Automated identification of circulating tumor cells by image cytometry
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Jan Greve | Leon W M M Terstappen | Tycho M Scholtens | Frederik Schreuder | Sjoerd T Ligthart | Joost F Swennenhuis | J. Greve | L. Terstappen | J. Swennenhuis | Sjoerd T. Ligthart | S. Ligthart | T. Scholtens | F. Schreuder
[1] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[2] S. Linder,et al. Cytokeratin-18 Is a Useful Serum Biomarker for Early Determination of Response of Breast Carcinomas to Chemotherapy , 2007, Clinical Cancer Research.
[3] Giandomenico Spezzano,et al. An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams , 2007 .
[4] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[5] E. Kubista,et al. Circulating breast cancer cells are frequently apoptotic. , 2001, The American journal of pathology.
[6] S. Digumarthy,et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology , 2007, Nature.
[7] Pau-Choo Chung,et al. A Fast Algorithm for Multilevel Thresholding , 2001, J. Inf. Sci. Eng..
[8] A. Weiss,et al. Detection and characterization of carcinoma cells in the blood. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[9] Jan Greve,et al. CellTracks TDI: An image cytometer for cell characterization , 2011, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[10] Leon W M M Terstappen,et al. Statistical considerations for enumeration of circulating tumor cells , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[11] Jason P. Gleghorn,et al. Capture of circulating tumor cells from whole blood of prostate cancer patients using geometrically enhanced differential immunocapture (GEDI) and a prostate-specific antibody. , 2010, Lab on a chip.
[12] J. Fleiss. Measuring nominal scale agreement among many raters. , 1971 .
[13] K. Pienta,et al. Circulating Tumor Cells Predict Survival Benefit from Treatment in Metastatic Castration-Resistant Prostate Cancer , 2008, Clinical Cancer Research.
[14] I. Nagtegaal,et al. Circulating tumour cells early predict progression-free and overall survival in advanced colorectal cancer patients treated with chemotherapy and targeted agents. , 2010, Annals of oncology : official journal of the European Society for Medical Oncology.
[15] Alison Stopeck,et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. , 2004, The New England journal of medicine.
[16] Anne-Michelle Noone,et al. Circulating tumor cells: a useful predictor of treatment efficacy in metastatic breast cancer. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[17] M. Omary,et al. Apoptosis Generates Stable Fragments of Human Type I Keratins* , 1997, The Journal of Biological Chemistry.
[18] Jieyue Li,et al. Automated analysis of Human Protein Atlas immunofluorescence images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[19] A W Partin,et al. Identification and characterization of circulating prostate carcinoma cells , 2000, Cancer.
[20] Claire Holloway,et al. Enumeration of Circulating Tumor Cells in the Blood of Breast Cancer Patients After Filtration Enrichment: Correlation with Disease Stage , 2004, Breast Cancer Research and Treatment.
[21] D. Dearnaley,et al. Characterization of ERG, AR and PTEN gene status in circulating tumor cells from patients with castration-resistant prostate cancer. , 2009, Cancer research.
[22] David Ward,et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..
[23] K. Pienta,et al. Apoptosis of circulating tumor cells in prostate cancer patients , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[24] E. Lane,et al. Keratin 8/18 breakdown and reorganization during apoptosis. , 2004, Experimental cell research.
[25] Michael D. Abràmoff,et al. Image processing with ImageJ , 2004 .
[26] Peter Kuhn,et al. High speed detection of circulating tumor cells. , 2006, Biosensors & bioelectronics.
[27] K. Schütze,et al. Isolation by size of epithelial tumor cells : a new method for the immunomorphological and molecular characterization of circulatingtumor cells. , 2000, The American journal of pathology.
[28] N. Lai,et al. CYFRA 21-1 enzyme-linked immunosorbent assay. Evaluation as a tumor marker in non-small cell lung cancer. , 1996, Chest.
[29] K. Wiman,et al. A Novel High-Through-Put Assay for Screening of Pro-Apoptotic Drugs , 2002, Investigational New Drugs.
