Feature Ranking Importance from Multimodal Radiomic Texture Features using Machine Learning Paradigm: A Biomarker to Predict the Lung Cancer

Big Data Research - Tập 29 - Trang 100331 - 2022
Seong-O Shim1, Monagi H. Alkinani1, Lal Hussain2,3, Wajid Aziz2
1College of Computer Science and Engineering, University of Jeddah, P.O. Box 80327, Jeddah 21589, Saudi Arabia
2Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, Pakistan
3Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, Athmuqam 13230, Pakistan

Tài liệu tham khảo

Jemal, 2010, Cancer statistics, CA Cancer J. Clin., 60, 277, 10.3322/caac.20073 Rios Ataxca, 2018, A passive state simulation of an anal sphincter using simmechanics, J. Mech. Med. Biol., 18, 10.1142/S0219519418500598 Siegel, 2018, Cancer statistics, CA Cancer J. Clin., 68, 7, 10.3322/caac.21442 Oser, 2015, Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin, Lancet Oncol., 16, e165, 10.1016/S1470-2045(14)71180-5 Johnson, 1996, Demographics of brain metastasis, Neurosurg. Clin. North. Am., 7, 337, 10.1016/S1042-3680(18)30365-6 Zappa, 2016, Non-small cell lung cancer: current treatment and future advances, Transl. Lung Cancer Res., 5, 288, 10.21037/tlcr.2016.06.07 Grossman, 2021, Differentiating small-cell lung cancer from non-small-cell lung cancer brain metastases based on MRI using efficientnet and transfer learning approach, Technol. Cancer Res. Treat., 20, 10.1177/15330338211004919 Adelstein, 1986, Mixed small cell and non-small cell lung cancer, Chest, 89, 699, 10.1378/chest.89.5.699 Ochiai, 2015, Comparison of therapeutic results from radiofrequency ablation and stereotactic body radiotherapy in solitary lung tumors measuring 5 cm or smaller, Int. J. Clin. Oncol., 20, 499, 10.1007/s10147-014-0741-z Chi, 2010, Treatment of brain metastasis from lung cancer, Cancers (Basel), 2, 2100, 10.3390/cancers2042100 Zheng, 2016, Classification and pathology of lung cancer, Surg. Oncol. Clin. N. Am., 25, 447, 10.1016/j.soc.2016.02.003 Kriegsmann, 2020, Deep learning for the classification of small-cell and non-small-cell lung cancer, Cancers (Basel), 12, 1604, 10.3390/cancers12061604 Teramoto, 2017, Automated classification of lung cancer types from cytological images using deep convolutional neural networks, BioMed Res. Int., 2017, 1, 10.1155/2017/4067832 Wang, 2020, Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy, Open Med., 15, 190, 10.1515/med-2020-0028 Park, 2012, Development and validation of a prognostic gene-expression signature for lung adenocarcinoma, PLoS One, 7 Potti, 2006, A genomic strategy to refine prognosis in early-stage non–small-cell lung cancer, N. Engl. J. Med., 355, 570, 10.1056/NEJMoa060467 Zhang, 2016, A nomogram to predict brain metastases of resected non-small cell lung cancer patients, Ann. Surg. Oncol., 23, 3033, 10.1245/s10434-016-5206-3 Fernandes, 2009, Expression profiles of thioredoxin family proteins in human lung cancer tissue: correlation with proliferation and differentiation, Histopathology, 55, 313, 10.1111/j.1365-2559.2009.03381.x Li, 2015, An array-based approach to determine different subtype and differentiation of non-small cell lung cancer, Theranostics, 5, 62, 10.7150/thno.10145 Calbo, 2011, A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer, Cancer Cell, 19, 244, 10.1016/j.ccr.2010.12.021 Krishnaiah, 2013, Diagnosis of lung cancer prediction system using data mining classification techniques, Int. J. Comput. Sci. Inf. Technol., 4, 39 Silvestri, 2007, 178S Travis, 2011, International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society: International Multidisciplinary Classification