A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT
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Jing Wang | Bin Li | Steve B. Jiang | Ning Yang | Puneeth Iyengar | Hongxia Hao | Zhiguo Zhou | Steve Jiang | Shulong Li | Kenneth Westover | Michael R. Folkert | Hak Choy | Robert Timmerman | Zhiguo Zhou | Jing Wang | M. Folkert | P. Iyengar | K. Westover | H. Choy | R. Timmerman | Hongxia Hao | Shulong Li | N. Yang | Bin Li
[1] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[2] L V Rubinstein,et al. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. , 1995, The Annals of thoracic surgery.
[3] J. Alison Noble,et al. Learning-based prediction of gestational age from ultrasound images of the fetal brain , 2015, Medical Image Anal..
[4] Vicky Goh,et al. Radiomics in PET: principles and applications , 2014, Clinical and Translational Imaging.
[5] Steve B. Jiang,et al. Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[6] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[7] Ronald M. Summers,et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.
[8] Suresh Senan,et al. Patterns of disease recurrence after stereotactic ablative radiotherapy for early stage non-small-cell lung cancer: a retrospective analysis. , 2012, The Lancet. Oncology.
[9] Renato Martins,et al. Non-small cell lung cancer. , 2012, Journal of the National Comprehensive Cancer Network : JNCCN.
[10] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[11] I. El Naqa,et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015, Physics in medicine and biology.
[12] Andreas Uhl,et al. Directional wavelet based features for colonic polyp classification , 2016, Medical Image Anal..
[13] Nico Karssemeijer,et al. Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..
[14] Shan Tan,et al. Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. , 2013, International journal of radiation oncology, biology, physics.
[15] Thierry Denoeux,et al. Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction , 2016, Medical Image Anal..
[16] Ronald M. Summers,et al. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.
[17] L. Lathauwer,et al. Signal Processing based on Multilinear Algebra , 1997 .
[18] Sunita Ghosh,et al. Outcomes in stage I non-small cell lung cancer following the introduction of stereotactic body radiotherapy in Alberta - A population-based study. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[19] Chong Li,et al. Existence of solutions for variational inequalities on Riemannian manifolds , 2009 .
[20] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[21] Xuelong Li,et al. Supervised tensor learning , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[22] Ashek Ahmmed. Image denoising using Gabor filter banks , 2011, 2011 IEEE Symposium on Computers & Informatics.
[23] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[24] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[25] D. Rubin,et al. Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. , 2016, Radiology.
[26] Bram van Ginneken,et al. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box , 2015, Medical Image Anal..
[27] Wei Shen,et al. Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.
[28] Fei Wang,et al. Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..
[29] Masoom A. Haider,et al. Discovery Radiomics for Computed Tomography Cancer Detection , 2015, ArXiv.
[30] Zhongxing Liao,et al. Systemic review of the patterns of failure following stereotactic body radiation therapy in early-stage non-small-cell lung cancer: clinical implications. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[31] Zvi Friedman,et al. Automatic apical view classification of echocardiograms using a discriminative learning dictionary , 2017, Medical Image Anal..
[32] N. Otsu. A threshold selection method from gray level histograms , 1979 .
[33] Joshua D. Lawson,et al. Locoregional and distant failure following image-guided stereotactic body radiation for early-stage primary lung cancer. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[34] Ninon Burgos,et al. Voxelwise atlas rating for computer assisted diagnosis: Application to congenital heart diseases of the great arteries , 2015, Medical Image Anal..
[35] Steve B. Jiang,et al. Multi-objective radiomics model for predicting distant failure in lung SBRT , 2017, Physics in medicine and biology.
[36] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[37] Nima Tajbakhsh,et al. Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.
[38] George Lee,et al. Image analysis and machine learning in digital pathology: Challenges and opportunities , 2016, Medical Image Anal..
[39] Quynh-Thu Le,et al. Non-small cell lung cancer: Clinical practice guidelines in oncology , 2006 .
[40] P Giraud,et al. Chapter 44 – Non–Small Cell Lung Cancer , 2016 .
[41] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[42] D. Aberle,et al. Results of initial low-dose computed tomographic screening for lung cancer. , 2013, The New England journal of medicine.
[43] Sébastien Jodogne,et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy , 2016, European Journal of Nuclear Medicine and Molecular Imaging.
[44] A. Jemal,et al. Cancer treatment and survivorship statistics, 2016 , 2016, CA: a cancer journal for clinicians.
[45] Dinggang Shen,et al. View‐aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi‐modality data , 2017, Medical Image Anal..
[46] P. Dupont,et al. Prognostic importance of the standardized uptake value on (18)F-fluoro-2-deoxy-glucose-positron emission tomography scan in non-small-cell lung cancer: An analysis of 125 cases. Leuven Lung Cancer Group. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[47] H. Abdi,et al. Principal component analysis , 2010 .
[48] P. Lambin,et al. Machine Learning methods for Quantitative Radiomic Biomarkers , 2015, Scientific Reports.
[49] H. Benali,et al. Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI , 2009, Neuroradiology.
[50] Amnon Shashua,et al. Linear image coding for regression and classification using the tensor-rank principle , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[51] Masoom A. Haider,et al. Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection , 2015, ArXiv.
[52] M E Burt,et al. Incidence of local recurrence and second primary tumors in resected stage I lung cancer. , 1995, The Journal of thoracic and cardiovascular surgery.
[53] R. Fletcher,et al. Clinical Epidemiology: The Essentials , 1982 .
[54] John Cho,et al. Stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC): is FDG-PET a predictor of outcome? , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[55] Xi Chen,et al. Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion , 2017, ArXiv.
[56] Xiaowei Yang,et al. A Linear Support Higher-Order Tensor Machine for Classification , 2013, IEEE Transactions on Image Processing.
[57] Andrea Bezjak,et al. Stereotactic body radiation therapy for inoperable early stage lung cancer. , 2010, JAMA.
[58] Andrew J. Saykin,et al. Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease , 2017, Scientific Reports.
[59] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[60] Anil K. Jain,et al. Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[61] C Shang,et al. Significance of Tumor Cell Histology for Local Control of Early-Stage Non-Small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy: A Stratified Retrospective Analysis. , 2016 .
[62] J. M. Michalski,et al. Long-term Results of RTOG 0236: A Phase II Trial of Stereotactic Body Radiation Therapy (SBRT) in the Treatment of Patients with Medically Inoperable Stage I Non-Small Cell Lung Cancer , 2014 .
[63] Lionel M. Raff,et al. Quasiclassical trajectory studies using 3D spline interpolation of ab initio surfaces , 1975 .
[64] Fang Liu,et al. Object information based interactive segmentation for fatty tissue extraction , 2013, Comput. Biol. Medicine.
[65] Larry S. Davis,et al. Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[66] F. Turkheimer,et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015 .
[67] Liangpei Zhang,et al. A Multifeature Tensor for Remote-Sensing Target Recognition , 2011, IEEE Geoscience and Remote Sensing Letters.
[68] Nenghai Yu,et al. Camera Model Identification for JPEG Images via Tensor Analysis , 2010, 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.
[69] Marko Robnik-Sikonja,et al. Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.
[70] Peyman Milanfar,et al. Linear Support Tensor Machine With LSK Channels: Pedestrian Detection in Thermal Infrared Images , 2016, IEEE Transactions on Image Processing.