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Zhen J, Liu C, Zhang J, Liao F, Xie H, Tan C, An P, Liu Z, Jiang C, Shi J, Wu K, Dong W. Evaluating Inflammatory Bowel Disease-Related Quality of Life Using an Interpretable Machine Learning Approach: A Multicenter Study in China. J Inflamm Res 2024; 17:5271-5283. [PMID: 39139580 PMCID: PMC11321795 DOI: 10.2147/jir.s470197] [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: 04/03/2024] [Accepted: 07/30/2024] [Indexed: 08/15/2024] Open
Abstract
Purpose Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments. Patients and Methods An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Results The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk. Conclusion An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL.
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Affiliation(s)
- Junhai Zhen
- Department of General Practice, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Chuan Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Jixiang Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Fei Liao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Huabing Xie
- Department of General Practice, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Cheng Tan
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Ping An
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 430060, People’s Republic of China
| | - Changqing Jiang
- Department of Clinical Psychology, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, People’s Republic of China
| | - Jie Shi
- Department of Medical Psychology, Chinese People’s Liberation Army Rocket Army Characteristic Medical Center, Beijing, 100032, People’s Republic of China
| | - Kaichun Wu
- Department of Gastroenterology, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Weiguo Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China
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Papalia GF, Brigato P, Sisca L, Maltese G, Faiella E, Santucci D, Pantano F, Vincenzi B, Tonini G, Papalia R, Denaro V. Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review. Cancers (Basel) 2024; 16:2700. [PMID: 39123427 PMCID: PMC11311270 DOI: 10.3390/cancers16152700] [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: 06/19/2024] [Revised: 07/20/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases. METHODS We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies. RESULTS We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney. CONCLUSIONS Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.
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Affiliation(s)
- Giuseppe Francesco Papalia
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy; (G.F.P.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Paolo Brigato
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy; (G.F.P.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Luisana Sisca
- Department of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Girolamo Maltese
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy; (G.F.P.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Eliodoro Faiella
- Department of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 00128 Rome, Italy
- Research Unit of Radiology and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Domiziana Santucci
- Department of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Francesco Pantano
- Department of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Bruno Vincenzi
- Department of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Giuseppe Tonini
- Department of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy
| | - Rocco Papalia
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy; (G.F.P.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Vincenzo Denaro
- Operative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy; (G.F.P.)
- Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
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Karabulut S, Afsar CU, Khanmammadov N, Karahan L, Paksoy N, Dogan I, Ferhatoğlu F, Tastekin D. Disease characteristics and prognostic factors of colorectal cancer patients with bone metastasis: A real-world data from Turkey. J Cancer Res Ther 2024:01363817-990000000-00056. [PMID: 38261430 DOI: 10.4103/jcrt.jcrt_392_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 03/16/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Bone metastasis is rarely seen in colorectal cancer (CRC) patients, and there is insufficient data available regarding such cases. The study aimed to identify the prognostic factors and characteristics associated with overall survival in patients with bone metastatic CRC. METHOD Data from bone metastatic CRC patients referred to a high-volume tertiary cancer center in Turkey, between January 2018 and April 2021, were retrospectively collected. The records of 150 consecutive patients treated for bone metastases due to CRC were reviewed. Overall survival curves were generated by the Kaplan-Meier method and analyzed using the log-rank test. RESULTS Median age was 55 years (19-86 years). Bone metastases were more common in men and those with metachronous metastases. The axial skeleton was the most commonly involved site, and patients were frequently presented with single bone metastasis. Peritoneal metastases were significantly correlated with extra-axial metastases (P = 0.002), and radiotherapy was applied to axial metastases significantly, more frequently (P = 0.02). Lung metastasis was also more prevalent in K-RAS mutated patients (P = 0.008). The median survival time from diagnosis of bone metastasis was 8.3 months (95% confidence interval (CI), 5.5-10.6), and the three-year survival rate was 76.9% (95% CI, 69.8-84.0). Multivariate analysis revealed that brain metastases, right-sided colon tumor, high serum ALP, and Ca 19-9 levels were independent poor prognostic factors (P = 0.01, 0.02, <0.001, and 0.04, respectively). CONCLUSIONS The location of CRC correlates significantly with the site of bone metastasis; the prognosis of CRC patients with bone metastasis is very poor, and the significant poor prognostic factors are brain metastases, right-sidedness, high serum ALP, and Ca 19-9 levels. More attention should be paid to bone metastasis in CRC patients.
