - Thanh-VAN Ta
- Hanoi Medical University Hospital, Hanoi Medical University, Hanoi, Vietnam;
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Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review. J Clin Epidemiol 2023; 157:120-133. [PMID: 36935090 PMCID: PMC11913775 DOI: 10.1016/j.jclinepi.2023.03.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/03/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
OBJECTIVES In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Meta-Research Centre, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK, ST5 5BG
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands; EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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He S, Sun D, Li H, Cao M, Yu X, Lei L, Peng J, Li J, Li N, Chen W. Real-World Practice of Gastric Cancer Prevention and Screening Calls for Practical Prediction Models. Clin Transl Gastroenterol 2023; 14:e00546. [PMID: 36413795 PMCID: PMC9944379 DOI: 10.14309/ctg.0000000000000546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 10/11/2022] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Some gastric cancer prediction models have been published. Still, the value of these models for application in real-world practice remains unclear. We aim to summarize and appraise modeling studies for gastric cancer risk prediction and identify potential barriers to real-world use. METHODS This systematic review included studies that developed or validated gastric cancer prediction models in the general population. RESULTS A total of 4,223 studies were screened. We included 18 development studies for diagnostic models, 10 for prognostic models, and 1 external validation study. Diagnostic models commonly included biomarkers, such as Helicobacter pylori infection indicator, pepsinogen, hormone, and microRNA. Age, sex, smoking, body mass index, and family history of gastric cancer were frequently used in prognostic models. Most of the models were not validated. Only 25% of models evaluated the calibration. All studies had a high risk of bias, but over half had acceptable applicability. Besides, most studies failed to clearly report the application scenarios of prediction models. DISCUSSION Most gastric cancer prediction models showed common shortcomings in methods, validation, and reports. Model developers should further minimize the risk of bias, improve models' applicability, and report targeting application scenarios to promote real-world use.
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Affiliation(s)
- Siyi He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Dianqin Sun
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - He Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Maomao Cao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Xinyang Yu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Lin Lei
- Department of Cancer Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong Province, China
| | - Ji Peng
- Department of Cancer Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong Province, China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
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Afrash MR, Shafiee M, Kazemi-Arpanahi H. Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors. BMC Gastroenterol 2023; 23:6. [PMID: 36627564 PMCID: PMC9832798 DOI: 10.1186/s12876-022-02626-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors. METHODS This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models' performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation. RESULTS This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer. CONCLUSIONS The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.
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Affiliation(s)
- Mohammad Reza Afrash
- grid.411705.60000 0001 0166 0922Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Mohsen Shafiee
- Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
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Mokhria RK, Singh J. Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma. Artif Intell Gastroenterol 2022; 3:96-104. [DOI: 10.35712/aig.v3.i4.96] [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: 06/07/2022] [Revised: 07/30/2022] [Accepted: 09/14/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.
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Affiliation(s)
- Rajesh Kumar Mokhria
- Government Model Sanskriti Senior Secondary School, Chulkana, 132101, Panipat, Haryana, India
| | - Jasbir Singh
- Department of Biochemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
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Chen A, Chen DO. Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data. Sci Rep 2022; 12:17917. [PMID: 36289292 PMCID: PMC9606301 DOI: 10.1038/s41598-022-23011-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/21/2022] [Indexed: 01/20/2023] Open
Abstract
When enabled by machine learning (ML), Learning Health Systems (LHS) hold promise for improving the effectiveness of healthcare delivery to patients. One major barrier to LHS research and development is the lack of access to EHR patient data. To overcome this challenge, this study demonstrated the feasibility of developing a simulated ML-enabled LHS using synthetic patient data. The ML-enabled LHS was initialized using a dataset of 30,000 synthetic Synthea patients and a risk prediction XGBoost base model for lung cancer. 4 additional datasets of 30,000 patients were generated and added to the previous updated dataset sequentially to simulate addition of new patients, resulting in datasets of 60,000, 90,000, 120,000 and 150,000 patients. New XGBoost models were built in each instance, and performance improved with data size increase, attaining 0.936 recall and 0.962 AUC (area under curve) in the 150,000 patients dataset. The effectiveness of the new ML-enabled LHS process was verified by implementing XGBoost models for stroke risk prediction on the same Synthea patient populations. By making the ML code and synthetic patient data publicly available for testing and training, this first synthetic LHS process paves the way for more researchers to start developing LHS with real patient data.