of Lung Adenocarcinoma, Proc. Am. Thorac Soc., 8, 381, 10.1513/pats.201107-042ST Loo, 2010, Subtyping of undifferentiated non-small cell carcinomas in bronchial biopsy specimens, J. Thorac. Oncol., 5, 442, 10.1097/JTO.0b013e3181d40fac Nicholson, 2010, Refining the diagnosis and EGFR status of non-small cell lung carcinoma in biopsy and cytologic material, using a panel of mucin staining, TTF-1, cytokeratin 5/6, and P63, and EGFR mutation analysis, J. Thorac. Oncol., 5, 436, 10.1097/JTO.0b013e3181c6ed9b Jiang, 2017, A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images, 556 Wu, 2013, Can diffusion-weighted imaging be used as a reliable sequence in the detection of malignant pulmonary nodules and masses?, Magn. Reson. Imaging, 31, 235, 10.1016/j.mri.2012.07.009 Khalil, 2020, A new expert system in prediction of lung cancer disease based on fuzzy soft sets, Soft Comput., 24, 14179, 10.1007/s00500-020-04787-x Lee, 2001, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique, IEEE Trans. Med. Imaging, 20, 595, 10.1109/42.932744 Rathore, 2014, Ensemble classification of colon biopsy images based on information rich hybrid features, Comput. Biol. Med., 47, 76, 10.1016/j.compbiomed.2013.12.010 Rathore, 2013, A recent survey on colon cancer detection techniques, IEEE/ACM Trans. Comput. Biol. Bioinform., 10, 545, 10.1109/TCBB.2013.84 Rathore, 2012, Capture largest included circles: an approach for counting red blood cells, vol. 281, 373 Rathore, 2015, Automated colon cancer detection using hybrid of novel geometric features and some traditional features, Comput. Biol. Med., 65, 279, 10.1016/j.compbiomed.2015.03.004 Hussain, 2019, Detecting brain tumor using machine learning techniques based on different features extracting strategies, Curr. Med. Imaging, 14, 595, 10.2174/1573405614666180718123533 Hussain, 2018, Automated breast cancer detection using machine learning techniques by extracting different feature extracting strategies, 327 Hussain, 2018, Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies, Cancer Biomark., 21, 393, 10.3233/CBM-170643 Fenton, 2006, The lung cancer alliance, J. Oncol. Pract., 2, 306, 10.1200/jop.2006.2.6.306 Tiwari, 2016, Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering, 1 Farid, 2001, Blind inverse gamma correction, IEEE Trans. Image Process., 10, 1428, 10.1109/83.951529 Bhandari, 2016, Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD, vol. 27, 453 Ngo, 2021, Taylor-series-based reconfigurability of gamma correction in hardware designs, Electronics, 10, 1959, 10.3390/electronics10161959 Hussain, 2019, Automated lung cancer detection based on multimodal features extracting strategy using machine learning techniques, 134 Hussain, 2019, Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features, IEEE Access, 7, 64704, 10.1109/ACCESS.2019.2917303 Zhou, 2018, Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches, Am. J. Neuroradiol., 39, 208, 10.3174/ajnr.A5391 Goh, 2011, Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker, Radiology, 261, 165, 10.1148/radiol.11110264 Giraud, 2019, Radiomics and machine learning for radiotherapy in head and neck cancers, Front. Oncol., 9 Nioche, 2018, LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity, Cancer Res., 78, 4786, 10.1158/0008-5472.CAN-18-0125 Weninger, 2019, Robustness of radiomics for survival prediction of brain tumor patients depending on resection status, Front. Comput. Neurosci., 13, 10.3389/fncom.2019.00073 Lohmann, 2021, Radiomics in neuro-oncology: basics, workflow, and applications, Methods, 188, 112, 10.1016/j.ymeth.2020.06.003 de Leon, 2019, Radiomics