Prediction of Pathological Complete Response after Neoadjuvant Chemotherapy for breast cancer using ensemble machine learning

Informatics in Medicine Unlocked - Tập 16 - Trang 100219 - 2019
Raghvi Bhardwaj1, Nishtha Hooda1
1Computer Science and Engineering Department, Chandigarh University, Mohali, Punjab, India

Tài liệu tham khảo

Asano, 2017, Prediction of survival after neoadjuvant chemotherapy for breast cancer by evaluation of tumor-infiltrating lymphocytes and residual cancer burden, BMC Canc, 17, 888, 10.1186/s12885-017-3927-8 Borchert, 1997, Elevated levels of prostate-specific antigen in serum of women with fibroadenomas and breast cysts, J Natl Cancer Inst, 89, 587, 10.1093/jnci/89.8.587 Kong, 2011, Meta-analysis confirms achieving pathological complete response after neoadjuvant chemotherapy predicts favourable prognosis for breast cancer patients, Eur J Cancer, 47, 2084, 10.1016/j.ejca.2011.06.014 Asri, 2016, Using machine learning algorithms for breast cancer risk prediction and diagnosis, Procedia Comput Sci, 83, 1064, 10.1016/j.procs.2016.04.224 Tahmassebi, 2019, Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients, Investig Radiol, 54, 110, 10.1097/RLI.0000000000000518 Bibault, 2016, Big data and machine learning in radiation oncology: state of the art and future prospects, Cancer Lett, 382, 110, 10.1016/j.canlet.2016.05.033 Aslan, 2018, Breast cancer diagnosis by different machine learning methods using blood analysis data, Int J Intell Syst Appl Eng, 6, 289, 10.18201/ijisae.2018648455 Huang, 2008, Prediction model building and feature selection with support vector machines in breast cancer diagnosis, Expert Syst Appl, 34, 578, 10.1016/j.eswa.2006.09.041 Yang, 2010, A review of ensemble methods in bioinformatics, Curr Bioinform, 5, 296, 10.2174/157489310794072508 Kourou, 2015, Machine learning applications in cancer prognosis and prediction, Comput Struct Biotechnol J, 13, 817, 10.1016/j.csbj.2014.11.005 Cain, 2019, Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set, Breast Canc Res Treat, 173, 455, 10.1007/s10549-018-4990-9 Bashiri, 2017, Improving the prediction of survival in cancer patients by using machine learning techniques: experience of gene expression data: a narrative review, Iran J Public Health, 46, 165 Wang, 2018, A support vector machine-based ensemble algorithm for breast cancer diagnosis, Eur J Oper Res, 267, 687, 10.1016/j.ejor.2017.12.001 Pearl, 2000 McCallum, 1998, A comparison of event models for naive bayes text classification, vol. 752, 41 Syarif, 2002, Study on multi stage logistic chain network: a spanning tree-based genetic algorithm approach, Comput Ind Eng, 43, 299, 10.1016/S0360-8352(02)00076-1 Orhan, 2011, EEG signals classification using the K-means clustering and a multilayer perceptron neural network model, Expert Syst Appl, 38, 13475, 10.1016/j.eswa.2011.04.149 Cortes, 1995, Support-vector networks, Mach Learn, 20, 10.1007/BF00994018 Huang, 2006, Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem, Nonlinear Anal Real World Appl, 7, 720, 10.1016/j.nonrwa.2005.04.006 Ho, 1995, Random decision forests (PDF) Kgl, 2013 Alfaro, 2013, Adabag: an R package for classification with boosting and bagging, J Stat Softw, 54, 1, 10.18637/jss.v054.i02