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Affiliation(s)
- Anjun Chen
- LHS Technology Forum Initiative, Learning Health Community, 748 Matadero Ave, Palo Alto, CA, 94306, USA.
| | - Drake O Chen
- LHS Technology Forum Initiative, Learning Health Community, 748 Matadero Ave, Palo Alto, CA, 94306, USA
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Baranovsky AY, Tsvetkova TL. The stomach cancer prognosis map is the basis for the formation of a register of patients with precancerous diseases. EXPERIMENTAL AND CLINICAL GASTROENTEROLOGY 2022:39-45. [DOI: 10.31146/1682-8658-ecg-205-9-39-45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
Abstract
A single-stage retrospective observational comparative study was conducted to find the most significant risk factors for stomach cancer. The analysis of 36 risk factors for stomach cancer in 143 patients aged 32 to 83 years, indigenous residents of cities, regions and republics of the Northwestern Federal District of Russia who underwent complex, including surgical treatment of this disease. The control group consisted of 128 people who underwent in-depth medical examination in the amount necessary for the program of this study. The ranking of the studied risk factors for gastric cancer according to their degree of prognostic significance is presented as follows: the presence of precancerous diseases accompanied by progressive atrophy of the gastric mucosa, intestinal metaplasia and/or dysplasia in combination with prolonged gastric helicobacteriosis; a decrease in the blood content of pepsinogen I and stimulated gastrin-17, as well as a decrease in the ratio of PG I/PG II; prolonged presence of anemia, leukopenia, neutropenia, lymphopenia, thrombocytopenia, especially in men over 50 years of age with bad habits (smoking, alcohol abuse); presence in patients with diabetes mellitus and obesity, especially when combined with them and under the condition of an increased index of glycosed hemoglobin; gross and multiple disorders of the rhythm of nutrition and the content of the diet. Using the obtained data, a mathematical assessment of the prognostic significance of each of the studied signs can be carried out, an algorithm for predicting stomach cancer and making individualized medical decisions is developed, without which it is impossible to create an effective and convenient register of patients with precancerous diseases at all stages of medical care for organizing and conducting personalized and effective cancer prevention measures.
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Affiliation(s)
- A. Yu. Baranovsky
- Federal State Budgetary Educational Institution of Higher Education “Saint Petersburg State University”
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Baranovskiy AY, Tcvetkova TL. Risk factors of gastric cancer as a basis for the development of a prognostic questionnaire for the register of patients with precancerous gastroduodenal diseases. EXPERIMENTAL AND CLINICAL GASTROENTEROLOGY 2022:29-38. [DOI: 10.31146/1682-8658-ecg-205-9-29-38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
Abstract
The article is a critical analysis of the world scientific literature devoted to the search for risk factors for stomach cancer for the timely prognosis of this disease and the implementation of cancer prevention measures. The paper presents data from numerous studies to determine the role of environmental factors, including unfavorable ecology, as well as gender, age, smoking, alcohol abuse. The authors’ opinions are presented on the essential role of the alimentary factor in the genesis of neoplasms in the stomach, including the predominance of animal fats in food, the abuse of overcooked, pickled foods rich in nitrosoamines, foods saturated with spices, the use of too hot food, the use of foods infected with mycotoxins in nutrition. The role of environmental factors in the prognosis of gastric cancer is noted: the state of secretory activity of the stomach, the dynamics of inflammatory and atrophic processes in the mucous membrane. A special role for the prognosis of stomach cancer is assigned by many authors to the pyloric helicobacter, as well as the quantitative indicator of glycated blood hemoglobin and its dynamics. The significance of genetic changes in the genesis of gastric cancer and their role as prognostic factors of the disease is ambiguous. The article draws attention to the multidirectional results of many authors in understanding a large number of factors they have studied that could be used as prognostic witnesses of stomach cancer. The expediency of searching for the most significant regional factors for the prognosis of gastric cancer is substantiated, on the basis of which it is very important to create registers of patients with precancerous diseases of the stomach for the organization and implementation of personalized and effective measures of cancer prevention.
