Early lung cancer diagnostic biomarker discovery by machine learning methods

Xie, Ying; Meng, Wei-Yu; Li, Runze; Wang, Yuwei; Qian, Xin; Chang, Chan; Yu, Zhifang; Fan, Xing‐Xing; Pan, Hudan; Xie, Chun; Wu, Qibiao; Yan, Pingkun; Liu, Liang; Tang, Yijun; Yao, Xiaojun; Wang, Meifang; Leung, Elaine Lai‐Han

doi:10.1016/j.tranon.2020.100907

Cited by 154 publications

(78 citation statements)

References 62 publications

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“…This classifier tends to outperform most other classification methods in terms of accuracy, variance, and bias, without overfitting issues [41]. XGBoost is a decision-tree-based ensemble algorithm that uses a gradient boosting framework and has been widely used in lung cancer studies [4,[42][43][44]. This classifier has been shown to yield superior predictive results using less computing resources in the shortest amount of time compared to other models due to its parallel processing, tree-pruning, sparse data handling, and regularization to present overfitting [45].…”

Section: Learning Modelsmentioning

confidence: 99%

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

et al. 2021

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The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.

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Section: Learning Modelsmentioning

confidence: 99%

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

et al. 2021

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“…In recent years, machine learning has been widely applied in biomarker discovery (3,(25)(26)(27)(28). Machine learning applies mathematical approaches to train a model to learn from data for a particular task (29).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data

2021

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Biomarkers are of great importance in many fields, such as cancer research, toxicology, diagnosis and treatment of diseases, and to better understand biological response mechanisms to internal or external intervention. Highthroughput gene expression profiling technologies, such as DNA microarrays and RNA sequencing, provide large gene expression data sets which enable datadriven biomarker discovery. Traditional statistical tests have been the mainstream for identifying differentially expressed genes as biomarkers. In recent years, machine learning techniques such as feature selection have gained more popularity. Given many options, picking the most appropriate method for a particular data becomes essential. Different evaluation metrics have therefore been proposed. Being evaluated on different aspects, a method' s varied performance across different datasets leads to the idea of integrating multiple methods. Many integration strategies are proposed and have shown great potential. This chapter gives an overview of the current research advances and existing issues in biomarker discovery using machine learning approaches on gene expression data.

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“…The machine learning method has been widely used in biomarker discovery in recent years [20,21]. Machine learning is a mathematical algorithm that trains a model on a training data set and applies the model to a test data set [22].…”

Section: Introductionmentioning

confidence: 99%

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Haam

Han

Lee

et al. 2021

Cancers

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Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

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Early lung cancer diagnostic biomarker discovery by machine learning methods

Cited by 154 publications

References 62 publications

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

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