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Albaradei S, Thafar M, Alsaedi A, Van Neste C, Gojobori T, Essack M, Gao X. Machine learning and deep learning methods that use omics data for metastasis prediction. Comput Struct Biotechnol J 2021; 19:5008-5018. [PMID: 34589181 PMCID: PMC8450182 DOI: 10.1016/j.csbj.2021.09.001] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [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: 01/18/2021] [Revised: 08/16/2021] [Accepted: 09/02/2021] [Indexed: 12/14/2022] Open
Abstract
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.
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Key Words
- AE, autoencoder
- ANN, Artificial Neural Network
- AUC, area under the curve
- Acc, Accuracy
- Artificial intelligence
- BC, Betweenness centrality
- BH, Benjamini-Hochberg
- BioGRID, Biological General Repository for Interaction Datasets
- CCP, compound covariate predictor
- CEA, Carcinoembryonic antigen
- CNN, convolution neural networks
- CV, cross-validation
- Cancer
- DBN, deep belief network
- DDBN, discriminative deep belief network
- DEGs, differentially expressed genes
- DIP, Database of Interacting Proteins
- DNN, Deep neural network
- DT, Decision Tree
- Deep learning
- EMT, epithelial-mesenchymal transition
- FC, fully connected
- GA, Genetic Algorithm
- GANs, generative adversarial networks
- GEO, Gene Expression Omnibus
- HCC, hepatocellular carcinoma
- HPRD, Human Protein Reference Database
- KNN, K-nearest neighbor
- L-SVM, linear SVM
- LIMMA, linear models for microarray data
- LOOCV, Leave-one-out cross-validation
- LR, Logistic Regression
- MCCV, Monte Carlo cross-validation
- MLP, multilayer perceptron
- Machine learning
- Metastasis
- NPV, negative predictive value
- PCA, Principal component analysis
- PPI, protein-protein interaction
- PPV, positive predictive value
- RC, ridge classifier
- RF, Random Forest
- RFE, recursive feature elimination
- RMA, robust multi‐array average
- RNN, recurrent neural networks
- SGD, stochastic gradient descent
- SMOTE, synthetic minority over-sampling technique
- SVM, Support Vector Machine
- Se, sensitivity
- Sp, specificity
- TCGA, The Cancer Genome Atlas
- k-CV, k-fold cross validation
- mRMR, minimum redundancy maximum relevance
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Affiliation(s)
- Somayah Albaradei
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia
| | - Maha Thafar
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Taif University, Collage of Computers and Information Technology, Taif, Saudi Arabia
| | - Asim Alsaedi
- King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
- King Abdulaziz Medical City, Jeddah, Saudi Arabia
| | - Christophe Van Neste
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [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: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Fatima K, Dasgupta A, DiCenzo D, Kolios C, Quiaoit K, Saifuddin M, Sandhu M, Bhardwaj D, Karam I, Poon I, Husain Z, Sannachi L, Czarnota GJ. Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma. Clin Transl Radiat Oncol 2021; 28:62-70. [PMID: 33778174 PMCID: PMC7985224 DOI: 10.1016/j.ctro.2021.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 12/23/2020] [Revised: 02/23/2021] [Accepted: 03/07/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC). METHODS Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation. RESULTS The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment. CONCLUSION QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
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Key Words
- AAC, Average acoustic concentration
- ACE, Attenuation co-efficient estimate
- ASD, Average scatterer diameter
- AUC, Area under the curve
- Acc, Accuracy
- CON, Contrast
- COR, Correlation
- CR, Complete responders
- CT, Computed tomography
- Delta-radiomics
- EBV, Epstein-Barr virus
- ENE, Energy
- FDG-PET, 18F-fluorodeoxyglucose positron emission tomography
- FLD, Fisher’s linear discriminant
- FN, False negative
- FP, False positive
- GLCM, Grey level co-occurrence matrix
- HN, Head and neck
- HNSCC, Head and neck squamous cell carcinoma
- HOM, Homogeneity
- HPV, Human papillomavirus
- Head and neck malignancy
- IGRT, Image-guided radiation therapy
- IMRT, Intensity-modulated radiation therapy
- MBF, Mid-band fit
- MRI, Magnetic resonance imaging
- Machine learning
- NR, Non-recurrence
- PET, Positron emission tomography
- PR, Partial responders
- QUS, Quantitative ultrasound
- Quantitative ultrasound
- R, Recurrence
- RF, Radiofrequency
- RFS, Recurrence-free survival
- ROI, Region of interest
- RT, Radiotherapy
- Radiomics
- Radiotherapy squamous cell carcinoma
- Recurrence
- SAS, Spacing among scatterers
- SI, Spectral intercept
- SP, Specificity
- SS, Spectral slope
- SVM, Support vector machine
- Sn, Sensitivity
- TN, True negative
- TP, True positive
- US, Ultrasound
- kNN, k nearest neighbors
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Affiliation(s)
- Kashuf Fatima
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Archya Dasgupta
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Karina Quiaoit
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Michael Sandhu
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Irene Karam
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ian Poon
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zain Husain
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | | | - Gregory J. Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
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