Development and evaluation of a virtual microscopy application for automated assessment of Ki-67 expression in breast cancer

[1]  Vilppu J Tuominen,et al.  ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 , 2010, Breast Cancer Research.

[2]  Mårten Fernö,et al.  The prognostic value of Ki67 is dependent on estrogen receptor status and histological grade in premenopausal patients with node-negative breast cancer , 2010, Modern Pathology.

[3]  D. Rimm,et al.  Microvessel area using automated image analysis is reproducible and is associated with prognosis in breast cancer. , 2009, Human pathology.

[4]  J. Bartlett,et al.  Membranous and cytoplasmic staining of Ki67 is associated with HER2 and ER status in invasive breast carcinoma , 2009, Histopathology.

[5]  Kelli Montgomery,et al.  Inter-observer reproducibility of HER2 immunohistochemical assessment and concordance with fluorescent in situ hybridization (FISH): pathologist assessment compared to quantitative image analysis , 2009, BMC Cancer.

[6]  C Caldas,et al.  Proliferation markers and survival in early breast cancer: a systematic review and meta-analysis of 85 studies in 32,825 patients. , 2008, Breast.

[7]  V. Kataja,et al.  Molecular Subtypes of Breast Cancers Detected in Mammography Screening and Outside of Screening , 2008, Clinical Cancer Research.

[8]  L. Goldstein,et al.  Automated quantitative analysis of estrogen receptor expression in breast carcinoma does not differ from expert pathologist scoring: a tissue microarray study of 3,484 cases , 2008, Breast Cancer Research and Treatment.

[9]  H. Nevanlinna,et al.  Ki67 and cyclin A as prognostic factors in early breast cancer. What are the optimal cut‐off values? , 2007, Histopathology.

[10]  J Lundin,et al.  A public-domain image processing tool for automated quantification of fluorescence in situ hybridisation signals , 2007, Journal of Clinical Pathology.

[11]  D. Rimm,et al.  Quantitative analysis of estrogen receptor heterogeneity in breast cancer , 2007, Laboratory Investigation.

[12]  J. Kononen,et al.  A high‐throughput strategy for protein profiling in cell microarrays using automated image analysis , 2007, Proteomics.

[13]  C. Sotiriou,et al.  Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12 155 patients , 2007, British Journal of Cancer.

[14]  A. Sapino,et al.  Routine assessment of prognostic factors in breast cancer using a multicore tissue microarray procedure , 2006, Virchows Archiv.

[15]  V. Kosma,et al.  The important prognostic value of Ki-67 expression as determined by image analysis in breast cancer , 2005, Journal of Cancer Research and Clinical Oncology.

[16]  M Lundin,et al.  A digital atlas of breast histopathology: an application of web based virtual microscopy , 2004, Journal of Clinical Pathology.

[17]  V. Kataja,et al.  Risk for distant recurrence of breast cancer detected by mammography screening or other methods. , 2004, JAMA.

[18]  David J. Foran,et al.  A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics , 2004, IEEE Transactions on Information Technology in Biomedicine.

[19]  R. Bock,et al.  Combined Ki-67 and feulgen stain for morphometric determination of the Ki-67 labelling index , 1993, Histochemistry.

[20]  K. Bloom,et al.  Enhanced accuracy and reliability of HER-2/neu immunohistochemical scoring using digital microscopy. , 2004, American journal of clinical pathology.

[21]  D. Rimm,et al.  Automated subcellular localization and quantification of protein expression in tissue microarrays , 2002, Nature Medicine.

[22]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[23]  A. Gown,et al.  Assessment of proliferative activity in breast cancer: MIB-1 immunohistochemistry versus mitotic figure count. , 1999, Human pathology.

[24]  M. Fernö,et al.  Correlation between p53, c‐erbB‐2, and topoisomerase IIα expression, DNA ploidy, hormonal receptor status and proliferation in 356 node‐negative breast carcinomas: prognostic implications , 1999, The Journal of pathology.

[25]  B. Rittman Quantitation In Immunohistochemistry , 1998 .

[26]  J. Kononen,et al.  Tissue microarrays for high-throughput molecular profiling of tumor specimens , 1998, Nature Medicine.

[27]  F. Rilke,et al.  Evaluation of Residual Cellularity and Proliferation on Preoperatively Treated Breast Cancer: A Comparison between Image Analysis and Light Microscopy Analysis , 1998, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[28]  M. Indelli,et al.  MIB-1 proliferative activity in invasive breast cancer measured by image analysis. , 1996, Journal of clinical pathology.

[29]  L. Layfield,et al.  Estrogen and progesterone receptor status determined by the Ventana ES 320 automated immunohistochemical stainer and the CAS 200 image analyzer in 236 early‐stage breast carcinomas: Prognostic significance , 1996, Journal of surgical oncology.

[30]  M. Osborn,et al.  Prognostic significance of tumor cell proliferation rate as determined by the MIB-1 antibody in breast carcinoma: its relationship with vimentin and p53 protein. , 1996, Clinical cancer research : an official journal of the American Association for Cancer Research.

[31]  Stefano Ferretti,et al.  Application of quantitative analysis to biologic profile evaluation in breast cancer , 1995, Cancer.

[32]  H. Multhaupt,et al.  Quantitation in immunohistochemistry. A research method or a diagnostic tool in surgical pathology? , 1995, Pathologica.

[33]  I. Ellis,et al.  Assessment of the new proliferation marker MIB1 in breast carcinoma using image analysis: associations with other prognostic factors and survival. , 1995, British Journal of Cancer.

[34]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .