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Winter A, van de Water RP, Pfitzner B, Ibach M, Riepe C, Ahlborn R, Faraj L, Krenzien F, Dobrindt EM, Raakow J, Sauer IM, Arnrich B, Beyer K, Denecke C, Pratschke J, Maurer MM. Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case-Control Study on Machine Learning versus the International Esodata Study Group Risk Model for Predicting 90-Day Mortality in Oncologic Esophagectomy. Cancers (Basel) 2024; 16:3000. [PMID: 39272858 PMCID: PMC11394558 DOI: 10.3390/cancers16173000] [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: 06/03/2024] [Revised: 08/12/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
Risk prediction prior to oncologic esophagectomy is crucial for assisting surgeons and patients in their joint informed decision making. Recently, a new risk prediction model for 90-day mortality after esophagectomy using the International Esodata Study Group (IESG) database was proposed, allowing for the preoperative assignment of patients into different risk categories. However, given the non-linear dependencies between patient- and tumor-related risk factors contributing to cumulative surgical risk, machine learning (ML) may evolve as a novel and more integrated approach for mortality prediction. We evaluated the IESG risk model and compared its performance to ML models. Multiple classifiers were trained and validated on 552 patients from two independent centers undergoing oncologic esophagectomies. The discrimination performance of each model was assessed utilizing the area under the receiver operating characteristics curve (AUROC), the area under the precision-recall curve (AUPRC), and the Matthews correlation coefficient (MCC). The 90-day mortality rate was 5.8%. We found that IESG categorization allowed for adequate group-based risk prediction. However, ML models provided better discrimination performance, reaching superior AUROCs (0.64 [0.63-0.65] vs. 0.44 [0.32-0.56]), AUPRCs (0.25 [0.24-0.27] vs. 0.11 [0.05-0.21]), and MCCs (0.27 ([0.25-0.28] vs. 0.15 [0.03-0.27]). Conclusively, ML shows promising potential to identify patients at risk prior to surgery, surpassing conventional statistics. Still, larger datasets are needed to achieve higher discrimination performances for large-scale clinical implementation in the future.
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Affiliation(s)
- Axel Winter
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | | | - Bjarne Pfitzner
- Hasso Plattner Institute, University of Potsdam, 14476 Potsdam, Germany
| | - Marius Ibach
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Christoph Riepe
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Robert Ahlborn
- Department of Information Technology, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Lara Faraj
- Einstein Center for Neurosciences Berlin, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Felix Krenzien
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- BIH Charité (Digital) Clinician Scientist Program, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Charitéplatz 1, 10117 Berlin, Germany
| | - Eva M Dobrindt
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Jonas Raakow
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Igor M Sauer
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Bert Arnrich
- Hasso Plattner Institute, University of Potsdam, 14476 Potsdam, Germany
| | - Katharina Beyer
- Department of General and Abdominal Surgery, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Christian Denecke
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Johann Pratschke
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Max M Maurer
- Department of Surgery, Campus Charité Mitte and Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- BIH Charité (Digital) Clinician Scientist Program, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Charitéplatz 1, 10117 Berlin, Germany
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Nunez JJ, Leung B, Ho C, Ng RT, Bates AT. Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing. COMMUNICATIONS MEDICINE 2024; 4:69. [PMID: 38589545 PMCID: PMC11001970 DOI: 10.1038/s43856-024-00495-x] [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: 12/23/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.
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Affiliation(s)
- John-Jose Nunez
- BC Cancer, Vancouver, BC, Canada.