[30] Jonathan W. Uhr,et al. Tumor Cells Circulate in the Peripheral Blood of All Major Carcinomas but not in Healthy Subjects or Patients With Nonmalignant Diseases , 2004, Clinical Cancer Research.
[31] A. Marchetti,et al. Int6 Expression Can Predict Survival in Early-Stage Non–Small Cell Lung Cancer Patients , 2005, Clinical Cancer Research.
[32] J. Bono,et al. All circulating EpCAM+CK+CD45- objects predict overall survival in castration-resistant prostate cancer. , 2010, Annals of oncology : official journal of the European Society for Medical Oncology.
[33] Jacob Cohen,et al. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .
[34] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[35] Johannes R. Sveinsson,et al. Random Forests for land cover classification , 2006, Pattern Recognit. Lett..
[36] L. Terstappen,et al. Characterization of circulating tumor cells by fluorescence in situ hybridization , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[37] H. Jörnvall,et al. An immunohistochemical study of the clearance of apoptotic cellular fragments , 2002, Cellular and Molecular Life Sciences CMLS.
[38] J. Sim,et al. The kappa statistic in reliability studies: use, interpretation, and sample size requirements. , 2005, Physical therapy.
[39] M C Miller,et al. Expression of epithelial cell adhesion molecule in carcinoma cells present in blood and primary and metastatic tumors. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[40] Chwee Teck Lim,et al. Versatile label free biochip for the detection of circulating tumor cells from peripheral blood in cancer patients. , 2010, Biosensors & bioelectronics.
[41] Michael Morse,et al. Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[42] Giovanna Bises,et al. Detection of EpCAM positive and negative circulating tumor cells in metastatic breast cancer patients , 2011, Acta oncologica.
[43] G. Kramer,et al. Differentiation between Cell Death Modes Using Measurements of Different Soluble Forms of Extracellular Cytokeratin 18 , 2004, Cancer Research.
[44] Pamela C. Cosman,et al. Automatic tracking, feature extraction and classification of C. elegans phenotypes , 2004, IEEE Transactions on Biomedical Engineering.
[45] Thierry Maudelonde,et al. Characterization and enumeration of cells secreting tumor markers in the peripheral blood of breast cancer patients. , 2005, Journal of immunological methods.
[46] A. Stopeck,et al. Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with non-malignant diseases. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[47] David A Basiji,et al. Sensitivity measurement and compensation in spectral imaging , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[48] Ws. Rasband. ImageJ, U.S. National Institutes of Health, Bethesda, Maryland, USA , 2011 .
[49] Juan Manuel Górriz,et al. SPECT image classification using random forests , 2009 .
[50] Nathalie Harder,et al. Feature Selection for Evaluating Fluorescence Microscopy Images in Genome-Wide Cell Screens , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[51] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[52] D. Dearnaley,et al. Circulating tumour cell (CTC) counts as intermediate end points in castration-resistant prostate cancer (CRPC): a single-centre experience. , 2009, Annals of oncology : official journal of the European Society for Medical Oncology.
[53] F. O. Fackelmayer,et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. , 2001, Cancer research.
[54] F. Becker,et al. Isolation of rare cells from cell mixtures by dielectrophoresis , 2009, Electrophoresis.
[55] Jan Greve,et al. Magnetic field design for selecting and aligning immunomagnetic labeled cells. , 2002, Cytometry.
[56] Joydeep Ghosh,et al. Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[57] T. Kroll,et al. Monitoring the response of circulating epithelial tumor cells to adjuvant chemotherapy in breast cancer allows detection of patients at risk of early relapse. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[58] Taghi M. Khoshgoftaar,et al. An Empirical Study of Learning from Imbalanced Data Using Random Forest , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).
[59] Peter Kuhn,et al. A rare-cell detector for cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[60] Frederik Schreuder,et al. Laser image cytometer for analysis of circulating tumor cells , 2008 .