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Artificial intelligence for distinguishment of hammering sound in total hip arthroplasty. Sci Rep 2022; 12:9826. [PMID: 35701656 PMCID: PMC9198079 DOI: 10.1038/s41598-022-14006-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 05/31/2022] [Indexed: 11/30/2022] Open
Abstract
Recent studies have focused on hammering sound analysis during insertion of the cementless stem to decrease complications in total hip arthroplasty. However, the nature of the hammering sound is complex to analyse and varies widely owing to numerous possible variables. Therefore, we performed a preliminary feasibility study that aimed to clarify the accuracy of a prediction model using a machine learning algorithm to identify the final rasping hammering sound recorded during surgery. The hammering sound data of 29 primary THA without complication were assessed. The following definitions were adopted. Undersized rasping: all undersized stem rasping before the rasping of the final stem size, Final size rasping: rasping of the final stem size, Positive example: hammering sound during final size rasping, Negative example A: hammering sound during minimum size stem rasping, Negative example B: hammering sound during all undersized rasping. Three datasets for binary classification were set. Finally, binary classification was analysed in six models for the three datasets. The median values of the ROC-AUC in models A–F among each dataset were dataset a: 0.79, 0.76, 0.83, 0.90, 0.91, and 0.90, dataset B: 0.61, 0.53, 0.67, 0.69, 0.71, and 0.72, dataset C: 0.60, 0.48, 0.57, 0.63, 0.67, and 0.63, respectively. Our study demonstrated that artificial intelligence using machine learning was able to distinguish the final rasping hammering sound from the previous hammering sound with a relatively high degree of accuracy. Future studies are warranted to establish a prediction model using hammering sound analysis with machine learning to prevent complications in THA.
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Abineza C, Balas VE, Nsengiyumva P. A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-219270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a progressive, obstructive lung disease that restricts airflow from the lungs. COPD patients are at risk of sudden and acute worsening of symptoms called exacerbations. Early identification and classification of COPD exacerbation can reduce COPD risks and improve patient’s healthcare and management. Pulse oximetry is a non-invasive technique used to assess patients with acutely worsening symptoms. As part of manual diagnosis based on pulse oximetry, clinicians examine three warning signs to classify COPD patients. This may lack high sensitivity and specificity which requires a blood test. However, laboratory tests require time, further delayed treatment and additional costs. This research proposes a prediction method for COPD patients’ classification based on pulse oximetry three manual warning signs and the resulting derived few key features that can be obtained in a short time. The model was developed on a robust physician labeled dataset with clinically diverse patient cases. Five classification algorithms were applied on the mentioned dataset and the results showed that the best algorithm is XGBoost with the accuracy of 91.04%, precision of 99.86%, recall of 82.19%, F1 measure value of 90.05% with an AUC value of 95.8%. Age, current and baseline heart rate, current and baseline pulse ox. (SPO2) were found the top most important predictors. These findings suggest the strength of XGBoost model together with the availability and the simplicity of input variables in classifying COPD daily living using a (wearable) pulse oximeter.
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Affiliation(s)
- Claudia Abineza
- African Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda
| | - Valentina E. Balas
- Department of Automatics and Applied Software, “Aurel Vlaicu” University, Arad, Romania
| | - Philibert Nsengiyumva
- African Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda
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Huang RJ, Kwon NSE, Tomizawa Y, Choi AY, Hernandez-Boussard T, Hwang JH. A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams. JCO Clin Cancer Inform 2022; 6:e2200039. [PMID: 35763703 DOI: 10.1200/cci.22.00039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Noncardia gastric cancer (NCGC) is a leading cause of global cancer mortality, and is often diagnosed at advanced stages. Development of NCGC risk models within electronic health records (EHR) may allow for improved cancer prevention. There has been much recent interest in use of machine learning (ML) for cancer prediction, but few studies comparing ML with classical statistical models for NCGC risk prediction. METHODS We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University of Washington (UW). The LR model contained well-established NCGC risk factors (intestinal metaplasia histology, prior Helicobacter pylori infection, race, ethnicity, nativity status, smoking history, anemia), whereas ML models agnostically selected variables from the EHR. Models were developed and internally validated in the Stanford data, and externally validated in the UW data. Hyperparameter tuning of models was achieved using cross-validation. Model performance was compared by accuracy, sensitivity, and specificity. RESULTS In internal validation, LR performed with comparable accuracy (0.732; 95% CI, 0.698 to 0.764), sensitivity (0.697; 95% CI, 0.647 to 0.744), and specificity (0.767; 95% CI, 0.720 to 0.809) to penalized lasso, support vector machine, K-nearest neighbor, and random forest models. In external validation, LR continued to demonstrate high accuracy, sensitivity, and specificity. Although K-nearest neighbor demonstrated higher accuracy and specificity, this was offset by significantly lower sensitivity. No ML model consistently outperformed LR across evaluation criteria. CONCLUSION Drawing data from two independent EHRs, we find LR on the basis of established risk factors demonstrated comparable performance to optimized ML algorithms. This study demonstrates that classical models built on robust, hand-chosen predictor variables may not be inferior to data-driven models for NCGC risk prediction.