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
| | | | | | - Raymond T Ng
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Alan T Bates
- BC Cancer, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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Alotaibi FM, Khan YD. A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer. Diagnostics (Basel) 2023; 13:2291. [PMID: 37443684 DOI: 10.3390/diagnostics13132291] [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: 03/15/2023] [Revised: 06/05/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Mutations in genes can alter their DNA patterns, and by recognizing these mutations, many carcinomas can be diagnosed in the progression stages. The human body contains many hidden and enigmatic features that humankind has not yet fully understood. A total of 7539 neoplasm cases were reported from 1 January 2021 to 31 December 2021. Of these, 3156 were seen in males (41.9%) and 4383 (58.1%) in female patients. Several machine learning and deep learning frameworks are already implemented to detect mutations, but these techniques lack generalized datasets and need to be optimized for better results. Deep learning-based neural networks provide the computational power to calculate the complex structures of gastric carcinoma-driven gene mutations. This study proposes deep learning approaches such as long and short-term memory, gated recurrent units and bi-LSTM to help in identifying the progression of gastric carcinoma in an optimized manner. This study includes 61 carcinogenic driver genes whose mutations can cause gastric cancer. The mutation information was downloaded from intOGen.org and normal gene sequences were downloaded from asia.ensembl.org, as explained in the data collection section. The proposed deep learning models are validated using the self-consistency test (SCT), 10-fold cross-validation test (FCVT), and independent set test (IST); the IST prediction metrics of accuracy, sensitivity, specificity, MCC and AUC of LSTM, Bi-LSTM, and GRU are 97.18%, 98.35%, 96.01%, 0.94, 0.98; 99.46%, 98.93%, 100%, 0.989, 1.00; 99.46%, 98.93%, 100%, 0.989 and 1.00, respectively.
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Affiliation(s)
- Fahad M Alotaibi
- Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore 54770, Pakistan
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Afrash MR, Mirbagheri E, Mashoufi M, Kazemi-Arpanahi H. Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study. BMC Med Inform Decis Mak 2023; 23:54. [PMID: 37024885 PMCID: PMC10080884 DOI: 10.1186/s12911-023-02154-y] [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: 09/05/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an individual's prognosis. Numerous clinical and pathological features are generally used in predicting gastric cancer survival, and their influence on the survival of this cancer has not been fully elucidated. Moreover, the five-year survivability prognosis performances of feature selection methods with machine learning (ML) classifiers for gastric cancer have not been fully benchmarked. Therefore, we adopted several well-known feature selection methods and ML classifiers together to determine the best-paired feature selection-classifier for this purpose. METHODS This was a retrospective study on a dataset of 974 patients diagnosed with gastric cancer in the Ayatollah Talleghani Hospital, Abadan, Iran. First, four feature selection algorithms, including Relief, Boruta, least absolute shrinkage and selection operator (LASSO), and minimum redundancy maximum relevance (mRMR) were used to select a set of relevant features that are very informative for five-year survival prediction in gastric cancer patients. Then, each feature set was fed to three classifiers: XG Boost (XGB), hist gradient boosting (HGB), and support vector machine (SVM) to develop predictive models. Finally, paired feature selection-classifier methods were evaluated to select the best-paired method using the area under the curve (AUC), accuracy, sensitivity, specificity, and f1-score metrics. RESULTS The LASSO feature selection algorithm combined with the XG Boost classifier achieved an accuracy of 89.10%, a specificity of 87.15%, a sensitivity of 89.42%, an AUC of 89.37%, and an f1-score of 90.8%. Tumor stage, history of other cancers, lymphatic invasion, tumor site, type of treatment, body weight, histological type, and addiction were identified as the most significant factors affecting gastric cancer survival. CONCLUSIONS This study proved the worth of the paired feature selection-classifier to identify the best path that could augment the five-year survival prediction in gastric cancer patients. Our results were better than those of previous studies, both in terms of the time required to form the models and the performance measurement criteria of the algorithms. These findings may be very promising and can, therefore, inform clinical decision-making and shed light on future studies.
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Affiliation(s)
- Mohammad Reza Afrash
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Esmat Mirbagheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrnaz Mashoufi
- Department of Health Information Management, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
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Talebi A, Celis-Morales CA, Borumandnia N, Abbasi S, Pourhoseingholi MA, Akbari A, Yousefi J. Predicting metastasis in gastric cancer patients: machine learning-based approaches. Sci Rep 2023; 13:4163. [PMID: 36914697 PMCID: PMC10011363 DOI: 10.1038/s41598-023-31272-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.
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Affiliation(s)
- Atefeh Talebi
- Colorectal Research Center, Iran University of Medical Center, Tehran, Iran.,British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Carlos A Celis-Morales
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.,Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Nasrin Borumandnia
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Somayeh Abbasi
- Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
| | - Mohamad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abolfazl Akbari
- Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Javad Yousefi
- Department of Internal Medicine, Iran University of Medical Sciences, Tehran, Iran
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Nunez JJ, Leung B, Ho C, Bates AT, Ng RT. Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing. JAMA Netw Open 2023; 6:e230813. [PMID: 36848085 PMCID: PMC9972192 DOI: 10.1001/jamanetworkopen.2023.0813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
IMPORTANCE Predicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer. OBJECTIVE To investigate whether natural language processing can predict survival of patients with general cancer from a patient's initial oncologist consultation document. DESIGN, SETTING, AND PARTICIPANTS This retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded. EXPOSURES Initial oncologist consultation documents were analyzed using traditional and neural language models. MAIN OUTCOMES AND MEASURES The primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used. RESULTS Of the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found. CONCLUSIONS AND RELEVANCE These findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.
