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Sharma D, Deepali, Garg VK, Kashyap D, Goel N. A deep learning-based integrative model for survival time prediction of head and neck squamous cell carcinoma patients. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07615-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Raghavan A, Sandra SC, Madan Kumar PD. Application of artificial intelligence in the diagnosis and survival prediction of patients with oral cancer: A systematic review. JOURNAL OF ORAL RESEARCH AND REVIEW 2022. [DOI: 10.4103/jorr.jorr_65_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Alabi RO, Youssef O, Pirinen M, Elmusrati M, Mäkitie AA, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif Intell Med 2021; 115:102060. [PMID: 34001326 DOI: 10.1016/j.artmed.2021.102060] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 01/27/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. OBJECTIVES This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. DATA SOURCES We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. ELIGIBILITY CRITERIA Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. DATA EXTRACTION Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. RESULTS A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. CONCLUSION Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
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Alabi RO, Mäkitie AA, Pirinen M, Elmusrati M, Leivo I, Almangush A. Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer. Int J Med Inform 2020; 145:104313. [PMID: 33142259 DOI: 10.1016/j.ijmedinf.2020.104313] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/04/2020] [Accepted: 10/20/2020] [Indexed: 01/15/2023]
Abstract
BACKGROUND The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling. OBJECTIVES This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population. METHODS The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver-operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients. RESULTS The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model's performance to predict overall survival. CONCLUSION The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients' outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram - machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, University of Misurata, Misurata, Libya
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Jothi N, Husain W, Rashid NA. Predicting generalized anxiety disorder among women using Shapley value. J Infect Public Health 2020; 14:103-108. [PMID: 32273237 DOI: 10.1016/j.jiph.2020.02.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 06/29/2019] [Accepted: 02/26/2020] [Indexed: 01/29/2023] Open
Abstract
Mental illness is a set of health problems that affect the way individuals perceive themselves, relate to others, and interact with the world around them. Due to the myriad of underlying causes and subsequent effects of mental illness, these conditions often trigger fear and misunderstanding among the general population. Common mental illnesses such as depression and anxiety disorders often affect an individual's thoughts, feelings, abilities, and behaviours. Anxiety disorder is characterized by an irrational fear of certain things or events. It is often attributed as the feeling of worry about anticipated events and fear in response to current events. This work has identified several related research efforts on the general well-being and psychological distress using data mining. However, there is inadequate research done using a similar method on specific mental health issues, especially related to generalized anxiety disorder (GAD). In view of this gap, this study focuses on implementing a novel feature selection and data mining classifier system. Under the proposed method, Shapley value will be implemented as the feature selection of the data mining classifier on the mental health data. The approach is used to predict GAD among women. The methodology for this research is adapted from the process of Knowledge Discovery in Databases (KDD). This methodology consists of 5 main phases; namely data acquisition, data pre-processing, feature selection, classification prediction, and evaluation. Using this enhanced prediction algorithm, any women can get help if they are perceived to be suffering from GAD. By designing an effective way of identifying individuals who may be suffering from mental illnesses, we hope that our work would improve the awareness surrounding mental health issues especially among women and enable them to undertake autonomous decision in seeking mental health services.
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Affiliation(s)
- Neesha Jothi
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Penang, Malaysia
| | - Wahidah Husain
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Penang, Malaysia
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Du M, Haag D, Song Y, Lynch J, Mittinty M. Examining Bias and Reporting in Oral Health Prediction Modeling Studies. J Dent Res 2020; 99:374-387. [DOI: 10.1177/0022034520903725] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.
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Affiliation(s)
- M. Du
- School of Public Health, The University of Adelaide, Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - D. Haag
- School of Public Health, The University of Adelaide, Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - Y. Song
- Australian Research Centre for Population Oral Health, Adelaide Dental School, The University of Adelaide, Adelaide, Australia
| | - J. Lynch
- School of Public Health, The University of Adelaide, Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
- Population Health Sciences, University of Bristol, Bristol, UK
| | - M. Mittinty
- School of Public Health, The University of Adelaide, Adelaide, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, Australia
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Nguyen D, Luo W, Phung D, Venkatesh S. LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.07.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A. An artificial intelligence approach to early predict symptom-based exacerbations of COPD. BIOTECHNOL BIOTEC EQ 2018. [DOI: 10.1080/13102818.2018.1437568] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
| | - Daniel Sanchez-Morillo
- Biomedical Engineering and Telemedicine Lab, School of Engineering, University of Cádiz, Cádiz, Spain
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Sharma N, Om H. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer. ScientificWorldJournal 2015; 2015:234191. [PMID: 26171415 PMCID: PMC4485993 DOI: 10.1155/2015/234191] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 05/29/2015] [Accepted: 05/30/2015] [Indexed: 12/20/2022] Open
Abstract
In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.
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Affiliation(s)
- Neha Sharma
- Dr. D.Y. Patil Institute of Master of Computer Applications, Akurdi, Savitribai Phule Pune University, Maharashtra 411007, India
| | - Hari Om
- Computer Science and Engineering Department, Indian School of Mines, Dhanbad, Jharkhand 826004, India
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Significant patterns for oral cancer detection: association rule on clinical examination and history data. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/s13721-014-0050-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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