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Affiliation(s)
- Robert J Huang
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA
| | - Nicole Sung-Eun Kwon
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA
| | - Yutaka Tomizawa
- Division of Gastroenterology, University of Washington, Seattle, WA
| | - Alyssa Y Choi
- Division of Gastroenterology and Hepatology, University of California Irvine, Irvine, CA
| | | | - Joo Ha Hwang
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA
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Xu X, Fairley CK, Chow EPF, Lee D, Aung ET, Zhang L, Ong JJ. Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages. Sci Rep 2022; 12:8757. [PMID: 35610227 PMCID: PMC9128330 DOI: 10.1038/s41598-022-12033-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/07/2022] [Indexed: 11/09/2022] Open
Abstract
Timely and regular testing for HIV and sexually transmitted infections (STI) is important for controlling HIV and STI (HIV/STI) among men who have sex with men (MSM). We established multiple machine learning models (e.g., logistic regression, lasso regression, ridge regression, elastic net regression, support vector machine, k-nearest neighbour, naïve bayes, random forest, gradient boosting machine, XGBoost, and multi-layer perceptron) to predict timely (i.e., within 30 days) clinic attendance and HIV/STI testing uptake after receiving a reminder message via short message service (SMS) or email). Our study used 3044 clinic consultations among MSM within 12 months after receiving an email or SMS reminder at the Melbourne Sexual Health Centre between April 11, 2019, and April 30, 2020. About 29.5% [899/3044] were timely clinic attendance post reminder messages, and 84.6% [761/899] had HIV/STI testing. The XGBoost model performed best in predicting timely clinic attendance [mean [SD] AUC 62.8% (3.2%); F1 score 70.8% (1.2%)]. The elastic net regression model performed best in predicting HIV/STI testing within 30 days [AUC 82.7% (6.3%); F1 score 85.3% (1.8%)]. The machine learning approach is helpful in predicting timely clinic attendance and HIV/STI re-testing. Our predictive models could be incorporated into clinic websites to inform sexual health care or follow-up service.
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Affiliation(s)
- Xianglong Xu
- Central Clinical School, Monash University, Melbourne, Australia.,Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia
| | - Christopher K Fairley
- Central Clinical School, Monash University, Melbourne, Australia.,Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia
| | - Eric P F Chow
- Central Clinical School, Monash University, Melbourne, Australia.,Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia.,Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - David Lee
- Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia
| | - Ei T Aung
- Central Clinical School, Monash University, Melbourne, Australia.,Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia
| | - Lei Zhang
- Central Clinical School, Monash University, Melbourne, Australia. .,Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia. .,China Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, Shaanxi, China. .,Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China.
| | - Jason J Ong
- Central Clinical School, Monash University, Melbourne, Australia. .,Melbourne Sexual Health Centre, The Alfred, Melbourne, 3053, Australia. .,Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
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Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol 2022; 22:101. [PMID: 35395724 PMCID: PMC8991704 DOI: 10.1186/s12874-022-01577-x] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 03/18/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. METHODS We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. RESULTS Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. CONCLUSIONS The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.