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Affiliation(s)
- John-Jose Nunez
- BC Cancer, Vancouver, British Columbia, Canada
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Cheryl Ho
- BC Cancer, Vancouver, British Columbia, Canada
| | - Alan T. Bates
- BC Cancer, Vancouver, British Columbia, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Raymond T. Ng
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
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Bektaş M, Burchell GL, Bonjer HJ, van der Peet DL. Machine learning applications in upper gastrointestinal cancer surgery: a systematic review. Surg Endosc 2023; 37:75-89. [PMID: 35953684 PMCID: PMC9839827 DOI: 10.1007/s00464-022-09516-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/26/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies. METHODS A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models. RESULTS From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy. CONCLUSIONS Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML.
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Affiliation(s)
- Mustafa Bektaş
- Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - George L. Burchell
- Medical Library, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - H. Jaap Bonjer
- Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Donald L. van der Peet
- Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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Afrash MR, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms. J Biomed Phys Eng 2022; 12:611-626. [PMID: 36569564 PMCID: PMC9759642 DOI: 10.31661/jbpe.v0i0.2105-1334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 01/20/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA). OBJECTIVE This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. MATERIAL AND METHODS In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models. RESULTS A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. CONCLUSION The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.
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Affiliation(s)
- Mohammad Reza Afrash
- PhD, Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- PhD, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- PhD, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- PhD, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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9
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P D, C G. A systematic review on machine learning and deep learning techniques in cancer survival prediction. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 174:62-71. [PMID: 35933043 DOI: 10.1016/j.pbiomolbio.2022.07.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/13/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Cancer is a disease which is characterised by the unusual and uncontrollable growth of body cells. This usually happens asymptomatically and gets spread to other parts of the body. The major problem in treating cancer is that its progress is not monitored once it is diagnosed. The progress or the prognosis can be done through survival analysis. The survival analysis is the branch of statistics that deals in predicting the time of event of occurrence. In the case of cancer prognosis the event is the survival time of the patient from the onset of the disease or it can be the recurrence of the disease after undergoing a treatment. This study aims to bring out the machine learning and deep learning models involved in providing the prognosis to the cancer patients.
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Affiliation(s)
- Deepa P
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Gunavathi C
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
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Afrash MR, Shanbehzadeh M, Kazemi-Arpanahi H. Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer. Clin Med Insights Oncol 2022; 16:11795549221116833. [PMID: 36035639 PMCID: PMC9403452 DOI: 10.1177/11795549221116833] [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: 04/11/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022] Open
Abstract
Background Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. Methods A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. Results The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance. Conclusions The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.
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Affiliation(s)
- Mohammad Reza Afrash
- Department of Health Information
Technology and Management, School of Allied Medical Sciences, Shahid Beheshti
University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information
Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam,
Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information
Technology, Abadan University of Medical Sciences, Abadan, Iran
- Student Research Committee, Abadan
University of Medical Sciences, Abadan, Iran
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11
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Shin HJ, Roh CK, Son SY, Hur H, Han SU, Choi YO. Reply to: Letter to the Editor: Comment on "Prediction of Survival Outcomes Based on Preoperative Clinical Parameters in Gastric Cancer" by Bektaş, Mustafa et al. Ann Surg Oncol 2022; 29:8300-8301. [PMID: 35982204 DOI: 10.1245/s10434-022-12382-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Ho-Jung Shin
- Department of Surgery, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Chul-Kyu Roh
- Department of Surgery, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Sang-Yong Son
- Department of Surgery, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Hoon Hur
- Department of Surgery, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Sang-Uk Han
- Department of Surgery, Ajou University School of Medicine, Suwon, Republic of Korea.
