Feasibility study of a multi-criteria decision-making based hierarchical model for multi-modality feature and multi-classifier fusion: Applications in medical prognosis prediction

Gillies, 2016, Radiomics: images are more than pictures, they are data, Radiology, 278, 563, 10.1148/radiol.2015151169 Lambin, 2017, the bridge between medical imaging and personalized medicine, Nat. Rev. Clin. Oncol., 14, 749, 10.1038/nrclinonc.2017.141 Kamran, 2017, CT-Based radiomic biomarker features predict prognosis in patients with limited stage small cell lung cancer, Int. J. Radiat. Oncol. Biol. Phys., 99, S12, 10.1016/j.ijrobp.2017.06.046 Lucia, 2018, Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy, Eur. J. Nucl. Med. Mol. Imaging, 45, 768, 10.1007/s00259-017-3898-7 Parmar, 2015, Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer, Sci. Rep., 5, 11044, 10.1038/srep11044 Xin, 2017, Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study, Phys. Med. Biol., 62, 8246, 10.1088/1361-6560/aa8d09 Chen, 2018, Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy, Radiat. Oncol., 13, 125, 10.1186/s13014-018-1068-0 Li, 2017, Feature selection: a data perspective, J. ACM Comput. Surv., 50, 1, 10.1145/3136625 Oikonomou, 2018, Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy, Sci. Rep., 8, 4003, 10.1038/s41598-018-22357-y van Timmeren, 2017, Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images, Radiother. Oncol., 123, 363, 10.1016/j.radonc.2017.04.016 Parmar, 2015, Machine learning methods for quantitative radiomic biomarkers, Sci. Rep., 5, 13087, 10.1038/srep13087 Vickers, 2011, Revolutionary in principle, but do they do more good than harm?, J. Clin. Oncol., 29, 2951, 10.1200/JCO.2011.36.1329 Baltrušaitis, 2019, Multimodal machine learning: a survey and taxonomy, IEEE Trans. Pattern Anal. Mach. Intell., 41, 423, 10.1109/TPAMI.2018.2798607 Haghighat, 2016, Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition, IEEE Trans. Inf. Forensics Secur., 11, 1984, 10.1109/TIFS.2016.2569061 Yap, 2018, Multimodal skin lesion classification using deep learning, Exp. Dermatol., 27, 1261, 10.1111/exd.13777 N. Emaminejad, W. Qian, Y. Guan, M. Tan, Y. Qiu, H. Liu, B. Zheng, Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients, 2015. Z. Zhou, D. Sher, Q. Zhang, P. Yan, J. Shah, N.-L. Pham, M. Folkert, S. Jiang, J. Wang, Multifactorial cancer treatment outcome prediction through multifaceted radiomics, (2018), arXiv:1807.09212. B. Lei, S. Chen, D. Ni, T. Wang, Discriminative learning for alzheimer's disease diagnosis via canonical correlation analysis and multimodal fusion, 8 (2016). Cables, 2016, RIM-reference ideal method in multicriteria decision making, Inf. Sciences, 337-338, 1, 10.1016/j.ins.2015.12.011 D. Dua, E. Karra Taniskidou, UCI Machine Learning Repository, (2017. [Online]. Available:http://archive.ics.uci.edu/ml). Cai, 2018, Feature selection in machine learning: a new perspective, Neurocomputing, 300, 70, 10.1016/j.neucom.2017.11.077 V. Bachu, J. Anuradha, A review of feature selection and its methods, 2019. Z. Zhao, F. Morstatter, S. Sharma, S. Alelyani, A. Anand, H. Liu, Advancing feature selection research, 2010. Brown, 2012, Conditional likelihood maximisation: a unifying framework for information theoretic feature selection, J. Mach. Learn. Res, 13, 27 Nie, 2010, Efficient and robust feature selection via joint ℓ2,1-norms minimization, 2, 1813 Yu, 2003, Feature selection for high-dimensional data: a fast correlation-based filter solution, 856 He, 2005, Laplacian score for feature selection, 507 Robnik-Šikonja, 2003, Theoretical and empirical analysis of ReliefF and RReliefF, Mach. Learn., 53, 23, 10.1023/A:1025667309714 Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. B., 58, 267 Friedman, 2010, Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw., 33, 1, 10.18637/jss.v033.i01 Tong Tong, 2008, Coordinate descent algorithms for lasso penalized regression, Ann. Appl. Stat., 2, 224, 10.1214/07-AOAS147 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Chen, 2016, XGBoost: A Scalable Tree Boosting System, 785 Pedregosa, 2011, Scikit-learn: machine learning in python, J. Mach. Learn. Res., 12, 2825 Moreno-Seco, 2006, Comparison of classifier fusion methods for classification in pattern recognition tasks, 705 Opitz, 1999, Generating accurate and diverse members of a neural-network ensemble, 535 Wolpert, 1992, Stacked generalization, Neural Netw., 5, 241, 10.1016/S0893-6080(05)80023-1 J. Demsar, Statistical comparisons of classifiers over multiple data sets, 2006. Zhou, 2017, One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies, Inf. Fusion, 36, 80, 10.1016/j.inffus.2016.11.009 Smith, 2018, Cancer screening in the United States, 2018: a review of current American cancer society guidelines