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Affiliation(s)
- Senem Karabulut
- Medical Oncology, Istanbul University Institute of Oncology, Istanbul, Turkey
| | | | - Nijat Khanmammadov
- Medical Oncology, Istanbul University Institute of Oncology, Istanbul, Turkey
| | - Latif Karahan
- Internal Medicine, Istanbul University Faculty of Medicine, Istanbul, Turkey
| | - Nail Paksoy
- Medical Oncology, Istanbul University Institute of Oncology, Istanbul, Turkey
| | - Izzet Dogan
- Medical Oncology, Istanbul University Institute of Oncology, Istanbul, Turkey
| | - Ferhat Ferhatoğlu
- Medical Oncology, Istanbul University Institute of Oncology, Istanbul, Turkey
| | - Didem Tastekin
- Medical Oncology, Istanbul University Institute of Oncology, Istanbul, Turkey
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Ajithkumar P, Vasantharajan SS, Pattison S, McCall JL, Rodger EJ, Chatterjee A. Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis. Int J Mol Sci 2024; 25:874. [PMID: 38255946 PMCID: PMC10815915 DOI: 10.3390/ijms25020874] [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: 11/30/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.
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Affiliation(s)
- Priyadarshana Ajithkumar
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
| | - Sai Shyam Vasantharajan
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
| | - Sharon Pattison
- Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - John L. McCall
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - Euan J. Rodger
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; (P.A.)
- School of Health Sciences and Technology, UPES University, Dehradun 248007, India
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Yi X, Xu W, Tang G, Zhang L, Wang K, Luo H, Zhou X. Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population. Front Oncol 2023; 13:1105224. [PMID: 37434968 PMCID: PMC10332636 DOI: 10.3389/fonc.2023.1105224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/06/2023] [Indexed: 07/13/2023] Open
Abstract
Background This study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM). Methods Patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM. Results For the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set. Conclusion The ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM.
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Affiliation(s)
- Xinglin Yi
- Department of Respiratory Medicine, Southwest Hospital of Third Military Medical University, Chongqing, China
| | - Wenhao Xu
- Department of Urinary Medicine Center, Southwest Hospital of Third Military Medical University, Chongqing, China
| | - Guihua Tang
- Department of Respiratory Medicine, Southwest Hospital of Third Military Medical University, Chongqing, China
| | - Lingye Zhang
- Department of Respiratory Medicine, Southwest Hospital of Third Military Medical University, Chongqing, China
| | - Kaishan Wang
- Department of Neurosurgery Department, Southwest Hospital of Third Military Medical University, Chongqing, China
| | - Hu Luo
- Department of Respiratory Medicine, Southwest Hospital of Third Military Medical University, Chongqing, China
| | - Xiangdong Zhou
- Department of Respiratory Medicine, Southwest Hospital of Third Military Medical University, Chongqing, China
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Koçak B, Cuocolo R, dos Santos DP, Stanzione A, Ugga L. Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning. Balkan Med J 2023; 40:3-12. [PMID: 36578657 PMCID: PMC9874249 DOI: 10.4274/balkanmedj.galenos.2022.2022-11-51] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/06/2022] [Indexed: 12/30/2022] Open
Abstract
In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence-and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.
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Affiliation(s)
- Burak Koçak
- Clinic of Radiology, University of Health Sciences Turkey, Başakşehir Çam and Sakura City Hospital, İstanbul, Turkey
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry University of Salerno, Baronissi, Italy
| | - Daniel Pinto dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Napoli, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Napoli, Italy
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