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Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Open and Crowd-Based Platforms: Impact on Organizational and Market Performance. SUSTAINABILITY 2022. [DOI: 10.3390/su14042223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The aim of the research was to present the state of the art on the use of open and crowd-based platforms and the advantages in terms of business performance that emerging practices employing such technologies are able to provide. The analysis was performed by extracting information on emerging practices from the repository Business Process Framework for Emerging Technologies developed by the Department of Industrial Engineering of the University of Salerno (Italy). Contingency tables allowed analysis of the association of such practices with industry, business function, business process, and impact on performance. From the analysis of the results, many implementation opportunities emerge, mainly in manufacturing, healthcare, and transportation industries, providing benefits not only in terms of efficiency and productivity, cost reduction, and information management but also in product/service differentiation. Therefore, the research provides an overview of opportunities for organizations employing open and crowd-based platforms in order to improve market and organizational performance. Moreover, the article highlights in what specific business contexts these technologies can be mainly useful.
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Gu J, Chen R, Wang SM, Li M, Fan Z, Li X, Zhou J, Sun K, Wei W. Prediction models for gastric cancer risk in the general population: a systematic review. Cancer Prev Res (Phila) 2022; 15:309-318. [PMID: 35017181 DOI: 10.1158/1940-6207.capr-21-0426] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/15/2021] [Accepted: 01/07/2022] [Indexed: 11/16/2022]
Abstract
Risk prediction models for gastric cancer (GC) could identify high-risk individuals in the general population. The objective of this study was to systematically review the available evidence about the construction and verification of GC predictive models. We searched PubMed, Embase, and Cochrane Library databases for articles that developed or validated GC risk prediction models up to November 2021. Data extracted included study characteristics, predictor selection, missing data, and evaluation metrics. Risk of bias (ROB) was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We identified a total of 12 original risk prediction models that fulfilled the criteria for analysis. The area under the receiver operating characteristic curve ranged from 0.73 to 0.93 in derivation sets (n=6), 0.68 to 0.90 in internal validation sets (n=5), 0.71 to 0.92 in external validation sets (n=7). The higher-performing models usually include age, salt preference, Helicobacter pylori, smoking, BMI, family history, pepsinogen and sex. According to PROBAST, at least one domain with a high ROB was present in all studies mainly due to methodologic limitations in the analysis domain. In conclusion, although some risk prediction models including similar predictors have displayed sufficient discriminative abilities, many have a high ROB due to methodological limitations and are not externally validated efficiently. Future prediction models should adherence to well-established standards and guidelines to benefit GC screening.
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Affiliation(s)
- Jianhua Gu
- National Central Cancer Registry, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Ru Chen
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Shao-Ming Wang
- National Central Cancer Registry Office, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Minjuan Li
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Zhiyuan Fan
- National Cancer Registry Office, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Xinqing Li
- 1. Office of National Central Cancer Registry, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiachen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center
| | - Kexin Sun
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College
| | - Wenqiang Wei
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
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Network Biology and Artificial Intelligence Drive the Understanding of the Multidrug Resistance Phenotype in Cancer. Drug Resist Updat 2022; 60:100811. [DOI: 10.1016/j.drup.2022.100811] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023]
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Li ZM, Zhuang X. Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer. Artif Intell Gastroenterol 2021; 2:105-110. [DOI: 10.35712/aig.v2.i4.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/22/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
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A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11136194] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Gastric cancer is the fifth most common cancer type worldwide and one of the most frequently diagnosed cancers in South Korea. In this study, we propose DeepPrevention, which comprises a prediction module to predict the possibility of progression from atrophic gastritis to gastric cancer and an explanation module to identify risk factors for progression from atrophic gastritis to gastric cancer, to identify patients with atrophic gastritis who are at high risk of gastric cancer. The data set used in this study was South Korea National Health Insurance Service (NHIS) medical checkup data for atrophic gastritis patients from 2002 to 2013. Our experimental results showed that the most influential predictors of gastric cancer development were sex, smoking duration, and current smoking status. In addition, we found that the average age of gastric cancer diagnosis in a group of high-risk patients was 57, and income, BMI, regular exercise, and the number of endoscopic screenings did not show any significant difference between groups. At the individual level, we identified that there were relatively strong associations between gastric cancer and smoking duration and smoking status.
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