in kidney cancer: MR imaging, Magn. Reson. Imaging Clin. N. Am., 27, 1, 10.1016/j.mric.2018.08.005 Kalkhaire, 2017, Remote detection of photoplethysmographic signal and SVM based classification, 128 Tariq, 2019, Breast cancer classification using global discriminate features in mammographic images, Int. J. Adv. Comput. Sci. Appl., 10, 381 Raghtate, 2015, Comparison of classification methods with second order statistical analysis and wavelet transform for texture image classification, 312 Jain, 2019 Thibault, 2013, Shape and texture indexes application to cell nuclei classification, Int. J. Pattern Recognit. Artif. Intell., 27, 10.1142/S0218001413570024 Chu, 1990, Use of gray value distribution of run lengths for texture analysis, Pattern Recognit. Lett., 11, 415, 10.1016/0167-8655(90)90112-F Kairuddin, 2017, Texture feature analysis for different resolution level of kidney ultrasound images, IOP Conf. Ser., Mater. Sci. Eng., 226, 10.1088/1757-899X/226/1/012136 Wang, 2010, A comparative study of filter-based feature ranking techniques, 43 Shakir, 2019, Radiomics based likelihood functions for cancer diagnosis, Sci. Rep., 9, 9501, 10.1038/s41598-019-45053-x Wu, 2016, Exploratory study to identify radiomics classifiers for lung cancer histology, Front. Oncol., 6, 187, 10.3389/fonc.2016.00071 Yu, 2019, A Matlab toolbox for feature importance ranking, 1 Teng, 2019, Unsupervised feature selection with adaptive residual preserving, Neurocomputing, 367, 259, 10.1016/j.neucom.2019.05.097 Saeys, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093/bioinformatics/btm344 Solorio-Fernández, 2020, A review of unsupervised feature selection methods, Artif. Intell. Rev., 53, 907, 10.1007/s10462-019-09682-y Venkatesh, 2019, A review of feature selection and its methods, Cybern. Inf. Technol., 19, 3 Gu Roffo, 2017, Infinite latent feature selection: a probabilistic latent graph-based ranking approach, 1407 Chien, 2018, Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names, Medicine, 97, 10.1097/MD.0000000000012418 Zhao, 2007, Spectral feature selection for supervised and unsupervised learning, 1151 Roffo, 2020, Infinite feature selection: a graph-based feature filtering approach, IEEE Trans. Pattern Anal. Mach. Intell., 1 Hou, 2014, Joint embedding learning and sparse regression: a framework for unsupervised feature selection, IEEE Trans. Cybern., 44, 793, 10.1109/TCYB.2013.2272642 Lind, 1993, The continuity principle in psychological research: an introduction to robust statistics, Can. J. Psychol., 34, 407, 10.1037/h0078861 Li, 2018, Feature selection, ACM Comput. Surv., 50, 1, 10.1145/3136625 Zeng, 2011, Feature selection and kernel learning for local learning-based clustering, IEEE Trans. Pattern Anal. Mach. Intell., 33, 1532, 10.1109/TPAMI.2010.215 Cai, 2010, Unsupervised feature selection for multi-cluster data, 333 Kim, 2015, T test as a parametric statistic, Korean J. Anesthesiol., 68, 540, 10.4097/kjae.2015.68.6.540 Heyer, 1982, 142 Hoeffding, 1994, 409 Bradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit., 30, 1145, 10.1016/S0031-3203(96)00142-2 Wilcoxon, 1992, Individual comparisons by ranking methods, Biom. Bull., 196 Kononenko, 1995, Induction of decision trees using relieff, 199 Tibshirani, 1996, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. B, 58, 267 Hu, 2013, Minimum–maximum local structure information for feature selection, Pattern Recognit. Lett., 34, 527, 10.1016/j.patrec.2012.11.012 Li, 2014, Clustering-guided sparse structural learning for unsupervised feature selection, IEEE Trans. Knowl. Data Eng., 26, 2138, 10.1109/TKDE.2013.65 