and current issues in cancer screening, CA Cancer J. Clin., 68, 297, 10.3322/caac.21446 Kalbasi, 2015, Dose-escalated irradiation and overall survival in men with nonmetastatic prostate cancer, JAMA Oncol., 1, 897, 10.1001/jamaoncol.2015.2316 Boladeras, 2018, Association between EBRT dose volume histograms and quality of life in prostate cancer patients, Rep. Pract. Oncol. Radiother., 23, 360, 10.1016/j.rpor.2018.07.009 Kim, 2014, Predictors of rectal tolerance observed in a dose-escalated phase 1-2 trial of stereotactic body radiation therapy for prostate cancer, Int. J. Radiat. Oncol., 89, 509, 10.1016/j.ijrobp.2014.03.012 Haibo, 2008, ADASYN: adaptive synthetic sampling approach for imbalanced learning, 1322 Chen, 2018, Cytokine-induced killer cells as a feasible adoptive immunotherapy for the treatment of lung cancer, Cell Death Dis., 9, 366, 10.1038/s41419-018-0404-5 Senthi, 2012, Patterns of disease recurrence after stereotactic ablative radiotherapy for early stage non-small-cell lung cancer: a retrospective analysis, Lancet Oncol., 13, 802, 10.1016/S1470-2045(12)70242-5 Bradley, 2010, Stereotactic body radiation therapy for early-stage non-small-cell lung cancer: the pattern of failure is distant, Int. J. Radiat. Oncol., 77, 1146, 10.1016/j.ijrobp.2009.06.017 Aerts, 2014, Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat. Commun., 5, 4006, 10.1038/ncomms5006 Clark, 2013, The cancer imaging archive (TCIA): maintaining and operating a public information repository, J. Digit. Imaging, 26, 1045, 10.1007/s10278-013-9622-7 Pavan, 2009, 1.19 - Multicriteria decision-making methods, 591 Z. Zhou, Z.J. Zhou, H. Hao, S. Li, X. Chen, Y. Zhang, M. Folkert, J. Wang, Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion, (2017), arXiv:1710.01614. Liu, 2018, Classifier fusion with contextual reliability evaluation, IEEE Trans. Cybern., 48, 1605, 10.1109/TCYB.2017.2710205 Blakeley, 1995, Noninvasive carotid artery testing: a meta-analytic review, Ann. Intern. Med., 122, 360, 10.7326/0003-4819-122-5-199503010-00007 D. Pamučar, Ž. Stević, S. Sremac, A new model for determining weight coefficients of criteria in MCDM models: full consistency method (FUCOM), 10 (2018) 393. R. Roberts, P. Goodwin, Weight approximations in multi-attribute decision models, 11 (2002) 291–303. Wang, 2010, Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making, Math. Comput. Model., 51, 1, 10.1016/j.mcm.2009.07.016 Diakoulaki, 1995, Determining objective weights in multiple criteria problems: the critic method, Comput. Oper. Res., 22, 763, 10.1016/0305-0548(94)00059-H Caruana, 2006, An empirical comparison of supervised learning algorithms, 161 Zhang, 2017, An up-to-date comparison of state-of-the-art classification algorithms, Expert Syst. Appl., 82, 128, 10.1016/j.eswa.2017.04.003 Boutaba, 2018, A comprehensive survey on machine learning for networking: evolution, applications and research opportunities, J. Internet Serv. Appl., 9, 16, 10.1186/s13174-018-0087-2 Ma, 2019, Breast cancer molecular subtype prediction by mammographic radiomic features, Acad. Radiol., 26, 196, 10.1016/j.acra.2018.01.023 Yin, 2019, Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features, Eur. Radiol., 29, 1841, 10.1007/s00330-018-5730-6 Qian, 2019, Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers, Cancer Lett., 451, 128, 10.1016/j.canlet.2019.02.054 Li, 2019, Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection, Eur. Radiol., 29, 3079, 10.1007/s00330-018-5861-9 Zhang, 2017, Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma, Clin. Cancer Res., 23, 4259, 10.1158/1078-0432.CCR-16-2910 Huang, 2016, A potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer, Radiology, 281, 947, 10.1148/radiol.2016152234 Ali, 2017, Accurate multi-criteria decision making methodology for recommending machine learning algorithm, Expert Syst. Appl., 71, 257, 10.1016/j.eswa.2016.11.034 G. Kou, Y. Lu, Y. Peng, Y. Shi, Evaluation of classification algorithms using MCDM and rank correlation, 11 (2012) 197–225. Peng, 2011, FAMCDM: a fusion approach of MCDM methods to rank multiclass classification algorithms, Omega, 39, 677, 10.1016/j.omega.2011.01.009 Adunlin, 2015, Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis, Health Expect., 18, 1894, 10.1111/hex.12287 Hancerliogullari, 2017, The use of multi-criteria decision making models in evaluating anesthesia method options in circumcision surgery, BMC Med. Inf. Decis. Mak., 17, 14, 10.1186/s12911-017-0409-5 Kishore, 2018, Multi criteria decision making methods to predict the prevalence of coronary artery disease, J. Med. Imaging Health Inform., 8, 719, 10.1166/jmihi.2018.2357 Sałabun, 2017, Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome, Artif. Intell. Rev., 48, 557, 10.1007/s10462-016-9511-9