1
|
Chang CC, Lin CY, Liu YS, Chen YY, Huang WL, Lai WW, Yen YT, Ma MC, Tseng YL. Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy? Cancers (Basel) 2024; 16:773. [PMID: 38398164 PMCID: PMC10886806 DOI: 10.3390/cancers16040773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical-radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.
Collapse
Affiliation(s)
- Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-C.C.); (Y.-Y.C.); (W.-L.H.); (W.-W.L.); (Y.-L.T.)
| | - Chia-Ying Lin
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-Y.L.); (Y.-S.L.)
| | - Yi-Sheng Liu
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-Y.L.); (Y.-S.L.)
| | - Ying-Yuan Chen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-C.C.); (Y.-Y.C.); (W.-L.H.); (W.-W.L.); (Y.-L.T.)
| | - Wei-Li Huang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-C.C.); (Y.-Y.C.); (W.-L.H.); (W.-W.L.); (Y.-L.T.)
| | - Wu-Wei Lai
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-C.C.); (Y.-Y.C.); (W.-L.H.); (W.-W.L.); (Y.-L.T.)
- Division of Thoracic Surgery, Department of Surgery, An-Nan Hospital, China Medical University, Tainan 70965, Taiwan
| | - Yi-Ting Yen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-C.C.); (Y.-Y.C.); (W.-L.H.); (W.-W.L.); (Y.-L.T.)
- Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan
| | - Mi-Chia Ma
- Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan 701401, Taiwan
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan; (C.-C.C.); (Y.-Y.C.); (W.-L.H.); (W.-W.L.); (Y.-L.T.)
| |
Collapse
|
2
|
Alabi RO, Almangush A, Elmusrati M, Leivo I, Mäkitie AA. Interpretable machine learning model for prediction of overall survival in laryngeal cancer. Acta Otolaryngol 2024:1-7. [PMID: 38279817 DOI: 10.1080/00016489.2023.2301648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/21/2023] [Indexed: 01/29/2024]
Abstract
Background: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.Objectives: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.Methods: A total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with LSCC were reviewed. The global model-agnostic interpretability was examined using SHapley Additive exPlanations (SHAP) technique. Likewise, individual interpretation of the prediction was made using Local Interpretable Model Agnostic Explanations (LIME).Results: The state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, the examined ensemble algorithms showed comparable weighted area under receiving curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, respectively. The global methods of interpretability (SHAP) demonstrated that the age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital status are among the prominent parameters.Conclusions: A ML model for OS prediction may serve as an ancillary tool for treatment planning of LSCC patients.
Collapse
Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine, University of Turku, Pathology, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, University of Turku, Pathology, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, 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 Institute and Karolinska University Hospital, Stockholm, Sweden
| |
Collapse
|
3
|
Sraitih M, Jabrane Y, Hajjam El Hassani A. An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques. J Clin Med 2021; 10:jcm10225450. [PMID: 34830732 PMCID: PMC8618527 DOI: 10.3390/jcm10225450] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 12/29/2022] Open
Abstract
The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.
Collapse
Affiliation(s)
- Mohamed Sraitih
- MSC Laboratory, Cadi Ayyad University, Marrakech 40000, Morocco;
| | - Younes Jabrane
- MSC Laboratory, Cadi Ayyad University, Marrakech 40000, Morocco;
- Correspondence: ; Tel.: +212-524-434-745
| | - Amir Hajjam El Hassani
- Nanomedicine Imagery & Therapeutics Laboratory, EA4662—UBFC, UTBM, 90000 Belfort, France;
| |
Collapse
|
4
|
Peppes N, Daskalakis E, Alexakis T, Adamopoulou E, Demestichas K. Performance of Machine Learning-Based Multi-Model Voting Ensemble Methods for Network Threat Detection in Agriculture 4.0. Sensors (Basel) 2021; 21:7475. [PMID: 34833551 DOI: 10.3390/s21227475] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 10/22/2021] [Accepted: 11/08/2021] [Indexed: 11/28/2022]
Abstract
The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.
Collapse
|
5
|
Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets. Diagnostics (Basel) 2021; 11:diagnostics11111990. [PMID: 34829338 PMCID: PMC8617960 DOI: 10.3390/diagnostics11111990] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/20/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023] Open
Abstract
An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.
Collapse
Affiliation(s)
- Syed Nisar Hussain Bukhari
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
- Correspondence:
| | - Amit Jain
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Ehtishamul Haq
- Department of Biotechnology, University of Kashmir, Srinagar 190006, India;
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City 13133, Kuwait;
| | - Julian Webber
- Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka 560-8531, Japan;
| |
Collapse
|
6
|
El-Kenawy ESM, Ibrahim A, Mirjalili S, Eid MM, Hussein SE. Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images. IEEE Access 2020; 8:179317-179335. [PMID: 34976558 PMCID: PMC8545288 DOI: 10.1109/access.2020.3028012] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 09/27/2020] [Indexed: 05/07/2023]
Abstract
Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.
Collapse
Affiliation(s)
- El-Sayed M. El-Kenawy
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and OptimizationTorrens University AustraliaFortitude ValleyQLD4006Australia
- Yonsei Frontier Laboratory (YFL)Yonsei UniversitySeoul03722South Korea
| | - Marwa Metwally Eid
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Sherif E. Hussein
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
| |
Collapse
|