Guo, 2017, Unsupervised feature selection with ordinal locality, 1213 Happy, 2017, An effective feature selection method based on pair-wise feature proximity for high dimensional low sample size data, 1574 Shi, 2014, Robust spectral learning for unsupervised feature selection, 977 Gravier, 2000, A Markov random field model for automatic speech recognition, 254 Vapnik, 1999, An overview of statistical learning theory, IEEE Trans. Neural Netw., 10, 988, 10.1109/72.788640 Toccaceli, 2017, Combination of conformal predictors for classification, vol. 60, 39 Subasi, 2013, Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Comput. Biol. Med., 43, 576, 10.1016/j.compbiomed.2013.01.020 Dobrowolski, 2012, Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders, Comput. Methods Programs Biomed., 107, 393, 10.1016/j.cmpb.2010.12.006 List, 1997, Characterization of bovine endothelial nitric oxide synthase as a homodimer with down-regulated uncoupled NADPH oxidase activity: tetrahydrobiopterin binding kinetics and role of haem in dimerization, Biochem. J., 323, 159, 10.1042/bj3230159 Smith, 2015, Conformal anomaly detection of trajectories with a multi-class hierarchy, 281 Aitkenhead, 2008, A co-evolving decision tree classification method, Expert Syst. Appl., 34, 18, 10.1016/j.eswa.2006.08.008 Hussain, 2018, Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach, Cogn. Neurodyn., 12, 271, 10.1007/s11571-018-9477-1 Rissanen, 1996, Fisher information and stochastic complexity, IEEE Trans. Inf. Theory, 42, 40, 10.1109/18.481776 Zaidi, 2012, Bayesian reliability models of Weibull systems: state of the art, Int. J. Appl. Math. Comput. Sci., 22, 585, 10.2478/v10006-012-0045-2 Yang, 2012, Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric, Med. Phys., 39, 6929, 10.1118/1.4754305 Huang, 2012, Retrieval of brain tumors with region-specific bag-of-visual-words representations in contrast-enhanced MRI images, Comput. Math. Methods Med., 2012, 1 Huang, 2014, Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images, PLoS One, 9 Cheng, 2016, Retrieval of brain tumors by adaptive spatial pooling and Fisher vector representation, PLoS One, 11 Hajian-Tilaki, 2013, Summary for policymakers, 1 Kashyap, 2015, Breast cancer detection in digital mammograms, IEEE Int Conf Imaging Syst Tech, 6 Kanakatte, 2008, Pulmonary tumor volume detection from positron emission tomography images, 213 Liu, 2010, A method of pulmonary nodule detection utilizing multiple support vector machines Parveen, 2013, Detection of lung cancer nodules using automatic region growing method, 1 Turkki, 2015, Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis, J. Clin. Pathol., 68, 614, 10.1136/jclinpath-2015-202888 Dennie, 2016, Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules, Quant. Imaging Med. Surg., 6, 6 Roth, 2015, 1 Shaffie, 2018, A novel autoencoder-based diagnostic system for early assessment of lung cancer, 1393 Chen Y-J, 2015, Computer-aided classification of lung nodules on computed tomography images via deep learning technique, OncoTargets Ther., 2015, 10.2147/OTT.S80733 Krewer, 2013, Effect of texture features in computer aided diagnosis of pulmonary nodules in low-dose computed tomography, 3887 L, 2019, Optimal deep learning model for classification of lung cancer on CT images, Future Gener. Comput. Syst., 92, 374, 10.1016/j.future.2018.10.009 Singh, 2019, Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans, Neural Comput. Appl., 31, 6863, 10.1007/s00521-018-3518-x Nasrullah, 2019, Automated detection and classification for early stage lung cancer on CT images using deep learning, 27