| | - Yong-Ok Choi
- School of Economics, Chung-Ang University, Seoul, Republic of Korea
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Bektaş M, Pereira JC, van der Peet DL. Letter to the Editor: Comment on "Prediction of Survival Outcomes Based on Preoperative Clinical Parameters in Gastric Cancer". Ann Surg Oncol 2022; 29:8298-8299. [PMID: 35933543 DOI: 10.1245/s10434-022-12381-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Mustafa Bektaş
- Department of Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jaime Costa Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
| | - Donald L van der Peet
- Department of Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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Jung JO, Crnovrsanin N, Wirsik NM, Nienhüser H, Peters L, Popp F, Schulze A, Wagner M, Müller-Stich BP, Büchler MW, Schmidt T. Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer. J Cancer Res Clin Oncol 2022; 149:1691-1702. [PMID: 35616729 PMCID: PMC10097798 DOI: 10.1007/s00432-022-04063-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Surgical oncologists are frequently confronted with the question of expected long-term prognosis. The aim of this study was to apply machine learning algorithms to optimize survival prediction after oncological resection of gastroesophageal cancers. METHODS Eligible patients underwent oncological resection of gastric or distal esophageal cancer between 2001 and 2020 at Heidelberg University Hospital, Department of General Surgery. Machine learning methods such as multi-task logistic regression and survival forests were compared with usual algorithms to establish an individual estimation. RESULTS The study included 117 variables with a total of 1360 patients. The overall missingness was 1.3%. Out of eight machine learning algorithms, the random survival forest (RSF) performed best with a concordance index of 0.736 and an integrated Brier score of 0.166. The RSF demonstrated a mean area under the curve (AUC) of 0.814 over a time period of 10 years after diagnosis. The most important long-term outcome predictor was lymph node ratio with a mean AUC of 0.730. A numeric risk score was calculated by the RSF for each patient and three risk groups were defined accordingly. Median survival time was 18.8 months in the high-risk group, 44.6 months in the medium-risk group and above 10 years in the low-risk group. CONCLUSION The results of this study suggest that RSF is most appropriate to accurately answer the question of long-term prognosis. Furthermore, we could establish a compact risk score model with 20 input parameters and thus provide a clinical tool to improve prediction of oncological outcome after upper gastrointestinal surgery.
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Affiliation(s)
- Jin-On Jung
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Nerma Crnovrsanin
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Naita Maren Wirsik
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Henrik Nienhüser
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Leila Peters
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Felix Popp
- Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - André Schulze
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Martin Wagner
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Beat Peter Müller-Stich
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Markus Wolfgang Büchler
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Thomas Schmidt
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany. .,Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
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Kaur I, Doja M, Ahmad T. Data Mining and Machine Learning in Cancer Survival Research: An Overview and Future Recommendations. J Biomed Inform 2022; 128:104026. [DOI: 10.1016/j.jbi.2022.104026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 12/29/2022]
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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6342226. [PMID: 34992648 PMCID: PMC8727098 DOI: 10.1155/2021/6342226] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 11/27/2021] [Indexed: 12/31/2022]
Abstract
Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.
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Yakar M, Etiz D. Artificial intelligence in radiation oncology. Artif Intell Med Imaging 2021; 2:13-31. [DOI: 10.35711/aimi.v2.i2.13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/30/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input. Machine learning (ML) is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience. Deep learning is an ML technique that uses deep neural networks to create a model. The growth and sharing of data, increasing computing power, and developments in AI have initiated a transformation in healthcare. Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging, dosimetry, and imaging performed before each fraction. Of the many algorithms used in radiation oncology, has advantages and limitations with different computational power requirements. The aim of this review is to summarize the radiotherapy (RT) process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML. The RT stage is divided into seven stages: patient evaluation, simulation, contouring, planning, quality control, treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations, and advantages of AI algorithms has been done for each stage.
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Affiliation(s)
- Melek Yakar
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
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Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F, Michiels S, Souris K, Sterpin E, Lee JA. Artificial intelligence and machine learning for medical imaging: A technology review. Phys Med 2021; 83:242-256. [PMID: 33979715 PMCID: PMC8184621 DOI: 10.1016/j.ejmp.2021.04.016] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/15/2021] [Accepted: 04/18/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.
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Affiliation(s)
- Ana Barragán-Montero
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium.
| | - Umair Javaid
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Gilmer Valdés
- Department of Radiation Oncology, Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, USA
| | - Paul Desbordes
- Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium
| | - Benoit Macq
- Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Belgium
| | - Siri Willems
- ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium
| | | | | | | | - Steven Michiels
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Kevin Souris
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium; KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Belgium
| | - John A Lee
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
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